Advanced Search
Article Contents

Analyses and Forecasts of a Tornadic Supercell Outbreak Using a 3DVAR System Ensemble


doi: 10.1007/s00376-015-5072-0

  • As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.
  • 加载中
  • Aksoy A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805- 1824.4ff70edc7106f4b2ade206a5b4a03de5http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2009MWRv..137.1805A%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20314/s?wd=paperuri%3A%28817381ab5d9867d9b84aa2f84c3772ca%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2009MWRv..137.1805A%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20314&ie=utf-8
    Albers S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1996: The local analysis and prediction system (LAPS): Analyses of clouds, precipitation, and temperature. Wea.Forecasting, 11, 273- 287.10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;226708041e58d4e55890019b5592d65behttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1996WtFor..11..273Ahttp://adsabs.harvard.edu/abs/1996WtFor..11..273AABSTRACT The Local Analysis and Prediction System (LAPS) combines numerous data sources into a set of analyses and forecasts on a 10-km grid with high temporal resolution. To arrive at an analysis of cloud cover, several input analyses are combined with surface aviation observations (SAOs) and pilot reports of cloud layers. These input analyses are: a skin temperature analysis (used to solve for cloud layer heights and coverage) derived from Geostationary Operational Environmental Satellite (GOES) IR 11.24 m data, other visible and multispectral imagery, a three-dimensional temperature analysis, and a threedimensional radar reflectivity analysis derived from full volumetric radar data. Use of a model first guess for clouds is currently being phased in. The goal is to combine the data sources to take advantage of their strengths, thereby automating the synthesis similar to that of a human forecaster. The design of the analysis procedures and output displays focuses on forecaster utility. A numb...
    Anderson J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884- 2903.10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;25a7ccea0916644ffbe03b37855cac972http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126175%2Fhttp://ci.nii.ac.jp/naid/10013126175/An ensemble adjustment Kalman filter for data assimilation ANDERSON J. L. Mon. Wea. Rev. 129, 2884-2903, 2001
    Anderson J. L., N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 1452- 1463.10.1175/JTECH2049.1178eb9ff1d10d90754004ab07818d36fhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007JAtOT..24.1452Ahttp://adsabs.harvard.edu/abs/2007JAtOT..24.1452AAbstract A variant of a least squares ensemble (Kalman) filter that is suitable for implementation on parallel architectures is presented. This parallel ensemble filter produces results that are identical to those from sequential algorithms already described in the literature when forward observation operators that relate the model state vector to the expected value of observations are linear (although actual results may differ due to floating point arithmetic round-off error). For nonlinear forward observation operators, the sequential and parallel algorithms solve different linear approximations to the full problem but produce qualitatively similar results. The parallel algorithm can be implemented to produce identical answers with the state variable prior ensembles arbitrarily partitioned onto a set of processors for the assimilation step (no caveat on round-off is needed for this result). Example implementations of the parallel algorithm are described for environments with low (high) communication latency and cost. Hybrids of these implementations and the traditional sequential ensemble filter can be designed to optimize performance for a variety of parallel computing environments. For large models on machines with good communications, it is possible to implement the parallel algorithm to scale efficiently to thousands of processors while bit-wise reproducing the results from a single processor implementation. Timing results on several Linux clusters are presented from an implementation appropriate for machines with low-latency communication. Most ensemble Kalman filter variants that have appeared in the literature differ only in the details of how a prior ensemble estimate of a scalar observation is updated given an observed value and the observational error distribution. These details do not impact other parts of either the sequential or parallel filter algorithms here, so a variety of ensemble filters including ensemble square root and perturbed observations filters can be used with all the implementations described.
    Anderson J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The data assimilation research testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283- 1296.10.1175/2009BAMS2618.15e2309fa474dc306b6bd79877b6514b2http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009BAMS...90.1283Ahttp://adsabs.harvard.edu/abs/2009BAMS...90.1283AAbstract The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DART's ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a model's state and an observation's expected value. Forward operators for standard, in situ observations and novel types, like GPS radio occultation soundings, are available. DART algorithms scale well on a variety of parallel architectures, allowing large data assimilation problems to be studied. DART also includes many low-order models and an ensemble assimilation tutorial appropriate for undergraduate and graduate instruction.
    Brewster K. A., 2002: Recent advances in diabatic initialization of a non-hydrostatic numerical model. Preprints, 15th Conf. on Numerical Weather Prediction/21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., CD-ROM, J6. 3.
    [ Available online at http://arps.ou.edu/ARPS/ARPS5DOC/BrewsterNWP2002.pdf.]
    Brewster K., M. Hu, M. Xue, and J. D. Gao, 2005: Efficient assimilation of radar data at high resolution for short-range numerical weather prediction. World Weather Research Program Symp . on Nowcasting and Very Short-Range Forecasting, WSN05, Toulouse, France, WMO World Weather Research Programme, Symp. CD,Paper 3. 06.
    Calhoun K. M., T. M. Smith, D. M. Kingfield, J. D. Gao, and D. J. Stensrud, 2014: Forecaster use and evaluation of real-time 3DVAR analyses during severe thunderstorm and tornado warning operations in the hazardous weather testbed. Wea.Forecasting, 29, 601- 613.10.1175/WAF-D-13-00107.1420a2ed8-a11e-4ac2-9dca-eb78ad33799a7fc627eae8c0f96059794a40067b53b0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014WtFor..29..601Chttp://adsabs.harvard.edu/abs/2014WtFor..29..601CAbstract A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4-6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4-6 min reduces the utility of the products when new radar scans are available.
    Clark A. J., J. S. Kain, P. T. Marsh, J. Correia, M. Xue, and F. Y. Kong, 2012a: Forecasting tornado path lengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea.Forecasting, 27, 1090- 1113.10.1175/WAF-D-11-00147.1c8dfbbc6-8603-4a92-a654-bad4f95bd5b7591cae742da649a69bcd5aa9afbfbf17http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012WtFor..27.1090Crefpaperuri:(ca02e2cd48f6610852c88e2f243f3ac6)http://adsabs.harvard.edu/abs/2012WtFor..27.1090CA three-dimensional (in space and time) object identification algorithm is applied to high-resolution forecasts of hourly maximum updraft helicity (UH)-a diagnostic that identifies simulated rotating storms-with the goal of diagnosing the relationship between forecast UH objects and observed tornado pathlengths. UH objects are contiguous swaths of UH exceeding a specified threshold. Including time allows tracks to span multiple hours and entire life cycles of simulated rotating storms. The object algorithm is applied to 3 yr of 36-h forecasts initialized daily from a 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run in real time at the National Severe Storms Laboratory (NSSL), and forecasts from the Storm Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms for the 2010 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Methods for visualizing UH object attributes are presented, and the relationship between pathlengths of UH objects and tornadoes for corresponding 18- or 24-h periods is examined. For deterministic NSSL-WRF UH forecasts, the relationship of UH pathlengths to tornadoes was much stronger during spring (March-May) than in summer (June-August). Filtering UH track segments produced by high-based and/or elevated storms improved the UH-tornado pathlength correlations. The best ensemble results were obtained after filtering high-based and/or elevated UH track segments for the 20 cases in April-May 2010, during which correlation coefficients were as high as 0.91. The results indicate that forecast UH pathlengths during spring could be a very skillful predictor for the severity of tornado outbreaks as measured by total pathlength.
    Clark, A. J., Coauthors, 2012b: An overview of the 2010 hazardous weather testbed experimental forecast program spring experiment. Bull. Amer. Meteor. Soc., 93, 55- 74.10.1175/BAMS-D-11-00040.1df963977-b7a5-4bfc-8e08-0c29b946068af4766b545da35c541f04ba5bdb81a5fbhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FADS%3Fid%3D2012BAMS...93...55Crefpaperuri:(4c05d4ea86e7578b866436541d7b624f)http://onlinelibrary.wiley.com/resolve/reference/ADS?id=2012BAMS...93...55CThe NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC. A supplement to this article is available online (DOI: 10.1175/BAMS-D-11-00040.2 )
    Dawson D. T., L. J. Wicker, E. R. Mansell, and R. L. Tanamachi, 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696- 716.
    Dawson D. T., M. Xue, J. A. Milbrand t, and A. Shapiro, 2015: Sensitivity of real-data simulations of the 3 May 1999 Oklahoma City tornadic supercell and associated tornadoes to multimoment microphysics. Part I: Storm-and tornado-scale numerical forecasts. Mon. Wea. Rev., 143, 2241- 2265.10.1175/MWR-D-14-00279.1851a8498-b476-4f5d-b2a8-392bd8d3c763440975f27091849effca87d5dfb2e0bahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.2241Dhttp://adsabs.harvard.edu/abs/2015MWRv..143.2241DAbstractNumerical predictions of the 3 May 1999 Oklahoma City, Oklahoma tornadic supercell are performed in a real-data framework utilizing telescoping nested grids of 3-km, 1-km, and 250-m horizontal grid spacing. Radar reflectivity and radial velocity from the Oklahoma City WSR-88D radar are assimilated using a cloud analysis procedure coupled with a cycled 3DVAR system to analyze storms on the 1-km grid for subsequent forecast periods. Single-, double- and triple-moment configurations of a multi-moment bulk microphysics scheme are used in several experiments on the 1-km and 250-m grids to assess the impact of varying the complexity of the microphysics scheme on the storm structure, behavior, and tornadic activity (on the 250-m grid). This appears to be the first study of its type to investigate single- vs. multi-moment microphysics in a real-data context.It is found that the triple-moment scheme overall performs the best, producing the smallest track errors for the mesocyclone on the 1-km grid, and str...
    Doswell, C. A., III, H. E. Brooks, N. Dotzek, 2009: On the implementation of the enhanced Fujita scale in the USA. Atmos. Res., 93, 554- 563.10.1016/j.atmosres.2008.11.0039239ff3385e6b4af57f7300f2d729057http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809508003438http://www.sciencedirect.com/science/article/pii/S0169809508003438The history of tornado intensity rating in the United States of America (USA), pioneered by T. Fujita, is reviewed, showing that non-meteorological changes in the climatology of the tornado intensity ratings are likely, raising questions about the temporal (and spatial) consistency of the ratings. Although the Fujita scale (F-scale) originally was formulated as a peak wind speed scale for tornadoes, it necessarily has been implemented using damage to estimate the wind speed. Complexities of the damage-wind speed relationship are discussed.Recently, the Fujita scale has been replaced in the USA as the official system for rating tornado intensity by the so-called Enhanced Fujita scale (EF-scale). Several features of the new rating system are reviewed and discussed in the context of a proposed set of features of a tornado intensity rating system.It is concluded that adoption of the EF-scale in the USA may have been premature, especially if it is to serve as a model for how to rate tornado intensity outside of the USA. This is in large part because its degree of damage measures used for estimating wind speeds are based on USA-specific construction practices. It is also concluded that the USA's tornado intensity rating system has been compromised by secular changes in how the F-scale has been applied, most recently by the adoption of the EF-scale. Several recommendations are offered as possible ways to help develop an improved rating system that will be applicable worldwide.
    Dowell D. C., F. Q. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble kalman filter experiments. Mon. Wea. Rev., 132, 1982- 2005.26e7960473172f8beb0c69dab3593615http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004mwrv..132.1982d/s?wd=paperuri%3A%28abb3b44783fba72b01284112f6ceacc4%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004mwrv..132.1982d&ie=utf-8
    Elmore K. L., D. J. Stensrud, and K. C. Crawford, 2002: Explicit cloud-scale models for operational forecasts: A note of caution. Wea.Forecasting, 17, 873- 884.10.1175/1520-0434(2002)0172.0.CO;2ddc31e030e0fcf49e1c67d738851c48ahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002WtFor..17..873Ehttp://adsabs.harvard.edu/abs/2002WtFor..17..873EAbstract As computational capacity has increased, cloud-scale numerical models are slowly being modified from pure research tools to forecast tools. Previous studies that used cloud-scale models as explicit forecast tools, in much the same way as a mesoscale model might be used, have met with limited success. Results presented in this paper suggest that this is due, at least in part, to the nature of cloud-scale models themselves. Results from over 700 cloud-scale model runs indicate that, in some cases, differences in the initial soundings that are smaller than can be measured by the current observing system result in unexpected differences in storm longevity. In other cases, easily measurable differences in the initial soundings do not result in significant differences in storm longevity. There unfortunately appears to be no set of parameters that can be used to determine whether the initial sounding is near some part of the cloud-model parameter space that displays this sensitivity. Because different cloud models share similar philosophies, if not similar design, this sensitivity to initial soundings places a fundamental limit on how well the current slate of cloud-scale models can be expected to perform as explicit forecast tools. Given these results, it is not clear that using state-of-the-art cloud-scale models as explicit forecasting tools is appropriate. However, cloud-model ensembles may help to address some inescapable problems with explicit forecasts from cloud models. The most useful application of cloud-scale models in operational forecasts may be a probabilistic one in which the models are used as members of ensembles, a process that has been demonstrated for models of larger-scale processes.
    Fierro A. O., E. R. Mansell, C. L. Ziegler, and D. R. MacGorman, 2012: Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011. Mon. Wea. Rev., 140, 2609- 2627.10.1175/MWR-D-11-00299.124408e1ef1bb14ea283b70edf48fae4chttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140.2609Fhttp://adsabs.harvard.edu/abs/2012MWRv..140.2609FAbstract This study presents the assimilation of total lightning data to help initiate convection at cloud-resolving scales within a numerical weather prediction model. The test case is the 24 May 2011 Oklahoma tornado outbreak, which was characterized by an exceptional synoptic/mesoscale setup for the development of long-lived supercells with large destructive tornadoes. In an attempt to reproduce the observed storms at a predetermined analysis time, total lightning data were assimilated into the Weather Research and Forecasting Model (WRF) and analyzed via a suite of simple numerical experiments. Lightning data assimilation forced deep, moist precipitating convection to occur in the model at roughly the locations and intensities of the observed storms as depicted by observations from the National Severe Storms Laboratory- three-dimensional National Mosaic and Multisensor Quantitative Precipitation Estimation (QPE)-.e., NMQ-攔adar reflectivity mosaic product. The nudging function for the total lightning data locally increases the water vapor mixing ratio (and hence relative humidity) via a simple smooth continuous function using gridded pseudo-Geostationary Lightning Mapper (GLM) resolution (9 km) flash rate and simulated graupel mixing ratio as input variables. The assimilation of the total lightning data for only a few hours prior to the analysis time significantly improved the representation of the convection at analysis time and at the 1-h forecast within the convective permitting and convective resolving grids (i.e., 3 and 1 km, respectively). The results also highlighted possible forecast errors resulting from errors in the initial mesoscale thermodynamic variable fields. Although this case was primarily an analysis rather than a forecast, this simple and computationally inexpensive assimilation technique showed promising results and could be useful when applied to events characterized by moderate to intense lightning activity.
    Fujita T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 1846- 1868.10.1175/MWR3391.116280acd-bb3f-46ba-8ef4-f8082f50302fe7a41d331f5a0cb66ff19e5121c8f522http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135.1846Frefpaperuri:(3e6b6c5e065ed3d1245bc094ab995d22)http://adsabs.harvard.edu/abs/2007MWRv..135.1846FThe assimilation of surface observations using an ensemble Kalman filter (EnKF) approach is evaluated for the potential to improve short-range forecasting. Two severe weather cases are examined, in which the assimilation is performed over a 6-h period using hourly surface observations followed by an 18-h simulation period. Ensembles are created in three different waysy using different initial and boundary conditions, by using different model physical process schemes, and by using both different initial and boundary conditions and different model physical process schemes. The ensembles are compared in order to investigate the role of uncertainties in the initial and boundary conditions and physical process schemes in EnKF data assimilation. In the initial condition ensemble, spread is associated largely with the displacement of atmospheric baroclinic systems. In the physics ensemble, spread comes from the differences in model physics, which results in larger spread in temperature and dewpoint temperature than the initial condition ensemble, and smaller spread in the wind fields. The combined initial condition and physics ensemble has properties from both of the previous two ensembles. It provides the largest spread and produces the best simulation for most of the variables, in terms of the rms difference between the ensemble mean and observations. Perhaps most importantly, this combined ensemble provides very good guidance on the mesoscale features important to the severe weather events of the day.
    Gao J. D., M. Xue, A. Shapiro, and K. K. Droegemeier, 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127, 2128- 2142.10.1175/1520-0493(1999)127<2128:AVMFTA>2.0.CO;2346ce470c76cafb3a5725afd8d57ae47http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1999MWRv..127.2128Ghttp://adsabs.harvard.edu/abs/1999MWRv..127.2128GAbstract This paper proposes a new method of dual-Doppler radar analysis based on a variational approach. In it, a cost function, defined as the distance between the analysis and the observations at the data points, is minimized through a limited memory, quasi-Newton conjugate gradient algorithm with the mass continuity equation imposed as a weak constraint. The analysis is performed in Cartesian space. Compared with traditional methods, the variational method offers much more flexibility in its use of observational data and various constraints. Using the radar data directly at observation locations avoids an interpolation step, which is often a source of error, especially in the presence of data voids. In addition, using the mass continuity equation as a weak instead of strong constraint avoids the error accumulation and the subsequent somewhat arbitrary adjustment associated with the explicit vertical integration of the continuity equation. The current method is tested on both model-simulated and observed datasets of supercell storms. It is shown that the circulation inside and around the storms, including the strong updraft and associated downdraft, is well analyzed in both cases. Furthermore, the authors found that the analysis is not very sensitive to the specification of boundary conditions and to data contamination. The method also has the potential for retrieving, with reasonable accuracy, the wind in regions of single-Doppler radar coverage.
    Gao J. D., M. Xue, K. Brewster, F. H. Carr, and K. K. Droegemeier, 2002: New development of a 3DVAR system for a non-hydrostatic NWP model. Preprints, 15th Conf. on Numerical Weather Prediction/19th Conf. on Weather Analysis and Forecasting, San Antonio, TX, Amer. Meteor. Soc., 12. 4.85a7c656-e339-4025-8c16-ff71fc08246952fae44ff16acb1ef9abab08f28434fahttp%3A%2F%2Fams.confex.com%2Fams%2FSLS_WAF_NWP%2Ftechprogram%2Fpaper_47480.htmhttp://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47480.htmAt the previous NWP conference, we reported on an incremental 3DVAR data assimilation system developed for small-scale nonhydrostatic models, in particular, the ARPS model. Within the system, a cost function is defined as a sum of background and observation constraints. The background field is typically provided by previous model forecasts. For special applications, e.g., for local-scale Doppler radar data retrievals, the background can also be provided by a single environmental sounding. In non-cycled mode, as is often unavoidable in research settings, the background can be from forecasts of an external model. In the system, the background covariances are modeled using recursive spatial filters. The system currently handles all traditional observations as well as raw Doppler velocity measurements. In this paper, we will report on the further development of this 3DVAR system, in particular, the addition of (weak) equation constraints to the cost function. These equations are based on nonhydrostatic equations of motion and the three dimensional mass continuity equation. These equations are important in providing the coupling among the velocity components and with thermodynamic fields. The paper will present the 3DVAR formulation and the results of numerical experiments.
    Gao J. D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457- 469.e485d4c3dc07366bc3f77e8a55f22a67http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JAtOT..21..457G%26db_key%3DPHY%26link_type%3DABSTRACT/s?wd=paperuri%3A%28bea7369f63db7495437d458da988726a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JAtOT..21..457G%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8
    Gao, J. D., Coauthors, 2013: A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea.Forecasting, 28, 727- 745.10.1175/WAF-D-12-00093.15f79e832-4d95-4623-b3ff-112497bb6bacd969c0799f0422274530442a429cbc5bhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1175%2FWAF-D-12-00093.1refpaperuri:(31cc02dc7cee02c1cfa219702ccf2ccc)http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1175/WAF-D-12-00093.1Abstract A real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAA's Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the assessment of this type of 3DVAR analysis for use in severe weather warnings. The eventual goal of this real-time 3DVAR system is to help meteorologists better track severe weather events and eventually provide better warning information to the public, ultimately saving lives and reducing property damage.
    Ge G. Q., J. D. Gao, and M. Xue, 2013a: Impacts of assimilating measurements of different state variables with a simulated supercell storm and three-dimensional variational method. Mon. Wea. Rev., 141, 2759- 2777.10.1175/mwr-d-12-00193.18fd6e3430a8e9d16cda5496c45e25a3ehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.2759Ghttp://adsabs.harvard.edu/abs/2013MWRv..141.2759GThis paper investigates the impacts of assimilating measurements of different state variables, which can be potentially available from various observational platforms, on the cycled analysis and short-range forecast of supercell thunderstorms by performing a set of observing system simulation experiments (OSSEs) using a storm-scale three-dimensional variational data assimilation (3DVAR) method. The control experiments assimilate measurements every 5 min for 90 min. It is found that the assimilation of horizontal wind can reconstruct the storm structure rather accurately. The assimilation of vertical velocity , potential temperature , or water vapor can partially rebuild the thermodynamic and precipitation fields but poorly retrieves the wind fields. The assimilation of rainwater mixing ratio can build up the precipitation fields together with a reasonable cold pool but is unable to properly recover the wind fields. Overall, data have the greatest impact, while have the second largest impact. The impact of is the smallest. The impact of assimilation frequency is examined by comparing results using 1-, 5-, or 10-min assimilation intervals. When is assimilated every 5 or 10 min, the analysis quality can be further improved by the incorporation of additional types of observations. When are assimilated every minute, the benefit from additional types of observations is negligible, except for . It is also found that for , , and measurements, more frequent assimilation leads to more accurate analyses. For and , a 1-min assimilation interval does not produce a better analysis than a 5-min interval.
    Ge G. Q., J. D. Gao, and M. Xue, 2013b: Impact of a diagnostic pressure equation constraint on tornadic supercell thunderstorm forecasts initialized using 3DVAR radar data assimilation. Advances in Meteorology, 2013, 947874.10.1155/2013/9478741abf3ba32962d9fc7c63271cae09b71ehttp%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066630http://www.oalib.com/paper/3066630A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR) data assimilation system. This diagnostic pressure equation constraint (DPEC) is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012) described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients. 1. Introduction A dynamic consistent initial condition is very important for making a quality storm-scale numerical weather prediction (NWP) forecast. For this purpose, a large number of studies have been focused on utilizing high-resolution radar data to provide better storm-scale initial conditions (e.g., [1-7]). Since radars primarily observe the radial velocity and reflectivity, most state variables have to be -渞etrieved- in the data assimilation (DA) process. This makes the assimilation of radar data a very challenging problem. Three-dimensional variational (3DVAR), four-dimensional variational (4DVAR), and ensemble Kalman filter (EnKF) methods have been applied to the previously mentioned radar DA problem. The 4DVAR method uses a NWP model as a strong constraint and hence naturally produces a dynamically consistent analysis. Sun and Crook [8, 9] and Sun [10] have shown encouraging results using a 4DVAR cloud model. However, it is very difficult to develop and maintain complex adjoint codes for NWP models. Complex ice microphysics, which are important for storm-scale applications but contain discontinuities and strong nonlinearities, introduce more difficulties in this situation. All of these difficulties limit the adoption of the 4DVAR method in storm-scale NWP operations. The EnKF technique is expected to
    Gilmore M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610- 2627.287c5843da3cd2fd8b908873b8609c04http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132.2610G/s?wd=paperuri%3A%28373f33e68425442f611cf6d8439a24ce%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132.2610G&ie=utf-8
    Hong S. Y., J. O. J. Lim, 2006: The WRF single moment 6 class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129- 151.641165bb-3609-45e6-b22a-3a85d1612e967308c59e0fe08d8147ff5b2869261e63http%3A%2F%2Fwww.dbpia.co.kr%2FJournal%2FArticleDetail%2F773025refpaperuri:(6e4d91088728a0340628b3f4166a8af2)http://www.dbpia.co.kr/Journal/ArticleDetail/773025This study examines the performance of the Weather Research and Forecasting (WRF)-Single-Moment- Microphysics scheme (WSMMPs) with a revised ice-microphysics of the Hong et al. In addition to the simple (WRF Single-Moment 3-class Microphysics scheme; WSM3) and mixed-phase (WRF Single-Moment 5-class Microphysics scheme; WSM5) schemes of the Hong et al., a more complex scheme with the inclusion of graupel as another predictive variable (WRF Single-Moment 6-class Microphysics scheme; WSM6) was developed. The characteristics of the three categories of WSMMPs were examined for an idealized storm case and a heavy rainfall event over Korea. In an idealized thunderstorm simulation, the overall evolutionary features of the storm are not sensitive to the number of hydrometeors in the WSMMPs; however, the evolution of surface precipitation is significantly influenced by the complexity in microphysics. A simulation experiment for a heavy rainfall event indicated that the evolution of the simulated precipitation with the inclusion of graupel (WSM6) is similar to that from the simple (WSM3) and mixed-phase (WSM5) microphysics in a low-resolution grid; however, in a high-resolution grid, the amount of rainfall increases and the peak intensity becomes stronger as the number of hydrometeors increases.
    Hong S. Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103- 120.
    Hu M., M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, 507- 525.28b4d2bd5164a0a469d91feb411adc27http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2007MWRv..135..507H%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D05623/s?wd=paperuri%3A%2830f4214278c1b303c36b065505da786f%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2007MWRv..135..507H%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D05623&ie=utf-8
    Hu M., M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II Data for the prediction of the fort worth, Texas, Tornadic Thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675- 698.10.1175/MWR3092.18c356373-44bd-4e76-954e-4ed1d13d2d1e97de4b71474781242f9bc2176f727b52http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F242369318_3DVAR_and_Cloud_Analysis_with_WSR-88D_Level-II_Data_for_the_Prediction_of_the_Fort_Worth_Texas_Tornadic_Thunderstorms._Part_I_Cloud_Analysis_and_Its_Impactrefpaperuri:(2a1b9c7a13ebb23ea77ced12a2fbf3d1)http://www.researchgate.net/publication/242369318_3DVAR_and_Cloud_Analysis_with_WSR-88D_Level-II_Data_for_the_Prediction_of_the_Fort_Worth_Texas_Tornadic_Thunderstorms._Part_I_Cloud_Analysis_and_Its_ImpactIn this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model are studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) scheme that contains a mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are examined while Part II focuses on the impact of radial velocity and the mass divergence constraint. The case studied is that of the 28 March 2000 Fort Worth, Texas, tornado outbreaks. The same case was studied by Xue et al. using the ARPS Data Analysis System (ADAS) and an earlier version of the cloud analysis procedure with WSR-88D level-III data. Since then, several modifications to the cloud analysis procedure, including those to the in-cloud temperature adjustment and the analysis of precipitation species, have been made. They are described in detail with examples. The assimilation and predictions use a 3-km grid nested inside a 9-km one. The level-II reflectivity data are assimilated, through the cloud analysis, at 10-min intervals in a 1-h period that ends a little over 1 h preceding the first tornado outbreak. Experiments with different settings within the cloud analysis procedure are examined. It is found that the experiment using the improved cloud analysis procedure with reflectivity data can capture the important characteristics of the main tornadic thunderstorm more accurately than the experiment using the early version of cloud analysis. The contributions of different modifications to the above improvements are investigated.
    Hu M., M. Xue, J. D. Gao, and K. Brewster, 2006b: 3DVAR and cloud analysis with WSR-88D Level-II Data for the prediction of the fort worth, Texas, Tornadic Thunderstorms. Part II: Impact of radial Velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699- 721.e68995d56eb95b68ae2302a542d45823http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006mwrv..134..675h/s?wd=paperuri%3A%28d3aa07fe46728396cb8e318184d5e05c%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006mwrv..134..675h&ie=utf-8
    Jones T. A., D. Stensrud, L. Wicker, P. Minnis, and R. Palikonda, 2015: Simultaneous radar and satellite data storm-scale assimilation using an ensemble Kalman filter approach for 24 May 2011. Mon. Wea. Rev., 143, 165- 194.10.1175/MWR-D-14-00180.1cf3cbe7a5ce02b80db430829cad4a263http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143..165Jhttp://adsabs.harvard.edu/abs/2015MWRv..143..165JAbstract Assimilating high-resolution radar reflectivity and radial velocity into convection permitting numerical weather prediction (NWP) models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines GOES-13 cloud water path (CWP) retrievals with WSR-88D Doppler radar reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a two-hour assimilation window between 1800 - 2000 UTC. Forecasts are then generated for 90 min at 5 min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3-D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.
    Kain J.S., Coauthors, 2010: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting-research environment. Wea.Forecasting, 25, 1510- 1521.10.1175/2010WAF2222405.1383a9cf8f8f88b655810a920b7880f57http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010WtFor..25.1510Khttp://adsabs.harvard.edu/abs/2010WtFor..25.1510KABSTRACT The impacts of assimilating radar data and other mesoscale observations in real-time, convection-allowing model forecasts were evaluated during the spring seasons of 2008 and 2009 as part of the Hazardous Weather Test Bed Spring Experiment activities. In tests of a prototype continental U. S.-scale forecast system, focusing primarily on regions with active deep convection at the initial time, assimilation of these observations had a positive impact. Daily interrogation of output by teams of modelers, forecasters, and verification experts provided additional insights into the value-added characteristics of the unique assimilation forecasts. This evaluation revealed that the positive effects of the assimilation were greatest during the first 3-6 h of each forecast, appeared to be most pronounced with larger convective systems, and may have been related to a phase lag that sometimes developed when the convective-scale information was not assimilated. These preliminary results are currently being evaluated further using advanced objective verification techniques.
    Lange H., G. C. Craig, 2014: The impact of data assimilation length scales on analysis and prediction of convective storms. Mon. Wea. Rev., 142, 3781- 3808.
    Morrison H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665- 1677.bcca90161eb1dd3cb83115f6fce9ac9ahttp%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JAtS...62.1665M%26db_key%3DPHY%26link_type%3DABSTRACT/s?wd=paperuri%3A%28bae32659961a3733306de95f73c92bd3%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JAtS...62.1665M%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8
    Purser R. J., W. S. Wu, D. F. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 1524- 1535.10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;256d483d1-5dde-413a-8990-3196c53935a64395fcf3678f5c02804e8414016f397fhttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DGZS200305019.htmrefpaperuri:(890f83670dacb224c43fb34449155e49)http://en.cnki.com.cn/Article_en/CJFDTOTAL-DGZS200305019.htmThe construction and application of efficient numerical recursive filters for the task of convolving a spatial distribution of "forcing" terms with a quasi-Gaussian self-adjoint smoothing kernel in two or three dimensions are described. In the context of variational analysis, this smoothing operation may be interpreted as the convolution of a covariance function of background error with the given forcing terms, which constitutes one of the most computationally intensive components of the iterative solution of a variational analysis problem. Among the technical aspects of the recursive filters, the problems of achieving acceptable approximations to horizontal isotropy and the implementation of both periodic and nonperiodic boundary conditions that avoid the appearance of spurious numerical artifacts are treated herein. A multigrid approach that helps to minimize numerical noise at filtering scales greatly in excess of the grid step is also discussed. It is emphasized that the methods are not inherently limited to the construction of purely Gaussian shapes, although the detailed elaboration of methods by which a more general set of possible covariance profiles may be synthesized is deferred to the companion paper (Part II).
    Purser R. J., W. S. Wu, D. F. Parrish, and N. M. Roberts, 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131, 1536- 1548.7ac8ed6d-e3a9-4439-86ae-1d9c3c126431b035f84e95d20e3c8282fef9aadb6a43http%3A%2F%2Fwww-frd.fsl.noaa.gov%2Fwgs%2Fdawg%2Fonrfa.pdfrefpaperuri:(389baceeaafcb965a57a1608157a9f1d)http://www-frd.fsl.noaa.gov/wgs/dawg/onrfa.pdfAbstract We describe the application of eHcient numerical recursive filters to the task of convolving a spatial distribution of'forcing'terms with a quasi-Gaussian self-adjoint smoothing kernel. In the context of variational analysis, this smoothing operation is
    Schwartz, C. S., Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea.Forecasting, 25, 263- 280.280f16c3-9aeb-4edc-92c9-6c2655103c2f0563636fc5fe544f8e41e3af18160a9chttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010wtfor..25..263srefpaperuri:(31dcb9aed6bb84995676b04de0407711)/s?wd=paperuri%3A%2831dcb9aed6bb84995676b04de0407711%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010wtfor..25..263s&ie=utf-8
    Skamarock W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. Barker, M. G. Duda, X. Y. Huang, and W. Wang, 2008: A description of the advanced research WRF Version 3. NCAR Tech. Note TN-475+STR,113 pp.133fdf5edd3fc85654e5fe959ecf2a0ahttp%3A%2F%2Fntis.library.gatech.edu%2Fhandle%2F123456789%2F2086http://ntis.library.gatech.edu/handle/123456789/2086The development of the Weather Research and Forecasting (WRF) modeling system is a multi-agency effort intended to provide a next-generation mesoscale forecast model and data assimilation system that will advance both the understanding and prediction of mesoscale weather and accelerate the transfer of research advances into operations. The model is being developed as a collaborative effort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), along with the participation of a number of university scientists. The WRF model is designed to be a flexible, state-of-the-art, portable code that is efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modeling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g boundary-layer eddies, convection, baroclinic waves). With WRF as a common tool in the university and operational centers, closer ties will be promoted between these communities, and research advances will have a direct path to operations. These hallmarks make the WRF modeling system unique in the history of NWP in the United States.
    Smith T. M., Coauthors, 2014: Examination of a real-time 3DVAR analysis system in the hazardous weather testbed. Wea.Forecasting, 29, 63- 77.10.1175/WAF-D-13-00044.179c90e73df997cb9773ca07b9f3f5e58http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F94254576%2Fexamination-real-time-3dvar-analysis-system-hazardous-weather-testbedhttp://connection.ebscohost.com/c/articles/94254576/examination-real-time-3dvar-analysis-system-hazardous-weather-testbedABSTRACT Forecasters and research meteorologists tested a real-time three-dimensional variational data assimilation (3DVAR) system in the Hazardous Weather Testbed during the springs of 2010-12 to determine its capabilities to assist in the warning process for severe storms. This storm-scale system updates a dynamically consistent three-dimensional wind field every 5 min, with horizontal and average vertical grid spacings of 1 km and 400 m, respectively. The system analyzed the life cycles of 218 supercell thunderstorms on 27 event days during these experiments, producing multiple products such as vertical velocity, vertical vorticity, and updraft helicity. These data are compared to multiradar-multisensor data from the Warning Decision Support System-Integrated Information to document the performance characteristics of the system, such as how vertical vorticity values compare to azimuthal shear fields calculated directly from Doppler radial velocity. Data are stratified by range from the nearest radar, as well as by the number of radars entering into the analysis of a particular storm. The 3DVAR system shows physically realistic trends of updraft speed and vertical vorticity for a majority of cases. Improvements are needed to better estimate the near-surface winds when no radar is nearby and to improve the timeliness of the input data. However, the 3DVAR wind field information provides an integrated look at storm structure that may be of more use to forecasters than traditional radar-based proxies used to infer severe weather potential.
    Snook N., M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35,L24803, doi: 10.1029/2008 GL035866.10.1029/2008GL035866a7c09b7cce404dee2cc99a9d6b6b7007http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008GL035866%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1029/2008GL035866/abstract[1] Idealized simulations of tornadogenesis in supercell storms are performed using a grid of 100 m spacing. The cold pool intensity and low-level storm dynamics are found to be very sensitive to the intercept parameters of rain and hail drop size distributions (DSD). DSDs favoring smaller (larger) hydrometeors result in stronger (weaker) cold pools due to enhanced (reduced) evaporative cooling/melting over a larger (smaller) geographic region. Sustained tornadic circulations of EF2 intensity are produced in two of the simulations with relatively weak cold pools. When the cold pool is strong, the updraft is tilted rearward by the strong, surging gust front, causing a disconnect between low-level circulation centers near gust front and the mid-level mesocyclone. Weaker cold pool cases have strong, sustained, vertical updrafts positioned near and above the low-level circulation centers, providing strong dynamic lifting and vertical stretching to the low-level parcels and favoring tornadogenesis.
    Snook N., M. Xue, and Y. Jung, 2012: Ensemble probabilistic forecasts of a tornadic mesoscale convective system from ensemble kalman filter analyses using WSR-88D and CASA Radar Data. Mon. Wea. Rev., 140, 2126- 2146.10.1175/MWR-D-11-00117.12afd06ea-19bc-43aa-938e-a7fb0ec51d4ba7b639dde86de332162bcd181b554f7ahttp%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F77570093%2Fensemble-probabilistic-forecasts-tornadic-mesoscale-convective-system-from-ensemble-kalman-filter-analyses-using-wsr-88d-casa-radar-datarefpaperuri:(284aca0f5150632674080eff2cba378c)http://connection.ebscohost.com/c/articles/77570093/ensemble-probabilistic-forecasts-tornadic-mesoscale-convective-system-from-ensemble-kalman-filter-analyses-using-wsr-88d-casa-radar-dataAbstract This study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8-9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential). Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physics uncertainty and effectively plays a role of flow-dependent inflation (in precipitation regions) during EnKF data assimilation. The ensemble predicts with high probability (approximately 0.65) the near-surface mesovortex associated with the first of three reported tornadoes. Though a bias toward stronger precipitation is noted in the ensemble forecasts, all experiments produce skillful probabilistic forecasts of radar reflectivity on a 0-3-h time scale as evaluated by multiple probabilistic verification metrics. These results suggest that both the inclusion of CASA radar data and use of a mixed-microphysics ensemble during EnKF data assimilation positively impact the skill of 2-3-h ensemble forecasts of mesovortices, despite having little impact on the quality of precipitation forecasts (analyzed in terms of predicted radar reflectivity), and are important steps toward an operational EnKF-based ensemble analysis and probabilistic forecast system to support convective-scale warn-on-forecast operations.
    Snyder C., F. Q. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble kalman filter. Mon. Wea. Rev., 131, 1663- 1677.10.1175//2555.1a194066e15b2ee2df62c951e4a125be3http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003MWRv..131.1663Shttp://adsabs.harvard.edu/abs/2003MWRv..131.1663SAssimilation of Doppler radar data into cloud models is an important obstacle to routine numerical weather prediction for convective-scale motions; the difficulty lies in initializing fields of wind, temperature, moisture, and condensate given only observations of radial velocity and reflectivity from the radar. This paper investigates the potential of the ensemble Kalman filter (EnKF), which estimates the covariances between observed variables and the state through an ensemble of forecasts, to assimilate radar observations at convective scales. In the basic experiment, simulated observations are extracted from a reference simulation of a splitting supercell and assimilated using the EnKF and the same numerical model that produced the reference simulation. The EnKF produces accurate analyses, including the unobserved variables, after roughly 30 min (or six scans) of radial velocity observations. Additional experiments, in which forecasts are made from the ensemble-mean analysis, reveal that forecast errors grow significantly in this simple system, so that the ability of the EnKF to track the reference solution is not simply because of stable system dynamics. It is also found that the covariances between radial velocity and temperature, moisture, and condensate are important to the quality of the analyses, as is the initialization chosen for the ensemble members prior to assimilating the first observations. These results are promising, especially given the ease of implementing the EnKF. A number of important issues remain, however, including the initialization of the ensemble prior to the first observation, the treatment of uncertainty in the environmental sounding, the role of error in the forecast model (particularly the microphysical parameterizations), and the treatment of lateral boundary conditions.
    Stensrud D. J., J. D. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts. Mon. Wea. Rev., 138, 1250- 1272.f8e6aebd9aa7c95c33e343b3fcd40897http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2010MWRv..138.1250S%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D15332/s?wd=paperuri%3A%2891eabccb47f1fa5510fcf297f65a8688%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2010MWRv..138.1250S%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D15332&ie=utf-8
    Stensrud D. J., J. W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077- 2107.10.1175/1520-0493(2000)1282.0.CO;2ce1af61652babce38ebdb00741c1bf71http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2000MWRv..128.2077Shttp://adsabs.harvard.edu/abs/2000MWRv..128.2077STwo separate numerical model ensembles are created by using model configurations with different model physical process parameterization schemes and identical initial conditions, and by using different model initial conditions from a Monte Carlo approach and the identical model configuration. Simulations from these two ensembles are investigated for two 48-h periods during which large, long-lived mesoscale convective systems develop. These two periods are chosen because, in some respects, they span the range of convective forecast problems routinely handled by operational forecasters. Calculations of the root-mean-square error, equitable threat score, and ranked probability score from both ensembles indicate that the model physics ensemble is more skillful than the initial-condition ensemble when the large-scale forcing for upward motion is weak. When the large-scale forcing for upward motion is strong, the initial-condition ensemble is more skillful than the model physics ensemble. This result is consistent with the expectation that model physics play a larger role in model simulations when the large-scale signal is weak and the assumptions used within the model parameterization schemes largely determine the evolution of the simulated weather events. The variance from the two ensembles is created at significantly different rates, with the variance in the physics ensemble being produced two to six times faster during the first 12 h than the variance in the initial-condition ensemble. Therefore, within a very brief time period, the variance from the physics ensemble often greatly exceeds that produced by the initial-condition ensemble. These results suggest that varying the model physics is a potentially powerful method to use in creating an ensemble. In essence, by using different model configurations, the systematic errors of the individual ensemble members are different and, hence, this may allow one to determine a probability density function from this...
    Stensrud, D. J., Coauthors, 2009: Convective-scale warn-on-forecast system a vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487- 1499.10.1175/2009BAMS2795.15eba901df3a24da0c9813e34b832235bhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237966541_Convective-Scale_Warn-on-Forecast_System_A_Vision_for_2020http://www.researchgate.net/publication/237966541_Convective-Scale_Warn-on-Forecast_System_A_Vision_for_2020The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that .ire largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times. A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high resolution convection resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn on-forecast system is a vision worth pursuing.
    Stensrud, D. J., Coauthors, 2013: Progress and challenges with warn-on-forecast. Atmos. Res., 123, 2- 16.10.1016/j.atmosres.2012.04.004c3691d105fd03a0114de3772f8be7f41http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS016980951200110Xhttp://www.sciencedirect.com/science/article/pii/S016980951200110XThe current status and challenges associated with two aspects of Warn-on-Forecast- National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations-re outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.
    Sun J. Z., N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55, 835- 852.70068e67-1c47-4977-ae04-28300fae3968f106af5455fb04d67ca933016ba0f47chttp%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F326692%2Fdynamical-microphysical-retrieval-from-doppler-radar-observations-using-cloud-model-itsrefpaperuri:(fdb9a0f14d4a4d67838d5b1420bdea2c)http://connection.ebscohost.com/c/articles/326692/dynamical-microphysical-retrieval-from-doppler-radar-observations-using-cloud-model-itsPresents a study using radar observations of a Florida airmas storm to examine the applicability of the retrieval technique in variational Doppler radar analysis system (VDRAS) to real data. When the data was collected; Discussion on the results of the retrieval technique; Comparison of the results with aircraft measurements; Summary and discussion on the study.
    Thompson G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519- 542.10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;206703f06f81842553d28e8acfa526f3ehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132..519Thttp://adsabs.harvard.edu/abs/2004MWRv..132..519TThis study evaluates the sensitivity of winter precipitation to numerous aspects of a bulk, mixed-phase microphysical parameterization found in three widely used mesoscale models [the fifth-generation Pennsylvania State University锟絅ational Center for Atmospheric Research Mesoscale Model (MM5), the Rapid Update Cycle (RUC), and the Weather Research and Forecast (WRF) model]. Sensitivities of the microphysics to primary ice initiation, autoconversion, cloud condensation nuclei (CCN) spectra, treatment of graupel, and parameters controlling the snow and rain size distributions are tested. The sensitivity tests are performed by simulating various cloud depths (with different cloud-top temperatures) using flow over an idealized two-dimensional mountain. The height and width of the two-dimensional barrier are designed to reproduce an updraft pattern with extent and magnitude consistent with documented freezing-drizzle cases. By increasing the moisture profile to saturation at low temperatures, a deep, precipitating snow cloud is also simulated. Upon testing the primary sensitivities of the microphysics scheme in two dimensions as reported in the present study, the MM5 with the modified scheme will be tested in multiple case studies and the results will be compared to observations in a forthcoming companion paper, Part II. The key results of this study are 1) the choice of ice initiation schemes is relatively unimportant for deep precipitating snow clouds but more important for shallow warm clouds having cloud-top temperature greater than -13锟紺, 2) the assumed snow size distribution and associated snow diffusional growth along with the assumed graupel size distribution and method of transforming rimed snow into graupel have major impacts on the mass of cloud water and formation of freezing drizzle, and 3) a proper simulation of drizzle using a single-moment scheme and exponential size distribution requires an increase in the rain intercept parameter, thereby reducing rain terminal velocities to values more characteristic of drizzle.
    Thompson T. E., L. J. Wicker, X. G. Wang, and C. Potvin, 2015: A comparison between the local ensemble transform Kalman filter and the ensemble square root filter for the assimilation of radar data in convective-scale models. Quart. J. Roy. Meteor. Soc., 141, 1163- 1176.10.1002/qj.24230e7a1fb7-8066-40d0-adaa-8d282ff580a65b21dc62cf646348a54793f87512c85dhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2423%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1002/qj.2423/abstractTwo ensemble data assimilation methods are used to assimilate Doppler radar observations into a convection-allowing model. The analyses and subsequent forecasts from the two systems are compared. The Local Ensemble Transform Kalman Filter (LETKF) simultaneously assimilates all observations that can impact the model state at a given location. It is compared to the Ensemble Square Root Filter (EnSRF), which assimilates observations sequentially and has commonly been used for convective-scale Doppler radar data assimilation. While the filters should behave the same for ideal systems, a comparison between the serial and simultaneous filters has not previously been explored at the convective scale where significant nonlinear effects are present. Observing System Simulation Experiments (OSSEs) are first used to compare the assimilation systems for the analysis and forecast of a supercell thunderstorm. Both the EnSRF and LETKF produce reasonable analyses from the Doppler velocity and reflectivity observations of the true supercell. Small improvements in analysis errors and system noise from the LETKF simultaneous update do not significantly impact the subsequent forecasts. This result is consistent across a range of localization length-scales and is independent of the manner in which localization is applied. Tests comparing the EnSRF and LETKF for a real-data case also have small differences. The magnitudes of these differences are similar to those that arise from the sampling variability associated with a finite ensemble. Overall, the results suggest the EnSRF and LETKF approaches are equally capable methods for radar data assimilation at convective scales.
    Tong M. J., M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789- 1807.10.1175/MWR2898.1609c3c42-daf6-4d0c-86a4-c40407221c9a6eecf15f2735ef19a2bf07069229222bhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228650237_Ensemble_Kalman_filter_assimilation_of_Doppler_radar_data_with_a_compressible_nonhydrostatic_model_OSS_experimentsrefpaperuri:(71c4106c16a3b96ae6d7390ebdad2bc2)http://www.researchgate.net/publication/228650237_Ensemble_Kalman_filter_assimilation_of_Doppler_radar_data_with_a_compressible_nonhydrostatic_model_OSS_experimentsAbstract A Doppler radar data assimilation system is developed based on an ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, it is assumed that the forward models are perfect and that the radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multiclass ice microphysics. New aspects of this study compared to previous work include the demonstration of the ability of the EnKF method to retrieve multiple microphysical species associated with a multiclass ice microphysics scheme, and to accurately retrieve the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of the radial velocity and reflectivity data as well as their spatial coverage in recovering the full-flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside of the precipitation regions, are used. Significant positive impact of the reflectivity assimilation is found even though the observation operator involved is nonlinear. The results also show that a compressible model that contains acoustic modes, hence the associated error growth, performs at least as well as an anelastic model used in previous EnKF studies at the cloud scale. Flow-dependent and dynamically consistent background error covariances estimated from the forecast ensemble play a critical role in successful assimilation and retrieval. When the assimilation cycles start from random initial perturbations, better results are obtained when the updating of the fields that are not directly related to radar reflectivity is withheld during the first few cycles. In fact, during the first few cycles, the updating of the variables indirectly related to reflectivity hurts the analysis. This is so because the estimated background covariances are unreliable at this stage of the data assimilation process, which is related to the way the forecast ensemble is initialized. Forecasts of supercell storms starting from the best-assimilated initial conditions are shown to remain very good for at least 2 h.
    Toth Z., O. Talagrand , G. Cand ille, and Y. J. Zhu, 2003: Probability and ensemble forecasts. Forecast Verification: A Practitioner's Guide in Atmospheric Science, I. T. Jolliffe and D. B. Stephenson, Eds., John Wiley & Sons Ltd., England, 137- 163.a1606106-10fa-4232-ab4a-424dc6b9cc56ac49480c31b67a12776c6fca90daca0dhttp%3A%2F%2Fci.nii.ac.jp%2Fncid%2FBB08575029refpaperuri:(00b0ce1ade09334ee96851df8540bcc6)http://ci.nii.ac.jp/ncid/BB08575029the quality of individual forecasts 11.5 Decadal and longer-range forecast verification 11.6Summary 12: Epilogue 1.1 A brief history and current practice Forecasts are made in a wide rangeof It gives a good up-to-date overview of verification methods and issues associated with
    van den Heever, S. C., W. R. Cotton, 2004: The impact of hail size on simulated supercell storms. J. Atmos. Sci., 61, 1596- 1609.10.1175/1520-0469(2004)061<1596:TIOHSO>2.0.CO;25bfe2c24f5922ed197d4b53f5248e5b6http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004JAtS...61.1596Vhttp://adsabs.harvard.edu/abs/2004JAtS...61.1596VAbstract Variations in storm microstructure due to updraft strength, liquid water content, and the presence of dry layers, wind shear, and cloud nucleating aerosol concentrations are likely to lead to changes in hail sizes within deep convective storms. The focus of this paper is to determine how the overall dynamics and microphysical structure of deep convective storms are affected if hail sizes are somehow altered in a storm environment that is otherwise the same. The sensitivity of simulated supercell storms to hail size distributions is investigated by systematically varying the mean hail diameter from 3 mm to 1 cm using the Regional Atmospheric Modeling System (RAMS) model. Increasing the mean hail diameter results in a hail size distribution in which the number concentration of smaller hailstones is decreased, while that of the larger hailstones is increased. This shift in the hail size distribution as a result of increasing the mean hail diameter leads to an increase in the mean terminal fall speed of the hail species and to reduced melting and evaporation rates. The sensitivity simulations demonstrate that the low-level downdrafts are stronger, the cold pools are deeper and more intense, the left-moving updraft is shorter-lived, the right-moving storm is stronger but not as steady, and the low-level vertical vorticity is greater in the cases with smaller hail stones. The maximum hail mixing ratios are greater in the larger hail simulations, but they are located higher in the storm and farther away from the updraft core in the smaller hail runs. Changes in the hail size distribution also appear to influence the type of supercell that develops.
    Wei M. Z., Z. Toth, R. Wobus, and Y. J. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus A, 60, 62- 79.10.1111/j.1600-0870.2007.00273.xc630b34e2d19a77224c2d239c5c9c31dhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2007.00273.x%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1111/j.1600-0870.2007.00273.x/citedbySince modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations. DA can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM). The results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled with an ETR generation scheme), or with an ETKF-type DA scheme.
    Wheatley D. M., D. J. Stensrud, D. C. Dowell, and N. Yussouf, 2012: Application of a WRF mesoscale data assimilation system to springtime severe weather events 2007-09. Mon. Wea. Rev., 140, 1539- 1557.10.1175/MWR-D-11-00106.1a2a9f8252efd4eb7694b4657cf610443http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140.1539Whttp://adsabs.harvard.edu/abs/2012MWRv..140.1539WAbstract An ensemble-based data assimilation system using the Weather Research and Forecasting Model (WRF) has been used to initialize forecasts of prolific severe weather events from springs 2007 to 2009. These experiments build on previous work that has shown the ability of ensemble Kalman filter (EnKF) data assimilation to produce realistic mesoscale features, such as drylines and convectively driven cold pools, which often play an important role in future convective development. For each event in this study, severe weather parameters are calculated from an experimental ensemble forecast started from EnKF analyses, and then compared to a control ensemble forecast in which no ensemble-based data assimilation is performed. Root-mean-square errors for surface observations averaged across all events are generally smaller for the experimental ensemble over the 0-6-h forecast period. At model grid points nearest to tornado reports, the ensemble-mean significant tornado parameter (STP) and the probability that STP > 1 are often greater in the experimental 0-6-h ensemble forecasts than in the control forecasts. Likewise, the probability of mesoscale convective system (MCS) maintenance probability (MMP) is often greater with the experimental ensemble at model grid points nearest to wind reports. Severe weather forecasts can be sharpened by coupling the respective severe weather parameter with the probability of measurable rainfall at model grid points. The differences between the two ensembles are found to be significant at the 95% level, suggesting that even a short period of ensemble data assimilation can yield improved forecast guidance for severe weather events.
    Wheatley D. M., N. Yussouf, and D. J. Stensrud, 2014: Ensemble kalman filter analyses and forecasts of a severe mesoscale convective system using different choices of microphysics schemes. Mon. Wea. Rev., 142, 3243- 3263.10.1175/MWR-D-13-00260.1904e631a21f4b567110d47bea8663a6ahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014MWRv..142.3243Whttp://adsabs.harvard.edu/abs/2014MWRv..142.3243WA Weather Research and Forecasting Model (WRF)-based ensemble data assimilation system is used to produce storm-scale analyses and forecasts of the 4-5 July 2003 severe mesoscale convective system (MCS) over Indiana and Ohio, which produced numerous high wind reports across the two states. Single-Doppler observations are assimilated into a 36-member, storm-scale ensemble during the developing stage of the MCS with the ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART). The storm-scale ensemble is constructed from mesoscale EnKF analyses produced from the assimilation of routinely available observations from land and marine stations, rawinsondes, and aircraft, in an attempt to better represent the complex mesoscale environment for this event. Three EnKF simulations were performed using the National Severe Storms Laboratory (NSSL) one- and two-moment and Thompson microphysical schemes. All three experiments produce a linear convective segment at the final analysis time, similar to the observed system at 2300 UTC 4 July 2003. The higher-order schemes-in particular, the Thompson scheme-are better able to produce short-range forecasts of both the convective and stratiform components of the observed bowing MCS, and produce the smallest temperature errors when comparing surface observations and dropsonde data to corresponding model data. Only the higher-order microphysical schemes produce any appreciable rear-to-front flow in the stratiform precipitation region that trailed the simulated systems. Forecast performance by the three microphysics schemes is discussed in context of differences in microphysical composition produced in the stratiform precipitation regions of the rearward expanding MCSs.
    Xue M., Coauthors, 2011: Realtime convection-permitting ensemble and convection-resolving deterministic forecasts of CAPS for the hazardous weather testbed 2010 spring experiment. Proc. 25th Conference on Wea. Forecasting, 20th Conference on Numerical Weather Prediction, Amer. Meteor. Soc., Seattle, WA.
    Yussouf N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013a: The ensemble kalman filter analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell storm using single-and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388- 3412.8472b2e9-f747-440f-a374-4da5fac40e5b8c2fa4cef92e4be3cdb7d9698afb8c6fhttp%3A%2F%2Fams.confex.com%2Fams%2F94Annual%2Fwebprogram%2FPaper233114.htmlhttp://ams.confex.com/ams/94Annual/webprogram/Paper233114.htmlA convective-scale ensemble data assimilation and prediction system is developed using the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed (DART) software for a short-range ensemble forecast of the 24 May 2011 Oklahoma tornadic supercell storms. A 45-member convective-scale ensemble is initialized from 3-km High-Resolution Rapid Refresh (HRRR) model using time-lagged ensemble method. Two sets of data assimilation and forecast experiments are conducted using either fixed physics or multiple physics parameterization schemes across the ensemble members. The reflectivity observations from Multi Radar Multi Sensor (MRMS) system, radial velocity observations from three WSR-88D radars and surface observations from Oklahoma Mesonets are assimilated into the ensembles for 1-h period. Preliminary work seeks to evaluate which ensemble system forecasts more accurate reflectivity structure, coldpool and low level mesocyclone tracks of the supercell storms.
    Yussouf N., J. D. Gao, D. J. Stensrud, and G. Q. Ge, 2013b: The impact of mesoscale environmental uncertainty on the prediction of a tornadic supercell storm using ensemble data assimilation approach. Advances in Meteorology, 2013, 731647.10.1155/2013/731647a681222afbb519ebde79e69b2730b5fahttp%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066639http://www.oalib.com/paper/3066639Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA's Warn-on-Forecast initiative.
    Yussouf N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 3044- 3066.10.1175/MWR-D-14-00268.1cf1d2afc418811798f2295ae856758f3http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.3044Yhttp://adsabs.harvard.edu/abs/2015MWRv..143.3044YAbstract As part of the NOAA- Warn-on-Forecast initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection-allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0-1 h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h long update system demonstrate the potential of a frequently-updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.
    Zhang J., F. Carr, and K. Brewster, 1998: ADAS cloud analysis. Preprints, 12th Conf. on Numerical Weather Prediction, Phoenix, AZ, Amer. Meteor.Soc., 185- 188.70031d4fa69761d4722fd70a4204a20ehttp%3A%2F%2Fciteseer.uark.edu%3A8080%2Fciteseerx%2Fshowciting%3Bjsessionid%3D05418B57AE3833CDE4F39B9457B4CEE0%3Fcid%3D4330007http://citeseer.uark.edu:8080/citeseerx/showciting;jsessionid=05418B57AE3833CDE4F39B9457B4CEE0?cid=4330007CiteSeerX - Scientific documents that cite the following paper: ADAS cloud analysis
    Zhang F., C. Snyder, and J. Z. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble kalman filter. Mon. Wea. Rev., 132, 1238- 1253.10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;21c8bab37fb7048cd6759d6fb79c469c0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132.1238Zhttp://adsabs.harvard.edu/abs/2004MWRv..132.1238ZThe ensemble Kalman filter (EnKF) uses an ensemble of short-range forecasts to estimate the flow-dependent background error covariances required in data assimilation. The feasibility of the EnKF for convective-scale of radial velocity from a supercell storm. The present study further explores the potential and behavior of the EnKF at convective scales by considering more realistic initial analyses and variations in the availability and quality of radar observations. Assimilation of simulated radial-velocity observations every 5 min where there is significant reflectivity using 20 ensemble members proves to be successful in most realistic observational scenarios for simulated supercell thunderstorms, although the same degree of success may not be readily expected with real observations and an imperfect model, at least with the present EnKF implementation. Even though the filter converges toward the truth simulation faster from a better initial estimate, an experiment with the initial estimate of supercell displaced by 10 km still yields an accurate estimate of the state of the storm given a slightly longer assimilation period. An experiment with radar observations only above 4km fails to assimilate the storm properly, but, with an addition of hypothetical surface mesonet taking wind and temperature observations, the EnKF can again provide a good estimate of the storm. The supercell can also be successfully assimilated in the case of radar observations only below 4km (such as those from the ground-based mobile radars). More frequent observations can help the storm assimilitation initially, but the benefit diminishes after half an hour. Results presented here indicate that the vertical resolution and the uncertainty of observations, for the typical range of most of the observational radars, would have little impact on the overall performance of the EnKF in assimilating the storm.
    Zhang, J., Coauthors, 2011: National mosaic and multi-sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321- 1338.10.1175/2011BAMS-D-11-00047.182a747d4c3553e14079fbb7469c436abhttp%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F70339129%2Fnational-mosaic-multi-sensor-qpe-nmq-system-description-results-future-planshttp://connection.ebscohost.com/c/articles/70339129/national-mosaic-multi-sensor-qpe-nmq-system-description-results-future-plansThe National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or -淣MQ-, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development. Through about ten years of R&D, a real-time NMQ system has been implemented ( http://nmq.ou.edu ). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time. The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.
  • [1] ZHENG Kailin, CHEN Baojun, 2014: Sensitivities of Tornadogenesis to Drop Size Distribution in a Simulated Subtropical Supercell over Eastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 657-668.  doi: 10.1007/s00376-013-3143-7
    [2] Shibo GAO, Haiqiu YU, Chuanyou REN, Limin LIU, Jinzhong MIN, 2021: Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 132-146.  doi: 10.1007/s00376-020-0081-z
    [3] Yujie PAN, Mingjun WANG, 2019: Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 160-172.  doi: 10.1007/s00376-018-8087-5
    [4] ZHU Kefeng, YANG Yi, Ming XUE, 2015: Percentile-based Neighborhood Precipitation Verification and Its Application to a Landfalling Tropical Storm Case with Radar Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1449-1459.  doi: 10.1007/s00376-015-5023-9
    [5] LU Huijuan, Qin XU, YAO Mingming, GAO Shouting, 2011: Time-Expanded Sampling for Ensemble-Based Filters: Assimilation Experiments with Real Radar Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 743-757.  doi: 10.1007/s00376-010-0021-4
    [6] Lu ZHANG, Xiangjun TIAN, Hongqin ZHANG, Feng CHEN, 2020: Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012), ADVANCES IN ATMOSPHERIC SCIENCES, 37, 873-892.  doi: 10.1007/s00376-020-9274-8
    [7] ZHU Jiangshan, Fanyou KONG, LEI Hengchi, 2012: A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China. Part I: A Case Study, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 201-216.  doi: 10.1007/s00376-011-0137-1
    [8] ZHU Jiangshan, KONG Fanyou, LEI Hengchi, 2013: A Regional Ensemble Forecast System for Stratiform Precipitation Events in the Northern China Region. Part II: Seasonal Evaluation for Summer 2010, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 15-28.  doi: 10.1007/s00376-012-1043-x
    [9] ZHOU Baiquan, NIU Ruoyun, ZHAI Panmao, 2015: An Assessment of the Predictability of the East Asian Subtropical Westerly Jet Based on TIGGE Data, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 401-412.  doi: 10.1007/s00376-014-4026-2
    [10] MA Juhui, Yuejian ZHU, Richard WOBUS, Panxing WANG, 2012: An Effective Configuration of Ensemble Size and Horizontal Resolution for the NCEP GEFS, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 782-794.  doi: 10.1007/s00376-012-1249-y
    [11] Jian YUE, Zhiyong MENG, Cheng-Ku YU, Lin-Wen CHENG, 2017: Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot (2009) Using WRF-based Ensemble Kalman Filter Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 66-78.  doi: 10.1007/s00376-016-6028-8
    [12] Xingchao CHEN, Kun ZHAO, Juanzhen SUN, Bowen ZHOU, Wen-Chau LEE, 2016: Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1106-1119.  doi: 10.1007/s00376-016-5290-0
    [13] FU Weiwei, 2012: Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model: Impact on the Simulation of Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 823-837.  doi: 10.1007/s00376-011-1022-7
    [14] ZHU Jiang, LIN Caiyan, WANG Zifa, 2009: Dust Storm Ensemble Forecast Experiments in East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1053-1070.  doi: 10.1007/s00376-009-8218-0
    [15] Dongmei XU, Zhiquan LIU, Shuiyong FAN, Min CHEN, Feifei SHEN, 2021: Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 661-676.  doi: 10.1007/s00376-020-0219-z
    [16] Xiaoli XIA, Jinzhong MIN, Feifei SHEN, Yuanbing WANG, Chun YANG, 2019: Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1-14.  doi: 10.1007/s00376-018-8075-9
    [17] Kefeng ZHU, Ming XUE, Yujie PAN, Ming HU, Stanley G. BENJAMIN, Stephen S. WEYGANDT, Haidao LIN, 2019: The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1308-1326.  doi: 10.1007/s00376-019-9011-3
    [18] ZHENG Xiaogu, WU Guocan, ZHANG Shupeng, LIANG Xiao, DAI Yongjiu, LI Yong, , 2013: Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1303-1312.  doi: 10.1007/s00376-012-2133-5
    [19] Xiaogu ZHENG, 2009: An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 154-160.  doi: 10.1007/s00376-009-0154-5
    [20] Jo-Han LEE, Dong-Kyou LEE, Hyun-Ha LEE, Yonghan CHOI, Hyung-Woo KIM, 2010: Radar Data Assimilation for the Simulation of Mesoscale Convective Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1025-1042.  doi: 10.1007/s00376-010-9162-8

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 27 March 2015
Manuscript revised: 21 September 2015
Manuscript accepted: 29 September 2015
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Analyses and Forecasts of a Tornadic Supercell Outbreak Using a 3DVAR System Ensemble

  • 1. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 73072, USA
  • 2. Center of Numerical Weather Prediction, National Meteorological Center, China Meteorological Administration, Beijing 100081
  • 3. NOAA/National Severe Storms Laboratory, Norman, OK 73072, USA

Abstract: As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

1. Introduction
  • Accurate convective-scale forecasts of severe weather events like tornadoes, hailstorms, flash floods, and damaging windstorms are crucial to reduce the loss of lives, injuries and economic cost. However, there are many challenges in accurately forecasting high impact weather events, partly due to their small spatial and temporal scales, inherent nonlinearity of the dynamics and physics, limitations in weather forecast models and assimilation methods, and incomplete observation coverage (e.g., Stensrud et al., 2009; Xue et al., 2011; Snook et al., 2012). Nevertheless, with the rapid increase in computational power, progress has been made in the past decade in assimilating Doppler radar and other available observations of the ongoing convection in convective-scale NWP models with the goal to improve forecasts of severe thunderstorm events (e.g., Kain et al., 2010; Clark et al., 2012a). Due to the high sensitivity of convective-scale forecasts to both the storm environment and internal storm processes (e.g., Elmore et al., 2002; Gilmore et al., 2004; Snook and Xue, 2008), uncertainties associated with high-impact weather are large. Ensemble-based forecasting is currently among the most promising techniques for the purpose of better assessing uncertainty on the convective scale, and enabling probabilistic forecast guidance that can be made available to the public (Stensrud et al., 2009, 2013). With the advent of the "Warn-on-Forecast" (Stensrud et al., 2009, 2013) research and development project, which envisions a numerical model-based probabilistic convective-scale analysis and forecast system to support warning operations within the NOAA, the time is right to extensively explore ensemble data assimilation and forecasting for severe weather events.

    The promising data assimilation approaches for convective-scale forecasting are the ensemble Kalman filter (EnKF) (Snyder and Zhang, 2003; Zhang et al., 2004; Dowell et al., 2004; Tong and Xue, 2005; Aksoy et al., 2009; Yussouf et al., 2013a; Wheatley et al., 2014) and localized ensemble transfer Kalman filter method (Lange and Craig, 2014; Thompson et al., 2015). However, a limitation of the convective-scale EnKF based approach is the rapid error growth in forecasts due to the lack of balance in the model dynamics (Lange and Craig, 2014). The 3D variational data assimilation scheme (3DVAR) can improve the balance among model variables by using weak constraints in the cost function (Gao et al., 1999, 2002, 2004; Hu et al., 2006a, b; Stensrud and Gao, 2010; Ge et al., 2013a, b). More advanced techniques, such as the 4D variational method (4DVAR; Sun and Crook, 1998) can also be used to assimilate radar observations with a much more balanced analysis, but it is computationally quite expensive in high-resolution storm-scale NWP.

    The ARPS 3DVAR system has been successfully used in NOAA's "Hazardous Weather Testbed" spring forecast experiments (Clark et al., 2012b) for the past several years to analyze and detect convective-scale severe weather events (Gao et al., 2013; Calhoun et al., 2014; Smith et al., 2014). While the results from the 3DVAR-based deterministic forecasts are very encouraging and reveal the potential value of a convective-scale 3DVAR system, applying the 3DVAR approach to an ensemble system is crucial to quantify the large uncertainties associated with high-impact weather events. Using convective-scale ensembles and a cycled 3DVAR data assimilation system, (Yussouf et al., 2013b) reported that an ensemble with multiphysics background fields provided more realistic probabilistic forecasts of the low-level rotation of an isolated tornadic supercell event (8 May 2003) than that with a fixed physics mesoscale background. To evaluate how well the system performs in forecasting a supercell outbreak with multiple storms and storm interactions, the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA, was selected for investigation in the present study. A WRF model-based interface to the ARPS 3DVAR system was developed and used to assimilate radar observations into each member of a convective-scale ensemble system. The initial and boundary conditions were provided from a multiscale, multiphysics ensemble (Yussouf et al., 2013b). Five different data assimilation and forecasts experiments were conducted to evaluate the sensitivity of a convective-scale ensemble forecast system initialized with 3DVAR radar data assimilation. The experiments included two different data assimilation time frequencies (5, 10 min), two in-cloud temperature adjustment schemes, and a fixed- and mixed-microphysics ensemble, with the goal to determine which configuration works best for supercell forecasts in an ensemble framework.

    A brief overview of the tornadic event is provided in section 2, followed by a description of the experimental design in section 3. The results of the analyses and forecasts are assessed in section 4, and a final discussion provided in section 5.

2. Tornadic outbreak event
  • On 24 May 2011, multiple violent tornadoes touched down across Oklahoma, causing extensive damage along their paths. An overview of the environmental conditions and the evolution of the convective storm for this severe weather outbreak event can be found in (Fierro et al., 2012) and (Jones et al., 2015). A total of 12 tornadoes were reported during the afternoon and evening hours (Table 1), and three of those were violent tornadoes with ratings on the enhanced Fujita (EF) scale of EF-4 or greater (Doswell et al., 2009) (Fig. 1a). Convective cells initiated in west-central Oklahoma along the dry line at 1900 UTC (Fig. 1b) and developed into several supercell thunderstorms that eventually moved into central Oklahoma during the next few hours, producing several significant tornadoes. There were four supercell storms (H, A, I, and B in Fig. 1c) ongoing at 2030 UTC. The first tornado (A1) was reported at around 2020 UTC at the position of supercell storm A and persisted until 2043 UTC (Table 1). The second tornado (B1), generated by supercell storm B, touched down at 2031 UTC and dissipated at 2046 UTC. Soon after, storm B produced the longest and most violent tornado (B2), which started at 2050 UTC and ended at 2235 UTC, having passed through Hinton, El Reno, Piedmont and Guthrie with a total path length of 101 km, and rated at EF-5. The second most violent tornado (Tornado C1) traveled from Chickasha to Newcastle, a distance of 53.11 km, and was rated at EF-4. The third most violent tornado (Tornado D1) was an EF-4 tornado that passed through Bradley and Goldsby with a total path length of 37 km, causing severe damage, killing 10 people, and injuring 290 people. Multiple operational Weather Surveillance Radar-1988 Doppler (WSR-88D) radars documented the life cycle of this tornado outbreak. These radars provide a unique dataset with which convective-scale analysis experiments could be conducted to assess the capability of the data assimilation and forecast system to predict low-level rotation within supercell storms.

    Figure 1.  (a) Storm-scale domain with WSR-88D locations (black dots) and NWS damage swaths (intensity ratings are indicated by colored lines) during the afternoon and evening hours. NSSL NMQ composite reflectivity (dB$Z$) observations valid at (b) 1900 UTC and (c) 2030 UTC 24 May 2011 over the region of interest.

3. Data assimilation system and experimental design
  • A multiscale ensemble analysis and prediction system was employed in this study, based on the WRF-ARW model (Skamarock et al., 2008). The parent mesoscale model domain covered the contiguous United States with a horizontal grid resolution of 15 km and with a 3 km horizontal grid resolution convective-scale domain nested within the mesoscale domain covering Oklahoma and parts of the surrounding states (Fig. 1a). Both domains had 51 vertical grid levels from the surface up to 10 hPa. A 36-member ensemble was designed using the NCEP's three-hourly 21-member GEFS (Toth et al., 2003; Wei et al., 2008). The first 18 of the 21 members were used to initialize a 36-member multiscale ensemble system at 0000 UTC 24 May 2011. Different combinations of physics schemes were applied to each member (Table 2) as the same design in Yussouf (2015) to address the uncertainties in model physics parameterization schemes (e.g., Stensrud et al., 2000, 2009; Fujita et al., 2007; Wheatley et al., 2012, Yussouf et al., 2013b). The same physics options for both the parent and nested grid ensembles were used in our experiments except that the cumulus parameterization scheme was turned off in the specific convective-scale experiments.

    In order to obtain the background for mesoscale simulation, these data such as rawinsondes, marine, mesonet, metar, satellite-derived winds and aircraft from NOAA's Meteorological Assimilation Data Ingest System were assimilated every hour from 0100 UTC 24 May to 0000 UTC 25 May 2011 with the ensemble adjustment Kalman filter (Anderson, 2001) implemented in the Data Assimilation Research Testbed software system (Anderson and Collins, 2007; Anderson et al., 2009) using a configuration similar to that employed by Yussouf et al. (2013a, 2013b), (Wheatley et al., 2014) and (Jones et al., 2015). Radar observations were excluded to be assimilated in the mesoscale domain. Both 1-way nested runs were conducted simultaneously in our setup. The boundary conditions of the nested storm-scale ensemble were provided by the parent mesoscale ensemble.

  • The data assimilation system used to assimilate radar velocity and reflectivity at the convective scale was the 3DVAR system with the WRF model (version 3.4.1) interface that includes a complex cloud analysis package (Gao et al., 2002, 2004; Brewster et al., 2005; Hu et al., 2006a). The system was computationally very efficient, and therefore relatively large model domains could be used on available computers to reduce the effects of lateral boundaries on the convective storms of interest (Stensrud and Gao, 2010). The 3DVAR system used a recursive filter (Purser et al., 2003a, 2003b) with a mass continuity equation and other constraints that were incorporated into a cost function, yielding 3D analyses of the wind components and other model variables. For accurately representing convective-scale storms, multiple analysis passes with different spatial scales of influence were applied in this system.

    The cloud analysis package was initially developed using the Local Analysis and Prediction System (Albers et al., 1996), and then later applied to the ARPS system (Zhang et al., 1998; Brewster, 2002; Hu et al., 2006a). The cloud analysis package uses radar reflectivity and other cloud observational information to update several hydrometeor variables and potential temperature in the 3DVAR analysis step. One limitation of the cloud analysis package is that it does not update the number concentration variables from the double-moment microphysics schemes, and therefore zeroes-out any update made in the analysis to the hydrometeor mixing ratios in the forecast step from the double-moment microphysics schemes. Future studies will address this limitation by using simple methods to diagnose these fields (e.g., see Dawson et al., 2015).

    There are two different temperature adjustment schemes within the cloud analysis package: the latent heat adjustment scheme (LH) and the moist-adiabatic scheme (MA). The LH scheme adjusts the in-cloud temperature based on the latent heat release corresponding to the added cloud water and ice. The MA scheme calculates in-cloud temperature from the moist adiabatic temperature profile corresponding to an air parcel lifted from the low levels. The MA adjustment scheme is therefore more consistent with the physics of convective storms since it reflects the change in temperature in an ascending moist air parcel (Hu and Xue, 2007).

    Figure 2.  Time lines for the storm-scale data assimilation and forecast experiments. Exp5* refers to Exp5LH, Exp5MA and Exp5MA_MP.

  • As mentioned earlier, the focus of this paper is to evaluate the convective-scale analyses and forecasts of the three tornadic supercells that occurred in central Oklahoma on 24 May 2011 using the 3DVAR data assimilation technique. The hourly updated 36-member 15-km mesoscale ensemble was used as the boundary conditions for the 3-km convective scale domain. Radial velocity and reflectivity observations from four operational WSR-88D radars over Oklahoma (Vance Air Force Base (KVNX), Tulsa (KINX), Oklahoma City (KTLX), and Frederick (KFDR)) were assimilated (Fig. 1a) using the 3DVAR technique and cloud analyses package.

    The analysis variables in the 3DVAR system included the U, V and W components of wind, the potential temperature, pressure, and the water vapor mixing ratio. In addition, the potential temperature, rain, snow, and hail mixing ratios were adjusted using the cloud analysis package. Five different ensemble experiments were conducted (Table 3), with different combinations of assimilation frequency, in-cloud temperature adjustment schemes, and microphysics schemes. For the first four experiments, the same assimilation time window of 30 min, starting at 2000 UTC and ending at 2030 UTC, was used. In Exp10LH, radar observations were assimilated every 10 min using the LH in-cloud temperature adjustment scheme. In Exp5LH and Exp5MA, radar observations were assimilated every 5 min using the LH and MA in-cloud temperature adjustment schemes, respectively. These three experiments used the same Thompson microphysics scheme (Thompson et al., 2004). In Exp5MA_MP, the first 12 ensemble members used the semi-double moment Thompson microphysics scheme, the next 12 members used the Morrison double-moment scheme (Morrison et al., 2005), and the last 12 members used the WRF single-moment 6-class (WSM6) scheme (Hong et al., 2004; Hong and Lim, 2006). At the end of the 30-min data assimilation period, 1-hr ensemble forecasts were generated from the above four experiments (i.e., Exp10LH, Exp5LH, Exp5MA and Exp5MA_MP), starting at 2030 UTC and ending at 2130 UTC, which covered the lifetimes of tornadoes A2 and B1, and the partial lifetime of tornadoes A1 and B2 (Table 1). The last experiment, MultiExp, was similar to Exp5MA_MP, but instead of assimilating observations at 5-min intervals for 30 minutes, the assimilation window extended out to 2300 UTC for a total of 180 minutes and 1-h ensemble forecasts were generated from the analyses every 30 min. MultiExp covered the lifetime of all the violent tornadoes over Oklahoma on 24 May 2011 (Table 1 and Fig. 2).

4. Results
  • The overall structure, location, and intensity of the supercell storms from the convective-scale reflectivity analyses and forecasts were compared against the National Mosaic and Multi-Sensor QPE (NMQ) 3D radar reflectivity mosaic (Zhang et al., 2011). The NMQ reflectivity was initially gridded using 1-km grid spacing, and thinned to a 3-km grid spacing to match the convective-scale WRF grid. In addition, the ETS, RMSE and bias of the convective-scale ensemble forecasts were calculated using the continuously cycled 3DVAR analyses produced by MultiExp.

    Figure 3.  Ensemble mean reflectivity (color scale; 5-dB$Z$ increments) and vertical vorticity (black contours; starting at $200\times 10^-5$ s$^{-1}$, with $200\times 10^-5$ s$^{-1}$ increments) analyses at 2030 UTC from (a, b) Exp10LH and (c, d) Exp5LH. (e, f) NSSL NMQ reflectivity observations interpolated to the model grid. Panels (a, c, e) are the horizontal cross sections at 5 km MSL, while panels (b, d, f) are the vertical cross sections along the red lines in the (a, c, e).

    Figure 4.  As in Fig. 3 but for the 30-min ensemble mean forecasts valid at 2100 UTC.

  • The ensemble mean analyses for reflectivity and vertical vorticity at 5 km MSL from the 10-min and 5-min assimilation frequencies are shown in Fig. 3. With only 30 mins of radar observation assimilation, both Exp10LH and Exp5LH were able to analyze the four ongoing supercells (H, A, I, and B in Fig. 3e) reasonably well (Figs. 3a and c). The analysis reflectivity cores at 2030 UTC from both experiments had larger dBZ values than those from the observed cores. The analyzed vorticity for supercell A was along the NWS surveyed damage path of tornado A1 (which was ongoing at 2030 UTC), and the analyzed vorticity for supercell B was also collocated with the damage path of tornado B1 in both experiments. However, closer inspection revealed that the maximum vorticity at 5 km MSL in Exp5LH (6.5× 10-3 s-1) was larger than that in Exp10LH (4.9× 10-3 s-1). The vertical cross section (red line shown in Figs. 3a, c and e) from the 2030 UTC analysis indicated that the strong vertical vorticity was collocated with the high reflectivity columns in both Exp10LH and Exp5LH (Figs. 3b and d). However, the maximum vorticity along the vertical cross section in Exp5LH (6.44× 10-3 s-1) was higher than that in Exp10LH (5.30× 10-3 s-1; Figs. 3b and d).

    The 30-min ensemble mean reflectivity forecast (Figs. 4a and c) indicated that the supercells tended to move too fast northeastwards and generated higher dBZ values in the reflectivity core compared with the synthesized observed reflectivity (Fig. 4e). This was likely due to model error. In addition, storms A and I nearly merged with each other in both experiments, contrary to observations, and the vorticity slowly weakened in the model over the forecast period. The ensemble mean forecasts in Exp5LH generated higher vertical vorticity along the high reflectivity core (Fig. 4d) of supercell B compared to that in Exp10LH (Fig. 4b). However, the 30-min forecasts in Exp5LH and Exp10LH indicated that increasing the assimilation frequency from 10 min to 5 min in this study did not result in any obvious improvements in the forecasts.

    Figure 5.  As in Fig. 3 but for the ensemble mean analysis valid at 2030 UTC for (a, b) Exp5LH and (c, d) Exp5MA.

    Figure 6.  As in Fig. 3 but for the 20-min ensemble mean forecast valid at 2050 UTC for (a, b) Exp5LH and (c, d) Exp5MA.

    Figure 7.  Perturbation potential temperature (K) cross section through storm B at the initial time 2000 UTC in (a) Exp5LH and (b) Exp5MA. These vertical cross sections are drawn along the center of storm B.

  • The ensemble mean reflectivity and vertical vorticity analyses at 5 km MSL from the two different in-cloud temperature adjustment schemes are shown in Fig. 5. While the reflectivity structure in both Exp5LH and Exp5MA were similar (Figs. 5a and c), the vertical vorticity analyses differed in magnitude. Exp5MA generated stronger circulations for all storm cells. The vertical vorticity centers collocated very well with the reflectivity core in both experiments (Figs. 5b and d).

    The 20-min ensemble mean forecast valid at 2050 UTC is shown in Fig. 6. This was the time when storm B spawned tornado B2 (Fig. 1a). The forecast reflectivity field indicated that the supercells moved to the east and split into multiple cells (Fig. 6). Among these cells, strong mesocyclones associated with storm B existed in both Exp5LH and Exp5MA (Figs. 6a-d). Two reflectivity cores existed in experiment Exp5LH, which were caused by a new born cell, formed to the south of the supercell B (Figs. 6a and b). However, the 20-min forecast of Exp5MA associated with storm B looked more reasonable, with the vertical vorticity center embedded within the single reflectivity core (Figs. 6c and d). Overall, the results indicate that the moist adiabatic scheme generates more accurate dynamical fields than the latent heat adjustment scheme. This is probably due to the more realistic moist adiabatic adjustment scheme, as it reflects the temperature change in an ascending moist air parcel and heats the atmosphere through a greater depth compared with that in the latent heat scheme (Fig. 7). This agrees with the findings of another study, by (Hu et al., 2006a).

  • The ensemble spread of reflectivity at 3-km MSL in Exp5MA (dashed line) and Exp5MA_MP (solid line) during the 30-min assimilation period are shown in Fig. 8. The mixed microphysics experiment, Exp5MA_MP, generated larger spread compared with the fixed microphysics experiment, Exp5MA. The larger ensemble spread from the mixed microphysics ensemble was due to the diversity in microphysics schemes across the mixed-microphysics ensemble members. The ensemble spread from Exp10LH, Exp5LH and Exp5MA was very similar (not shown).

    Figure 9.  One-hour forecast time series of RMSE (solid lines) and bias (dashed lines) for (a) reflectivity (dB$Z$), (b) temperature ($^\circ$C), (c) $U$-component wind (m s$^{-1}$) and (d) $V$-component wind (m s$^{-1}$) at 3 km MSL in Exp10LH (black lines), Exp5LH (red lines), Exp5MA (green lines) and Exp5MA_MP (blue lines).

    During the first 20 min of the forecast period, the RMSEs for reflectivity in Exp5LH and Exp5MA were smaller than those in Exp10LH (Fig. 9a). Afterward, the RMSE and bias for reflectivity in Exp10LH were smaller than those in Exp5LH and Exp5MA. The differences in the RMSE for temperature variables between Exp5LH and Exp10LH were more than 1°C, and the temperature bias in Exp5LH was smaller than that in Exp10LH (Fig. 9b). The RMSEs of the U and V components of wind in Exp5LH were slightly smaller than those in Exp10LH (Figs. 9c and d). Compared with the other experiments, Exp5MA_MP generated the smallest RMSE values (Fig. 9a) for the reflectivity field. In addition, the forecast of Exp5MA generated smaller RMSE and bias for the U and V components of wind than those of Exp5LH (Figs. 9c and d).

    Overall, the results indicated that the data assimilation and forecast experiments with a 5-min assimilation frequency, MA in-cloud temperature adjustment scheme, and mixed-microphysics ensemble (Exp5MA_MP) generatedmore accurate forecasts compared with the other three experiments. Thunderstorm simulations are known to be very sensitive to the microphysics parameterization scheme (Gilmore et al., 2004; van den Heever and Cotton, 2004; Snook and Xue, 2008), and one major source of error in storm-scale data assimilation and forecasts is the error introduced into the model from microphysics schemes. By using different microphysics schemes in the ensemble configurations, the different systematic errors associated with the microphysics schemes in the ensemble are diffused compared to that from the single model configuration. Thus, the better performance of Exp5MA_MP was likely due to a more accurate representation of model error associated with the microphysical parameterization schemes in the mixed-microphysics ensemble, which was consistent with the findings of (Snook et al., 2012).

    Figure 8.  Ensemble spread of reflectivity at 3 km MSL during the assimilation period from 2000 UTC to 2030 UTC for Exp5MA (dashed line) and Exp5MA_MP (solid line).

    Figure 9.  One-hour forecast time series of RMSE (solid lines) and bias (dashed lines) for (a) reflectivity (dBZ), (b) temperature (◦C), (c) U-component wind (m s−1) and (d) V-component wind (m s$^−1$) at 3 km MSL in Exp10LH (black lines), Exp5LH (red lines), Exp5MA (green lines) and Exp5MA MP (blue lines).

    Figure 10.  One-hour neighborhood ensemble probability forecasts of column maximum vertical vorticity between 0 to 5 km MSL at each horizontal grid point exceeding a threshold of 0.0025 s$^{-1}$ within a radius of 9 km in MultiExp from every 30-min analysis valid at (a) 2030 UTC, (b) 2100 UTC, (c) 2130 UTC, (d) 2200 UTC, (e) 2230 UTC and (f) 2300 UTC. Overlain in each panel is the NWS surveyed tornado damage tracks (black outline) and the start and end times of observed tornado tracks.

    Figure 11.  One-hour neighborhood ensemble probability forecasts of vorticity between 0 and 5 km MSL, exceeding 90% for thresholds of (a) 0.0025 s$^{-1}$ within a radius of 9 km and (b) 0.0015 s$^{-1}$ within a radius of 6 km in MultiExp at every 30-min analysis interval. Overlain in each panel is the NWS surveyed tornado damage track (black outline) and the start and end times of violent observed tornado tracks.

  • To evaluate how well the system performed in forecasting the rotation associated with the supercell outbreak, we continuously cycled the 5-min data assimilation system using the configuration of Exp5MA_MP for another 3-h period, and launched ensemble forecasts every 30 min from the cycled analyses (MultiExp). The ensemble probability of maximum vertical vorticity was calculated from this experiment.

    The 3-km model horizontal grid spacing used in this study was far too coarse to explicitly resolve any tornado circulation, so we instead focused on mesocyclone forecasts. One measure that can be used to infer the amount of rotation within supercells is the vertical vorticity (Stensrud and Gao, 2010; Dawson et al., 2012; Yussouf et al., 2013a). To do so, the column maximum vertical vorticity between 0 and 5 km MSL at each model horizontal grid point was identified from the 36-member ensemble to calculate the ensemble probability of maximum vertical vorticity using thresholds of 0.0015 s-1 and 0.0025 s-1. The vertical depth of 0-5 km was chosen to account for both low-and mid-level rotation within the supercells. A neighborhood approach (Schwartz et al., 2010; Snook et al., 2012; Yussouf et al., 2013a) was used to calculate the ensemble probabilities, instead of using model horizontal grid point values to account for the mesocyclone position differences across the ensemble members. The forecasts at 5 min intervals were checked to determine whether the 0-5 km maximum vertical vorticity exceeded the threshold within a horizontal radius of 9 km around a grid point. The ensemble probabilities were calculated as the percentage of ensemble members that met the criterion mentioned above. The 1-h ensemble probability forecasts of vertical vorticity exceeding a threshold of 0.0025 s-1 from MultiExp are shown in Fig. 10. A total of six 1-h ensemble forecasts were generated from the analyses starting at 2030 UTC and at 30-min intervals thereafter. The last forecast was generated from the 2300 UTC analyses.

    During this time period, around 11 tornadoes with ratings ranging from EF-0 to EF-5 occurred over Oklahoma (Table 1). The NWS-surveyed damage paths were overlaid on each panel only if that particular tornado was occurring during that forecast period. The forecasts from the 2030 UTC analyses generated a vorticity probability swath that overlapped the damage path of tornadoes A1, A2 and B1 with values above 95% (Fig. 10a). The 1-h forecast vorticity swath beginning at 2100 UTC overlaid the damage path of tornado B2 (Fig. 10b). The forecast probabilities for tornado B3, which was briefly on the ground from 2137 to 2138 UTC, were around 90%. There was another vorticity swath north of the B2-B3 track with 90% probabilities, and no damage path was associated with the swath. The vorticity swaths originating at 2130 UTC (Fig. 10c) aligned well with the damage paths of B3 and B2, with more than 90% probabilities earlier in the forecasts and above 70% later in the forecasts. The vorticity swaths for C1 were above 50%. The forecast vorticity swaths beginning at 2200 UTC matched the time of the observed tornado tracks of B2, C1 and C2, and the probabilities were almost all above 90% (Fig. 10d). The forecast probabilities starting at 2230 UTC were above 15%-70% nearby the observed tornado tracks, except tornado B4. For the forecasts initiated at 2300 UTC, the probabilities of vorticity were above 95% for tornado B4, but no substantial probability of strong rotation was associated with tornado D3, which occurred at 2336 UTC.

    The 1-h forecast probabilities with threshold values of 0.0025 s-1 and exceeding 90%, generated from the every 30-min analysis of MultiExp (Fig. 11a), covered the timing of the observed tornado tracks for most of the tornados, except C2, D1, D2 and D3. Among those four tornadoes, C2 was rated EF-0, D2 was EF-1, D3 was EF-2, and only D1 was EF-4. This indicates that relatively weaker tornadoes may need lower threshold values to display the vorticity swaths. To the left (northwest) of the B tornado family, there was another model-generated high-probability rotation, but no tornado was associated with that forecast vorticity swath. The system was able to forecast vorticity swaths for those weaker (C2, D1 and D2) tornadoes with probabilities above 90% (Fig. 11b) when the vorticity threshold value was reduced to 0.0015 s-1. Further inspection revealed that the strength of the forecast vorticity swaths with thresholds lower than the values used in this study (i.e., 0.0025 s-1 and 0.0015 s-1) showed a gradual increase in probabilities for all storms, including the weaker tornadic storms (not shown). However, lowering the threshold value also increased the rotation area northwest of the B tornado family, where there were no tornadoes. Therefore, while decreasing the threshold value may increase the probability of rotation of weaker tornadoes, it may also increase the false alarm rate. Similar results also were reported by (Yussouf et al., 2015). This indicates that, even using a 3-km grid resolution, the ensemble may be skillful in forecasting an individual storm's rotation intensity, but it also raises an important question on how to calibrate or differentiate between tornadic and non-tornadic supercells at grid resolutions that cannot explicitly resolve tornado-scale circulations. This question was outside the scope of the current study and remains open for future investigation.

5. Summary and discussion
  • In this study, several analysis and forecast experiments were conducted for the tornadic supercell storms that occurred on 24 May 2011 in Oklahoma, USA, using the ARPS 3DVAR analysis system and WRF-ARW forecast model. A 36-member multiphysics, multiscale ensemble was used to provide the initial and boundary conditions for the ensemble 3DVAR analysis and forecast system. Radial velocity and reflectivity observations from four WSR-88D radars were assimilated into the convective-scale ensemble using the ensemble 3DVAR approach. Five different data assimilation and forecasts experiments were conducted at the convective scale to evaluate the sensitivity of the system to the observation assimilation time frequencies, in-cloud temperature adjustment schemes, fixed- and mixed-microphysics ensembles, and assimilation time window. The results indicated that the assimilation of radar observations with a 5-min frequency produced more accurate analyses and forecasts of temperature and U and V wind components than those with a 10-min assimilation frequency. The ensemble forecasts from the moist adiabatic scheme (Exp5MA) generated more accurate dynamical fields than in the experiment with the latent heating scheme (Exp5LH), and the RMSEs for these fields were lower in Exp5MA. This was likely due to the more realistic representation of temperature adjustments of the ascending moist air parcel in the moist adiabatic adjustment scheme.

    Overall, the results of the sensitivity experiments revealed that Exp5MA_MP performed better than the other experiments. This was likely due to the better representation of the microphysical errors in the mixed-microphysics ensemble. The composite neighborhood ensemble probabilistic forecasts of vertical vorticity with different threshold values indicated that the ensemble was able to forecast the rotation embedded in the supercells for most of the observed storms, and the vorticity swaths aligned well with the observed damage tracks with high probabilities. The overall encouraging results obtained from this study provide reasons for cautious optimism and motivate us to conduct further studies on how best to assimilate radar observations using an ensemble-based 3DVAR data assimilation and forecast system for NOAA's "Warn-on-Forecast" program.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return