doi:10.1007/s00376-015-5072-0

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- 1824.4ff70edc7106f4b2ade206a5b4a03de5

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2009MWRv..137.1805A%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20314

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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;2

26708041e58d4e55890019b5592d65be

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1996WtFor..11..273A

http://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;2

5a7ccea0916644ffbe03b37855cac972

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126175%2F

http://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/JTECH2049.1

178eb9ff1d10d90754004ab07818d36f

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007JAtOT..24.1452A

http://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- 1296.10.1175/2009BAMS2618.1

5e2309fa474dc306b6bd79877b6514b2

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009BAMS...90.1283A

http://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-13-00107.1

420a2ed8-a11e-4ac2-9dca-eb78ad33799a

7fc627eae8c0f96059794a40067b53b0

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014WtFor..29..601C

http://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-11-00147.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012WtFor..27.1090C

refpaperuri:(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-11-00040.1

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FADS%3Fid%3D2012BAMS...93...55C

refpaperuri:(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-14-00279.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.2241D

http://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.2008.11.003

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http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809508003438

http://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- 2005.26e7960473172f8beb0c69dab3593615

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004mwrv..132.1982d

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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;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002WtFor..17..873E

http://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-11-00299.1

24408e1ef1bb14ea283b70edf48fae4c

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140.2609F

http://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/MWR3391.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007MWRv..135.1846F

refpaperuri:(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;2

346ce470c76cafb3a5725afd8d57ae47

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1999MWRv..127.2128G

http://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-ff71fc082469

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http%3A%2F%2Fams.confex.com%2Fams%2FSLS_WAF_NWP%2Ftechprogram%2Fpaper_47480.htm

http://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.e485d4c3dc07366bc3f77e8a55f22a67

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JAtOT..21..457G%26db_key%3DPHY%26link_type%3DABSTRACT

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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-12-00093.1

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1175%2FWAF-D-12-00093.1

refpaperuri:(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-12-00193.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.2759G

http://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, 947874.10.1155/2013/947874

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http%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066630

http://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- 2627.287c5843da3cd2fd8b908873b8609c04

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132.2610G

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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-3a85d1612e96

7308c59e0fe08d8147ff5b2869261e63

http%3A%2F%2Fwww.dbpia.co.kr%2FJournal%2FArticleDetail%2F773025

refpaperuri:(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- 525.28b4d2bd5164a0a469d91feb411adc27

http%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/MWR3092.1

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http%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_Impact

refpaperuri:(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.e68995d56eb95b68ae2302a542d45823

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006mwrv..134..675h

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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-14-00180.1

cf3cbe7a5ce02b80db430829cad4a263

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143..165J

http://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- 1521.10.1175/2010WAF2222405.1

383a9cf8f8f88b655810a920b7880f57

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010WtFor..25.1510K

http://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.bcca90161eb1dd3cb83115f6fce9ac9a

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JAtS...62.1665M%26db_key%3DPHY%26link_type%3DABSTRACT

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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;2

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http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DGZS200305019.htm

refpaperuri:(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-1d9c3c126431

b035f84e95d20e3c8282fef9aadb6a43

http%3A%2F%2Fwww-frd.fsl.noaa.gov%2Fwgs%2Fdawg%2Fonrfa.pdf

refpaperuri:(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-92c9-6c2655103c2f

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010wtfor..25..263s

refpaperuri:(31dcb9aed6bb84995676b04de0407711)

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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.133fdf5edd3fc85654e5fe959ecf2a0a

http%3A%2F%2Fntis.library.gatech.edu%2Fhandle%2F123456789%2F2086

http://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-13-00044.1

79c90e73df997cb9773ca07b9f3f5e58

http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F94254576%2Fexamination-real-time-3dvar-analysis-system-hazardous-weather-testbed

http://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 GL035866.10.1029/2008GL035866

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2008GL035866%2Fabstract

http://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-11-00117.1

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http%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-data

refpaperuri:(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//2555.1

a194066e15b2ee2df62c951e4a125be3

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2003MWRv..131.1663S

http://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.f8e6aebd9aa7c95c33e343b3fcd40897

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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;2

ce1af61652babce38ebdb00741c1bf71

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2000MWRv..128.2077S

http://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- 1499.10.1175/2009BAMS2795.1

5eba901df3a24da0c9813e34b832235b

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F237966541_Convective-Scale_Warn-on-Forecast_System_A_Vision_for_2020

http://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.2012.04.004

c3691d105fd03a0114de3772f8be7f41

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS016980951200110X

http://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-ae04-28300fae3968

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http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F326692%2Fdynamical-microphysical-retrieval-from-doppler-radar-observations-using-cloud-model-its

refpaperuri:(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;2

06703f06f81842553d28e8acfa526f3e

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132..519T

http://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.2423

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2423%2Fabstract

http://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/MWR2898.1

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F228650237_Ensemble_Kalman_filter_assimilation_of_Doppler_radar_data_with_a_compressible_nonhydrostatic_model_OSS_experiments

refpaperuri:(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-424dc6b9cc56

ac49480c31b67a12776c6fca90daca0d

http%3A%2F%2Fci.nii.ac.jp%2Fncid%2FBB08575029

refpaperuri:(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;2

5bfe2c24f5922ed197d4b53f5248e5b6

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004JAtS...61.1596V

http://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.x

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2007.00273.x%2Fcitedby

http://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-11-00106.1

a2a9f8252efd4eb7694b4657cf610443

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140.1539W

http://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-13-00260.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014MWRv..142.3243W

http://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-a374-4da5fac40e5b

8c2fa4cef92e4be3cdb7d9698afb8c6f

http%3A%2F%2Fams.confex.com%2Fams%2F94Annual%2Fwebprogram%2FPaper233114.html

http://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, 731647.10.1155/2013/731647

a681222afbb519ebde79e69b2730b5fa

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066639

http://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-14-00268.1

cf1d2afc418811798f2295ae856758f3

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.3044Y

http://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- 188.70031d4fa69761d4722fd70a4204a20e

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http://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;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132.1238Z

http://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-11-00047.1

82a747d4c3553e14079fbb7469c436ab

http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F70339129%2Fnational-mosaic-multi-sensor-qpe-nmq-system-description-results-future-plans

http://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.