doi:10.1007/s00376-016-6004-3

Albers S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1997: 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..273AThe Local Analysis and Prediction System 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 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 IR 11.24-渭m data, other visible and multispectral imagery, a three-dimensional temperature analysis, and a three-dimensional 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 number of derived fields are calculated including cloud type, liquid water content, ice content, and icing severity, as well as precipitation type, concentration, and accumulation. Results from validating the cloud fields against independent data obtained during the Winter Icing and Storms Project are presented. Forecasters can now make use of these analyses in a variety of situations, such as depicting sky cover and radiation characteristics over a region, three-dimensionally delineating visibility and icing conditions for aviation, depicting precipitation type, rain and snow accumulation, etc.

Auligné, T., A. Lorenc, Y. Michel, T. Montmerle, A. Jones, M. Hu, J. Dudhia, 2011: Toward a new cloud analysis and prediction system. Bull. Amer. Meteor. Soc., 92, 207- 210.c050669426c2b0923f37aeee6240f9f5

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2011BAMS...92..207A%26db_key%3DPHY%26link_type%3DABSTRACT

http://xueshu.baidu.com/s?wd=paperuri%3A%2829ce78e3dff252beeea849d14fdcee0d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2011BAMS...92..207A%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8&sc_us=9076614415181720180

Barker D., Coauthors, 2012: The weather research and forecasting model's community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc., 93, 831- 843.10.1175/BAMS-D-11-00167.1

d0ce9a1e48e6f6f742201862bf6def02

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F239866111_Weather_Research_and_Forecasting_Model%27s_Community_VariationalEnsemble_Data_Assimilation_System_WRFDA

http://www.researchgate.net/publication/239866111_Weather_Research_and_Forecasting_Model's_Community_VariationalEnsemble_Data_Assimilation_System_WRFDAData assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

Bauer P., Coauthors, 2011: Satellite cloud and precipitation assimilation at operational NWP centres. Quart. J. Roy. Meteor. Soc., 137, 1934- 1951.10.1002/qj.905

a855659020018f1528ced7752c8b91c9

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.905%2Ffull

http://onlinelibrary.wiley.com/doi/10.1002/qj.905/fullAbstract Top of page Abstract 1.Introduction 2.NWP systems 3.Assimilation of clouds and precipitation 4.Discussion and future plans Acknowledgements References The status of current efforts to assimilate cloud- and precipitation-affected satellite data is summarised with special focus on infrared and microwave radiance data obtained from operational Earth observation satellites. All global centres pursue efforts to enhance infrared radiance data usage due to the limited availability of temperature observations in cloudy regions where forecast skill is estimated to strongly depend on the initial conditions. Most systems focus on the sharpening of weighting functions at cloud top providing high vertical resolution temperature increments to the analysis, mainly in areas of persistent high and low cloud cover. Microwave radiance assimilation produces impact on the deeper atmospheric moisture structures as well as cloud microphysics and, through control variable and background-error formulation, also on temperature but to lesser extent than infrared data. Examples of how the impacts of these two observation types are combined are shown for subtropical low-level cloud regimes. The overall impact of assimilating such data on forecast skill is measurably positive despite the fact that the employed assimilation systems have been constructed and optimized for clear-sky data. This leads to the conclusion that a better understanding and modelling of model processes in cloud-affected areas and data assimilation system enhancements through inclusion of moist processes and their error characterization will contribute substantially to future forecast improvement. Copyright 漏 2011 Royal Meteorological Society, Crown in the right of Canada, and British Crown copyright, the Met Office

Benjamin, S. G., Coauthors, 2004: An hourly assimilation-forecast cycle: The RUC. Mon. Wea. Rev., 132, 495- 518.10.1175/1520-0493(2004)1322.0.CO;2

99e98620a0eb9aac9822fca6d5c3a124

http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126454%2F

http://ci.nii.ac.jp/naid/10013126454/The Rapid Update Cycle (RUC), an operational regional analysis09“forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic09“sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed. A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.

Bukovsky M. S., D. J. Karoly, 2009: Precipitation simulations using WRF as a nested regional climate model. J. Appl. Meteor. Climatol., 48, 2152- 2159.10.1175/2009JAMC2186.1

f6e20e421126ef243da92938bd56d699

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009JApMC..48.2152B

http://adsabs.harvard.edu/abs/2009JApMC..48.2152BThis note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast–type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction–National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis.

Chen Y. D., S. R. H. Rizvi, X.-Y. Huang, J. Z. Min, and X. Zhang, 2013. Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and arctic regions. Meteor. Atmos. Phys.,121, 79-98, doi: 10.1007/s00703-013-0251-y.f3f74776060ffa3cd20f90cfdf2089c8

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2013MAP...121...79C%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20314

http://xueshu.baidu.com/s?wd=paperuri%3A%28f3637b3f6b679fb61de5714b6abf63f4%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2013MAP...121...79C%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20314&ie=utf-8&sc_us=10306275418552469499

Chen Y. D., H. L. Wang, J. Z. Min, X. Y. Huang, P. Minnis, R. Z. Zhang, J. Haggerty, and R. Palikonda, 2015: Variational assimilation of cloud liquid/ice water path and its impact on NWP. J. Appl. Meteor. Climatol., 54, 1809- 1825.10.1175/JAMC-D-14-0243.1

4dcb260bd54ae2f0f52962a7ee5c185f

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JApMC..54.1809C

http://adsabs.harvard.edu/abs/2015JApMC..54.1809CNot Available

Desroziers G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385- 3396.10.1256/qj.05.108

bb20c7b84df4cd405f24960f4b4632fd

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1256%2Fqj.05.108%2Fabstract

http://onlinelibrary.wiley.com/doi/10.1256/qj.05.108/abstractAbstract Most operational assimilation schemes rely on linear estimation theory. Under this assumption, it is shown how simple consistency diagnostics can be obtained for the covariances of observation, background and estimation errors in observation space. Those diagnostics are shown to be nearly cost-free since they only combine quantities available after the analysis, i.e. observed values and their background and analysis counterparts in observation space. A first application of such diagnostics is presented on analyses provided by the French 4D-Var assimilation. A procedure to refine background and observation-error variances is also proposed and tested in a simple toy analysis problem. The possibility to diagnose cross-correlations between observation errors is also investigated in this same simple framework. A spectral interpretation of the diagnosed covariances is finally presented, which allows us to highlight the role of the scale separation between background and observation errors. Copyright 2005 Royal Meteorological Society

Errico R. M., P. Bauer, and J. F. Mahfouf, 2007: Issues regarding the assimilation of cloud and precipitation data. J. Atmos. Sci., 64, 3785- 3798.10.1175/2006JAS2044.1

ad2bcfd54779918fcc8c0e803284665b

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2007JAtS...64.3785E

http://adsabs.harvard.edu/abs/2007JAtS...64.3785EThe assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation鈥搊perator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.

Hong S.-Y., J.-H. Kim, J.-O. Lim, and J. Dudhia, 2006: The WRF single moment microphysics scheme (WSM). J. Korean Meteor. Soc., 2006, 42, 129- 151.15215204854ecc11e3e7bb96546229a6

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F241516528_The_WRF_single_moment_microphysics_scheme_WSM

http://www.researchgate.net/publication/241516528_The_WRF_single_moment_microphysics_scheme_WSM

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

97de4b71474781242f9bc2176f727b52

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006MWRv..134..675H

http://adsabs.harvard.edu/abs/2006MWRv..134..675HIn 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- 721.10.1175/MWR3092.1

0ed978d0-1b69-424f-b8e8-3e07a2a37390

97de4b71474781242f9bc2176f727b52

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006MWRv..134..675H

refpaperuri:(2a1b9c7a13ebb23ea77ced12a2fbf3d1)

Jones T. A., D. J. Stensrud, P. Minnis, and R. Palikonda, 2003: Evaluation of a forward operator to assimilate cloud water path into WRF-DART. Mon. Wea. Rev., 141, 2272- 2289.10.1175/MWR-D-12-00238.1

75870dccda8678c31db4605922999d93

http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1175%2FMWR-D-12-00238.1

http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1175/MWR-D-12-00238.1Not Available

Lim K. S. S., S. Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587- 1612.10.1175/2009MWR2968.1

99ea76b73c96b8b762e69f85ba5dae04

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2010mwrv..138.1587l

http://adsabs.harvard.edu/abs/2010mwrv..138.1587lA new double-moment bulk cloud microphysics scheme, the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) Microphysics scheme, which is based on the WRF Single-Moment 6-class (WSM6) Microphysics scheme, has been developed. In addition to the prediction for the mixing ratios of six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel) in the WSM6 scheme, the number concentrations for cloud and rainwater are also predicted in the WDM6 scheme, together with a prognostic variable of cloud condensation nuclei (CCN) number concentration. The new scheme was evaluated on an idealized 2D thunderstorm test bed. Compared to the simulations from the WSM6 scheme, there are greater differences in the droplet concentration between the convective core and stratiform region in WDM6. The reduction of light precipitation and the increase of moderate precipitation accompanying a marked radar bright band near the freezing level from the WDM6 simulation tend to alleviate existing systematic biases in the case of the WSM6 scheme. The strength of this new microphysics scheme is its ability to allow fle xibility in variable raindrop size distribution by predicting the number concentrations of clouds and rain, coupled with the explicit CCN distribution, at a reasonable computational cost.

Lin Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk Parameterization of the snow field in a cloud model. J. Appl. Meteor., 22, 1065- 1092.10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2

9190891c3775ec6ca868fe681504eba0

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1983japme..22.1065l

http://adsabs.harvard.edu/abs/1983japme..22.1065lA two-dimensional, time-dependent cloud model has been used to simulate a moderate intensity thunderstorm for the High Plains region. Six forms of water substance (water vapor, cloud water, cloud ice, rain, snow and hail, i.e., graupel) are simulated. The model utilizes the `bulk water' microphysical parameterization technique to represent the precipitation fields which are all assumed to follow exponential size distribution functions. Autoconversion concepts are used to parameterize the collision-coalescence and collision-aggregation processes. Accretion processes involving the various forms of liquid and solid hydrometeors are simulated in this model. The transformation of cloud ice to snow through autoconversion (aggregation) and Bergeron process and subsequent accretional growth or aggregation to form hail are simulated. Hail is also produced by various contact mechanisms and via probabilistic freezing of raindrops. Evaporation (sublimation) is considered for all precipitation particles outside the cloud. The melting of hail and snow are included in the model. Wet and dry growth of hail and shedding of rain from hail are simulated.The simulations show that the inclusion of snow has improved the realism of the results compared to a model without snow. The formation of virga from cloud anvils is now modeled. Addition of the snow field has resulted in the inclusion of more diverse and physically sound mechanisms for initiating the hail field, yielding greater potential for distinguishing dominant embryo types characteristically different from warm- and cold-based clouds.

Lin Y. L., B. A. Colle, 2011: A new bulk microphysical scheme that includes riming intensity and temperature-dependent ice characteristics. Mon. Wea. Rev., 139, 1013- 1035.10.1175/2010MWR3293.1

13db92973bf83d35bb5cc37f8776f0e5

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011MWRv..139.1013L

http://adsabs.harvard.edu/abs/2011MWRv..139.1013LA new bulk microphysical parameterization (BMP) scheme is presented that includes a diagnosed riming intensity and its impact on ice characteristics. As a result, the new scheme represents a continuous spectrum from pristine ice particles to heavily rimed particles and graupel using one prognostic variable [[precipitating ice (PI)]] rather than two separate variables (snow and graupel). In contrast to most existing parameterization schemes that use fixed empirical relationships to describe ice particles, general formulations are proposed to consider the influences of riming intensity and temperature on the projected area, mass, and fall velocity of PI particles. The proposed formulations are able to cover the variations of empirical coefficients found in previous observational studies. The new scheme also reduces the number of parameterized microphysical processes by 芒藛录芒藛录50%% as compared to conventional six-category BMPs and thus it is more computationally efficient. The new scheme (called SBU-YLIN) has been implemented in the Weather Research and Forecasting (WRF) model and compared with three other schemes for two events during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) over the central Oregon Cascades. The new scheme produces surface precipitation forecasts comparable to more complicated BMPs. The new scheme reduces the snow amounts aloft as compared to other WRF schemes and compares better with observations, especially for an event with moderate riming aloft. Sensitivity tests suggest both reduced snow depositional growth rate and more efficient fallout due to the contribution of riming to the reduction of ice water content aloft in the new scheme, with a larger impact from the partially rimed snow and fallout.

McNally A. P., 2009: The direct assimilation of cloud-affected satellite infrared radiances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 135, 1214- 1229.10.1002/qj.426

a9be0aa105ebe57299791063aa447c92

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.426%2Ffull

http://onlinelibrary.wiley.com/doi/10.1002/qj.426/fullNot Available

Migliorini S., 2012: On the equivalence between radiance and retrieval assimilation. Mon. Wea. Rev., 140, 258- 265.10.1175/MWR-D-10-05047.1

f6327737209964a5951420f32718e749

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140..258M

http://adsabs.harvard.edu/abs/2012MWRv..140..258MNot Available

Minnis P., 2007: Cloud retrievals from GOES-R. Proc. OSA Hyperspec. Imaging Sounding of Environ. Topical Mtg., Santa Fe, NM, Feb. 11-15, CD-ROM, HWC3m.

Minnis, P., Coauthors, 2008: Near-real time cloud retrievals from operational and research meteorological satellites. Proc. SPIE Europe Remote Sens., Cardiff, Wales, UK, 15-18 September, 7107, No. 2, 8 pp.10.1117/12.800344

cb4fab43e12a8d3b3e6fa9d189d8ba6f

http%3A%2F%2Fspie.org%2FPublications%2FProceedings%2FPaper%2F10.1117%2F12.800344

http://spie.org/Publications/Proceedings/Paper/10.1117/12.800344A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications. 漏 2008 SPIE.

Minnis P., Coauthors, 2011: CERES edition-2 cloud property retrievals using TRMM VIRS and terra and aqua MODIS Data art I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 4374- 4400.10.1109/TGRS.2011.2144601

b793d3ae-80e9-4c8b-8f27-9677586f92f5

dfc68b00cb5577519824cab39a9b6539

http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Ficp.jsp%3Farnumber%3D5783916

refpaperuri:(aed471c8e89dbd756569b3a9039a4dff)

http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5783916Not Available

Minnis P., Coauthors, 2012: Simulations of infrared radiances over a deep convective cloud system observed during TC4: Potential for enhancing nocturnal ice cloud retrievals. Remote Sensing, 4, 3022- 3054.10.3390/rs4103022

d67d179693ae42f15e9d9855f158328e

http%3A%2F%2Fwww.oalib.com%2Fpaper%2F166746

http://www.oalib.com/paper/166746Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 mm can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses t 20, the 3.7–10.8 μm and 3.7–6.7 μm BTDs are the most sensitive to De. Satellite imagery appears to be consistent with these results suggesting that t and De could be retrieved for greater optical thicknesses than previously assumed. But, because of sensitivity of the BTDs to uncertainties in the atmospheric profiles of temperature, humidity, and ice water content, and sensor noise, exploiting the small BTD signals in retrieval algorithms will be very challenging.

Okamoto K., A. P. McNally, and W. Bell, 2014: Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects. Quart. J. Roy. Meteor. Soc., 140, 1603- 1614.10.1002/qj.2242

242062da1e003efc3d1674add536ea66

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.2242%2Fcitedby

http://onlinelibrary.wiley.com/doi/10.1002/qj.2242/citedbyAs a step toward the assimilation of cloud‐affected infrared radiances in multi‐layer cloud conditions, this study evaluates cloud effects on model first‐guess simulations (background) and observations using the Infrared Atmospheric Sounding Interferometer (IASI) radiances. It is found from an extensive statistical analysis that over oceans the magnitude of observation‐minus‐background departures (O–B) – even in the most cloud‐sensitive window channels – is typically less than 10 K for 85% of all‐sky IASI data. A parameter has been developed to express the magnitude of the cloud effect based upon observed and simulated cloudy radiances. It is shown that the variations in the standard deviation (SD) of O–B departures can be described (and thus predicted) by this cloud effect parameter – such that the probability density function (PDF) of O–B normalized with predicted O–B SD exhibits a near‐Gaussian form. It is argued that the predicted cloud effect can be used in an assimilation context to define cloud‐dependent quality controls and aid observation error assignment. Simple linear estimation theory is used to simulate the possible benefits of state‐dependent observation errors according to cloud effect.

Parrish D. F., J. C. Derber, 1992: The national meteorological center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747- 1763.10.1175/1520-0493(1992)1202.0.CO;2

4214772c1895942c49e785d413b108e0

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1992MWRv..120.1747P

http://adsabs.harvard.edu/abs/1992MWRv..120.1747PAt the National Meteorological Center (NMC), a new analysis system is being extensively tested for possible use in the operational global data assimilation system. This analysis system is called the because the spectral coefficients used in the NMC spectral model are analyzed directly using the same basic equations as statistical (optimal) interpolation. Results from several months of parallel testing with the NMC spectral model have been very encouraging. Favorable features include smoother analysis increments, greatly reduced changes from initialization, and significant improvement of 1-5-day forecasts. Although the analysis is formulated as a variational problem, the objective function being minimized is formally the same one that forms the basis of all existing optimal interpolation schemes. This objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the invent of the corresponding forecast- and observation-error covariance matrices. There are two principal differences in how the SSI implements the minimization of this functional as compared to the current OI used at NMC. First, the analysis variables are spectral coefficients instead of gridpoint values. Second, all observations are used at once to solve a single global problem. No local approximations are made, and there is no special data selection. Because of these differences, it is straightforward to include unconventional data, such as radiances, in the analysis. Currently temperature, wind, surface pressure, mixing, ratio, and Special Sensor Microwave/lmager (SSM/I) total precipitable water can be used as the observation variables. Soon to be added are the scatterometer surface winds. This paper provides a detailed description of the SSI and presents a few results.

Pincus R., R. J. Patrick Hofmann, J. L. Anderson, K. Raeder, N. Collins, and J. S. Whitaker, 2011: Can fully accounting for clouds in data assimilation improve short-term forecasts by global models? Mon. Wea. Rev., 139, 946- 957.10.1175/2010MWR3412.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011MWRv..139..946P

http://adsabs.harvard.edu/abs/2011MWRv..139..946PNot Available

Polkinghorne R., T. Vukicevic, 2011: Data assimilation of cloud-affected radiances in a cloud-resolving model. Mon. Wea. Rev., 139, 755- 773.10.1175/2010MWR3360.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011MWRv..139..755P

http://adsabs.harvard.edu/abs/2011MWRv..139..755PAssimilation of cloud-affected infrared radiances from the () is performed using a four-dimensional variational data assimilation (4DVAR) system designated as the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). A cloud mask is introduced in order to limit the assimilation to points that have the same type of cloud in the model and observations, increasing the linearity of the minimization problem. A series of experiments is performed to determine the sensitivity of the assimilation to factors such as the maximum-allowed residual in the assimilation, the magnitude of the background error decorrelation length for water variables, the length of the assimilation window, and the inclusion of other data such as ground-based data including data from the Atmospheric Emitted Radiance Interferometer (AERI), a microwave radiometer, radiosonde, and cloud radar. In addition, visible and near-infrared satellite data are included in a separate experiment. The assimilation results are validated using independent ground-based data. The introduction of the cloud mask where large residuals are allowed has the greatest positive impact on the assimilation. Extending the length of the assimilation window in conjunction with the use of the cloud mask results in a better-conditioned minimization, as well as a smoother response of the model state to the assimilation.

Rao, Rama Y. V., H. R. Hatwar, A. Kamal Salah, Y. Sudhakar, 2007: An experiment using the high resolution eta and WRF models to forecast heavy precipitation over India. Pure Appl. Geophys., 164, 1593- 1615.10.1007/s00024-007-0244-1

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http%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fklu%2F24%2F2007%2F00000164%2FF0020008%2F00000244

http://www.ingentaconnect.com/content/klu/24/2007/00000164/F0020008/00000244In the present study using the Weather Research and Forecasting (WRF) and Eta models, recent heavy rainfall events that occurred (i) over parts of Maharastra during 26 to 27 July, 2005, (ii) over coastal Tamilnadu and south coastal Andhra Pradesh during 24 to 28 October, 2005, and (iii) the tropical cyclone of 30 September to 3 October, 2004/Monsoon Depression of 2 to 5 October 2004, that developed during the withdrawal phase of the southwest monsoon season of 2004 have been investigated. Also sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather events. The results show that the WRF model with the microphysical process and cumulus parameterization schemes of Ferrier et al . and Betts-Miller-Janjic was able to capture the heavy rainfall events better than the other schemes. It is also observed that the WRF model was able to predict mesoscale rainfall more realistically in comparison to the Eta model of the same resolution.

Roberts N. M., H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78- 97.10.1175/2007MWR2123.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008mwrv..136...78r

http://adsabs.harvard.edu/abs/2008mwrv..136...78rThe development of NWP models with grid spacing down to 鈭1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10-15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40-70 km, some 10-20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.

Shen Y., P. Zhao, Y. Pan, and J. J. Yu, 2014: A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res.: Atmos., 119, 3063- 3075.10.1002/2013JD020686

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http://onlinelibrary.wiley.com/doi/10.1002/2013JD020686/abstracthourly rain gauge data at more than 30,000 automatic weather stations in China, in conjunction with the Climate Precipitation Center Morphing (CMORPH) precipitation product for the 2008-2010 warm seasons (from May through September), we assess the capability of the probability density function-optimal interpolation (PDF-OI) methods in generating the daily, 0.25° × 0.25° and hourly, 0.1° × 0.1° merged precipitation products between gauge observations and the CMORPH product. We find that error correlation, error variances of gauge and satellite data, and matching strategy in the PDF-OI method are dependent on the spatial and temporal resolutions of the used data. Efforts to improve the parameters and matching strategy for the hourly and 0.1°× 0.1° product have been conducted. These improvements are not only suitable to a high-frequency depiction of no-rain events, but accurately describe the error structures of hourly gauge and satellite fields. The successive merged precipitation algorithm or product is called the original PDF-OI (Orig_PDF-OI) and the improved PDF-OI, respectively. The cross-validation results show that the improved method reduces systematic bias and random errors effectively compared with both the CMORPH precipitation and the Orig_PDF-OI. The improved merged precipitation product over China at hourly, 0.1° resolution is generated from 2008 to 2010. Compared with the Orig_PDF-OI, the improved product reduces the underestimation greatly and has smaller bias and root-mean-square error, and higher spatial correlation. The improved product can better capture some varying features of hourly precipitation in heavy weather events.

Sun J. Z., N. A. Crook, 2010: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642- 1661.10.1175/1520-0469(1997)0542.0.CO;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1997jats...54.1642s

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http://adsabs.harvard.edu/abs/1997jats...54.1642sThe purpose of the research reported in this paper is to develop a variational data analysis system that can be used to assimilate data from one or more Doppler radars. In the first part of this two-part study, the technique used in this analysis system is described and tested using data from a simulated warm rain convective storm. The analysis system applies the 4D variational data assimilation technique to a cloud-scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields are determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities (or rainwater mixing ratio) and their model predictions. The adjoint of the numerical model is used to provide the sensitivity of the cost function with respect to the control variables.Experiments using data from a simulated convective storm demonstrated that the variational analysis system is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler information. However, less accurate velocity fields are obtained when single-Doppler data were used. In both cases, retrieving the temperature field is more difficult than the retrieval of the other fields. Results also show that assimilating the rainwater mixing ratio obtained from the reflectivity data results in a better performance of the retrieval procedure than directly assimilating the reflectivity. It is also found that the system is robust to variations in the Z-qrelation, but the microphysical retrieval is quite sensitive to parameters in the warm rain scheme. The technique is robust to random errors in radial velocity and calibration errors in reflectivity.

Sun J. Z., H. L. Wang, 2013: WRF-ARW variational storm-scale data assimilation: Current capabilities and future developments. Advances in Meteorology, 2013, 815910.10.1155/2013/815910

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http%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066673

http://www.oalib.com/paper/3066673The variational radar data assimilation system has been developed and tested for the Advanced Research Weather Research and Forecasting (WRF-ARW) model since 2005. Initial efforts focused on the assimilation of the radar observations in the 3-dimensional variational framework, and recently the efforts have been extended to the 4-dimensional system. This article provides a review of the basics of the system and various studies that have been conducted to evaluate and improve the performance of the system. Future activities that are required to further improve the system and to make it operational are also discussed. 1. Introduction In the past two decades active research was conducted on the development of techniques to initialize storm-scale numerical prediction models. It has been recognized that the success will critically depend on the optimal use of the national operational WSR-88D radar network that covers the United States with single Doppler coverage in most areas. Although the network provides observations at a resolution that is able to resolve atmospheric convection, they are only limited to radial wind and reflectivity. Therefore several early studies focused on the feasibility of retrieving meteorological fields from these single Doppler observations. Techniques with different complexities have been developed which aim at obtaining the unobserved meteorological variables such as 3-dimensional (3D) wind, temperature, and microphysical fields from the radar observations of radial velocity and reflectivity (e.g., [1-5]). The techniques that make use of a numerical model in a data assimilation (DA) context received particular attention because they combine the retrieval, initialization, and forecast in one system. The first radar DA system for the storm-scale was developed based on the 4-dimensional variational data assimilation (4D-Var) technique and a boundary layer fluid dynamics model for the retrieval of the 3D wind and temperature [1]. This system, known as VDRAS (Variational Doppler Radar Analysis System), was later expanded to include microphysical retrieval, as well as short-term forecasts initialized by these retrieved fields [6-9]. Another variational-based radar DA system was developed by Gao et al. [4] using a 3-dimensional variational data assimilation (3D-Var) technique in the framework of the ARPS (Advanced Research and Prediction System [10]) model. A so-called 3.5-dimensional variational radar data assimilation based on Navy鈥檚 COAMPS (The Coupled Ocean/Atmosphere Mesoscale Prediction System) was developed and demonstrated

Wang H. L., J. Z. Sun, X. Zhang, X.-Y. Huang, and T. Auligné, 2013: Radar data assimilation with WRF 4D-Var. Part I: System development and preliminary testing. Mon. Wea. Rev., 141, 2224- 2244.10.1175/MWR-D-12-00168.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.2224W

http://adsabs.harvard.edu/abs/2013MWRv..141.2224WThe major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows.

Zhu T., D. L. Zhang, 2006: Numerical simulation of hurricane bonnie (1998). Part II: Sensitivity to varying cloud microphysical processes. J. Atmos. Sci., 63, 109- 126.10.1175/JAS3599.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006JAtS...63..109Z

http://adsabs.harvard.edu/abs/2006JAtS...63..109ZIn this study, the effects of various cloud microphysics processes on the hurricane intensity, precipitation, and inner-core structures are examined with a series of 5-day explicit simulations of Hurricane Bonnie (1998), using the results presented in Part I as a control run. It is found that varying cloud microphysics processes produces little sensitivity in hurricane track, except for very weak and shallow storms, but it produces pronounced departures in hurricane intensity and inner-core structures. Specifically, removing ice microphysics produces the weakest (15-hPa underdeepening) and shallowest storm with widespread cloud water but little rainwater in the upper troposphere. Removing graupel from the control run generates a weaker hurricane with a wider area of precipitation and more cloud coverage in the eyewall due to the enhanced horizontal advection of hydrometeors relative to the vertical fallouts (or increased water loading). Turning off the evaporation of cloud water and rainwater leads to the most rapid deepening storm (i.e., 90 hPa in 48 h) with the smallest radius but a wider eyewall and the strongest eyewall updrafts. The second strongest storm, but with the most amount of rainfall, is obtained when the melting effect is ignored. It is found that the cooling due to melting is more pronounced in the eyewall where more frozen hydrometeors, especially graupel, are available, whereas the evaporative cooling occurs more markedly when the storm environment is more unsaturated. It is shown that stronger storms tend to show more compact eyewalls with heavier precipitation and more symmetric structures in the warm-cored eye and in the eyewall. It is also shown that although the eyewall replacement scenarios occur as the simulated storms move into weak-sheared environments, the associated inner-core structural changes, timing, and location differ markedly, depending on the hurricane intensity. That is, the eyewall convection in weak storms tends to diminish shortly after being encircled by an outer rainband, whereas both the cloud band and the inner eyewall in strong storms tend to merge to form a new eyewall with a larger radius. The results indicate the importance of the Bergeron processes, including the growth and rapid fallout of graupel in the eyewall, and the latent heat of fusion in determining the intensity and inner-core structures of hurricanes, and the vulnerability of weak storms to the influence of large-scale sheared flows in terms of track, inner-core structures, and intensity changes.

Zhu T., D. L. Zhang, and F. Z. Weng, 2004: Numerical simulation of Hurricane Bonnie (1998). Part I: Eyewall evolution and intensity changes. Mon. Wea. Rev., 132, 225- 241.10.1175/1520-0493(2004)1322.0.CO;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004MWRv..132..225Z

http://adsabs.harvard.edu/abs/2004MWRv..132..225ZAbstract In this study, a 5-day explicit simulation of Hurricane Bonnie (1998) is performed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with the finest grid length of 4 km. The initial mass, wind, and moisture fields of the hurricane vortex are retrieved from the Advanced Microwave Sounding Unit-A (AMSU-A) satellite measurements, and the sea surface temperature (SST) is updated daily. It is shown that the simulated track is within 3° latitude–longitude of the best track at the end of the 5-day integration, but with the landfalling point close to the observed. The model also reproduces reasonably well the hurricane intensity and intensity changes, asymmetries in cloud and precipitation, as well as the vertical structures of dynamic and thermodynamic fields in the eye and eyewall. It is shown that the storm deepens markedly in the first 2 days, during which period its environmental vertical shear increases substantially. It is found...