Stratiform Cloud-Hydrometeor Assimilation for HRRR and RAP Model Short-Range Weather Prediction

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spin-up problems, a non-variational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1-9h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.

[1]  Patrick Minnis,et al.  CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[2]  S. Benjamin,et al.  Commercial-Aircraft-Based Observations for NWP: Global Coverage, Data Impacts, and COVID-19 , 2020, Journal of Applied Meteorology and Climatology.

[3]  Patrick Minnis,et al.  CERES MODIS Cloud Product Retrievals for Edition 4—Part I: Algorithm Changes , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[4]  C. Draxl,et al.  Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development , 2019, Bulletin of the American Meteorological Society.

[5]  Patrick Minnis,et al.  Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Runhua Yang,et al.  Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP , 2019, Monthly Weather Review.

[7]  David A Clausi,et al.  Data fusion and data assimilation of ice thickness observations using a regularisation framework , 2019, Tellus A: Dynamic Meteorology and Oceanography.

[8]  Brett Candy,et al.  All‐sky satellite data assimilation of microwave temperature sounding channels at the Met Office , 2019, Quarterly Journal of the Royal Meteorological Society.

[9]  David D. Turner,et al.  Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations , 2018, Monthly Weather Review.

[10]  Dennis G. Atkinson.  Expanded Cloud Base and Backscatter Detection Using the ASOS Ceilometer , 2018 .

[11]  Ming Hu,et al.  GSI Three-Dimensional Ensemble–Variational Hybrid Data Assimilation Using a Global Ensemble for the Regional Rapid Refresh Model , 2017 .

[12]  Bob Glahn,et al.  A LAMP–HRRR MELD for Improved Aviation Guidance , 2017 .

[13]  Eric P. James,et al.  Observation System Experiments with the Hourly-Updating Rapid Refresh (RAP) Model Using GSI Hybrid Ensemble/Variational Data Assimilation , 2017 .

[14]  Walker S. Ashley,et al.  Fatal weather-related general aviation accidents in the United States , 2016 .

[15]  G. Grell,et al.  A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh , 2016 .

[16]  Siebren de Haan,et al.  Cloud Initialization in the Rapid Update Cycle of HIRLAM , 2014 .

[17]  G. Thompson,et al.  A Study of Aerosol Impacts on Clouds and Precipitation Development in a Large Winter Cyclone , 2014 .

[18]  Andrew K. Heidinger,et al.  Entering the Era of +30-Year Satellite Cloud Climatologies: A North American Case Study , 2014 .

[19]  William Bell,et al.  Progress towards the assimilation of all‐sky infrared radiances: an evaluation of cloud effects , 2014 .

[20]  R. Crawford,et al.  Parameterization of Runway Visual Range as a Function of Visibility: Implications for Numerical Weather Prediction Models , 2012 .

[21]  Peter N. Francis,et al.  Variational assimilation of cloud fraction in the operational Met Office Unified Model , 2011 .

[22]  Umran S. Inan,et al.  Long‐range lightning geolocation using a VLF radio atmospheric waveform bank , 2010 .

[23]  Peter Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part I: Implementation , 2010 .

[24]  J. Derber,et al.  Introduction of the GSI into the NCEP Global Data Assimilation System , 2009 .

[25]  A. McNally.  The direct assimilation of cloud‐affected satellite infrared radiances in the ECMWF 4D‐Var , 2009 .

[26]  Stephen S. Weygandt,et al.  The High Resolution Rapid Refresh (HRRR): an hourly updated convection resolving model utilizing radar reflectivity assimilation from the RUC / RR , 2009 .

[27]  Paul J. Roebber,et al.  Visualizing Multiple Measures of Forecast Quality , 2009 .

[28]  G. Thompson,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization , 2008 .

[29]  S. Corfidi,et al.  Elevated Convection and Castellanus: Ambiguities, Significance, and Questions , 2008 .

[30]  Ari Karppinen,et al.  Retrieval of mixing height and dust concentration with lidar ceilometer , 2007 .

[31]  P. Kollias,et al.  Observations of marine stratocumulus in SE Pacific during the PACS 2003 cruise , 2004 .

[32]  W. Paul Menzel,et al.  Variational Retrieval of Cloud Parameters from GOES Sounder Longwave Cloudy Radiance Measurements , 2001 .

[33]  Robert M. Aune,et al.  NWP Cloud Initialization Using GOES Sounder Data and Improved Modeling of Nonprecipitating Clouds , 2000 .

[34]  Stefan Gollvik,et al.  Mesan, an operational mesoscale analysis system , 2000 .

[35]  Thomas T. Warner,et al.  Nonhydrostatic, Mesobeta-Scale Model Simulations of Cloud Ceiling and Visibility for an East Coast Winter Precipitation Event , 1999 .

[36]  Brian A. Colle,et al.  Evaluation of MM5 and Eta-10 Precipitation Forecasts over the Pacific Northwest during the Cool Season , 1999 .

[37]  Kenneth L. Cummins,et al.  A Combined TOA/MDF Technology Upgrade of the U.S. National Lightning Detection Network , 1998 .

[38]  Gregory Thompson,et al.  Using Satellite Data to Reduce Spatial Extent of Diagnosed Icing , 1997 .

[39]  John A. McGinley,et al.  The Local Analysis and Prediction System ( LAPS ) : Analyses of Clouds, Precipitation, and Temperature , 1996 .

[40]  B. Macpherson,et al.  The Impact of MOPS Moisture Data in the U.K. Meteorological Office Mesoscale Data Assimilation Scheme , 1996 .

[41]  Donald W. Burgess,et al.  Recording, Archiving, and Using WSR-88D Data , 1993 .

[42]  Allan I. Carswell,et al.  Automated method for lidar determination of cloud-base height and vertical extent. , 1992, Applied optics.

[43]  C. Doswell,et al.  On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables , 1990 .

[44]  Philip J. Rasch,et al.  Cumulus Initialization in a Global Model for Numerical Weather Prediction , 1989 .

[45]  T. Carlson,et al.  Some Effects of Surface Heating and Topography on the Regional Severe Storm Environment. Part I: Three-Dimensional Simulations , 1986 .

[46]  John C. Wyngaard,et al.  Structure of the Planetary Boundary Layer and Implications for its Modeling , 1985 .

[47]  Stanley G. Benjamin,et al.  Elevated Mixed Layers in the Regional Severe Storm Environment: Conceptual Model and Case Studies , 1983 .

[48]  Robert A. Clark,et al.  Vertically Integrated Liquid Water—A New Analysis Tool , 1972 .

[49]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[50]  Joseph B. Olson,et al.  A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF–ARW , 2019 .

[51]  Stanley G. Benjamin,et al.  Implementation of a Digital Filter Initialization in the WRF Model and Its Application in the Rapid Refresh , 2016 .

[52]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[53]  S. Benjamin,et al.  ASSIMILATION OF SURFACE CLOUD , VISIBILITY , AND CURRENT WEATHER OBSERVATIONS IN THE RUC , 2003 .

[54]  K. Shadan,et al.  Available online: , 2012 .