A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

[1]  M. Xue,et al.  Direct Assimilation of Radar Reflectivity Data Using 3DVAR: Treatment of Hydrometeor Background Errors and OSSE Tests , 2019, Monthly Weather Review.

[2]  V. Wulfmeyer,et al.  Observational operators for dual polarimetric radars in variational data assimilation systems (PolRad VAR v1.0) , 2018, Geoscientific Model Development.

[3]  Yonghan Choi,et al.  Tuning of length‐scale and observation‐error for radar data assimilation using four dimensional variational (4D‐Var) method , 2017 .

[4]  Xuguang Wang,et al.  Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell , 2017 .

[5]  Xiang-Yu Huang,et al.  Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States , 2017 .

[6]  M. Xue,et al.  Comparison of Simulated Polarimetric Signatures in Idealized Supercell Storms Using Two-Moment Bulk Microphysics Schemes in WRF , 2016 .

[7]  Jinzhong Min,et al.  Assimilating AMSU-a radiance data with the WRF hybrid En3DVAR system for track predictions of Typhoon Megi (2010) , 2015, Advances in Atmospheric Sciences.

[8]  Derek J. Posselt,et al.  Assimilation of Dual-Polarization Radar Observations in Mixed- and Ice-Phase Regions of Convective Storms: Information Content and Forward Model Errors , 2015 .

[9]  Ming Xue,et al.  Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System , 2015 .

[10]  Thomas Auligné,et al.  Generalized background error covariance matrix model (GEN_BE v2.0) , 2014 .

[11]  Volker Wulfmeyer,et al.  Radar data assimilation experiments using the IPM WRF Rapid Update Cycle , 2014 .

[12]  Olivier Caumont,et al.  Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model , 2014 .

[13]  Guifu Zhang,et al.  The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter , 2014 .

[14]  Juanzhen Sun,et al.  Radar Data Assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a Squall Line over the U.S. Great Plains , 2013 .

[15]  Juanzhen Sun,et al.  Radar Data Assimilation with WRF 4D-Var. Part I: System Development and Preliminary Testing , 2013 .

[16]  Juanzhen Sun,et al.  Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events , 2013 .

[17]  Ming Xue,et al.  Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses using WSR-88D and CASA Radar Data , 2012 .

[18]  Yong-Run Guo,et al.  The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA , 2012 .

[19]  Jidong Gao,et al.  Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification , 2012 .

[20]  M. Xue,et al.  Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data , 2011 .

[21]  A. Ryzhkov,et al.  Polarimetric Radar Observation Operator for a Cloud Model with Spectral Microphysics , 2011 .

[22]  M. Xue,et al.  Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms , 2010 .

[23]  Olivier Caumont,et al.  1D+3DVar assimilation of radar reflectivity data: a proof of concept , 2010 .

[24]  Martin Hagen,et al.  A polarimetric radar forward operator for model evaluation , 2008 .

[25]  Jian Zhang,et al.  Brightband Identification Based on Vertical Profiles of Reflectivity from the WSR-88D , 2008 .

[26]  W. Collins,et al.  Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models , 2008 .

[27]  Jerry M. Straka,et al.  Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part II: Impact of Polarimetric Data on Storm Analysis , 2008 .

[28]  M. Xue,et al.  Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables , 2008 .

[29]  Véronique Ducrocq,et al.  A Radar Simulator for High-Resolution Nonhydrostatic Models , 2006 .

[30]  Ming Hu,et al.  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 , 2006 .

[31]  Mingjing Tong,et al.  Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments , 2005 .

[32]  Kevin W. Manning,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis , 2004 .

[33]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[34]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[35]  Guifu Zhang,et al.  A method for estimating rain rate and drop size distribution from polarimetric radar measurements , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[36]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[37]  Juanzhen Sun,et al.  Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part I: Model Development and Simulated Data Experiments. , 1997 .

[38]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[39]  T. A. Seliga,et al.  Radar polarimetric backscattering properties of conical Graupel , 1984 .

[40]  V. Ducrocq,et al.  Simulation of  W‐band radar reflectivity for model validation and data assimilation , 2018 .

[41]  Xiaoyan Zhang THE WEATHER RESEARCH AND FORECASTING MODEL ’ S COMMUNITY VARIATIONAL / ENSEMBLE DATA ASSIMILATION SYSTEM WRFDA , 2018 .

[42]  Philippe Lopez,et al.  Linearized Physics for Data Assimilation at ECMWF , 2013 .

[43]  Guifu Zhang,et al.  Simulations of Polarimetric Radar Signatures of a Supercell Storm Using a Two-Moment Bulk Microphysics Scheme , 2010 .

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

[45]  Ying-Hwa Kuo,et al.  An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall , 2007 .

[46]  Mingjing Tong,et al.  An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting , 2006 .

[47]  M. Xue,et al.  3 DVAR and Cloud Analysis with WSR-88 D Level-II Data for the Prediction of Fort Worth Tornadic Thunderstorms Part I : Impact of radial velocity analysis via 3 DVAR , 2004 .

[48]  Jean-Noël Thépaut,et al.  Simplified and Regular Physical Parameterizations for Incremental Four-Dimensional Variational Assimilation , 1999 .