A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data
暂无分享,去创建一个
[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 .