Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges

Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

[1]  C. K. M. Douglas,et al.  LOCAL WEATHER FORECASTING , 1957 .

[2]  B. L. Moiselwitsch The Variational Method , 1961 .

[3]  E. Lorenz The predictability of a flow which possesses many scales of motion , 1969 .

[4]  Keith A. Browning,et al.  Review Lecture: Local weather forecasting , 1980, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[5]  G. Austin,et al.  An evaluation of extrapolation techniques for the short‐term prediction of rain amounts , 1981 .

[6]  Charles A. Doswell,et al.  Short-Range Forecasting , 1986 .

[7]  Douglas K. Lilly,et al.  Numerical prediction of thunderstorms—has its time come? , 1990 .

[8]  Physical initialization for numerical weather prediction over the tropics , 1991 .

[9]  Juanzhen Sun,et al.  Recovery of Three-Dimensional Wind and Temperature Fields from Simulated Single-Doppler Radar Data , 1991 .

[10]  G. Evensen Using the Extended Kalman Filter with a Multilayer Quasi-Geostrophic Ocean Model , 1992 .

[11]  Peter Lynch,et al.  Initialization of the HIRLAM Model Using a Digital Filter , 1992 .

[12]  C. Qiu,et al.  A Simple Adjoint Method of Wind Analysis for Single-Doppler Data , 1992 .

[13]  Cynthia K. Mueller,et al.  The Utility of Sounding and Mesonet Data to Nowcast Thunderstorm Initiation , 1993 .

[14]  M. Dixon,et al.  TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology , 1993 .

[15]  Juanzhen Sun,et al.  Wind and Thermodynamic Retrieval from Single-Doppler Measurements of a Gust Front Observed during Phoenix II , 1994 .

[16]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[17]  A. Shapiro,et al.  Single-Doppler Velocity Retrievals with Phoenix II Data: Clear Air and Microburst Wind Retrievals in the Planetary Boundary Layer , 1995 .

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

[19]  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 .

[20]  B. Macpherson,et al.  A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model , 1997 .

[21]  X. Zou Tangent linear and adjoint of''''on-off''''processes and their feasibility for use in 4-dimensional , 1997 .

[22]  Juanzhen Sun,et al.  Nowcasting Thunderstorms: A Status Report , 1998 .

[23]  B. Golding Nimrod: a system for generating automated very short range forecasts , 1998 .

[24]  Juanzhen Sun,et al.  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 , 1998 .

[25]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[26]  Jidong Gao,et al.  A Variational Method for the Analysis of Three-Dimensional Wind Fields from Two Doppler Radars , 1999 .

[27]  Tammy M. Weckwerth,et al.  The Effect of Small-Scale Moisture Variability on Thunderstorm Initiation , 2000 .

[28]  John D. Tuttle,et al.  Inferences of Predictability Associated with Warm Season Precipitation Episodes , 2001 .

[29]  I. Zawadzki,et al.  Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology , 2002 .

[30]  Steven E. Koch,et al.  An Overview of the International H2O Project (IHOP_2002) and Some Preliminary Highlights , 2004 .

[31]  Tianyou Yu,et al.  Multi-scale Analysis and Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Assimilating Radar and Surface Network Data using EnKF , 2003 .

[32]  C. Snyder,et al.  Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter , 2003 .

[33]  Jidong Gao,et al.  The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation , 2003 .

[34]  Andrew C. Lorenc,et al.  The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var , 2003 .

[35]  Louis J. Wicker,et al.  Wind and Temperature Retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: Ensemble Kalman Filter Experiments , 2004 .

[36]  Elizabeth E. Ebert,et al.  Sydney 2000 Forecast Demonstration Project: Convective Storm Nowcasting , 2004 .

[37]  Rita D. Roberts,et al.  Summary of Convective Storm Initiation and Evolution during IHOP: Observational and Modeling Perspective , 2004 .

[38]  Juanzhen Sun,et al.  Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter , 2004 .

[39]  J. Done,et al.  The next generation of NWP: explicit forecasts of convection using the weather research and forecasting (WRF) model , 2004 .

[40]  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 .

[41]  I. Zawadzki,et al.  Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE) , 2004 .

[42]  Jidong Gao,et al.  A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars , 2004 .

[43]  Barry E. Schwartz,et al.  An Hourly Assimilation–Forecast Cycle: The RUC , 2004 .

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

[45]  Juanzhen Sun,et al.  Initialization and Numerical Forecasting of a Supercell Storm Observed during STEPS , 2005 .

[46]  Steven E. Koch,et al.  MULTIFUNCTIONAL MESOSCALE OBSERVING NETWORKS , 2005 .

[47]  Y. Honda,et al.  A pre‐operational variational data assimilation system for a non‐hydrostatic model at the Japan Meteorological Agency: formulation and preliminary results , 2005 .

[48]  Tadashi Tsuyuki,et al.  Assimilation of Precipitation Data to the JMA Mesoscale Model with a Four-dimensional Variational Method and its Impact on Precipitation Forecasts , 2005 .

[49]  Ying-Hwa Kuo,et al.  Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case , 2005 .

[50]  S. J. Weiss,et al.  Examination of convection-allowing configurations of the WRF model for the prediction of severe convective weather : The SPC/NSSL spring program 2004 , 2006 .

[51]  James E. Evans,et al.  Corridor Integrated Weather System , 2006 .

[52]  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 .

[53]  I. Zawadzki,et al.  Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction. , 2006 .

[54]  Mingjing Tong Ensemble Kalman filter assimilation of Doppler radar data for the initialization and prediction of convective storms. , 2006 .

[55]  M. Xue,et al.  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 , 2006 .

[56]  M. Xue 3B.1 CAPS REALTIME STORM-SCALE ENSEMBLE AND HIGH-RESOLUTION FORECASTS AS PART OF THE NOAA HAZARDOUS WEATHER TESTBED 2007 SPRING EXPERIMENT , 2007 .

[57]  Yuki Honda,et al.  An Assimilation and Forecasting Experiment of the Nerima Heavy Rainfa11 with a Cloud-Resolving Nonhydrostatic 4-Dimensional Variational Data Assimilation System , 2007 .

[58]  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 .

[59]  Ming Hu,et al.  Impact of Configurations of Rapid Intermittent Assimilation of Wsr-88d Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case , 2022 .

[60]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Radar Data and Ensemble Square-root Kalman Filter . Part I : Sensitivity Analysis and Parameter , 2007 .

[61]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability , 2008 .

[62]  M. Xue,et al.  A Comparison of Precipitation Forecast Skill between Small Near-Convection-Permitting and Large Convection-Parameterizing Ensembles , 2008 .

[63]  F. Kong Real-Time Storm-Scale Ensemble Forecast 2008 Spring Experiment , 2008 .

[64]  Ying Zhang,et al.  Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations , 2008 .

[65]  N. Roberts,et al.  Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events , 2008 .

[66]  Kevin W. Manning,et al.  Experiences with 0–36-h Explicit Convective Forecasts with the WRF-ARW Model , 2008 .

[67]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments , 2008 .

[68]  Christopher A. Davis,et al.  The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part I: Overview of the Modeling System, the Forecast Products, and How the Products Are Used , 2008 .

[69]  C. Schraff,et al.  Assimilation of radar‐derived rain rates into the convective‐scale model COSMO‐DE at DWD , 2008 .

[70]  S. Benjamin,et al.  Assimilation of radar reflectivity data using a diabatic digital filter within the Rapid Update Cycle , 2008 .

[71]  Stanley G. Benjamin,et al.  CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 , 2009 .

[72]  M. Xue,et al.  A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles , 2009 .

[73]  Louis J. Wicker,et al.  Additive Noise for Storm-Scale Ensemble Data Assimilation , 2009 .

[74]  Robin J. Hogan,et al.  Verification of cloud‐fraction forecasts , 2009 .

[75]  Rita D. Roberts,et al.  Nowcasting Challenges during the Beijing Olympics: Successes, Failures, and Implications for Future Nowcasting Systems , 2009 .

[76]  Eric Gilleland,et al.  Intercomparison of Spatial Forecast Verification Methods , 2009 .

[77]  Soichiro Sugimoto,et al.  An examination of WRF 3DVAR radar data assimilation on its capability in retrieving unobserved variables and forecasting precipitation through observing system simulation experiments , 2009 .

[78]  Nigel Roberts,et al.  Impact of Data Assimilation on Forecasting Convection over the United Kingdom Using a High-Resolution Version of the Met Office Unified Model , 2009 .

[79]  Marc Berenguer,et al.  The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part I: Methodology and Seasonal Comparison , 2010 .

[80]  Guifu Zhang,et al.  Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error , 2010 .

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

[82]  Guifu Zhang,et al.  State estimation of convective storms with a two-moment microphysics scheme and an ensemble Kalman filter: Experiments with simulated radar data , 2010 .

[83]  Stanley G. Benjamin,et al.  Advances in the Consolidated Storm Prediction for Aviation (CoSPA) [presentation] , 2010 .

[84]  Juanzhen Sun,et al.  A Frequent-Updating Analysis System Based on Radar, Surface, and Mesoscale Model Data for the Beijing 2008 Forecast Demonstration Project , 2010 .

[85]  S. J. Weiss,et al.  Assessing Advances in the Assimilation of Radar Data and Other Mesoscale Observations within a Collaborative Forecasting-Research Environment , 2010 .

[86]  S. J. Weiss,et al.  1 Assessing Advances in the Assimilation of Radar Data within a Collaborative Forecasting-Research Environment , 2010 .

[87]  Chris Snyder,et al.  A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts , 2010 .

[88]  Y. Weng,et al.  Performance of convection‐permitting hurricane initialization and prediction during 2008–2010 with ensemble data assimilation of inner‐core airborne Doppler radar observations , 2011 .

[89]  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 .

[90]  Sarah L. Dance,et al.  3D-Var Assimilation of Insect-Derived Doppler Radar Radial Winds in Convective Cases Using a High-Resolution Model , 2011 .

[91]  Alexander D. Schenkman,et al.  The Analysis and Prediction of the 8 – 9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88 D and CASA Radar Data Using 3 DVAR , 2011 .

[92]  Kazuo Saito,et al.  A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area , 2011 .

[93]  Jidong Gao,et al.  Impact of CASA Radar and Oklahoma Mesonet Data Assimilation on the Analysis and Prediction of Tornadic Mesovortices in an MCS , 2011 .

[94]  Alexander D. Schenkman,et al.  The Analysis and Prediction of the 8–9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88D and CASA Radar Data Using 3DVAR , 2011 .

[95]  Ying Zhang,et al.  Sensitivity of 0–12-h Warm-Season Precipitation Forecasts over the Central United States to Model Initialization , 2012 .

[96]  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 .

[97]  James W. Wilson,et al.  Impacts of Forecaster Involvement on Convective Storm Initiation and Evolution Nowcasting , 2012 .

[98]  Z. Sokol,et al.  Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity , 2012 .

[99]  M. Bailey,et al.  Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10): a World Weather Research Programme Project , 2012, Pure and Applied Geophysics.

[100]  Corey K. Potvin,et al.  Progress and challenges with Warn-on-Forecast , 2012 .

[101]  Mingjing Tong,et al.  Ensemble Kalman Filter Analyses of the 29–30 May 2004 Oklahoma Tornadic Thunderstorm Using One- and Two-Moment Bulk Microphysics Schemes, with Verification against Polarimetric Radar Data , 2012 .

[102]  I. Zawadzki,et al.  Predictability of Precipitation from Continental Radar Images. Part V: Growth and Decay , 2012 .

[103]  Louis J. Wicker,et al.  Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones , 2012 .

[104]  Xuguang Wang,et al.  Assimilation of Radar Radial Velocity Data with the WRF Hybrid Ensemble–3DVAR System for the Prediction of Hurricane Ike (2008) , 2012 .

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

[106]  M. Xue,et al.  Assimilation of radial velocity and reflectivity data from coastal WSR‐88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike (2008) , 2013 .

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

[108]  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 .

[109]  M. Xue,et al.  Impacts of Assimilating Measurements of Different State Variables with a Simulated Supercell Storm and Three-Dimensional Variational Method , 2013 .