WRF-LTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts

This study introduces WRF-LTNGDA, a lightning data assimilation technique implemented in the Weather Research and Forecasting (WRF) model. This technique employs lightning for improving the representation of convection by means of controlling the triggering of the model's convection parameterization scheme. The development and implementation of WRF-LTNGDA was carried out in a framework that could easily allow for its exploitation in real-time forecasting activities. The assimilation algorithm was evaluated over eight precipitation events that took place in Greece in the years 2010-2013. Results clearly show that lightning forcing has a positive impact on model performance. The conducted analysis revealed that the employment of WRF-LTNGDA induces statistically significant improvements in precipitation verification scores, especially for high rainfall accumulations. Separate examination of one of the eight case studies highlighted the overall better agreement between the modelled and observed spatial distribution of precipitation when lightning data assimilation was applied, than in the control simulation. WRF-LTNGDA: a lightning data assimilation technique for the WRF model.WRF-LTNGDA is evaluated over eight case studies in Greece.Lightning assimilation improves precipitation prediction.WRF-LTNGDA can be used for real-time weather forecasting applications.

[1]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[2]  Jidong Gao,et al.  Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event , 2014 .

[3]  Thomas M. Hamill,et al.  Hypothesis Tests for Evaluating Numerical Precipitation Forecasts , 1999 .

[4]  V. Kotroni,et al.  Lightning occurrence in relation with elevation, terrain slope, and vegetation cover in the Mediterranean , 2008 .

[5]  R. Hodur.  The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) , 1997 .

[6]  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 .

[7]  Yunjun Zhou,et al.  A study of the relationship between cloud-to-ground lightning and precipitation in the convective weather system in China , 2002 .

[8]  Vittorio E. Brando,et al.  Satellite data assimilation and estimation of a 3D coastal sediment transport model using error-subspace emulators , 2013, Environ. Model. Softw..

[9]  A. Fierro,et al.  Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011 , 2012 .

[10]  Arjo Segers,et al.  Data assimilation of ozone in the atmospheric transport chemistry model LOTOS , 2000, Environ. Model. Softw..

[11]  Lawrence D. Carey,et al.  Relationship between cloud‐to‐ground lightning and precipitation ice mass: A radar study over Houston , 2006 .

[12]  William Lau,et al.  Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales , 2015, Environ. Model. Softw..

[13]  P. Diaconis,et al.  Computer-Intensive Methods in Statistics , 1983 .

[14]  Winifred C. Lambert,et al.  A Simple Technique for Using Radar Data in the Dynamic Initialization of a Mesoscale Model , 2000 .

[15]  William S. Olson,et al.  The effect of spaceborne microwave and ground-based continuous lightning measurements on forecasts of the 1998 groundhog day storm , 2001 .

[16]  A. Speranza,et al.  Sensitivity of Precipitation Forecast Skill Scores to Bilinear Interpolation and a Simple Nearest-Neighbor Average Method on High-Resolution Verification Grids , 2003 .

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

[18]  Steven Businger,et al.  The Impact of Lightning Data Assimilation on a Winter Storm Simulation over the North Pacific Ocean , 2009 .

[19]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[20]  John S. Kain,et al.  The Kain–Fritsch Convective Parameterization: An Update , 2004 .

[21]  V. Kotroni,et al.  The sensitivity of numerical forecasts to convective parameterization during the warm period and the use of lightning data as an indicator for convective occurrence , 2009 .

[22]  Dawei Han,et al.  Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation , 2012 .

[23]  V. Kotroni,et al.  Evaluation of MM5 high-resolution real-time forecasts over the urban area of Athens, Greece , 2004 .

[24]  Kamal Puri,et al.  Tropical prediction using dynamical nudging, satellite-defined convective heat sources, and a cyclone bogus , 1992 .

[25]  Conrad L. Ziegler,et al.  A Lightning Data Assimilation Technique for Mesoscale Forecast Models , 2007 .

[26]  Kostas Lagouvardos,et al.  Precipitation forecast skill of different convective parameterization and microphysical schemes: Application for the cold season over Greece , 2001 .

[27]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation , 2001 .

[28]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

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

[30]  B. Lynn,et al.  An Evaluation of the Efficacy of Using Observed Lightning to Improve Convective Lightning Forecasts , 2015 .

[31]  John S. Kain,et al.  Convective parameterization for mesoscale models : The Kain-Fritsch Scheme , 1993 .

[32]  Marco Casaioli,et al.  Verification of Precipitation Forecasts from Two Limited-Area Models over Italy and Comparison with ECMWF Forecasts Using a Resampling Technique , 2005 .

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

[34]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[35]  V. M. Karyampudi,et al.  The Effect of Assimilating Rain Rates Derived from Satellites and Lightning on Forecasts of the 1993 Superstorm , 1999 .

[36]  Eric Defer,et al.  Study of a heavy precipitation event over southern France, in the frame of HYMEX project: Observational analysis and model results using assimilation of lightning , 2013 .

[37]  Zaviša I. Janić.  Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model , 2001 .

[38]  E. Anagnostou,et al.  Improving Convective Precipitation Forecasting through Assimilation of Regional Lightning Measurements in a Mesoscale Model , 2003 .

[39]  J. Dudhia.  A Nonhydrostatic Version of the Penn State–NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front , 1993 .

[40]  Xueke Wu,et al.  Application of total-lightning data assimilation in a mesoscale convective system based on the WRF model , 2014 .

[41]  J. M. Fritsch,et al.  Preliminary Numerical Tests of the Modification of Mesoscale Convective Systems , 1981 .

[42]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[43]  Christopher J. Schultz,et al.  Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends , 2011 .

[44]  W. David Rust,et al.  Lightning and precipitation history of a microburst‐producing storm , 1988 .

[45]  V. Kotroni,et al.  A comparison of lightning data provided by ZEUS and LINET networks over Western Europe , 2009 .

[46]  E. Anagnostou,et al.  Evaluating the impact of lightning data assimilation on mesoscale model simulations of a flash flood inducing storm , 2009 .