1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators

摘要: This paper addresses the impact of assimilating data from Earth Networks Total Lightning Network (ENTLN) during two cases severe weather. Data ENTLN serve as a substitute for those upcoming launch GOES Mapper (GLM). We use Weather Research and Forecast (WRF) model variational assimilation techniques at 9 km spatial resolution. The main goal is to examine potential lightning observations future GLM. Previous efforts assimilate mainly utilized nudging approaches. develop three more sophisticated approaches, 3D-VAR WRFDA 1D+nD-VAR (n=3,4) schemes that currently are being considered operational implementation by National Centers Environmental Prediction (NCEP) Naval Laboratory (NRL). research uses Convective Available Potential Energy (CAPE) proxy between variables. To test performance aforementioned schemes, we assess quality resulting analysis forecasts precipitation compared control experiment verify them against NCEP stage IV precipitation. Results demonstrate improves statistics window 3-7 h thereafter. 1D+4D-VAR approach performs best, significantly improving root mean square errors 25% 27.5% on cases. finding confirms nD-VAR accuracy moisture analyses forecasts. Finally, briefly discuss limitations inherent in current their implications, possible ways improve them.