Application of Improved NLS-4DVar in Assimilating Radar Data with Advanced research WRF (ARW)

Assimilating Doppler radar observations play an important role in improving the accuracy of initial condition and the prediction for the meso-micro scale numerical weather prediction (NWP). The improved NLS-4DVar (Non-linear Least Squares-based on Four-dimensional Variational Data Assimilation) is an advanced hybrid assimilation method that exploits the strengths of both four-dimensional variational methods and ensemble Kalman filter. The method adopts an efficient local correlation matrix decomposition approach and can be applied without invoking the adjoint models. Observing system simulation experiment (OSSE) study is first designed to generally evaluate the validity of the improved NLS-4DVar method. A heavy convective-rainfall cases in Ningbo from August 8 to August 9 in 2012 was selected to investigate the performance of the improved NLS-4DVar in assimilating real radar observations and the impacts of assimilating radar observations on numerical forecasts, with the Weather Research and Forecasting (WRF) model as our forecasting model. The results indicated that significant improvements in predicting heavy rainfall can be achieved due to the improved initial conditions for the convective system's dynamics after assimilating the radar observation with the improved NLS-4DVar.

Key words: NLS-4DVar, radar data assimilation, OEESs, WRF, heavy rainfall