Parameter sensitivity of the LETKF-WRF system for assimilation of radar observations in a case of deep convection in Argentina.

CELESTE SAULO; JUAN RUIZ; PAULA MALDONADO

Kobe

Congreso; 3rd RIKEN International Symposium on Data Assimilation; 2017

RIKEN-AICS

Very short-term weather forecasts are useful for identifying areas of high-impact weather events associated with deep convection that can significantly affect people?s lives. One way to generate adequate forecasts is using high-spatial-resolution numerical models initialized with high temporal and horizontal resolution observations, as those provided by weather radars. Thus, a data assimilation system capable of assimilating radar data is essential to improve very short-term weather forecasts. The aim of this work is to compare different approaches for the estimation of the error covariance matrix by using the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model for a case of deep convection over central Argentina. Several observing system simulation experiments were performed by assimilating synthetic radar observations, including reflectivity and Doppler velocity. In these experiments we studied the sensitivity to i) the specification of the initial and boundary perturbations, ii) the type and magnitude of the multiplicative inflation and iii) the localization scale. Results obtained will be presented at the symposium.