Preliminary evaluation of the SCALE-LETKF radar data assimilation system: a case of deep convection in Argentina

CELESTE SAULO; JUAN RUIZ; TAKEMASA MIYOSHI; PAULA MALDONADO; GUO-YUAN LIEN; TAKUMI HONDA

Florianópolis

Simposio; 7th International WMO Symposium on Data Assimilation; 2017

CPTEC, WMO, INPE

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 by using high-spatial-resolution numerical models initialized with high-horizontal-resolution and high-temporal-frequency observations containing information of the mesoscale, 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 present the progress made in the implementation of a radar data assimilation system that can assimilate reflectivity and Doppler velocity and is based on the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Scalable Computing for Advanced Library and Environment (SCALE) model. To test the performance of the system, a case of deep organized convection over central Argentina was chosen. Radar observations are obtained from the Anguil radar site (La Pampa, Argentina) with a 10-min frequency. The assimilation system uses 60 ensemble members in a 240 x 240 km domain with a 2 km horizontal resolution. Results obtained from the SCALE-LETKF system as well as very short-term ensemble forecasts will be presented at the symposium