A Rapid Refresh ensemble based Data Assimilation and Forecast system (RRA) for the RELAMPAGO field campaign in Argentina

DILLON, MARIA EUGENIA; CORRALES, PAOLA; MALDONADO, PAULA; GARCIA SKABAR, YANINA; RUIZ, JUAN; SACCO, MAXIMILIANO; CUTRARO, FEDERICO; MINGARI, LEONARDO; MATSUDO, CYNTHIA; HOBOUCHIAN, PAULA; VIDAL, LUCIANO; RUGNA, MARTIN; SALIO, PAOLA; NESBITT, STEPHEN; KALNAY, EUGENIA; MIYOSHI, TAKEMASA

CABA

Workshop; RELAMPAGO CACTI Data Analysis Workshop; 2019

This presentation describes lessons learned from the implementation of a regional ensemble data assimilation system motivated by RELAMPAGO, carried out by the Servicio Meteorológico Nacional (SMN) and the Centro de Investigaciones del Mar y la Atmósfera (CIMA), using CHEYENNE (NCAR) computational resources. The system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF), with 60 ensemble members. It combines multiple data sources both global and locally available like high resolution surface networks, AMDAR data from local flights, soundings, AIRS retrievals, high resolution GOES-16 wind estimates and local radar data. Ten kilometer warm-start hourly analyses generated by this rapid refresh system reduces the lag between the observation time and the availability of the analysis to 3 hours and allows a three hourly initialization of 36-hours ensemble forecasts. Preliminary evaluation of the ensemble forecast shows a positive impact with respect to cold-start initializations.