Ensemble Forecast Sensitivity to Observations applied to a regional data assimilation system over Argentina.

CASARETTO, GIMENA; DILLON, MARIA EUGENIA; GARCIA SKABAR, YANINA; RUIZ, JUAN JOSÉ; SACCO, MAXIMILIANO; LIEN, GUO-YUAN

Virtual

Simposio; WCRP-WWRP Symposium on data assimilation and reanalysis and 2021 ECMWF annual seminar on observations; 2021

Observations that are assimilated into numerical weather prediction systems are con-formed by numerous data sets and the impact of the observations must be objectively evalu-ated. The Forecast Sensitivity to Observation (FSO) provides an efficient impact evaluationof each observation on forecasts. This study proposes applying the simpler ensemble formu-lation of FSO (EFSO, Kalnay et al 2012) to the Weather Research and Forecasting modelcoupled with the Local Ensemble Transform Kalman Filter in Argentina (Dillon et al 2019),during 25 days of the intensive observing period of the RELAMPAGO-CACTI field campaignthat was conducted during the 2018-2019 austral warm season in the center of Argentina(Nesbitt et al 2021). Analyses were obtained every 6-h with a 20-km resolution, assimilatingdata from soundings, aircrafts, satelite, AIRS and surface and automatic stations. EFSO wasapplied in order to detect those observations that were beneficial or detrimental for regionalforecasts with evaluation forecast time of the EFSO computation set to 6-h. The resultsevidence that wind, temperature and humidity from automatic stations have almost nulepositive impact. On the other hand, sounding, aircrafts and atmospheric infrared sounderobservations present a larger positive impact. Also the fields of EFSO document the bene-ficial impact of observations in the forecasts for the central area of Argentina. It is shownthat the EFSO method can efficiently suggest data selection criteria.