Ensemble forecast sensitivity to observations impact (EFSOI) of a high impact weather event using a convection permitting data assimilation system

CASARETTO, GIMENA; DILLON, MARIA EUGENIA; GARCIA SKABAR, YANINA; RUIZ, JUAN JOSÉ; MALDONADO, PAULA; SACCO, MAXIMILIANO

Viena (modalidad mixta virtual presencial)

Congreso; EGU General Assembly 2023; 2023

The improvement of numerical weather forecasts is a key element to predict high-impact weatherevents, associated with deep moist convection. The observations that are assimilated intonumerical weather prediction systems are conformed by numerous data sets and their impactshould be objectively evaluated. This can be efficiently estimated by the Forecast Sensitivity toObservation Impact (FSOI) methodology. In this study, we explore the application of the ensembleformulation of FSOI (EFSOI) in a convective scale regional data assimilation system over Sierras deCórdoba (Argentina), a data-sparse region with complex terrain characterized by the periodicoccurrence of extreme precipitation and flash floods events. To evaluate the observation networksthat result beneficial and detrimental for the forecast, the Weather Research and Forecastingmodel coupled with the Local Ensemble Transform Kalman Filter was used with 40 members.Convective scale analyses were obtained every 5 minutes, assimilating reflectivity data from a C-band radar and conventional and non-conventional surface weather stations (CSWS and NSWS).The experiment was initialized on December 13 at 23 UTC and ran for 5 hours, until December 1403 UTC. The experiment conducted was a case study within the intensive observing period of theRELAMPAGO-CACTI field campaign that was carried out during the 2018-2019 austral warm seasonin the center of Argentina. An independent data assimilation cycle using more observations and adifferent configuration is used in the experiments as verifying truth for the computation offorecast errors in EFSOI.Results showed that all the observation sources had, on average, a positive impact on the 30minute forecasts with a positive impact rate above 50%. However, when observations impacts areanalyzed by geographic location, different results are evidenced. Most of the surface stations thatevidence a detrimental impact in forecasts are located in the northern part of the region, probablydue to a misrepresentation of the thermodynamic environment. Regarding radar reflectivityobservations, values of positive impact rate above 50% dominate over all the region,demonstrating that, in general, they reduce the forecast errors. The results suggest that the observations with values of reflectivity beneath 15 dBZ have a larger amount of beneficialobservations in lower levels than in upper levels.This methodology is an approximation to quantify the impact of reflectivity and surfaceobservations on a convective permitting forecast over the region. The results of this (and future)work can help to identify observation data sources detrimental for the data assimilation system,suggesting data selection criteria to assess improvements in this regional convective-scale dataassimilation system where nonconventional observations such as radar data plays an essentialrole.