Ensemble Forecast Sensitivity to Observations Impact (EFSOI) applied to a regional data assimilation system over south-eastern South America

CASARETTO, GIMENA; DILLON, MARIA EUGENIA; SKABAR, YANINA GARCÍA; RUIZ, JUAN J.; SACCO, MAXIMILIANO

ATMOSPHERIC RESEARCH

ELSEVIER SCIENCE INC

Año: 2023

0169-8095

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets, and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study, we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that are beneficial or detrimental to the forecast. In this paper, we focus on the impact of human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018-2019 austral warm season in central Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a beneficial impact on the 6-hour forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a detrimental impact. Two data denial experiments were carried out in order to support the EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observational data sources detrimental to the data assimilation system, suggesting data selection criteria to improve regional forecast accuracy.