Ensemble Forecast Sensitivity to Observations Impact (EFSOI) of a high impact weather event using a convection permitting data assimilation system.

The improvement of numerical weather forecasts is a key element to predict high-impact weather events, associated with deep moist convection. The 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 estimated 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 convective scale regional data assimilation system over Sierras de Córdoba (Argentina), a data-sparse region with complex terrain characterized by the periodic occurrence of extreme precipitation and flash floods events. To evaluate the observation networks that result beneficial and detrimental for the forecast, the Weather Research and Forecasting model 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 14 03 UTC. The experiment conducted was a case study within the intensive observing period of the RELAMPAGO-CACTI field campaign that was carried out during the 2018-2019 austral warm season in the center of Argentina. An independent data assimilation cycle using more observations and a different configuration is used in the experiments as verifying truth for the computation of forecast errors in EFSOI.

Results showed that all the observation sources had, on average, a positive impact on the 30 minute forecasts with a positive impact rate above 50%. However, when observations impacts are analyzed by geographic location, different results are evidenced. Most of the surface stations that evidence a detrimental impact in forecasts are located in the northern part of the region, probably due to a misrepresentation of the thermodynamic environment. Regarding radar reflectivity observations, 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 beneficial observations in lower levels than in upper levels.

This methodology is an approximation to quantify the impact of reflectivity and surface observations 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 data assimilation system where nonconventional observations such as radar data plays an essential role.