Abstract
The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied-a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Mathematik, Informatik und Statistik > Informatik |
| Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Sprache: | Englisch |
| Dokumenten ID: | 82317 |
| Datum der Veröffentlichung auf Open Access LMU: | 15. Dez. 2021, 15:01 |
| Letzte Änderungen: | 15. Dez. 2021, 15:01 |
- Dokument bearbeiten
