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.
Highlights
The advance of numerical weather prediction (NWP) models to increasingly higher grid spacings has lead to new grounds being reached for what concerns their potential synergy with spaceborne systems
The present state of the art HR NWP models coincides with the availability of several Earth Observation (EO) data characterized by high spatial and/or temporal resolution, such as the Sentinel missions developed by the European Space Agency (ESA) in the framework of the Copernicus programme (EU Regulation No 377/2014)
This paper introduces and discusses the results of a set of experiments on the assimilation in the Weather Research and Forecasting (WRF) model [13], of surface information derived from Sentinel products and information about the atmospheric water vapour content derived from both Sentinel-1 data and Global Navigation Satellite System (GNSS) measurements
Summary
The advance of numerical weather prediction (NWP) models to increasingly higher grid spacings (km and sub-km) has lead to new grounds being reached for what concerns their potential synergy with spaceborne systems. To feed high-resolution (HR) NWP models, HR input data and boundary conditions potentially updatable on, at least, a weekly basis are needed. The present state of the art HR NWP models coincides with the availability of several Earth Observation (EO) data characterized by high spatial and/or temporal resolution, such as the Sentinel missions developed by the European Space Agency (ESA) in the framework of the Copernicus programme (EU Regulation No 377/2014). Services like emergency management, land surface and marine applications make use of Sentinel data (https://www.copernicus.eu/en/services), while Copernicus atmospheric services focus on air quality and atmospheric composition, ozone layer and ultra-violet radiation, emissions and surface fluxes, solar radiation and climate forcing but not on NWP applications
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