A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

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.

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

“…The assimilation of GNSS products into NWP models can currently be done in terms of total delay in the zenith direction above a receiver. Their positive impact on the prediction of convective storms localization and timing has been proved in many research studies (Oigawa et al, 2018;Lagasio et al, 2019;Mascitelli et al, 2019;Hdidou et al, 2020;Yang et al, 2020).…”

“…To overcome this limitation, the concept of a geosynchronous SAR is under development, and this will allow for a continuous monitoring of integrated water vapor over large areas (Ruiz Rodon et al, 2013;Monti Guarnieri and Rocca, 2017;Monti Guarnieri et al, 2018). Several experiments were done to prove the positive impact of the SAR-derived water vapor maps in the prediction of extreme rain events when assimilated into NWP models (Pichelli et al, 2015;Mateus et al, 2018;Lagasio et al, 2019;Miranda et al, 2019;Pierdicca et al, 2020).…”

“…It is important to notice that, nowadays, assimilation techniques disregard the spatial correlation of SAR observations. This implies a sub-sampling of the available maps to reduce their spatial resolution and correlation (Lagasio et al, 2019).…”

Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

“…In recent years, several works have been done to integrate SAR measurements and Numerical Weather Prediction Models (NWPMs): the latter can provide an estimate of the atmospheric delay that can be used to correct SAR-derived products [9][10][11], while the first can deliver a measure useful as ingestion product into NWPMs where no other sources like ground-based radars or radio probes are available [12][13][14]. The measure that a SAR can provide is the delay induced by a variation of refractive index in the geometric path traveled by the electromagnetic wave, which, in turn, is related to the water vapor content in the atmosphere.…”

Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin

“…Researchers are trying to enhance the forecasting performances of convective rainfall events, integrating information extracted by the dataset available nowadays which couple traditional and innovative meteorological variables [28,29]. Other authors proposed to adopt a different paradigm, switching from the conventional physically-based meteorological model to more recent machine learning techniques [30][31][32][33][34] to exploit the huge amount of available data.…”