Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived products: analysis of the forecasts of a high impact weather event. (18th December 2019)

• Record Type: Journal Article
• Authors: Lagasio, Martina; Pulvirenti, Luca; Parodi, Antonio; Boni, Giorgio; Pierdicca, Nazzareno; Venuti, Giovanna; Realini, Eugenio; Tagliaferro, Giulio; Barindelli, Stefano; Rommen, Bjorn
• Is Part Of: European journal of remote sensing. Volume 52(2019)Supplement 4
• Page Start: 16
• Page End: 33
• Publication Date: 2019-12-18
• Subjects: Numerical weather model -- Sentinel-1 -- Sentinel-3 -- data assimilation; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2019.1642799
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast.