Abstract
ABSTRACTThis 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.
Highlights
IntroductionThe progresses achieved in numerical weather prediction (NWP) presently allow the models to produce forecasts with a grid spacing of the order of 1 km (the so-called cloud-resolving grid spacing) (e.g. Cardoso, Soares, Miranda, & Belo-Pereira, 2013)
The progresses achieved in numerical weather prediction (NWP) presently allow the models to produce forecasts with a grid spacing of the order of 1 km (e.g. Cardoso, Soares, Miranda, & Belo-Pereira, 2013)
To apply the multi-temporal Soil Moisture (SM) retrieval algorithm previously described, the S1 data acquired on 8 September 2017, and those acquired on September 2, August 27, August 21 and 15 August 2017 were used and the ground range detected (GRD) products were chosen
Summary
The progresses achieved in numerical weather prediction (NWP) presently allow the models to produce forecasts with a grid spacing of the order of 1 km (the so-called cloud-resolving grid spacing) (e.g. Cardoso, Soares, Miranda, & Belo-Pereira, 2013). The progresses achieved in numerical weather prediction (NWP) presently allow the models to produce forecasts with a grid spacing of the order of 1 km (the so-called cloud-resolving grid spacing) High-resolution NWP models are generally fed by low-resolution and/or not timely updated data and this implies a poor knowledge of the initial state of both atmosphere and surface at small scales. NWP models operating at cloudresolving grid spacing can take advantage of the availability of several free of charge Earth Observation (EO) data characterized by high spatial and/or temporal resolution. It can be expected that ingesting products derived from the aforementioned EO data into NWP models might significantly reduce weather forecast uncertainties. While some investigations on the assimilation in NWP models of low resolution (tens of km) EOderived products (e.g. soil moisture extracted from the Soil Moisture and Ocean Salinity mission data), are available in the literature
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