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. NumericalWeather 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.

A Synergistic use of a high-resolution numerical weather prediction model and high-resolution earth observation products to improve precipitation forecast / Lagasio, M.; Parodi, A.; Pulvirenti, L.; Meroni, A. N.; Boni, G.; Pierdicca, N.; Marzano, F. S.; Luini, L.; Venuti, G.; Realini, E.; Gatti, A.; Tagliaferro, G.; Barindelli, S.; Guarnieri, A. M.; Goga, K.; Terzo, O.; Rucci, A.; Passera, E.; Kranzlmueller, D.; Rommen, B.. - In: REMOTE SENSING. - ISSN 2072-4292. - 11:20(2019). [10.3390/rs11202387]

A Synergistic use of a high-resolution numerical weather prediction model and high-resolution earth observation products to improve precipitation forecast

Pierdicca N.;Marzano F. S.;
2019

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. NumericalWeather 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.
2019
data assimilation; GNSS water vapour; numerical weather prediction; sentinel 1
01 Pubblicazione su rivista::01a Articolo in rivista
A Synergistic use of a high-resolution numerical weather prediction model and high-resolution earth observation products to improve precipitation forecast / Lagasio, M.; Parodi, A.; Pulvirenti, L.; Meroni, A. N.; Boni, G.; Pierdicca, N.; Marzano, F. S.; Luini, L.; Venuti, G.; Realini, E.; Gatti, A.; Tagliaferro, G.; Barindelli, S.; Guarnieri, A. M.; Goga, K.; Terzo, O.; Rucci, A.; Passera, E.; Kranzlmueller, D.; Rommen, B.. - In: REMOTE SENSING. - ISSN 2072-4292. - 11:20(2019). [10.3390/rs11202387]
File allegati a questo prodotto
File Dimensione Formato  
Lagasio_Synergistic_2019.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 938.48 kB
Formato Adobe PDF
938.48 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1449535
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 42
  • ???jsp.display-item.citation.isi??? 32
social impact