Abstract
Abstract. The knowledge of tropospheric water vapor distribution can significantly improve the accuracy of Numerical Weather Prediction (NWP) models. The present work proposes an automatic and fast procedure for generating reliable water vapor products from the synergic use of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and Global Navigation Satellite System (GNSS) observations. Moreover, a compression method able to drastically reduce, without significant accuracy loss, the water vapor dataset dimension has been implemented to facilitate the sharing through cloud services. The activities have been carried in the EU H2020 TWIGA project framework, aimed at providing water vapor maps at Technology Readiness Level 7.
Highlights
Numerical Weather Prediction models (NWPMs) currently represent the most important tool for weather forecasting and provide crucial information for lives and property protection and conscious human activities managing
The assimilation in NWPMs of water vapor observations derived by meteorological and Global Navigation Satellite System (GNSS) stations represents a well-established and effective technique (Guerova et al, 2016), and several studies showed its positive impact on the prediction of precipitation events (Kuo et al, 1993; Nakamura et al, 2004; Marcus et al, 2007)
The presented work proposes a novel procedure developed for the generation of Zenith Total Delay (ZTD) products from the synergic use of Synthetic Aperture Radar (SAR) and GNSS data
Summary
Numerical Weather Prediction models (NWPMs) currently represent the most important tool for weather forecasting and provide crucial information for lives and property protection and conscious human activities managing. Enhancing the performance of these models, especially for the prediction of heavy convective precipitation events, is still a challenging task. It has been demonstrated in the literature that the knowledge of tropospheric water vapor distribution can significantly improve the accuracy of NWPMs forecasts. Characterized by high temporal resolution, the GNSS observations are usually provided as sparse datasets due to the low density of available measurements. This means that they cannot comprehensively describe the spatial variations of water vapor, which in storm phenomena can occur within tens of meters (Stensrud, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
