Speakers
Description
Nowcasting and understanding of locally evolving severe weather events is a demanding task that requires the combined investigation of different type (both ground- and space-based) of datasets. Atmospheric water vapor (WV) which is the most abundant greenhouse gas (accounting for ~70% of global warming) comprises a significant energy source which generates severe weather and climate phenomena. GNSS (Global Navigation Satellite System) WV has been proved a valuable data source for high-resolution limited area Numerical Weather Prediction (NWP) models. The rapid spatiotemporal variations of WV in the low atmosphere poses one of the main challenges to NWP models forecasting accuracy. Abrupt increase of WV several hours before extreme rainfall has been temporally correlated with rainfall in various studies, followed by a decrease after the event. Other studies have investigated the joint effect of GNSS-WV and atmospheric pressure on extreme rainfall. Though many studies have evidenced ongoing accumulation of WV before the heavy rainfall, there is still a great difficult to determine a tight relationship between rainfall and WV, that could be reproduced by a plain, physically motivated two-layer nowcasting model.
Lately, Commercial Microwave Links (CML), globally used in cellular telecommunication networks of base stations, are exploited as opportunistic sensors to estimate the average rainfall intensity along the radio path and to reconstruct rainfall maps over a region. Rainfall measured by the CML network has a vast application prospect in both densely populated and remote mountainous regions. Over tropical regions, such as Sri Lanka, the spatial comparison of CMLs with the high-quality satellite product GPM (global precipitation measurement) and with conventional rain gauge data confirmed the potential of CMLs to provide detailed monitoring of heavy rainfall events. The advantage of both the GNSS and CML opportunistic sensors networks is their high spatial and temporal resolutions.
In this context, the present study attempts a first comparison of GNSS tropospheric products (Precipitable Water Vapor) with the respective CML-derived rainfall measurements with the ultimate aim to investigate the possible correlation between WV and heavy rainfall, during selected extreme precipitation events occurring at the period June 2019 – June 2020 over the Lombardy region in Northern Italy. To achieve this, we will exploit CML network, owned by Vodafone Italia S.p.A., ground-based GNSS receivers network owned by GReD srl, as well as meteorological observations available through the Lombardy-based Advanced Meteorological Predictions and Observations (LAMPO) project.
| Are you an Early Career Scientist ? | No |
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