The metropolitan area of Milan is a high-risk territory due to the presence of four torrential rivers (Lambro, Seveso, Groane and Olona rivers) in which severe and short thunderstorms can frequently trigger flash floods. In 2014, considering only the Milan municipality, Seveso floods produced damages for several million euros. Funded by the Cariplo Foundation, the LAMPO project (Lombardy-based Advanced Meteorological Predictions and Observations) aims at mitigating the impact of severe thunderstorm in the Milan area by developing a nowcasting system based on the water vapor observations derived by an experimental dense network of low-cost GNSS receivers. The project is led by the Politecnico di Milano and ARPA della Lombardia, in cooperation with GReD srl and Università degli studi di Padova. With the objective of building a nowcasting algorithm based on neural networks, we started by analyzing time series of atmospheric parameters from 2001 to 2017 in the Lombardy Region (specifically the data collected by 183 weather stations homogenously distributed in the region and by the existent GNSS permanent networks) and creating a monthly and seasonal climatology of pressure, temperature, relative humidity, integrated water vapor and wind speed. Additional information about extreme weather events has been collected from radar measurements and rain gauges with one-minute and five-minute temporal resolution. Merging the information of convective cells database retrieved by radar data, rain gauge sensors and hydrometers on the Seveso area, we have first chosen two case studies of extreme events causing floods, and then we have systematically selected all the convective events generating a rain rate higher than the 95th percentile. We then started investigating the anomaly of each parameter during the selected events with respect to the climatological value of the same area, in order to characterize the atmospheric structure during the extreme convective events. The difference between the current atmospheric values with the climatology represents the perturbation due to the presence of the extreme event and thus provides fundamental information to understand the convective and pre-convective environment. With this work, we characterize the atmospheric structure for extreme convective events with particular emphasis on the water vapor gradient before the convection. A further step will be the densification of the existent GNSS permanent networks (station inter-distances in the order of 50km) by installing a set of low cost receivers (interdistance less than 10 km) in the Seveso area to evaluate the impact that a finer resolution monitoring of water vapor have on the nowcasting of locally developed convective cells.

LAMPO project - the neural network forecasting model input data

E. Solazzo;S. Barindelli;F. Zanini;G. Venuti
2019-01-01

Abstract

The metropolitan area of Milan is a high-risk territory due to the presence of four torrential rivers (Lambro, Seveso, Groane and Olona rivers) in which severe and short thunderstorms can frequently trigger flash floods. In 2014, considering only the Milan municipality, Seveso floods produced damages for several million euros. Funded by the Cariplo Foundation, the LAMPO project (Lombardy-based Advanced Meteorological Predictions and Observations) aims at mitigating the impact of severe thunderstorm in the Milan area by developing a nowcasting system based on the water vapor observations derived by an experimental dense network of low-cost GNSS receivers. The project is led by the Politecnico di Milano and ARPA della Lombardia, in cooperation with GReD srl and Università degli studi di Padova. With the objective of building a nowcasting algorithm based on neural networks, we started by analyzing time series of atmospheric parameters from 2001 to 2017 in the Lombardy Region (specifically the data collected by 183 weather stations homogenously distributed in the region and by the existent GNSS permanent networks) and creating a monthly and seasonal climatology of pressure, temperature, relative humidity, integrated water vapor and wind speed. Additional information about extreme weather events has been collected from radar measurements and rain gauges with one-minute and five-minute temporal resolution. Merging the information of convective cells database retrieved by radar data, rain gauge sensors and hydrometers on the Seveso area, we have first chosen two case studies of extreme events causing floods, and then we have systematically selected all the convective events generating a rain rate higher than the 95th percentile. We then started investigating the anomaly of each parameter during the selected events with respect to the climatological value of the same area, in order to characterize the atmospheric structure during the extreme convective events. The difference between the current atmospheric values with the climatology represents the perturbation due to the presence of the extreme event and thus provides fundamental information to understand the convective and pre-convective environment. With this work, we characterize the atmospheric structure for extreme convective events with particular emphasis on the water vapor gradient before the convection. A further step will be the densification of the existent GNSS permanent networks (station inter-distances in the order of 50km) by installing a set of low cost receivers (interdistance less than 10 km) in the Seveso area to evaluate the impact that a finer resolution monitoring of water vapor have on the nowcasting of locally developed convective cells.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1109704
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