An accurate measurement and monitoring of precipitation events is closely linked with different applications that have an impact on human welfare such as water resources management, and floods, landslides or wildfire risk assessments. Currently rain gauges, disdrometers, ground-based weather radars and satellite sensors (both active and passive) can be considered the conventional devices for precipitation measurements that are worldwide adopted. These devices have different measurement principles, time and space resolution, and accuracy (Gebremichael and Testik, 2013). In the last decade, a new technology that exploits the microwave satellite links has been investigated to retrieve precipitation information. The idea is to estimate the precipitation starting from the attenuation of the signal along its propagation path. Few studies have been carried out in this direction (such as Barthès and Mallet, 2013 and Mercier et al., 2015), showing promising results. In that regards, recently, an Italian project called NEFOCAST, funded by Tuscany Region (Italy), has been carried out with the aim of estimating rainfall rate from attenuation measurements made available by commercial interactive digital video broadcasting (DVB) receivers, called smartLNBs. During the NEFOCAST project, an ad hoc rainfall retrieval algorithm has been developed, tuned and tested. It allows to estimate, with 1-minute rate, the instantaneous rainfall rate (R, in mm/h) from the ratio η = Es/N0 between the received energy-per-symbol Es and the one-sided power spectral density of the additive white Gaussian noise N0, (Giannetti et al. 2017). To validate the algorithm, a 1-year field campaign (from January 2018 to January 2019) was conducted. The collected data allow to compare the SmartLNB precipitation estimates with the measurements gathered by ‘conventional’ meteorological devices such as rain gauges, weather radar and disdrometer. A network of 24 smartLNBs was deployed in Tuscany, along with 11 rain gauges and one X-band dual-polarization weather radar. Furthermore, the performance of the NEFOCAST algorithm has been preliminarily tested by comparing data provided from one SmartLNB installed at the Institute of Atmospheric Sciences and Climate (ISAC) of CNR in Rome (Italy) with a co-located laser disdrometer. For this site, data from a dual polarization C-band weather radar (Polar55C) could be compared with SmartLNB measurements along the Earth-satellite link. In fact, during the project the Polar55C has been aimed in the same direction as the SmartLNB, with the same elevation angle, thus scanning the same portion of atmosphere where the SmartLNB signal was propagating. Preliminary results show a good agreement between the total cumulative precipitation (in mm) obtained from SmartLNB data and the one collected by the co-located disdrometer during different rainfall events. The corresponding values of Normalized Mean Absolute Error (NMAE) and Root Mean Square Error (RMSE) obtained comparing the total cumulative precipitations obtained from SmartLNB and disdrometer are 41% and 4.71 mm, respectively. Encouraging results come also from the comparison of the total precipitation amounts as measured by the network of SmartLNBs and rain gauges, with values of NMAE (RMSE) that range between 39% and 53% (2.8 mm and 8.0 mm), depending on the specific site.

Validation of rainfall estimation derived from commercial DVB received signal with disdrometer, rain gauges and ground based radar

L. Facheris
Co-primo
Writing – Review & Editing
;
F. Giannetti
Co-primo
Writing – Review & Editing
;
A. Vaccaro
Co-primo
Writing – Review & Editing
;
2019-01-01

Abstract

An accurate measurement and monitoring of precipitation events is closely linked with different applications that have an impact on human welfare such as water resources management, and floods, landslides or wildfire risk assessments. Currently rain gauges, disdrometers, ground-based weather radars and satellite sensors (both active and passive) can be considered the conventional devices for precipitation measurements that are worldwide adopted. These devices have different measurement principles, time and space resolution, and accuracy (Gebremichael and Testik, 2013). In the last decade, a new technology that exploits the microwave satellite links has been investigated to retrieve precipitation information. The idea is to estimate the precipitation starting from the attenuation of the signal along its propagation path. Few studies have been carried out in this direction (such as Barthès and Mallet, 2013 and Mercier et al., 2015), showing promising results. In that regards, recently, an Italian project called NEFOCAST, funded by Tuscany Region (Italy), has been carried out with the aim of estimating rainfall rate from attenuation measurements made available by commercial interactive digital video broadcasting (DVB) receivers, called smartLNBs. During the NEFOCAST project, an ad hoc rainfall retrieval algorithm has been developed, tuned and tested. It allows to estimate, with 1-minute rate, the instantaneous rainfall rate (R, in mm/h) from the ratio η = Es/N0 between the received energy-per-symbol Es and the one-sided power spectral density of the additive white Gaussian noise N0, (Giannetti et al. 2017). To validate the algorithm, a 1-year field campaign (from January 2018 to January 2019) was conducted. The collected data allow to compare the SmartLNB precipitation estimates with the measurements gathered by ‘conventional’ meteorological devices such as rain gauges, weather radar and disdrometer. A network of 24 smartLNBs was deployed in Tuscany, along with 11 rain gauges and one X-band dual-polarization weather radar. Furthermore, the performance of the NEFOCAST algorithm has been preliminarily tested by comparing data provided from one SmartLNB installed at the Institute of Atmospheric Sciences and Climate (ISAC) of CNR in Rome (Italy) with a co-located laser disdrometer. For this site, data from a dual polarization C-band weather radar (Polar55C) could be compared with SmartLNB measurements along the Earth-satellite link. In fact, during the project the Polar55C has been aimed in the same direction as the SmartLNB, with the same elevation angle, thus scanning the same portion of atmosphere where the SmartLNB signal was propagating. Preliminary results show a good agreement between the total cumulative precipitation (in mm) obtained from SmartLNB data and the one collected by the co-located disdrometer during different rainfall events. The corresponding values of Normalized Mean Absolute Error (NMAE) and Root Mean Square Error (RMSE) obtained comparing the total cumulative precipitations obtained from SmartLNB and disdrometer are 41% and 4.71 mm, respectively. Encouraging results come also from the comparison of the total precipitation amounts as measured by the network of SmartLNBs and rain gauges, with values of NMAE (RMSE) that range between 39% and 53% (2.8 mm and 8.0 mm), depending on the specific site.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1026238
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