Rainfall estimation and its spatial distribution are key elements for agriculture. Actually, rainfall maps over cultivated areas are needed for efficient water resources management, while prediction and monitoring of severe precipitation events are required for the estimation of possible damages and risks for crops, animals and infrastructures. Spatial and temporal accuracies of rainfall estimates are crucial, especially in case of intense and localized phenomena. Conventional instruments such as rain gauges provide point estimations, but the setup of a dense network requires high installation and maintenance costs. On the other hand, techniques based on satellite remote sensing or weather radars present specific limitations either in terms of data availability, sources of error, cost, or spatial and temporal resolution. A promising alternative is the exploitation of modern telecommunication technologies that, albeit not specifically developed for rainfall estimation, can bring relevant information through the measurement of the attenuation caused by raindrops on broadcast satellite signals. NEFOCAST1 is a research project funded by Tuscany Region (Italy) which implements such an approach based upon a dense population of ground-based Interactive Satellite Terminals (ISTs). The IST employed in the project is an innovative two-way (i.e., transmit/receive) device named Smart Low-Noise Block converter (SmartLNB). Usage of smart LNBs has many advantages in terms of cost and setup and has a great potential for application worldwide including areas where hydro-meteorological networks are not fully developed. In the framework of this project, an experimental network of SmartLNBs has been installed in Tuscany Region in Central Italy and a dedicated platform (NEFOCAST Service Center) has been set up where the data is collected (via ground-to-satellite link), processed and shared with a number of value-added service providers (VASPs). Real-time estimates (with 1 minute update) of rain rates as ‘seen’ by the SmartLNBs are produced through a processing algorithm based on the relationship between the rain rate and the signal attenuation with respect to clear-sky conditions. The real-time point estimates are filtered with a space-time Kalman filter to predict the pattern and evolution of the rainfall field and produce high resolution maps. This work is focused on the simulation of a set of case studies, featuring several storms with different spatial-temporal patterns and intensities. The 3D simulated rainfall fields are used as a virtual reality for the synthetic reconstruction of a set of SmartLNBs measurements over randomly located points. The relevant rainfall maps are then produced by use of the above mentioned Kalman filter approach over the set of synthetic SmartLNBs measurements. Finally a simple linear model of the storm evolution is introduced to test the ability of such algorithm to reconstruct the dynamic of the precipitation system. The impacts of various factors such as SmartLNB density, satellite link geometry, rainfall characteristics (e.g. horizontal/vertical structure, convective/stratiform event), are investigated and the potential for practical applications is eventually discussed.

Real-time high resolution rainfall maps from a network of ground-based interactive satellite terminals: the NEFOCAST project

Filippo Giannetti
Co-primo
Writing – Review & Editing
;
Marco Moretti
Co-primo
Writing – Review & Editing
;
Ruggero Reggiannini
Co-primo
Writing – Review & Editing
;
Antonio Colicelli
Co-primo
Writing – Review & Editing
;
Giacomo Bacci
Co-primo
Writing – Review & Editing
2018-01-01

Abstract

Rainfall estimation and its spatial distribution are key elements for agriculture. Actually, rainfall maps over cultivated areas are needed for efficient water resources management, while prediction and monitoring of severe precipitation events are required for the estimation of possible damages and risks for crops, animals and infrastructures. Spatial and temporal accuracies of rainfall estimates are crucial, especially in case of intense and localized phenomena. Conventional instruments such as rain gauges provide point estimations, but the setup of a dense network requires high installation and maintenance costs. On the other hand, techniques based on satellite remote sensing or weather radars present specific limitations either in terms of data availability, sources of error, cost, or spatial and temporal resolution. A promising alternative is the exploitation of modern telecommunication technologies that, albeit not specifically developed for rainfall estimation, can bring relevant information through the measurement of the attenuation caused by raindrops on broadcast satellite signals. NEFOCAST1 is a research project funded by Tuscany Region (Italy) which implements such an approach based upon a dense population of ground-based Interactive Satellite Terminals (ISTs). The IST employed in the project is an innovative two-way (i.e., transmit/receive) device named Smart Low-Noise Block converter (SmartLNB). Usage of smart LNBs has many advantages in terms of cost and setup and has a great potential for application worldwide including areas where hydro-meteorological networks are not fully developed. In the framework of this project, an experimental network of SmartLNBs has been installed in Tuscany Region in Central Italy and a dedicated platform (NEFOCAST Service Center) has been set up where the data is collected (via ground-to-satellite link), processed and shared with a number of value-added service providers (VASPs). Real-time estimates (with 1 minute update) of rain rates as ‘seen’ by the SmartLNBs are produced through a processing algorithm based on the relationship between the rain rate and the signal attenuation with respect to clear-sky conditions. The real-time point estimates are filtered with a space-time Kalman filter to predict the pattern and evolution of the rainfall field and produce high resolution maps. This work is focused on the simulation of a set of case studies, featuring several storms with different spatial-temporal patterns and intensities. The 3D simulated rainfall fields are used as a virtual reality for the synthetic reconstruction of a set of SmartLNBs measurements over randomly located points. The relevant rainfall maps are then produced by use of the above mentioned Kalman filter approach over the set of synthetic SmartLNBs measurements. Finally a simple linear model of the storm evolution is introduced to test the ability of such algorithm to reconstruct the dynamic of the precipitation system. The impacts of various factors such as SmartLNB density, satellite link geometry, rainfall characteristics (e.g. horizontal/vertical structure, convective/stratiform event), are investigated and the potential for practical applications is eventually discussed.
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/950143
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