Motivated by a real world problem, this study develops a neural network approach to identify and evaluate the relationship between atmospheric radar reflectivity and ground level rainfall intensity. Rainfall is one of the most difficult elements of hydrologic cycle to measure and forecast. This is due to the tremendous range of variability it displays over a wide range of scales both in space and time. Weather radar constitutes an attractive possibility for improving the description of rainfall fields as it can provide high resolution images in space and time of the atmospheric reflectivity over large areas
Using neural networks to identify the relationship between radar reflectivity and rainfall intensity / Morlini, Isabella; Orlandini, Stefano. - STAMPA. - (1999), pp. 89-94. (Intervento presentato al convegno S.CO.99 tenutosi a Venezia, Italy nel 27-29 Settembre 1999).
Using neural networks to identify the relationship between radar reflectivity and rainfall intensity
MORLINI, Isabella;ORLANDINI, Stefano
1999
Abstract
Motivated by a real world problem, this study develops a neural network approach to identify and evaluate the relationship between atmospheric radar reflectivity and ground level rainfall intensity. Rainfall is one of the most difficult elements of hydrologic cycle to measure and forecast. This is due to the tremendous range of variability it displays over a wide range of scales both in space and time. Weather radar constitutes an attractive possibility for improving the description of rainfall fields as it can provide high resolution images in space and time of the atmospheric reflectivity over large areas
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