This work considers the comparison and accuracy estimation of atmospheric integrated precipitable water vapor and integrated liquid water content retrieved from brightness temperatures measured by ground based-microwave radiometers using different retrieval methods. Linear and non-linear algorithms including two different neural networks have been employed, tested and compared for both non-precipitating and rainy conditions.
Neural Networks and polynomial regressions for the retrieval of atmospheric water vapour and rain rate by ground-based microwave radiometry
BASILI, Patrizia;BONAFONI, Stefania;
2004
Abstract
This work considers the comparison and accuracy estimation of atmospheric integrated precipitable water vapor and integrated liquid water content retrieved from brightness temperatures measured by ground based-microwave radiometers using different retrieval methods. Linear and non-linear algorithms including two different neural networks have been employed, tested and compared for both non-precipitating and rainy conditions.File in questo prodotto:
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