Quantitative Precipitacion Estimation model with Spatial Variability based on Polarimetric Radar

This paper describes the development of a precipitation estimation model which considers the spatial variability inherent to measuring with meteorological radars. Data from radars is separated by 30km and 50km respectively using an ANFIS (Adaptive Network-based in Fuzzy Inference Systems) estimation...

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Bibliographic Details
Published in:Revista IEEE América Latina 2016-05, Vol.14 (5), p.2128-2137
Main Authors: Gomez, E., Obregon, N.
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive systems
ANFIS
Artificial neural networks
Classification
Estimation
Fuzzy logic
Gages
Mathematical models
Meteorological radar
quantitative precipitation estimation
Radar
radar measurements
Radar polarimetry
Rain
weather radar
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Summary:This paper describes the development of a precipitation estimation model which considers the spatial variability inherent to measuring with meteorological radars. Data from radars is separated by 30km and 50km respectively using an ANFIS (Adaptive Network-based in Fuzzy Inference Systems) estimation system for each group. Each ANFIS system is fed by radar input data and a rain gauge located at corresponding distances to each group of input data. This article shows the results of a data classification study of an S-band radar in Brisbane, Australia, along with rain gauge data from the impact area of the radar. The result from the classification is regarded as the base for the data separation for the different ANFIS systems of the estimation model. The obtained results show improvement in the MSE compared to other traditional methods.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2016.7530405