Continuous Forecasting and Evaluation of Derived - Relationships in a Sparse Rain Gauge Network Using NEXRAD
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 2
Abstract
Distributed rainfall information is necessary for making operational hydrologic predictions and retrospective studies. Rainfall measurements from radar require conversion of reflectivity into rainfall rates, but contain systematic errors (bias). Default - relationships that are commonly used are not adapted to particular radars and vary by season. Improved rainfall estimation may be obtained for a given season and radar installation by deriving - relationships. These derived - relationships are used before prospective storms cross over gauges within a network, i.e., before near real-time bias corrections are possible. This research focuses on the derivation of - relationships for rainfall estimation in the South Florida Water Management District. These relationships are derived through seasonal characterization of gauge and Next-Generation Radar (NEXRAD) system observations. Verification of the derived - relationships is evaluated using rain gauges withheld for cross validation. Methods used in forecasting include persistence, seasonal trends, autoregressive, and Kalman filter methods. Results demonstrate that radar-specific - relationships exhibit better efficiency than standard - relationships, and among the forecasting methods tested, all were advantageous over persistence with varying degrees of accuracy.
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Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 2 • February 2013
Pages: 175 - 182
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Feb 1, 2011
Accepted: Jan 6, 2012
Published online: Jan 11, 2012
Published in print: Feb 1, 2013
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