Merging radar and in situ rainfall measurements: An assessment of different combination algorithms. Issue 10 (9th October 2016)

• Record Type: Journal Article
• Authors: Hasan, Mohammad Mahadi; Sharma, Ashish; Johnson, Fiona; Mariethoz, Gregoire; Seed, Alan
• Is Part Of: Water resources research. Volume 52:Issue 10(2016:Oct.)
• Page Start: 8384
• Page End: 8398
• Publication Date: 2016-10-09
• Subjects: radar rainfall -- merging -- covariance -- correlation -- PDF overlap -- probability aggregation; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2015WR018441
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Merging radar and gauge rainfall estimates is an area of active research. Since rain gauges alone are often limited at representing the complete spatial distribution of rainfall, a combination of radar‐derived rainfall with spatially interpolated gauge estimates using alternate weighting approaches is investigated. This paper examines several merging methods that differ in the consideration of correlation among the estimation errors, their distribution, and the application of dynamic and static weighting. The merging process has been applied to the radar data from Terrey Hills radar located in Sydney, Australia, and spatially interpolated gauge rainfall on the same area. The performance of the merging methods is assessed by comparing the combined estimate with the gauge observation. It is however clear from our findings that rainfall estimation from any of the combination approaches assessed contains less error than any of the noncombination approaches. The results show that the correlation between these two rainfall estimation errors plays a significant role in the performance of the merging methods. The combination method should be chosen depending on the purpose, accuracy of the estimate, and complexity of the method. Key Points: The influence of error independence (uncorrelated and correlated) on the combined estimates The effect of static or dynamic weighting on the combined estimates The consequence of Gaussian or non‐Gaussian error distributions on the combined estimates