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Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia

Chappell, Adrian, Renzullo, Luigi J., Raupach, Tim H. and Haylock, Malcolm 2013. Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia. Journal of Hydrology 493 , pp. 105-114. 10.1016/j.jhydrol.2013.04.024

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Abstract

Rain gauges provide valuable information about the amount and frequency of rainfall. In Australia, the majority of rain gauges are located in populated, wet coastal regions. Approximately 2000 gauges reporting within 24 h of a target day were used to make near real-time (NRT) estimates of daily precipitation. The remaining ≈4000 gauges for the same target day were used to evaluate bias and estimation performance using several traditional statistics. There is considerable potential to improve the estimation of rainfall in Australia using related ancillary data, particularly in sparsely gauged areas. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA-RT) near real-time product (3B42RT) provided images (0.25° resolution) of precipitation across Australia. Daily precipitation was estimated in 2009/10 approximately every 5 km across Australia. This study evaluated selected geostatistical methods for estimating daily rainfall maps across Australia. It tackled the change of support problem and spatial intermittency of daily rainfall data in blending satellite and gauge data. Dissension occurred amongst traditional global statistical measures of performance which were compounded by extremes of gauge density. Overall, our assessment is that blending the 3B42RT satellite and rain gauge data was not worthwhile. However, the blending considerably reduced the estimation variance indicating that uncertainty of the map estimates was a neglected property necessary to detect change and difference in patterns

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Ocean Sciences
Publisher: Elsevier
ISSN: 0022-1694
Date of Acceptance: 13 April 2013
Last Modified: 01 Nov 2018 16:45
URI: http://orca.cf.ac.uk/id/eprint/116330

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