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An algorithm for integrating satellite precipitation estimates with in situ precipitation data on a pentad time scale
Author(s) -
Wang Xiaolan L.,
Lin Achan
Publication year - 2015
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd022788
Subject(s) - precipitation , rain gauge , data set , algorithm , gauge (firearms) , satellite , scale (ratio) , meteorology , mathematics , environmental science , statistics , physics , materials science , quantum mechanics , astronomy , metallurgy
Abstract This study proposes an algorithm for constructing pentad precipitation fields by integrating the popularly used Global Precipitation Climatology Project (GPCP) daily precipitation data set, GPCP1dd v1.2, with Canadian in situ daily precipitation data. This algorithm consists of two major steps. First, the GPCP data were adjusted to remove biases relative to the gauge data, with consideration of the differences between snowfall and rainfall, and of the gauge density. Then, a blended pentad precipitation field was constructed using the adjusted GPCP precipitation field and the differences between the gauge and adjusted GPCP precipitation fields (residual kriging). The skill of the algorithm is evaluated for three networks of sparse to medium gauge density, with the evaluation data set being much larger than the training data set. The results show that the algorithm produces better representation of pentad precipitation fields than the GPCP precipitation estimates or using the gauge data alone; it has smaller root‐mean‐square errors and higher correlation skill scores. This algorithm was used to produce the first blended pentad precipitation data set for the period of 1997–2007 for Canada (CanBP5dV1). It can be used for other regions around the world.