Extended Abstract

RaiNs= Ri+ ∑k = 1Pk( Rok−Rsk)Pk=NN( Wrk/ ∑ Wrj) ⋅ ∏ W θ fj = 1j = 1Wθ j=⎧1 − cos θj θj< π / 2⎨⎩1, rjelser≤0 .33 D0 .33 DWhereWθis a function of orientation. r is the distance between station j and grid point i , Dis the average station distanceθ j is the angle from the connection line between station j and gridi to the connection line between grid i and station k .2.3 The test scheme descriptionIn this study, the period from 20 th May, 2010 to 10 th Sep, 2010 in China were tested. First of all, theSatellite precipitation estimates data were downloaded, decoded, at the same time, the rain gaugesprecipitation data obtained, then the 2 steps was done:1: Quality control. By a simple quality control method, we eliminated the irrationality raingauges, then a relative immobile station list got.2: Data supplement. Based on the step1 and the daily and hourly rain gaugesprecipitation ,we supply the “zero” to the record in the station list in the step1 .That means if the raingauge doesn’t observed precipitation, we considered the precipitation in the rain gauge is “zero”.In the fusing arithmetic, we selected 0.6° as the adjusting radius, which is the best adjusting radiusafter we have done a series of test from 0.1° to 2°( table ignoring).At last we have done a series ofevaluation test by using the hydrological stations to examining the performance of the QPE products.3. Evaluation of the QPE products3.1 Evaluation methodology descriptionEvaluation was made with the threat score (TS), false alarm rate and missing alarm rate between theobservations and the QPE products, and with the brier score to evaluate the performance. Theprecipitation was divided into four categories with the consistency of operational forecasts at theNational Meteorological Center (NMC) of CMA as no rain, little rain, moderate rain and heavy rain3-94-