Abstract
Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.
Highlights
Thanks to the combined use of microwave and infrared sensors, the quality of available satellite rainfall estimates over land has significantly increased in the few last decades
We present the results obtained from the application of triple collocation (TC) by following the subsequent methodological steps: (1) calculating TC-based correlations for Triplets A, B, C, D and E over the contiguous United States (CONUS) and providing an assessment of the Climate Prediction Center (CPC) product (Sect. 3.1), (2) understanding the adequacy of TC results based on the spatial similarity between (TC-based) ρt,Xi and (CPC-based) RXi over the CONUS in order to identify the optimal configuration for applying TC and (3) applying the optimalconfigured TC on a global scale to calculate ρt,Xi globally for the selected rainfall products (Sect. 3.3)
Regardless of the triplet or error model applied, the TC analysis summarized in Table 1 indicates that CPC is the most accurate product, which strengthens our assumption that within CONUS, CPC can be used as a benchmark to evaluate the optimal TC configuration for rainfall product evaluation
Summary
Thanks to the combined use of microwave and infrared sensors, the quality of available satellite rainfall estimates over land has significantly increased in the few last decades. Through the Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm, rainfall estimates from the various precipitation-relevant satellite PMW and IR missions are intercalibrated, merged and interpolated with the GPM Combined Core Instrument product to produce rainfall accumulation estimates with an unprecedented accuracy. Despite these technical advancements, the precipitation community still struggles to show a clear picture of the actual increased accuracy of satellite rainfall estimates in many areas of the world because validation studies rely upon the availability. Massari et al.: Performance of global rainfall estimates of high-quality (and sufficiently dense) ground-based rainfall instrumentation (e.g. rain gauge and radars)
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