An assessment of the performance of global rainfall estimates without ground-based observations

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.

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

“…Figure 4 also displays the lower performances obtained for the eastern US. A similar performance pattern was also found by Massari et al (2017), who calculated global correlation of different rainfall data sets by applying the extended TC (McColl et al, 2014) analysis.…”

“…It is worth nothing that due to the calibration strategy, the data set is not suitable for trend analysis. Moreover, the SM2RAIN-CCI is completely independent from other existing state-of-the-art precipitation products, therefore offering an additional long-term data set that can be used for independently evaluating these global-scale precipitation products as shown by Massari et al (2017).…”

“…SM2RAIN has been used to estimate precipitation from various single-sensor SSM products, e.g. from ASCAT and SMOS (Brocca et al, , 2014Ciabatta et al, 2015Ciabatta et al, , 2017Koster et al, 2016;Massari et al, 2017).…”

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

“…It can be seen that P ERA performs relatively better than P 3B42RT for almost all catchments thus confirming the previous results in terms of K obtained in calibration. Similar results were also found by [44] who observed a higher quality of ERA-Interim in Europe with respect to 3B42RT. The only exception is the Mdouar catchment.…”

“…SM2RAIN is an algorithm for estimating rainfall accumulations from soil moisture observations. The method has demonstrated to provide accumulated rainfall estimates with an accuracy comparable (and higher depending on the regions) to state-of-the-art satellite rainfall products [24,26,44]. For further details on the method and its application, the reader is referred to [24].…”