Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data

Abstract

Measuring precipitation intensity is not straightforward; and over many areas, ground observations are lacking and satellite observations are used to fill this gap. The most common way of retrieving rainfall is by addressing the problem "top-down" by inverting the atmospheric signals reflected or radiated by atmospheric hydrometeors. However, most applications are interested in how much water reaches the ground, a problem that is notoriously difficult to solve from a top-down perspective. In this study, a novel "bottom-up" approach is proposed that, by doing "hydrology backward," uses variations in soil moisture (SM) sensed by microwave satellite sensors to infer preceding rainfall amounts. In other words, the soil is used as a natural rain gauge. Three different satellite SM data sets from the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR-E), and the Microwave Imaging Radiometer with Aperture Synthesis are used to obtain three new daily global rainfall products. The "First Guess Daily" product of the Global Precipitation Climatology Centre (GPCC) is employed as main benchmark in the validation period 2010-2011 for determining the continuous and categorical performance of the SM-derived rainfall products by considering the 5 day accumulated values. The real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis product, i.e., the TRMM-3B42RT, is adopted as a state-of-the-art satellite rainfall product. The SM-derived rainfall products show good Pearson correlation values (R) with the GPCC data set, mainly in areas where SM retrievals are found to be accurate. The global median R values (in the latitude band ±50°) are equal to 0.54, 0.28, and 0.31 for ASCAT-, AMSR-E-, and SMOS-derived products, respectively. For comparison, the median R for the TRMM-3B42RT product is equal to 0.53. Interestingly, the SM-derived products are found to outperform TRMM-3B42RT in terms of average global root-mean-square error statistics and in terms of detection of rainfall events. The regions for which the SM-derived products perform very well are Australia, Spain, South and North Africa, India, China, the Eastern part of South America, and the central part of the United States. The SM-derived products are found to estimate accurately the rainfall accumulated over a 5 day period, an aspect particularly important for their use for hydrological applications, and that address the difficulties of estimating light rainfall from TRMM-3B42RT.

Correction of real-time satellite precipitation with satellite soil moisture observations

“…Figure 6 (statistics provided in Table 1) confirms the effect of surface saturation on adjusted precipitation, which is well described in previous studies (e.g., Brocca et al, 2013Brocca et al, , 2014. The recovered precipitation, when the surface soil is saturated, only contributes more noise rather than an improvement to the rainfall estimates.…”

“…Studies have demonstrated that in situ (Brocca et al, 2009(Brocca et al, , 2013Matgen et al, 2012) and satellite (Francois et al, 2003;Pellarin et al, 2008Pellarin et al, , 2013Brocca et al, 2014) estimates of surface soil moisture could contribute to precipitation estimates by providing useful information concerning the sign and magnitude of antecedent rainfall accumulation errors. In particular, Brocca et al (2014) estimated daily rainfall on a global scale based on satellite SM products by inverting the soil water balance equation.…”

“…In particular, Brocca et al (2014) estimated daily rainfall on a global scale based on satellite SM products by inverting the soil water balance equation. Crow et al (2003Crow et al ( , 2009Crow et al ( , 2011 corrected space-borne rainfall retrievals by assimilating remotely sensed surface soil moisture retrievals into an Antecedent Precipitation Index (API) based soil water balance model using a Kalman filter (Kalman, 1960).…”

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

“…Recently, Brocca et al (2014) developed a method for estimating rainfall accumulation amounts directly from satellite SM observations based on the principle that the soil can be treated as a "natural rain gauge". In contrast with classical satellite rainfall products, this new bottom-up approach attempts to measure rainfall by calculating the difference between two successive SM measurements derived from a satellite SM product.…”

“…In this study, TC is applied to the rainfall accumulation estimates derived from (1) ERA-Interim (Dee et al, 2011), (2) SM2RAIN (Brocca et al, 2014) via inversion of Advanced SCATterometer (ASCAT; Wagner et al, 1999, SM data, (3) the NOAA Climate Prediction Center morphing (CMORPH, raw version) (Joyce et al, 2004) and (4) the TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42RT (Huffman et al, 2007) product over the CONUS (note that 3B42RT and CMORPH do not include gauge information in their retrieval algorithms). Thanks to the ability of TC to provide the correlation against the "un- known" truth (ETC; McColl et al, 2014), the assessment of the products will be carried out in terms of correlation against "true" rainfall values.…”