Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

Abstract

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different “bottom up” techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well‐known “top down” rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge‐corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real‐time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS‐based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root‐mean‐square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS‐based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches has the potential to improve the quality of near‐real‐time rainfall estimates from remote sensing in the near future.

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

“…SM2RAIN has also the main limitation of not being able to estimate rainfall if the soil is close to saturation, since no SM variations can be observed after rainfall events in such conditions. The algorithm has proved to accurately estimate rainfall both on a regional (Abera et al, 2016;Brocca et al, , 2015Brocca et al, , 2016Ciabatta et al, 2015Ciabatta et al, , 2017 and on a global scale (Brocca et al, 2014;Koster et al, 2016). For further details about the SM2RAIN formulation, the reader is referred to Brocca et al ( , 2014.…”

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

“…More recently, the number of publications on this topic is remarkably increasing likely due to the work by Brocca et al [137,138] who developed a "bottom-up" approach, called SM2RAIN, for directly estimating precipitation rates from soil moisture observations only. The method has been applied on a local scale with in situ observations [137,139] and on a regional/global scale with satellite data [138,140,141]. Moreover, the "bottom-up" approach was integrated with state-of-the-art rainfall products (i.e., "top-down" approach) for obtaining a superior rainfall product by Brocca et al [141] and Ciabatta et al [142] in Australia and Italy.…”

“…The method has been applied on a local scale with in situ observations [137,139] and on a regional/global scale with satellite data [138,140,141]. Moreover, the "bottom-up" approach was integrated with state-of-the-art rainfall products (i.e., "top-down" approach) for obtaining a superior rainfall product by Brocca et al [141] and Ciabatta et al [142] in Australia and Italy. Finally, the precipitation product corrected through soil moisture data were used in several recent studies for improving flood prediction [98,143,144].…”

SMOS near-real-time soil moisture product: processor overview and first validation results

“…J. Rodríguez-Fernández et al: SMOS near-real-time soil moisture product: processor overview itations, affects the partitioning of the water cycle (infiltration and run-off, and therefore the groundwater storage; see McColl et al, 2017) and it can also be used to improve rainfall estimations (Pellarin et al, 2008;Crow et al, 2009;Brocca et al, 2016). SM measurements have been used to perform data assimilation into land surface models (Xu et al, 2015;Blankenship et al, 2016;, SVAT (Soil Vegetation Atmosphere Transfer) models Ridler et al, 2014;Muñoz Sabater et al, 2007) and in carbon-cycle models (Scholze et al, 2016).…”