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

Author
item BROCCA, LUCA - National Research Council - Italy
item PELLARIN, T. - Grenoble Institute Of Technology
item Crow, Wade
item CIABATTTA, LUCA - National Research Council - Italy
item MASSARI, CHRISTIAN - National Research Council - Italy
item RYU,D. - Melbourne University
item RUDIGER, C. - Monash University
item KERR, Y. - Collaborator

Submitted to: Geophysical Research and Atmosphere
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2016
Publication Date: 11/1/2016
Citation: Brocca, Luca, Pellarin, T., Crow, W.T., Ciabattta, L., Massari, C., Ryu,D., Rudiger, C., Kerr, Y. 2016. Rainfall estimation by inverting SMOS soil moisture estimates: a comparison of different methods over Australia. Geophysical Research and Atmosphere. 121:12,062–12,079.

Interpretive Summary: The accurate measurement of daily rainfall accumulations is vital for the global monitoring of agricultural drought. However, such estimates are not typically available for large regions of the world lacking adequate ground-based rain gauge instrumentation (e.g., Africa, Central Asian and areas of South American). Recent rainfall leaves a clear impression on levels of surface soil moisture. Therefore, a potential solution to this problem is the use of remotely-sensed surface soil moisture retrievals to indirectly estimate recent rainfall accumulation. Within the past decade, a multiple algorithms have been proposed for this purpose. This paper describes the first inter-comparison analysis of these algorithms. As a result, it provides a valuable first step towards defining an optimal algorithm for use in operational drought monitoring activities. Eventually, the results of this analysis will be used to improve on-going USDA operational efforts to globally monitor the event, duration and severity of agricultural drought events.

Technical 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 European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis product (TMPA) using three different “bottom up” techniques: SM2RAIN, SMART and API-mod. The implementation of these three techniques aim at improving the well-known “top down” rainfall estimate derived from 3B42RT product, available in near real time. Ground observations provided by the Australian Water Availability Project (AWAP) are considered as independent validation dataset. The three algorithms are calibrated against the gauge-corrected TMPA re-analysis product, 3B42, and used for adjusting the 3B42RT product using the information provided by the 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. Based on the current results, it is shown that the integration of “bottom up” and “top down” approaches provides an important potential for delivering high-quality rainfall estimates from remote sensing in the near future.