Precipitation estimation using L‐band and C‐band soil moisture retrievals

Abstract

An established methodology for estimating precipitation amounts from satellite‐based soil moisture retrievals is applied to L‐band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C‐band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge‐based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L‐band SMAP and SMOS data sets is higher than that obtained with the C‐band product, as might be expected given that L‐band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP‐based precipitation estimates and the observations (for aggregations to ∼100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

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.…”

“…This new application is receiving more and more attention in the recent time, as also confirmed by a number of research projects funded by ESA, EUMETSAT, and NASA on this topic. Therefore, we believe that its further development and widespread application is highly beneficial for contributing to efforts in global precipitation estimation [140].…”

“…This new application is receiving more and more attention in the recent time, as also confirmed by a number of research projects funded by ESA, EUMETSAT, and NASA on this topic. Therefore, we believe that its further development and widespread application is highly beneficial for contributing to efforts in global precipitation estimation [140]. Soil moisture observations can be highly important for the detection and the quantification of irrigation, and specifically satellite datasets thanks to their large scale coverage [145,146].…”

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

“…The current SMOS SM retrievals by themselves have been found to be skillful (Al-Yaari et al, 2014;Fascetti et al, 2016), and research is ongoing to further improve them (Rodriguez-Fernandez et al, 2015;Ye et al, 2015;Zhao et al, 2015;van der Schalie et al, 2016;Wigneron et al, 2016). The use of these SMOS SM retrievals has been manifold, e.g., to derive enhanced estimates of precipitation (Wanders et al, 2015;Koster et al, 2016), to derive offline rootzone soil moisture estimates (Ford et al, 2014), or to offline downscale the data to higher-resolution soil moisture estimates (Piles et al, 2014). Other studies have assimilated SMOS SM retrievals online into land surface models to possibly downscale the retrievals and consistently improve soil moisture and other land surface variables (Ridler et al, 2014;Zhao et al, 2014;Lievens et al, 2015), leading to, e.g., improved estimates of floods (Alvarez-Garreton et al, 2015) and crop growth (Chakrabart et al, 2014).…”

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

“…In this study, in order to maximize the independence of SM2RAIN predictions, SM2RAIN parameters were not calibrated and were instead assumed constant in space as in Koster et al (2016). In particular, the drainage rate (the second term in Eq.…”