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

• Published in: Water resources research Vol. 52; no. 9; pp. 7213 - 7225
• Main Authors: Koster, Randal D., Brocca, Luca, Crow, Wade T., Burgin, Mariko S., De Lannoy, Gabrielle J. M.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-09-2016
• Subjects: Atmospheric precipitations; C band; Correlation; Correlation coefficient; Datasets; Density; Ecological aggregations; Estimates; Estimation; Global precipitation; Information dissemination; Meteorological satellites; Methods; Missions; Moisture; Precipitation; Precipitation estimation; Precipitation variability; Products; Rain; Rain gauges; remote sensing; Salinity; Salinity effects; Satellites; Scatterometers; Soil; Soil layers; Soil moisture; Soils; Variability; 1854 Precipitation; 1855 Remote sensing (1640, 4337); 1866 Soil moisture
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2016WR019024

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.
Key Points:
The time sequencing of rain is extracted from 4 soil moisture retrieval data sets
This rain estimation is of unprecedented accuracy for the L‐band retrievals
Known features of the instruments and algorithms explain their relative performance