Abstract
Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.
Highlights
Rainfall is the main driver of the hydrological cycle (Oki and Kanae, 2006) and plays an essential role in water resource management and agricultural applications (Vintrou et al, 2014; Gibon et al, 2018), drought monitoring (Garreaud et al, 2017) and flood forecasting (Maggioni and Massari, 2018).Ground networks of rain gauges are considered the most accurate rainfall observations across many regions of the world
We developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiplesatellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals
We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets
Summary
Rainfall is the main driver of the hydrological cycle (Oki and Kanae, 2006) and plays an essential role in water resource management and agricultural applications (Vintrou et al, 2014; Gibon et al, 2018), drought monitoring (Garreaud et al, 2017) and flood forecasting (Maggioni and Massari, 2018).Ground networks of rain gauges are considered the most accurate (and as a reflection the most used) rainfall observations across many regions of the world. In regions like Africa, South America and central Asia the gauge density is often less than one station per 10 000 km, which results in large interpolation errors of gauge-based gridded rainfall products. This is an interesting paradox, since gauges are insufficient exactly where they are more needed. In these areas the only source of “observed” rainfall with a timeliness suited for applications is derived from satellite rainfall estimates (SREs) and meteorological models
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