A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

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 10000 km 2 . 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 are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. 5In this study, we developed a short-latency (i.e., 2-3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration 10 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. 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.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. However, the difficulty and the costs associated with their maintenance along with the timeliness of their data availability are critical obstacles for their use in real-time and seasonal applications. Moreover, while in developed regions the rain gauge distribution is sufficiently dense and supported by well organized and funded organizations, in developing countries the data coverage is extremely poor. 5

The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

“…In this study, we used the IMERG-Early and IMERG-Final products. The two products were upscaled to 0.25 • by using a box-shaped kernel with antialiasing, which was found to outperform simple spatial averaging, and half-hourly rainfall were accumulated to obtain a daily product [36].…”

River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa

“…In addition to rain gauges, and ground meteorological radars, meteorological and numerical weather prediction models and satellite observations can be used. However, both data sources rely directly or indirectly on ground observations (e.g., through assimilation for modelling or used as reference for satellite products), and hence their reliability over scarcely gauged areas is highly uncertain 9 .…”

“…The bottom up approach clearly distinguishes from the state-of-the-art top down method (i.e., GPM) as the rainfall signal is obtained\enhanced from the knowledge of the soil moisture signal, i.e., from the bottom up. This relatively new approach has been tested with different satellite sensors (e.g., Soil Moisture Ocean Salinity, SMOS, mission 15 , 16 ; Soil Moisture Active and Passive, SMAP, mission 17 ; and Advanced SCATterometer, ASCAT, sensor 18 ), and recently also by integrating multiple satellite soil moisture products 9 , 19 .…”

“…Top down and bottom up approaches have their own advantages and limitations. Several studies have demonstrated clearly that their integration is providing a higher quality rainfall product 9 , 14 – 16 , 20 , 21 . For hydrological applications such as river flow prediction, the bottom up approach has been found to perform well 22 , 23 , and the integration with the top down approaches particularly useful 1 , 24 – 26 .…”

“…Recently, Massari et al 6 proposed the Triple Collocation approach 28 for the assessment of satellite rainfall products in ungauged areas by profiting from three independent datasets. Massari et al 9 and Brocca et al 18 have recently performed Triple Collocation for assessing newly derived satellite rainfall products obtaining that the products based on the bottom up approach 18 , and on the integration of bottom up and top down approaches 9 , have very good performance in Africa and South America. Based on these results, we believe it is important and urgent to carry out the hydrological validation of such products in Africa, where there is a strong need for accurate rainfall data.…”