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

Abstract

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

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

“…GPM+SM2RAIN ( https://zenodo.org/record/3345323 ) has a spatial resolution of 0.25° and daily temporal resolution. Two PRISM-based satellite rainfall products have been considered 15 , 36 . The PRISM method is applied to correct GPM-ER with SMOS (PRISM-SMOS) and SMAP (PRISM-SMAP) soil moisture observations.…”

“…PRISM-SMOS and PRISM-SMAP have a spatial resolution of 0.25° and 3-h temporal resolution. Details on the characteristic of GPM+SM2RAIN algorithm and on PRISM approach is given in 11 , 36 and we refer the interested reader to these publications for more specific information. Here, briefly, we underline the main difference between the two approaches that relies on the method used for integrating soil moisture observations.…”

“…Four satellite rainfall products have been assessed: (1) IMERG Early Run, (2) GPM+SM2RAIN ( 9 , available here: https://doi.org/10.5281/zenodo.3345322 ), (3) precipitation Inferred from Soil Moisture (PRISM) applied to SMOS, i.e., PRISM-SMOS, and (4) PRISM-SMAP. The two last PRISM-based products have been obtained from the integration of IMERG Early Run with SMOS and SMAP soil moisture through the procedure described in 15 , 36 . Even though not useful for our target application, for completeness, the 5th European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5 37 ), the ground-based Global Precipitation Climatology Centre product (GPCC 38 ) and the gauge-corrected IMERG Final Run product (GPM-FR) 11 have been considered.…”

Agro-ecological Lower Midland Zones IV and V in Kenya Using GIS and Remote Sensing for Climate-Smart Crop Management

“…In semi-arid regions, the issue of soil moisture becomes very important since moisture stress in crop is one of the key determinants of crop failure. In other studies, Pellarin et al (2020) has shown that soil moisture content can be used to infer precipitation using PrISM Model. This kind of modeling is said to provide useful information concerning crop yield estimates and irrigation demands over large areas.…”

Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi River Basin of East Africa

“…There has been increasing interest in the application of satellite precipitation products over East African regions of data scarcity (Näschen et al 2018;Oduor et al 2020;Pellarin et al 2020). A majority of the studies have focused around the greater Lake Victoria drainage basin considering its economic significance (Habib et al 2012;Williams et al 2015;Awange, 2021).…”