Remote sensing and precipitation retrieval
Postdoctoral Research Associate
Department of Hydrology & Atmospheric Sciences, University of Arizona
Satellite precipitation retrieval and evaluation across oceans, polar regions, and data-sparse environments.
Dr. Kwabena Kingsley Kumah is a remote sensing scientist whose research focuses on retrieving, evaluating, and extending satellite precipitation and cryosphere records. As a Postdoctoral Research Associate at the University of Arizona, he contributes to NASA-supported work on GPCP, passive-microwave and infrared precipitation retrievals, and the evaluation of satellite and reanalysis products.
His current research combines algorithm development with independent, uncertainty-aware evaluation. It includes multi-reference assessment of ocean precipitation products, observation-constrained estimates of Antarctic snowfall, comparison of GPROF retrieval versions over the United States, and development of AVHRR-based methods for high-latitude precipitation. Across these projects, he examines where precipitation products agree, where they diverge, and how their limitations depend on scale, surface conditions, and observing system.
Dr. Kumah earned his Ph.D. at the University of Twente / ITC, where he developed rainfall-estimation methods that combined commercial microwave-link attenuation with Meteosat observations. This work in Africa established the broader thread connecting his research: improving precipitation information in regions and environments where conventional observations are sparse or difficult to obtain.
Selected team and professional recognition.
Global Precipitation Climatology Project (GPCP) Team · American Geophysical Union
Member of the Global Precipitation Climatology Project team recognized by the American Geophysical Union with the 2026 Open Science Recognition Prize for long-term stewardship of open global precipitation data and trusted climate records.
Professional recognition · 2025
Who’s Who in America
Recognized as a 2025 honored listee.
Selected work across satellite retrieval, validation, and data-sparse rainfall monitoring.
Development and diagnostic evaluation of AVHRR-based precipitation retrievals for high-latitude regions where conventional satellite precipitation estimates remain uncertain.
A physically informed correction framework for reducing scan-angle-related brightness-temperature bias in AVHRR infrared observations used for precipitation retrieval.
A multi-reference validation framework comparing GPCP, IMERG, ERA5, and MERRA-2 with PAL, moored buoys, atolls, and OceanRAIN across daily to climatological scales.
A physically constrained assessment of Antarctic precipitation using GRACE-based storage change, ice discharge, basal melt, and sublimation inputs across IMBIE drainage basins.
A machine-learning extension of the Global Merged Analysis of Snow and Ice record back to 1980-1987 using ERA5-derived surface variables.
Rainfall detection and mapping methods combining commercial microwave link attenuation with Meteosat cloud-top observations for data-sparse regions.
Dr. Kumah works on AVHRR-based high-latitude precipitation retrievals, limb-darkening correction, machine learning, and diagnostic evaluation relevant to long-term precipitation records such as GPCP and IMERG. This work focuses on improving the physical consistency and interpretability of satellite precipitation information in challenging environments.
His validation work compares products including GPCP, IMERG, ERA5, and MERRA-2 with independent references such as PAL, moored buoys, atolls, OceanRAIN, and Antarctic mass-budget constraints. The emphasis is on uncertainty-aware validation across daily, seasonal, and climatological scales.
Earlier research combined commercial microwave link attenuation with MSG SEVIRI cloud-top observations to support rainfall detection and mapping in Sub-Saharan Africa. This work reflects the broader challenge of precipitation monitoring where gauge and radar networks are limited, including rainfall-monitoring relevance for Ghana and other African regions.
Precipitation is essential for weather, climate, water resources, agriculture, and hazard monitoring, but it remains difficult to measure over oceans, polar regions, complex terrain, and data-sparse regions. Dr. Kumah’s work aims to improve the reliability, transparency, and usefulness of satellite precipitation and cryosphere records by combining physical understanding, machine learning, independent observations, and careful validation. The broader goal is to support better climate monitoring, hydrologic understanding, weather-risk assessment, and water-resource decision-making.
I welcome collaborations related to satellite precipitation retrieval, product validation, high-latitude precipitation, ocean precipitation, cryosphere applications, and rainfall monitoring in data-sparse regions.