Dr. Kwabena Kingsley Kumah

About

I specialize in remote sensing-based precipitation retrieval, algorithm development, and uncertainty diagnostics. My work leverages infrared, microwave satellite data, and ground-based observations combined with machine learning techniques to improve global rainfall estimation. I am currently developing an AVHRR-based precipitation product aimed at high-latitude regions, supporting initiatives like GPCP and IMERG.

My research has also focused on leveraging commercial microwave links (CMLs) from telecommunication networks, combined with satellite data, to enhance rainfall estimation in data-scarce regions. This innovative approach utilizes signal attenuation in communication networks to infer precipitation, offering a cost-effective solution for improving rainfall monitoring where traditional sensors are limited.

Education

• Postdoc Scholar, Hydrometeorology, University of Arizona, 2023–Present
• PhD, GIS and Earth Observation, University of Twente, 2022
• MSc, GIS and EO, University of Twente, 2016
• BSc, Environmental Science, University of Cape Coast, Ghana, 2012

Experience

• Postdoctoral Research Associate, University of Arizona – 2023–Present
• Data Analyst, TAHMO – 2023–Present
• Visiting Scientist, EUMETSAT – 2022–2023
• Doctoral Researcher, University of Twente – 2016–2022

Technical Skills

• Geospatial Data Analysis: GIS, Remote Sensing, spatiotemporal modeling, and geospatial data fusion
• Programming Languages: Python (numpy, pandas, geopandas, xarray, matplotlib), Bash scripting
• Data Formats & Tools: netCDF, HDF5, GeoTIFF, shapefiles, GDAL, rasterio, QGIS, ArcGIS
• Satellite Data Handling: AVHRR, MSG SEVIRI, GPM/IMERG, CMORPH, CHIRPS
• Machine Learning: Scikit-learn, regression/classification models for rainfall retrieval
• Automation & Workflow: End-to-end geoprocessing workflows, batch satellite processing
• HPC Usage: High-performance computing for satellite data processing

Publications

• Near real-time rainfall estimation from MSG & microwave links
• The MSG Technique for CML Rainfall Enhancement
• Rain Area Detection in Kenya using MSG Data

Media Features

• ITC Algorithm Detects Rainfall in Rural Africa
• APS Physics: Rainfall Detection Algorithm
• Innovation Origins: Predicting Rainfall in Africa

Academic Profiles

• Google Scholar
• ResearchGate
• ORCID
• LinkedIn

Contact

Email: kkumah@arizona.edu

Alternate: kkumahkwabena@gmail.com

Office: JW Harshbarger Building, Room 316D, University of Arizona