The role of water vapor observations in satellite-based rainfall information highlighted by a Deep Learning approach

The role of water vapor observations in satellite-based rainfall information highlighted by a Deep Learning approach

Curzi, Fabio (TU Delft Civil Engineering & Geosciences)

ten Veldhuis, Marie-claire (mentor)
Estebanez Camarena, M. (graduation committee)
Taormina, R. (graduation committee)
van de Giesen, N.C. (graduation committee)

Delft University of Technology

Civil Engineering | Environmental Engineering

2022-06-28

Rain-fed agriculture is the main source of food in Ghana therefore improving quantitative rainfall estimates is essential for local farmers to predict crop growth using vegetation models. Rainfall dynamics in the tropics is an ongoing topic of research due to their complexity and sub-grid precipitation variability. At the same time, tropical areas such as Ghana are the most affected by a lack of proper rain gauge network coverage. Traditional methods rely on empirical assumptions and statistical theories that require continuous calibration and still struggle to accurately represent local variability. The aim of this paper is to demonstrate the potential of a Deep Learning (DL) approach using bi-spectral information of water vapor imagery (WV) and thermal infrared (TIR) as a starting point to develop an effective alternative to the Cold Cloud Duration (CCD)
method which is a widely applied statistical technique by satellite rainfall products like Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Tropical Applications of
Meteorology using SATellite data (TAMSAT) that are specifically designed for Africa.
WV inhibition of low-level features assures the correct depiction of strong convective motions usually related to heavy rainfall which is crucial in tropical areas where convective rainfall is dominant. The addition of WV 7.3𝜇m is particularly beneficial in North Ghana as tropical systems
are advecting dry air from the nearby Sahara desert creating discontinuities in precipitation events which translates into dry intrusions and dry slots seen in the images of the WV channel.
The developed Deep learning model showed strong performances in binary classification where it outperformed IMERG-Final false alarms count resulting in lower rainfall overestimation (FBias < 2.0), although further research is needed to overcome the very poor relation between GEO-IR images and actual rainfall estimates at the surface.

Rainfall Detection
Water Resources
Environmental Engineering
West Africa
TAHMO
Meteosat
Deep Learning

http://resolver.tudelft.nl/uuid:e79330f6-24dc-42a6-81f0-27a7316bebf1

2023-06-01

9.40, -0.83

Student theses

master thesis

© 2022 Fabio Curzi