Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall
from Cloud Top Properties of the MSG satellite and Commercial Microwave
Link Rainfall Intensities
Abstract
High spatiotemporal resolution rainfall is needed in predicting flash
floods, local climate impact studies and agriculture management.
Rainfall estimation techniques like satellites and the commercial
microwave links (MWL) rainfall estimation have independently made
significant advancements in high spatiotemporal resolution rainfall
estimation. However, their combination for rainfall estimation has
received little attention, while it could benefit many applications in
ungauged areas. This study investigated the usability of the random
forest (RF) algorithm trained with MWL rainfall and Meteosat Second
Generation (MSG) based cloud top properties for estimating high
spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift
Valley. Our approach retrieved cloud top properties for use as predictor
variables from rain areas estimated from the MSG data and estimated path
average rainfall intensities from the MWL to serve as the target
variable. We trained and validated the RF algorithm using parameters
derived through optimal parameter tuning. The RF rainfall intensity
estimates were compared with gauge, MWL, Global Precipitation
Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG)
and European Organisation for the Exploitation of Meteorological
Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to
evaluate its rainfall intensities from point and spatial perspectives.
The results can be described as good, considering they were achieved in
near real-time, pointing towards a promising rainfall estimation
alternative based on the RF algorithm applied to MWL and MSG data. The
applicative benefits of this technique could be huge, considering that
many ungauged areas have a growing MWL network and MSG and, in the
future, Meteosat Third Generation coverage.