Background
Africa faces a critical challenge: a lack of reliable, localized weather forecasts. This data gap
directly impacts sectors like agriculture, which forms the backbone of many African economies. While the
TAHMO initiative has deployed nearly 800 weather stations across the continent, spatial coverage remains
uneven, and significant gaps persist. High-quality, localised data is vital—not only for daily farming
decisions but also for climate resilience planning.
The primary objective of this research is to develop a high-precision,
fused precipitation product for East Africa that integrates the spatial continuity of satellite and
reanalysis data with the point-accuracy of the TAHMO network.
By creating a unified dataset that minimizes Root Mean Square Error (RMSE), we aim to overcome the
limitations of sparse monitoring networks and provide reliable meteorological inputs for the region’s
critical economic sectors.
Accurate rainfall estimates are particularly vital for East Africa, where the economy is heavily reliant
on rain-fed agriculture and hydroelectric power generation.
Providing farmers and policymakers with trustworthy data enables better decision-making regarding
planting schedules, crop insurance, and drought mitigation strategies.
Ultimately, this fused product serves as a foundational tool for enhancing climate resilience and food
security across the region.
The broader goal is to power a mobile or web-based app that serves localized forecasts directly to
African farmers—helping bridge the information gap in weather awareness.
To achieve these goals, we have the following experiments:
Spatial - Temporal Interpolation Experiments: To ensure the mathematical validity
of
this fusion
process,
we first conducted a rigorous temporal audit to quantify and correct systematic timing mismatches
between Gridded Precipitation Products (GPPs) and ground observations.
This step serves as a mandatory precursor to spatio-temporal interpolation; attempting to fuse
datasets
with misaligned timestamps would introduce significant noise, thereby increasing rather than
reducing
RMSE.
Our experiments utilize variance-stabilized feature spaces to diagnose specific phase shifts
inherent to
different retrieval algorithms, such as the assimilation windows of ERA5 or the morphing lag in
IMERG.
By establishing a physically coherent temporal baseline, we ensure that the subsequent fusion
algorithm
combines data that actually represent the same meteorological event.
Thus, these alignment experiments are the quality control filter that guarantees the input data is
worthy of the final interpolation model.
Harnessing Telecom Networks for Climate Resilience: We are transforming Africa’s
existing telecommunications infrastructure into a
high-resolution
weather monitoring network.
By using Commercial Microwave Links (CMLs) as "opportunistic sensors," we capture the signal
interference caused by rainfall to generate precise, real-time precipitation maps.
While traditional rain gauges are often sparse and expensive to maintain, our approach leverages the
massive footprint of Mobile Network Providers to fill critical data gaps.
By integrating these telecom datasets with advanced data processing, we provide the high-resolution
spatial information essential for flood alerts, precision agriculture, and climate resilience across
the continent.