Research

Rainfall in Southern Africa is highly variable, shaped by influences from the Inter-Tropical Convergence Zone, the South Indian Ocean, El Niño-Southern Oscillation, and local climate drivers. Understanding this variability improves predictability, strengthening early warning, preparedness, and planning. This is foundational to the work the RAMP Lab does.

Our research also examines rainfall onset dynamics and trends, late-season cessation patterns, intra-seasonal dry spells, and heat extremes that create food security risk even in seasonally high rainfall adequate seasons. We research methods for blending satellite-derived climate surfaces with surface station observations to produce the most accurate gridded datasets possible for Botswana and the broader southern Africa region.

Food security early warning depends on timely, accurate seasonal monitoring of weather and its impact on agricultural productivity. RAMP Lab assesses, researches and localizes operational tools that translate climate data into actionable information, supporting the local monitoring of crop and vegetation conditions and agricultural stress at district to national scales. Our work is aligned with the FEWS NET science ecosystem and SADC analytical frameworks, and draws on the same satellite-derived datasets and monitoring methods used across the FEWS NET network.

Drought is the most economically damaging natural hazard in Southern Africa, yet it is also among the most predictable, given the right tools and data. RAMP Lab researches multi-indicator drought monitoring systems, including the Combined Drought Index (CDI), that integrate rainfall deficits, vegetation anomalies, and hydrological signals into convergence-of-evidence drought assessments.

Climate services sit at the interface between science and decision-making. Operational climate monitoring in data-sparse regions depends heavily on satellite-based data. RAMP Lab works extensively with gridded agrometeorological datasets, helping translate climate science products, into decision-relevant information that governments, farmers, and humanitarian actors can use.

Our research looks at validating satellite-based datasets such as CHIRPS and CHIRTS against surface station observations in Botswana, as well as applying and researching blending methods that combine the spatial coverage of satellite products with the accuracy of station data, and collaborating with BDMS to produce improved gridded datasets for operational use by BDMS. The resulting datasets feed directly into BDMS monitoring tools, with support of the RAMP Lab’s technical infrastructure. In support of the important work BDMS is doing, we host the Botswana Seasonal Rainfall Monitoring Portal, an interactive rainfall monitoring tool that allows users to explore climate variability at their own level of detail.

High-quality surface observations are scarce across sub-Saharan Africa, limiting both weather forecasting and climate monitoring. The 3D-Printed Automatic Weather Station (3D-PAWS) programme, developed by UCAR’s COMET Program, offers a path to rapid, affordable expansion of observation networks using readily available hardware and locally fabricated sensor housings.

At the University of Botswana, RAMP Lab is establishing a 3D-printing facility that will help build a critical mass of young African expertise in 3D-printing and related emerging technologies, with 3D-PAWS fabrication as one concrete application. This will be done through teaching and research, giving UB physics and meteorology students direct hands-on experience with the kind of locally fabricated instruments they may go on to use, adapt, and improve in their careers.

In support of the work led by partners at UCAR and BDMS, the lab will contribute to the expansion of low-cost surface observation infrastructure across Botswana, while offering a teaching and research facility within the SADC region that can be drawn on to support this work as it grows.

Cattle production is central to rural livelihoods across Southern Africa, yet livestock in the region’s semi-arid environments face rising heat stress, irregular rainfall, and disease pressures that conventional monitoring methods struggle to detect early enough for effective intervention. Better visibility into how individual animals respond to climate and rangeland conditions opens the door to earlier, more targeted management decisions.

RAMP Lab is collaborating with BUAN, DVS and BDMS to develop low-cost IoT cattle-tag systems linking animal physiology and behaviour to climate and environmental conditions, supporting research into heat stress detection, disease early warning, and reproductive efficiency. The work draws on the lab’s 3D-printing capabilities for rapid prototyping of sensor enclosures, alongside its established strengths in environmental monitoring and data integration.