Research

Rainfall in Southern Africa is highly variable, shaped by competing 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 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 accurate, timely seasonal monitoring and reporting on the impact of weather on agricultural productivity. RAMP Lab localizes, develops and maintains operational tools used to translate climate data into actionable food security intelligence, and undertakes research to improve these tools and make them more impactful.

Drought is the most economically damaging natural hazard in Southern Africa, yet it is also the most predictable - given the right tools and data. RAMP Lab is researching multi-indicator drought monitoring systems like the Combined Drought Index (CDI) that combine meteorological, agricultural, and hydrological signals into actionable drought assessments.

Climate services sit at the interface between science and decision-making. Producing a good rainfall forecast or drought monitor is necessary, and equally important, delivering these products in a format accessible to users. RAMP Lab works on both sides of this interface: improving the quality of climate products and improving their usability.

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. We pay particular attention to how national meteorological services can build internal capacity to produce and communicate climate services themselves, rather than depending on external expertise.

Operational climate monitoring in data-sparse regions depends heavily on satellite-derived estimates. RAMP Lab works extensively with CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations), a globally produced satellite-gauge merged rainfall dataset developed at UCSB’s Climate Hazards Center.

Our research focuses on validating CHIRPS against surface station observations in Botswana, developing 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.

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.

RAMP Lab is building a 3D-PAWS fabrication and training facility at the University of Botswana. This facility allows the lab to produce low-cost stations locally, deploy them in collaboration with BDMS to fill critical gaps in Botswana’s observation network, and give UB meteorology students direct hands-on experience building the instruments they will eventually use in their careers. The facility serves as a regional training hub, with training events conducted in partnership with UCAR and African national meteorological services.

In collaboration with UCAR / COMET and BDMS.