Seeing the Storm Before It Strikes
March 5, 2026
Thunderstorms can develop quickly and intensify within minutes. In many parts of Africa, they bring strong winds, heavy rainfall, hail, and flash floods that affect communities, agriculture, and infrastructure. Farmers may lose crops in a single afternoon. Roads can become impassable. Power lines and buildings may be damaged before warnings can be widely communicated.
In such situations, timing is everything. Not tomorrow’s forecast, but the next 15 to 60 minutes. This is where nowcasting becomes essential. Nowcasting focuses on very short-term weather prediction, typically up to one hour ahead. Unlike traditional forecasts that describe broader weather patterns over days, nowcasting concentrates on rapidly evolving phenomena such as severe thunderstorms. It aims to answer a simple but critical question: what will happen here, very soon?
Within the SAFE4ALL project, MicroStep-MIS has developed a high-impact thunderstorm nowcasting tool designed specifically to strengthen early warning capacities in Ghana, Kenya, and Zimbabwe.
Why a Satellite-Based Approach
In many parts of the world, short-term storm prediction relies heavily on weather radar. Radar provides detailed information about precipitation structure and movement. However, radar coverage is not uniform everywhere, and in some regions it is limited or unavailable.
Satellite data offer a different perspective. They cover the entire continent continuously and provide a long historical archive. This archive makes it possible to analyse how storms have developed in the past and to use that knowledge to improve short-term forecasts.
The SAFE4ALL nowcasting tool is built primarily on satellite observations. By analysing sequences of satellite images, the system learns how convective systems grow, move, and intensify. It does not simply extrapolate cloud motion. Instead, it applies artificial intelligence methods that can recognize more complex patterns associated with severe weather.
From Observation to Short-Term Forecast
The tool processes satellite data and transforms it into information relevant for heavy rainfall and convective activity. One important step is converting raw satellite imagery into rainfall-related products that reveal precipitation intensity more clearly than standard visual composites.
Based on this processed data, the system generates nowcasts for 15, 30, 45, and 60 minutes ahead. These short lead times are particularly important for severe thunderstorms, which often evolve faster than traditional forecasting cycles.
The approach has been evaluated using established meteorological verification methods. Metrics such as probability of detection and false alarm ratio are used to assess how often storms are correctly predicted and how often warnings are issued unnecessarily. This is crucial because effective early warning systems must balance sensitivity with reliability. A system that misses events is dangerous, but one that produces too many false alarms can reduce trust.
Testing has shown solid performance across the case study countries, providing confidence that the tool can meaningfully support operational warning processes.
Adapting to Local Conditions
Thunderstorms do not behave the same way everywhere. Coastal regions, highlands, and inland plains each have distinct atmospheric dynamics. Seasonal patterns, local topography, and land surface characteristics all influence how convective storms form and develop.
For this reason, the SAFE4ALL tool is not a one-size-fits-all solution. The modeling approach is adapted to each country. Historical satellite data over Ghana, Kenya, and Zimbabwe are analysed separately. Local rainfall characteristics, typical storm structures, and regional climate features are taken into account during model development and evaluation.
This adaptation process is essential. A model trained only on European storm systems, for example, would not automatically perform well in tropical or subtropical environments. By training and validating the system with regional data, the tool becomes more sensitive to the specific types of high-impact thunderstorms that affect each country.
Collaboration with national meteorological institutes also plays an important role. Local expertise helps identify which storm types are most relevant, which impacts are most critical, and how forecast outputs can best support warning decisions.
Supporting Early Warnings and Risk Reduction
The primary objective of the thunderstorm nowcasting tool is to improve the ability of meteorological institutes to issue timely severe storm warnings.
Even 30 minutes of additional lead time can make a difference. It can allow authorities to alert communities in flood-prone areas, farmers to protect equipment or livestock, and infrastructure operators to take precautionary measures.
By combining satellite data, artificial intelligence, and local knowledge, the SAFE4ALL thunderstorm nowcasting tool contributes to strengthening resilience against high-impact weather. It is one of nine tools developed within the project, each addressing a different aspect of climate and disaster risk management.
Severe storms will always occur. The goal is not to prevent them, but to reduce their impact. Accurate and reliable short-term forecasting is an important step in that direction.
About MicroStep-MIS
MicroStep-MIS is a technology company specialising in advanced environmental monitoring, meteorological, and early-warning systems. With experience in meteorology, climatology, aviation systems, and data-driven decision support, the company develops solutions to reduce risk, enhance safety, and build climate resilience. Since 2014, MicroStep-MIS has been actively involved in European research and innovation projects in cooperation with universities and operational institutions.
Within SAFE4ALL, MicroStep-MIS develops the High Impact Thunderstorm Nowcast tool, an AI-based solution designed to strengthen short-term severe weather prediction in support of agriculture, disaster risk management, and ecosystem services.