SEWAA-Linked AI Climate Forecasting for Uganda's National Integrated Early Warning System (NIEWS)

Overview

Uganda's National Integrated Early Warning System (NIEWS), operated through the National Emergency Coordination and Operations Centre (NECOC) under the Office of the Prime Minister (OPM), is being enhanced through integration with the Strengthening Early Warning Systems for Anticipatory Action (SEWAA) project. SEWAA is a regional initiative led by the IGAD Climate Prediction and Applications Centre (ICPAC) in partnership with the World Food Programme (WFP), the University of Oxford, the European Centre for Medium-Range Weather Forecasts (ECMWF), and Google.org, which applies machine learning techniques to generate high-resolution probabilistic rainfall forecasts for Eastern Africa. Uganda joined the SEWAA programme in March 2025, following successful pilots in Kenya and Ethiopia, with the expansion supported by the Government of Denmark.

The core AI technology underpinning SEWAA is a Conditional Generative Adversarial Network (cGAN), a deep learning architecture that generates high-resolution calibrated probabilistic weather forecasts by learning from historical climate data. The cGAN approach works by training two neural networks in competition: a generator that produces synthetic high-resolution rainfall predictions from lower-resolution numerical weather prediction (NWP) ensemble inputs, and a discriminator that evaluates the realism of the generated outputs against observed rainfall patterns. The system produces probabilistic forecasts rather than single-point predictions, enabling decision-makers to assess the likelihood and range of potential rainfall outcomes. This is complemented by Ensemble Logistic Regression models and evaluation tools for assessing forecast reliability. A critical advantage of the cGAN approach is that it can run on standard personal computers and laptops, eliminating the need for costly supercomputing infrastructure that has historically limited forecasting capacity in low-income countries. As described by Oxford University researcher Shruti Nath, the approach starts from traditional forecasts and adds the AI model to correct what was not captured by conventional methods.

The intended application within Uganda's social protection context is to use SEWAA-generated probabilistic hazard forecasts to inform pre-agreed triggers and early actions before climate shocks materialise, supporting anticipatory and shock-responsive social protection responses. NECOC serves as the strategic coordination centre for whole-of-government emergency response, linking data from weather and hydrological monitoring stations to feed into the national early warning system. The NIEWS produces monthly multi-hazard bulletins (U-NIEWS) that disseminate forecasting information, hazard alerts, and information on crop and pasture conditions, food insecurity, and weather and climate forecasts. The Uganda National Meteorological Authority (UNMA) provides the national meteorological data that feeds into these systems. By integrating SEWAA's AI-enhanced forecasts into this existing infrastructure, Uganda aims to improve the accuracy and lead time of warnings that trigger anticipatory social protection scale-up, such as emergency cash transfers or in-kind assistance before droughts or floods affect vulnerable populations.

The system operates under a human-on-the-loop (HOTL) oversight model. NIEWS and NECOC remain the issuing authority for all warnings; the AI-generated forecasts serve as advisory inputs that inform activation decisions rather than triggering actions automatically. Government officials and meteorological authorities retain full decision-making authority over whether to issue warnings or activate social protection responses based on the probabilistic forecasts. This design reflects the early stage of the deployment and the need to build institutional confidence in AI-generated outputs before increasing automation.

As of 2025, the SEWAA expansion to Uganda is in its design and development phase. The project was formally launched at an event in Entebbe on 3 March 2025, attended by representatives from ICPAC, WFP, the Uganda Meteorological Services Department, and regional partners. Dr Bob Ogwang, Acting Commissioner of Uganda's Department of Meteorological Services, stated that the project would support Uganda's quest for improved forecasting capability in the face of rising temperatures and increasing frequency of extreme weather events. The operational deployment of SEWAA-linked triggers within Uganda's social protection system, including the specific governance frameworks for trigger adjudication and the pre-agreed financing mechanisms for anticipatory action, remains under development and has not yet been publicly documented in detail. No Uganda-specific evaluation of trigger performance or forecast skill has been published to date.

The SEWAA project addresses three primary climate hazards affecting Eastern Africa: droughts that threaten water and food availability, floods that cause infrastructure damage and population displacement, and tropical cyclones. The project serves meteorological institutions across four countries (Ethiopia, Kenya, Rwanda, and Uganda), with ICPAC providing regional climate services across 11 East African member states. Technical documentation, model source code, user guides, and evaluation tools are publicly available through the SEWAA platform at cgan.icpac.net, reflecting a commitment to open-source approaches that support institutional capacity building and technology transfer to national meteorological services.

Classification

Programme Details

NIEWS is Uganda's multi-hazard early warning system operated through NECOC under OPM, producing monthly U-NIEWS bulletins. The SEWAA integration aims to enhance NIEWS with AI-generated probabilistic climate forecasts to inform pre-agreed triggers for anticipatory social protection scale-up before climate shocks materialise.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-004-UGA-002 African Academy of Sciences (2025). 'AI Forecasting Equips East Africa for Extreme Weather'. Nairobi: AAS. Available at: https://aasciences.africa/news/ai-forecasting-equips-east-africa-for-extreme-weather (Accessed 24 March 2026).
   https://aasciences.africa/news/ai-forecasting-equips-east-africa-for-extreme-weather
2. SRC-001-UGA-002 IGAD Climate Prediction & Applications Centre (2025). 'SEWAA Project Expands to Uganda and Rwanda to Strengthen Climate Resilience'. Nairobi: IGAD/ICPAC. Available at: https://www.icpac.net/news/sewaa-project-expands-to-uganda-and-rwanda-to-strengthen-climate-resilience/ (Accessed 30 October 2025).
   https://www.icpac.net/news/sewaa-project-expands-to-uganda-and-rwanda-to-strengthen-climate-resilience/
3. SRC-003-UGA-002 ICPAC (2025). 'SEWAA – Strengthening Early Warning Systems for Anticipatory Action'. ICPAC cGAN Platform. Available at: https://cgan.icpac.net/ (Accessed 24 March 2026).
   https://cgan.icpac.net/
4. SRC-002-UGA-002 World Food Programme (2025). 'Strengthening Early Warning Systems for Anticipatory Action (SEWAA) – Mid-term Achievements' (23 September 2025). Rome: WFP. Available at: https://www.wfp.org/publications/strengthening-early-warning-systems-anticipatory-action-sewaa-project-mid-term (Accessed 30 October 2025).
   https://www.wfp.org/publications/strengthening-early-warning-systems-anticipatory-action-sewaa-project-mid-term