Transforming Anticipatory Action in Eastern Africa
Machine Learning for Early Warning Systems: Transforming Anticipatory Action in Eastern Africa
Introduction: A Unique Opportunity to Reimagine Early Warning
Today, a unique opportunity exists within the humanitarian community to reimagine the role of technology in building the resilience of vulnerable communities against the increasing frequency of extreme weather events. With only one-third of the world having access to life-saving early warnings, innovative solutions are imperative to prevent and mitigate the impacts of predictable severe weather events.
The core challenge:
To build trust in early warnings and enable governments to implement anticipatory actions at scale, early warning systems must demonstrate reliable predictions of extreme weather events at the localized level, days and weeks ahead of time.
This case study examines how the World Food Programme (WFP), with grant support from Google.org, is collaborating with a consortium of partners — including the University of Oxford, IGAD Climate Prediction and Applications Centre (ICPAC), the Kenya Meteorological Department (KMD), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the Ethiopia Meteorological Institute (EMI) — to leverage artificial intelligence and machine learning to enhance early warning systems in Eastern Africa.
The Challenge: From Reactive Response to Anticipatory Action
In regions such as Eastern Africa, communities face increasing risks from extreme weather events, including droughts and floods. While early warning systems exist, they often struggle with several critical limitations.
Global forecasting models provide lower-resolution predictions that are not sufficiently precise for localized decision-making at the community level.
Without reliable forecasts days or weeks in advance, humanitarian actors cannot trigger anticipatory actions such as early food distribution or cash assistance.
When forecasts lack demonstrated reliability, governments and humanitarian organizations hesitate to invest resources in actions that may not be needed.
Most national meteorological and hydrological centres lack the tools, computational infrastructure, and resources needed to generate reliable localized predictions.
As Professor Tim Palmer notes in the report, if reliable localized early warnings can be achieved, "early-warning will be the ubiquitous go-to tool to help society become more resilient to the ever-greater intensity of weather extremes."
Yet, despite the knowledge that investments in early warning systems are worth at least ten times their development costs relative to the damages they offset, the tools and resources needed for strengthening these systems remain a barrier for most National Meteorological and Hydrological centres.
The AI Solution: Machine Learning for Enhanced Forecasting
Generative Adversarial Networks (GANs)
To address these limitations, WFP and its partners introduced advanced machine learning techniques into early warning systems. Generative Adversarial Networks (GANs) are a type of machine learning and neural network model commonly used in image enhancement. They can transform low-resolution images into high-resolution ones, all without the need for costly supercomputers.
Oxford University has adapted this technology to improve the accuracy of rainfall forecasts. The team has taken global models (which provide lower-resolution forecasts) and fine-tuned them to be useful at local levels (yielding high-resolution results). This is accomplished by using many past examples of weather forecasts, together with the actual observed rainfall, allowing the model to "learn" how to enhance the forecasts.
Key innovation
This method requires less costly computational power than traditional local area models. Initial findings indicate that this approach not only matches but even outperforms existing techniques, especially in forecasting extreme rainfall events.
As Dr Guleid Artan, Director of ICPAC, states: "There is a practical requirement for the application of machine learning in improving weather forecasts, and Google support for this project is commendable."
The Partnership Ecosystem
Skillful early warnings and weather forecasts delivered by national and regional meteorological centres play a vital role in any effective disaster risk management system. The partnership brings together diverse expertise:
Serving as the regional centre of excellence in climate science and application, ensuring coordinated efforts to help member states tackle climate risks and adapt to climate change.
Providing technical support, global medium-range weather forecasting models, data, and computational resources. Their longstanding collaboration with WFP forms the foundation of this project's objective.
National meteorological centres that deliver tailored forecasts and alerts crucial for agriculture, water management, and public safety.
Developing and adapting the GAN technology for weather forecasting applications.
Integrating AI into Monitoring and Evaluation
Although not explicitly framed as a traditional M&E system, this initiative functions as a next-generation monitoring and evaluation model. It demonstrates how AI can transform M&E from a retrospective function into a predictive and decision-oriented system.
The Future: Scaling and Sustainability
With partners in place, support from Google.org, and the collective interest to challenge the status quo, the next steps entail training meteorological centres to run, adapt, and optimize the GAN model to the complex rainfall patterns in Eastern Africa. This includes:
- Establishing baseline scores to assess the reliability of current forecasts
- Enabling progress tracking in forecast improvements using GAN
- Adapting the GAN for seasonal and sub-seasonal forecast timescales
WFP remains committed to innovation aimed at shifting from reactive to proactive risk management. The organization is dedicated to supporting government partners in enhancing their capacities to deliver skillful early warning systems. However, as the report notes, this mission requires collective action.
A call for collective action
"We need partnerships with both public and private sector organizations that bring their technical expertise and funding to pre-arrange financing and strengthen capacity in early warning systems and anticipate risks before they escalate into disasters. Strengthening early warning systems to enable action before disasters not only saves more lives and livelihoods, but it is also a more efficient, cost-effective, and dignified approach to humanitarian response."
Results and Anticipated Impact
The integration of AI into early warning systems has led to several key improvements:
Enhanced rainfall forecasts, particularly for extreme events, with the GAN approach outperforming existing techniques.
Enhanced local-level predictions enabling more precise targeting of humanitarian interventions.
Ability to act days or weeks in advance, transforming the humanitarian response timeline.
Improved accuracy without requiring expensive computational infrastructure, making the approach scalable for developing contexts.
Key Lessons for M&E Professionals
From measurement to prediction
M&E systems can evolve beyond tracking past indicators to forecasting future risks and outcomes. This case demonstrates how predictive analytics can become a core component of monitoring frameworks.
Continuous learning systems
AI enables dynamic feedback loops where systems improve automatically over time using historical data and observed outcomes — a model for adaptive M&E.
Integration with decision-making
The value of M&E increases significantly when insights are directly linked to operational decisions and action triggers, rather than sitting in static reports.
Scalability and efficiency
AI solutions can enhance performance without requiring high-cost infrastructure, making them suitable for low-resource settings where traditional approaches may be prohibitive.
Frequently Asked Questions
What are Generative Adversarial Networks (GANs) and how are they used for weather forecasting?
GANs are a type of machine learning model commonly used in image enhancement. Oxford University adapted this technology to improve rainfall forecasts by taking low-resolution global models and fine-tuning them to produce high-resolution local predictions. The model learns from past forecasts and observed rainfall data to enhance future predictions.
What is anticipatory action and why does it matter?
Anticipatory action triggers the delivery of lifesaving interventions in the critical window between an early warning alert and the impact of a severe weather event. It enables humanitarian actors to act before disasters occur, which is more cost-effective, timely, and dignified for affected populations than reactive responses.
How does this initiative relate to Monitoring and Evaluation?
The system functions as a next-generation M&E model, incorporating continuous data collection, model validation against observed outcomes, adaptive improvement through feedback loops, and direct integration with decision-making and action triggers. It demonstrates how M&E can evolve from retrospective reporting to predictive, decision-oriented systems.
What are the next steps for this initiative?
The next steps include training meteorological centres to run, adapt, and optimize the GAN model for Eastern Africa's complex rainfall patterns, establishing baseline scores to assess current forecast reliability, enabling progress tracking, and adapting the GAN for seasonal and sub-seasonal forecast timescales.
A Shift from Reactive to Proactive Humanitarian Response
The WFP early warning initiative demonstrates how artificial intelligence can fundamentally transform humanitarian monitoring and evaluation. By embedding machine learning into forecasting systems, humanitarian actors can move from reactive response to proactive risk management.
Rather than evaluating crises after they occur, organizations can now anticipate, prepare, and act in advance — saving lives, protecting livelihoods, and improving the efficiency of interventions.
This case signals a broader shift in the M&E field: from reporting the past to shaping the future.
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