Smarter Rain Predictions for East Africa

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Author: Denis Avetisyan

A new machine learning approach is delivering more accurate and reliable rainfall forecasts, crucial for communities across East Africa.

Post-processing global ensemble forecasts with a conditional Generative Adversarial Network (cGAN) significantly improves probabilistic rainfall predictions over East Africa.

Despite advances in weather prediction, reliable high-resolution rainfall forecasts remain a challenge for resource-constrained meteorological services in East Africa. This paper, ‘Rainfall forecasts in daily use over East Africa improved by machine learning’, introduces a novel conditional Generative Adversarial Network (cGAN) system for post-processing global forecasts, delivering significantly improved probabilistic rainfall predictions at 10km resolution. Our computationally inexpensive approach-trainable on laptops and requiring no additional processing-offers a viable solution for generating numerous ensemble members, enhancing preparedness for critical weather events. Could this system represent a scalable pathway towards greater resilience to weather-related risks across data-sparse regions?

The Illusion of Prediction: Forecasting Rainfall in a Complex World

East Africa’s reliance on rain-fed agriculture and its vulnerability to climate extremes make accurate rainfall prediction profoundly important, yet the region presents a uniquely difficult forecasting challenge. Complex topography, including mountains and large water bodies, interacts with atmospheric circulations to generate highly localized and variable rainfall patterns. These patterns aren’t easily captured by broad-scale weather models, leading to frequent discrepancies between forecasts and actual precipitation. This unpredictability has cascading effects, hindering agricultural planning, exacerbating food insecurity, and increasing the risk of devastating floods and droughts for communities across the region. Improved forecasting therefore demands a deeper understanding of these local weather drivers and the development of models capable of resolving this inherent complexity.

East Africa’s rainfall exhibits a remarkable degree of localized fluctuation – both in terms of where it falls and when – presenting a formidable challenge to forecasting systems built on broader scales. Traditional methods, often reliant on regional averages or simplified atmospheric models, frequently fail to capture these intricate patterns, resulting in predictions that are inaccurate at the community level. This unreliability has significant consequences for vulnerable populations heavily dependent on rain-fed agriculture; crop failures, water scarcity, and increased food insecurity are common outcomes of flawed forecasts. The high temporal variability – sudden onset or cessation of rains, or shifts in seasonal timing – further exacerbates these issues, making it difficult for communities to effectively prepare for, or mitigate, the impacts of extreme weather events.

Reliable assessment of rainfall forecasts in East Africa hinges on the availability of comprehensive observational data, and the Integrated Multi-satellitE Retrievals for GPM (IMERG) provides a vital resource for this purpose. IMERG combines data from a constellation of satellites and ground-based radar to create a high-resolution map of precipitation, effectively serving as a benchmark against which the accuracy of various forecasting models can be measured. By comparing predicted rainfall amounts and locations to IMERG’s observations, scientists can identify systematic errors, refine model parameters, and ultimately improve the reliability of forecasts crucial for agriculture, water resource management, and disaster preparedness in this climatically sensitive region. This rigorous evaluation process, facilitated by IMERG, is not merely an academic exercise; it directly contributes to building resilience and mitigating the impacts of extreme weather events on vulnerable communities.

The Refinement of Imperfection: Post-Processing for Probabilistic Forecasts

Post-processing techniques like Quantile Mapping (QM) and Isotonic Distribution Regression (IDR) are crucial for refining probabilistic forecasts by addressing systematic errors in forecast distributions. QM operates by transforming the forecasted cumulative distribution function (CDF) to match the observed CDF derived from historical data, such as that provided by IMERG. IDR, conversely, is a non-parametric method that directly enforces the constraint that the forecast probabilities are monotonically increasing with the predicted rainfall amount, ensuring a valid probability distribution. Both techniques effectively calibrate forecasts, meaning that a forecast with a stated probability of, for example, 80% will, over time, be verified approximately 80% of the time, thereby increasing the reliability and usefulness of the information for downstream applications.

Post-processing techniques utilize observational data from the Integrated Multi-satellitE Retrievals for GPM (IMERG) to address systematic biases present in numerical weather prediction model forecasts. These biases can manifest as under- or over-estimation of rainfall intensity or frequency, leading to poorly calibrated probabilistic forecasts. By comparing model predictions with observed rainfall amounts from IMERG, post-processing algorithms adjust the forecast distributions to better align with the actual distribution of rainfall outcomes. This correction process doesn’t alter the underlying model physics, but rather statistically transforms the forecast to improve its reliability and ensure the predicted probabilities accurately reflect the likelihood of different rainfall scenarios. The result is a more representative forecast distribution that better captures the range of possible rainfall amounts, improving forecast skill and usability.

Enhancements to Rainfall Forecast Skill, achieved through post-processing techniques like Quantile Mapping and Isotonic Distribution Regression, directly translate to improved decision-making capabilities. These techniques refine probabilistic forecasts by reducing biases and better representing the observed range of rainfall amounts, which leads to more reliable predictions of both the average rainfall and the likelihood of extreme events. Consequently, decision-makers in sectors such as agriculture, water resource management, and disaster preparedness receive more actionable information, enabling them to optimize strategies and mitigate potential risks associated with rainfall variability and intensity. The increased skill is quantifiable through standard metrics like the Continuous Ranked Probability Score (CRPS) and the Brier Score, providing objective evidence of forecast improvement.

A Generative Illusion: cGAN and the Pursuit of High-Resolution Prediction

The conditional Generative Adversarial Network (cGAN) addresses the challenge of generating high-resolution rainfall forecasts, especially in regions with limited observational data. Traditional forecasting methods often struggle in data-sparse areas, leading to reduced accuracy and resolution. cGAN utilizes a generator-discriminator architecture; the generator creates synthetic rainfall forecasts, while the discriminator evaluates their realism based on available data. The “conditional” aspect of cGAN incorporates input data, such as large-scale weather patterns, to guide the generation process and improve forecast quality. This allows cGAN to effectively learn the relationship between sparse observations and high-resolution rainfall patterns, providing more detailed and reliable forecasts where data is scarce.

The cGAN model utilizes output from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF IFS) as a conditional input, enabling it to refine existing forecasts rather than generating them from scratch. This approach allows the cGAN to specifically target and mitigate systematic errors present in the ECMWF IFS predictions. By learning the residual distribution – the difference between the ECMWF IFS forecast and the actual observed rainfall – the cGAN effectively functions as an error correction mechanism. This process enhances forecast accuracy, particularly in scenarios where the ECMWF IFS exhibits consistent biases or limitations, without requiring the model to independently predict all aspects of the rainfall pattern.

Unlike traditional numerical weather prediction models that typically output a single deterministic forecast, the cGAN approach generates probabilistic forecasts. This means the model doesn’t predict a rainfall amount, but rather a range of possible outcomes and their associated probabilities. This probabilistic output is crucial for risk assessment and decision-making, as it provides information about the uncertainty inherent in weather forecasting. Users can then assess the likelihood of various scenarios-ranging from no rainfall to extreme precipitation events-and tailor their responses accordingly. This contrasts with single-value predictions, which offer no indication of forecast confidence or potential error margins.

The conditional Generative Adversarial Network (cGAN) demonstrates performance comparable to state-of-the-art weather forecasting models such as FuXi and GraphCast, as validated through quantitative evaluations of forecast accuracy. Critically, cGAN achieves this competitive level of performance with substantially reduced computational demands; training can be successfully completed using hardware configurations that are significantly less resource-intensive than those required for FuXi or GraphCast. This reduced hardware requirement lowers the barrier to entry for researchers and practitioners, enabling wider access to advanced high-resolution forecasting capabilities and facilitating more extensive experimentation and model development.

The Mirage of Accuracy: Validating Forecasts and Measuring Illusion

Assessing the accuracy of probabilistic weather forecasts demands more than simple error calculations; rigorous evaluation using metrics like the Continuous Ranked Probability Score (CRPS) is essential. Unlike deterministic forecasts that predict a single outcome, probabilistic forecasts provide a range of possibilities, and CRPS effectively measures how well that entire distribution aligns with observed reality. It considers both the forecast’s accuracy and its calibration – whether predicted probabilities match actual frequencies. This is particularly vital when comparing advanced techniques like conditional Generative Adversarial Networks (cGAN), Quantile Mapping, and Isotonic Distribution Regression, as it reveals not just if a model is accurate, but how confidently it makes correct predictions. A lower CRPS score signifies a better forecast, indicating that the predicted probabilities closely reflect the observed rainfall amounts and frequencies, which is critical for reliable decision-making in fields like disaster preparedness and resource management.

The assessment of any precipitation forecasting model necessitates comparison against established, high-quality observational datasets, and the Integrated Multi-satellitE Retrievals for GPM (IMERG) serves as a crucial benchmark in this regard. IMERG provides a consistently updated, multi-satellite-based reconstruction of precipitation, offering a reliable ‘ground truth’ against which to evaluate forecast accuracy. By comparing forecasted precipitation patterns and intensities to those observed in IMERG, researchers can quantitatively determine the strengths and weaknesses of different forecasting techniques – such as cGAN, Quantile Mapping, and Isotonic Distribution Regression. This comparison isn’t simply about assigning a score; it pinpoints specific areas where models struggle, allowing for targeted improvements in algorithms and data assimilation processes, ultimately leading to more skillful and dependable predictions.

The conditional Generative Adversarial Network (cGAN) has emerged as a competitive force in probabilistic forecasting, achieving Continuous Ranked Probability Scores (CRPS) on par with more established models like FuXi and GraphCast. This performance is particularly noteworthy given cGAN’s accessibility; unlike some computationally intensive forecasting systems, it can generate a robust 1000-member ensemble using standard desktop hardware. This capacity for high-resolution, probabilistic prediction – delivering a range of possible outcomes rather than a single value – allows for a more nuanced understanding of future conditions and enables better-informed decision-making across sectors reliant on accurate weather predictions, from disaster preparedness to agricultural planning.

The potential impact of improved precipitation forecasting extends far beyond mere statistical accuracy in East Africa. Enhanced skill in predicting rainfall – facilitated by techniques like cGAN, Quantile Mapping, and Isotonic Distribution Regression – directly supports more effective disaster preparedness initiatives, enabling timely evacuations and resource allocation during floods or droughts. Simultaneously, reliable forecasts empower agricultural planning, allowing farmers to optimize planting schedules, irrigation strategies, and crop selection for increased yields and food security. Crucially, these advancements contribute to sustainable resource management by informing decisions related to water storage, land use, and long-term environmental planning, ultimately bolstering the resilience of communities facing climate variability and change in a region highly vulnerable to extreme weather events.

The pursuit of increasingly accurate rainfall forecasting, as detailed in this work, echoes a humbling truth about modeling the world. The implementation of machine learning, specifically a conditional Generative Adversarial Network, seeks to refine probabilistic predictions – a noble effort, yet inherently limited. As Galileo Galilei observed, “You cannot teach a man anything; you can only help him discover it himself.” This study doesn’t create perfect foresight, but rather illuminates patterns within the complex system of East African rainfall, assisting in the discovery of more reliable predictions. Like any map attempting to chart a vast ocean, these models will always be approximations, bound by the inherent uncertainties of the phenomena they represent.

Beyond the Horizon

Multispectral observations of forecast skill enable calibration of probabilistic models, yet the inherent chaos of atmospheric systems presents a fundamental challenge. Comparison of theoretical predictions-in this case, cGAN-enhanced rainfall forecasts-with observed outcomes in East Africa demonstrates both the achievements and, crucially, the limitations of current machine learning approaches. The observed improvements, while significant, do not erase the possibility of catastrophic failure-a single, unpredicted deluge can overwhelm even the most refined probabilistic framework.

Future work must address the propagation of uncertainty within ensemble forecasts. Simply refining the post-processing stage, though valuable, risks polishing a flawed foundation. A more fruitful avenue lies in exploring methods to directly incorporate physical constraints into the generative models themselves, preventing the creation of physically implausible rainfall patterns. This necessitates a move beyond purely data-driven approaches, acknowledging that the universe does not conform to statistical convenience.

The pursuit of perfect forecasts is, perhaps, a vanity. Each successful prediction merely delays the inevitable encounter with the unknown. Nonetheless, the continued refinement of these techniques, alongside a healthy skepticism regarding their absolute authority, remains a worthwhile endeavor. The true measure of progress isn’t minimizing error, but maximizing preparedness for the errors that will inevitably occur.

Original article: https://arxiv.org/pdf/2512.24525.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-01 19:12