Forecasting the Future: Exploring the Role of AI in Weather Prediction

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GRC 2023 Global Essay Competition Top 10

By Carolina dos Santos

Hurricane Otis in Mexico, initially labeled a tropical storm by weather forecasts, revealed itself as a destructive category 5 hurricane, resulting in the loss of at least 48 lives. This tragedy underscores the crucial importance of accurate and timely weather information in the context of social and economic domains, especially amid growing concerns about climate change. Artificial intelligence (AI) has emerged as a revolutionary force in weather forecasting, offering transformative capabilities for reshaping the industry, creating more reliable predictions, and improving community preparedness.

Modern weather forecasting, evolving since the 1960s, has made significant strides in accuracy. However, limitations persist. Vilhelm Bjerknes' book, “The Problem of Weather Forecasting as a Problem in Mechanics and Physics,” underscores the current incompleteness of observations, particularly from land stations, offering limited data on higher atmospheric layers and no observations over the ocean. Furthermore, the quantity of these stations is crucial for accurate weather forecasting, but their implementation is hindered by the high cost associated with such infrastructure. This challenge becomes particularly evident in economically disadvantaged regions such as Africa, where the lack of weather stations inhibits precise forecasts. In a world where disasters are expected to become more common, the importance of early warning systems cannot be overstated, but unfortunately, many countries lack access to the required resources.
These elements underscore the critical need for advancements in technology and approaches, such as AI, to overcome the existing limitations and enhance the precision of weather predictions globally.
Traditional weather forecasting relies on numerical weather models solved on a global grid, demanding substantial computing resources and limiting the number of services capable of generating global weather forecasts. In contrast, AI weather models use reanalysis data from past weather to train, requiring less computational time and enabling quicker, more responsive, accessible, and economically viable forecasts.

Addressing these challenges, initiatives like’s radar readings from space and drone-based data collection are utilizing technologies to democratize access to weather observations, aiming to make forecasting more affordable and accessible, especially for countries lacking extensive ground infrastructure. For instance, Uganda has adopted an AI-based forecasting computer, attracted by its affordability, that can predict the weather for the entire country. Other private companies, including Salient, Huawei, Google DeepMind, and Nvidia, are also at the forefront of integrating AI into weather forecasting. Notably, DeepMind's GraphCast, a machine learning weather prediction (MLWP), demonstrated success in forecasting Hurricane Lee in the North Atlantic in September, predicting its landfall in Nova Scotia nine days before it occurred, three days earlier than traditional approaches.
The graphics below support this claim by describing the performance evaluation of two weather forecasting models, GraphCast, and HRES, a traditional forecasting method. GraphCast has achieved comparable forecast skill and efficiency when compared to HRES, indicating that machine learning weather prediction methods are now competitive with established techniques.

However, just as computational approaches face fundamental limits to their utility, so do AI-based ones. Amidst this challenge, collaborative efforts between public and private entities, exemplified by partnerships involving the World Meteorological Organization, Huawei, and Nvidia, highlight the potential synergy between AI technology and established forecasting methods in addressing the shortcomings of traditional models. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF) has seamlessly integrated real-time forecasts from AI models developed by private entities into its forecasting system, further underscoring the significant potential of AI. The World Bank's estimations add weight to this assertion, indicating that enhancements to weather forecasts and early warning systems could generate annual benefits totaling $162 billion and potentially save 23,000 lives each year, thereby contributing significantly to global resilience.

Moreover, AI's role extends beyond predictions. Accessibility to weather information is also crucial for ensuring that people can comprehend and utilize forecasts.'s AI plays a pivotal role in this by transforming forecasts into actionable advice, making weather information accessible to those most susceptible to climate-related challenges, such as farmers in impoverished regions. By doing so, advancements in weather prediction also directly benefit those who may face vulnerabilities due to their reliance on weather conditions for livelihoods or other critical activities.

As climate change intensifies, weather forecasting becomes pivotal in fostering resilience. The integration of AI into weather forecasting does not claim to eradicate all challenges but rather represents a transformative step, offering valuable solutions to address existing issues. AI stands as a dynamic and indispensable tool for adapting to the evolving weather patterns of our planet. Embracing this approach is not just an option but a necessity in the face of technological evolution, considering that historical patterns are less reliable guides to the future due to climate change.


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Forecasting the Future: Exploring the Role of AI in Weather Prediction
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