The Evolution and Future of AI in Weather Forecasting

1. Early Use of AI in Weather Forecasting

The roots of modern weather forecasting can be traced back to the mid-20th century, when meteorologists began using computer models to simulate atmospheric processes. One of the earliest breakthroughs occurred in the 1950s with the development of numerical weather prediction (NWP) models. These models relied on mathematical equations to represent the behavior of the atmosphere and required substantial computational power to process the data. NWP has been evolving and is now the authority on global weather forecasting.

One notable breakthrough in the 1980s was the use of Expert Systems, which relied on predefined rules to simulate the decision-making process of meteorologists. Although these systems lacked the sophistication of modern AI, they laid the groundwork for more complex algorithms. During this period, the limitations of computational power and limited ability of experts to relay complex human decision making meant its impact was modest compared to traditional methods.

In the following decades, AI techniques such as machine learning and neural networks began to emerge, offering new possibilities for weather forecasting. By the 1980s, researchers started exploring the use of AI to improve the accuracy of weather predictions.

I was involved in early neural network applications in weather forecasting in late 80’s and 90’s. Two projects I worked on were: lightning prediction where we adapted spatiotemporal neural networks to the prediction of lightning strikes at Kennedy Space Center for the Air Force Geophysics Laboratory. This project collected data from lighting sensors and other driving local weather data in an area of interest to predict the locations of lightning strikes. (Ref: “Use of Neural Networks to Predict Lightning at Kennedy Space Center”, Frankel, D.S., Schiller, I., Draper, J.S., Barnes, A.A , International Joint Conference on Neural Networks (IJCNN-91), Seattle, Washington, 8-12 Jul, 1991).  In other project, I used local cloud movements and winds to predict cloud clear line of sight for launch decisions. (Ref: “Cloud Prediction for RV Test Launch Decisions”, Schiller, I., Draper, J.S., King, S., Draper, S.C., Johnson D., Shields J., Final Report, BMO-TR-91-033, USAF Ballistic Missile Organization, Norton AFB, San Bernardino, CA, Nov, 1991).

These examples of early AI systems were relatively simple compared to modern standards, but they started to produce useful predictions and laid the groundwork for more sophisticated applications. In the early 2000s, AI research in weather forecasting saw significant developments that bridged traditional meteorological models with emerging machine learning techniques. Notable contributions from this period include: 1. Machine Learning for Pattern Recognition, where researchers began applying support vector machines (SVMs) and neural networks to recognize patterns in historical weather data, 2. Data-Driven Models for Severe Weather Events, where research focused on using machine learning to predict extreme weather phenomena, such as tornadoes and hurricanes, and 3. Integration with NWP Models, where efforts were made to incorporate machine learning for post-processing NWP outputs, improving forecasts for localized regions. These hybrid approaches helped correct systematic biases in physical models. These advances laid the groundwork for contemporary AI techniques in meteorology.

2. AI Transforming Weather Forecasting

In recent years, AI has become indispensable to weather forecasting. This shift is driven by advancements in computational capabilities, the availability of vast datasets, and the development of sophisticated AI algorithms. Today, machine learning models, deep learning, and data assimilation techniques are integral to modern meteorology.

AI Models Enhancing Numerical Weather Prediction (NWP)

While NWP models have been remarkably successful, they are computationally intensive and sometimes struggle with fine-scale details. AI augments these models by addressing their limitations in two primary ways:

• Error Correction: AI models can identify systematic errors in NWP outputs and adjust them accordingly. For example, machine learning models trained on historical forecast data and observations can detect biases and improve forecast accuracy without needing to reconfigure the entire NWP framework.
• Hybrid Approaches: AI is increasingly used to create hybrid models that combine physical simulations with data-driven insights. By blending physics-based predictions with machine learning outputs, these models enhance both the speed and precision of weather forecasting.

AI Enhancing Real-Time Data Assimilation

AI also plays a vital role in real-time data assimilation, the process of integrating observational data into forecasting models. By rapidly analyzing inputs from satellites, weather stations, and radar systems, AI algorithms can update forecasts more frequently and accurately. This capability is particularly critical for high-impact weather events, such as hurricanes and tornadoes, where timely predictions can save lives. Our Hurricane Hunter Satellites will produce such data and we plan to use it to augment existing forecasts with this new data using AI to speed up possible weather warnings.

Localized and Personalized Forecasts

AI has made localized weather forecasting more accessible. Platforms like IBM’s The Weather Company use AI to generate hyperlocal forecasts by analyzing data from IoT devices, mobile sensors, and citizen science initiatives. These forecasts provide personalized insights, tailoring updates for industries like agriculture, transportation, insurance and energy. Hyper-local and personal on-demand forecasting is the future that will benefit companies that deal with geographical and specialized local services.

3. Current Research and Innovations

AI research and development is active and vast. It spans from global forecasting improvements, hybrid NMP enhancements, to hype-local specialized products and services as well as on-demand reporting on consumers’ smart phones or integration with numerous software products. As they say: Improving Accuracy = Increased Productivity. Here are just a few examples of current innovations.

• Extended Forecast: The length and accuracy of weather forecasts have significantly improved over time. NWP models have extended their forecasting from just a few days to 7-10 days now with reasonable accuracy. This probably reached the limit to what first principles can deliver based on time and complexity. Current hybrid approaches combining NWP and AI are now standard showing accurate predictions for up to 10–15 days. In addition, medium- to long-term and seasonal forecasts are becoming more reliable. Extreme events forecasting improves early warnings for hurricanes, floods, and heatwaves. Google’s DeepMind model was used to create GenCast for standard weather forecasting. The team compared how the 15-day forecasts of both systems performed in predicting a designated set of 1,320 global wind speeds, temperatures and other atmospheric features. The Nature Report said the new agent outdid the center’s forecasts 97.2 percent of time. DeepMind is sharing its engine and underlying computer code on-line. (Ref: “Google Introduces A.I. Agent That Aces 15-Day Weather Forecasts”, New York Times, 2024/12/04).
• Medium Range Forecast: GraphCast is a state-of-the-art AI model designed for faster and more accurate global weather forecasting. It delivers 10-day weather predictions with unprecedented accuracy in under one minute, outperforming the industry standard High Resolution Forecast (HRES) by the European Centre for Medium-Range Weather Forecasts (ECMWF). GraphCast provides earlier warnings of extreme weather events, accurately predicting cyclone tracks, atmospheric rivers, and extreme temperatures. By open-sourcing the model, GraphCast supports global scientific collaboration and is already being tested by weather agencies like ECMWF.
• Nowcasting: DeepMind, in collaboration with the UK Met Office, has developed advanced AI systems for “nowcasting,” which focuses on predicting weather in the immediate future (up to two hours ahead). Their deep learning models analyze radar data to provide highly localized and accurate short-term forecasts, outperforming traditional methods in precision and speed.

• Chinese Research Advancements: China has been at the forefront of using AI for weather prediction. Researchers are leveraging AI to improve typhoon tracking and rainfall prediction. The Chinese Academy of Sciences has developed AI models that integrate satellite data with ground-based observations, significantly enhancing the accuracy of extreme weather event forecasts. In other initiative, the researchers created a hybrid model for longer-range tropical cyclone forecasting, combining the strength and high resolution of the physics-based Weather Research and Forecasting (WRF) Model with the large-scale circulation and storm path modeling capabilities of a machine learning model called Pangu-Weather (Pangu). They found that the hybrid approach significantly improved the tracking and intensity forecasts from 5 to 7 days to up to 2 weeks. (Ref: https://eos.org/research-spotlights/physics-meets-machine-learning-for-better-cyclone-predictions).

• AI-Driven Climate Modeling: Efforts are also underway to use AI for long-term climate modeling. Projects such as NVIDIA’s Earth-2 initiative aim to build digital twins of Earth’s climate systems using AI to simulate and predict future climate scenarios with unprecedented detail.
• Hyper-local Weather Forecasting: Generating highly precise weather predictions for very specific geographic areas, such as individual neighborhoods or ZIP codes is becoming more important because businesses will improve productivity, safety and readiness. By leveraging advanced weather models, IoT sensors, satellite imagery, and machine learning techniques, AI can provide accurate, location-specific forecasts. One such company, Truweather Solutions, is working on hyperlocal forecasting for many applications including drone operations. Delivery drones depend on accurate and localized forecasting for beyond the visual range operations that are being explored by many delivery companies.

4. Future Improvements and Prospects

The future of AI in weather forecasting holds even greater promise. As AI technology continues to advance, we can expect several key developments that will further enhance the accuracy and reliability of weather predictions.

• Enhanced Data Integration: AI will continue to improve its ability to integrate data from diverse sources, including new types of sensors and satellite technologies. This will provide a more comprehensive understanding of atmospheric conditions and lead to even more accurate forecasts. Traditionally NWP has an inherent difficulty to incorporate different data sources that have diverse resolution, accuracy and sources. This problem is much simpler for AI.
• Predictive Analytics: Long-term climate modeling will benefit from AI’s ability to analyze complex interactions between atmospheric, oceanic, and terrestrial systems. This will improve our understanding of climate change phenomena, such as melting polar ice caps and shifting monsoon patterns, helping policymakers make informed decisions.
• Improved Extreme Weather Prediction: AI will enhance our ability to predict extreme weather events with greater accuracy. Advanced machine learning models will analyze complex interactions between various atmospheric factors, providing more precise forecasts for hurricanes, typhoons, and other severe weather phenomena. Our own work at TWA is dedicated to mitigating damage from hurricanes and other climate-related events. This is achieved by a small constellation of nanosat pairs to make 3D global wind measurements and providing earlier and more accurate hurricane forecasts using hybrid approach of existing models and new modified models. The data collected by our Hurricane Hunter Satellite constellation will benefit from AI-based forecasting. TWA will deliver global coverage of tropical cyclones at a fraction of the cost while delivering up to 2X the accuracy every couple of hours.
• Unified Global Models: AI will facilitate the integration of diverse datasets from satellites, drones, and ground-based sensors into unified global forecasting models. These models will provide seamless predictions, reducing discrepancies between regional forecasts and enabling better coordination for international disaster response.
• Hyperlocal Predictions :AI will refine microforecasting, predicting conditions at street-level resolutions. This will benefit urban planning, outdoor events, and autonomous vehicles.
• Dynamic Updates :With advancements in sensor networks and edge computing, AI models will provide continuous updates tailored to specific locations or user preferences.
• Accessibility and Personalization: Many companies now can incorporate their own models or use general AI forecasting models developed by Google’s Deep Mind and others and incorporate them for their specific needs and specialized forecasting that improves their services and benefit their customers.

5. Conclusion

Despite its potential, the widespread adoption of AI in weather forecasting is not without challenges. Active research and development addressing the following issues:

• Data Quality: AI models require high-quality, diverse datasets, which may be unavailable in certain regions. This will drive the creation of new data sources like our Hurricane Hunter Satellites that will bring new 3D wind and cloud data that can be incorporated in AI models with relative ease.
• Model Interpretability: Deep learning models often function as “black boxes,” making it difficult to understand their decision-making processes. This is where meteorologists need to apply human intelligence to vet and scrutinize the AI results.

AI is making high-quality weather data more timely, accessible, and tailored to specific needs. User-friendly applications and platforms are providing individuals and companies with the tools to access accurate weather information tailored to their specific needs. For example, we, together with Canada Weather Analytics Inc., are developing, apps that will assist in extreme weather warnings specially targeting to the need of users delivering that information to their smart phones.

The integration of AI into weather forecasting has already led to significant advancements in the field, improving both global and local predictions. From its early use in numerical weather prediction models to its current applications in real-time analysis and hyper-local forecasting, AI has transformed the way we understand and predict the weather. As technology continues to evolve, AI will play an increasingly vital role in enhancing the accuracy and reliability of weather forecasts, ultimately helping to safeguard lives and property and better understand our planet’s complex climate systems.