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ยท 10 min read
Jake W. Casselman

A Glimpse into the Future: Unveiling the Power of AI in Weather Forecasting Mapping the Future of Meteorology: AI-driven Innovations by Google, NVIDIA, Microsoft, and Huawei Redefine Weather Prediction (AI-generated Image by OpenAI)

You might have recently seen that Google is making strides in AI-driven weather forecasting with their Graphcast model. But did you know that many other large tech companies are also entering the scene? In this article, we dive into the advancements in AI meteorology innovations from the private sector, putting a spotlight on key players like NVIDIA's FourCastNet, Microsoft's ClimaX, and Huawei's Pangu-Weather and exploring how these technologies have the potential to revolutionize the field of meteorological science.

๐ŸŽฏ AI Methods in Weather Forecasting: Same Goal, Different Approachesโ€‹

The quest for the most effective AI method in weather forecasting is a dynamic and ongoing challenge. While Google has been one of the frontrunners with models like Graphcast and Met-Net-3, the industry is witnessing a variety of innovative approaches by other tech giants.

Google's Graphcast and Met-Net-3โ€‹

Graphcast excels in providing rapid and accurate weather predictions. This model distinguishes itself with forecasts that extend up to 10 days into the future, demonstrating a resolution accuracy of about 28x28 km at the equator. While Graphcast is adept at short to medium-range forecasts, it encounters challenges in enhancing resolution and sustaining accuracy for longer forecast periods. In contrast, Met-Net-3, another innovative model from the same team, specializes in high-resolution forecasting. This model is capable of delivering forecasts with an impressive 1x1 km resolution, offering updates every few minutes. Its forte lies in its precision, although it primarily focuses on short-term forecasting, with a lead time of just 24 hours. For in-depth insights, visit Google's official Graphcast blog and Met-Net-3 blog. For the scientific paper on Graphcast, see here.

Technical Reading on Google's Graph Neural Network (GNN)
  • Approach: Google's GraphCast, developed by DeepMind, uses Graph Neural Networks (GNNs) for weather forecasting, focusing on spatially structured data.

  • Notable Strengths: It provides rapid and precise weather predictions, while being a relatively small model (36.7 million parameters). This is significant as a 10-day forecast can be run in under a minute on relatively basic infrastructure (in their study, they use a TPU v4 device).

  • Notable Limitations: Because the training data is ERA5 with a resolution of 0.25 degrees, this may produce challenges when trying to increase the resolution (such as to sub-km). Uncertainty is also handled by "spatially blurring" the forecasts, which becomes an important constraint at longer lead times (e.g., forecasting several days into the future).

NVIDIA's FourCastNetโ€‹

FourCastNet's strength lies in its exceptional speed, processing forecasts much faster than traditional models. Its current limitation is in sometimes producing results that deviate from established physical laws, a gap NVIDIA aims to bridge with future enhancements. Explore more on NVIDIA's official blog. For the scientific paper on FourCastNet, see here.

Technical Reading on NVIDIA's Fourier-based neural network
  • Approach: NVIDIA's model uses a Fourier-based neural network framework and runs on cuDNN-accelerated TensorFlow. It operates on NVIDIA GPUs, achieving high-speed computation (A100 GPU, similar to Chat GPT).

  • Notable Strengths: It can process extensive ensemble forecasts rapidly, making it suitable for real-time applications. To put this into context, NVIDIA claims that they are "45,000 times faster than traditional NWP models" (source). This is especially useful when considering ensemble forecasts and detection of extreme events. They also expect to eventually train the FourCastNet to "predict weather on sub-km scales."

  • Notable Limitations: Like many other purely data driven AI weather models, the output may not always strictly obey known physical laws (read: produces unrealist results). Noting this in their research paper, NVIDIA plans to incorporate "Physics-informed Machine Learning" methods in the future.

Microsoft's ClimaXโ€‹

ClimaX handles various datasets effectively, particularly in downscaling for local forecasts. It uses separate models for different forecast intervals, a limitation addressed by ClimaX-iter which allows iterative predictions, though this can affect long-term forecast accuracy. Learn more about their approach on Microsoft's ClimaX overview and access the scientific paper here.

Technical Reading on Microsoft's Vision Transformers (ViT)
  • Approach: Microsoft's ClimaX employs a multi-dimensional image-to-image translation architecture based on Vision Transformers (ViT).

  • Notable Strengths: It's designed to handle a wide variety of weather and climate modeling training data ("heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings"). Using a ViT method also allows for training the model on spatially-incomplete datasets. Notably, this method also works well for being able to downscale data to get more localized results.

  • Notable Limitations: In ClimaX, each model instance is trained for a specific lead time, necessitating separate models for different forecasting intervals. ClimaX-iter addresses this by enabling iterative predictions over multiple lead times with a single model. This approach offers greater efficiency and scalability, but it compromises accuracy in longer-term forecasts due to error accumulation.

Huawei's Pangu-Weatherโ€‹

Pangu-Weather stands out as one of the few models implementing a 3D Earth Specific Transformer architecture, a notable advancement in AI weather forecasting. This 3D approach is key to its accuracy, allowing the model to capture intricate atmospheric relationships. Its rapid forecasting capabilities are on par with NVIDIA's FourCastNet. However, the complexity of 3D data processing raises computational demands, which poses challenges for scalability and practical application. This mirrors the balance seen in advanced AI models like OpenAI's ChatGPT, where increased sophistication often comes with higher resource requirements. Learn more about their approach on Pangu-Weather's official page and access the scientific paper here.

Technical Reading on Pangu-Weather and Huawei's 3D Earth Specific Transformer (3DEST)
  • Approach: This model uses a 3D Earth-Specific Transformer (3DEST) architecture to process complex 3D meteorological data.

  • Notable Strengths: Pangu-Weather can produce forecasts extremely rapidly, suggesting that they are similar to NVIDIA's FourCastNet (i.e., 45,000 times faster than traditional numerical models). This means that forecasts can be done in "real-time" (i.e., second(s)). They also use a 3D deep neural network, which can be more accurate than 2D networks (i.e., NVIDIA's FourCastNet), as it can capture intrinsic relationship between different atmospheric levels.

  • Notable Limitations: The primary challenge for the Pangu-Weather system, akin to advanced AI models like OpenAI's ChatGPT, lies in the trade-off between model sophistication and computational demands. As the system incorporates more complex 3D data and hierarchical algorithms for accuracy, it faces increased computational overhead, particularly in memory usage, which restricts the scalability and practical application of the technology.

๐ŸŒ Broadening the Horizon: Beyond the Leading Tech Giantsโ€‹

This article offers a snapshot of the private sector's advancements in meteorological innovation. There are also many other private companies like Jua (Swiss Startup) and Meteomatics (Swiss Company) at the forefront, with Jua developing AI models and Meteomatics using meteodrones for unique weather insights.

The European Centre for Medium-Range Weather Forecasts (ECMWF) plays an indispensable role that extends beyond the private sector. Their models are benchmarks in the field, often used by private companies for comparison. Furthermore, ECMWF, in collaboration with Copernicus, provides the ERA5 reanalysis model, which is a crucial training dataset for many of these emerging AI models. Demonstrating their support for innovation, ECMWF actively embraces the private industry by hosting groundbreaking models like Google's Graphcast, NVIDIA's FourCastNet, and Huawei's Pangu-Weather on their platform. For an in-depth look, visit ECMWF's AI showcase.

ECMWF has also launched their own AI model, the AIFS (Artificial Intelligence/Integrated Forecasting System), enhancing their Integrated Forecasting System (IFS). Live model runs of AIFS can be seen at ECMWF's AIFS charts. Learn more from their blog: ECMWF unveils alpha version of new ML model.

The evolving field of AI in weather forecasting, driven by private companies, academia, and inter-governmental collaborations, promises more precise and efficient meteorological predictions.

๐Ÿงฉ Practical Challenges: The Initial Conditions Hurdleโ€‹

While a growing number of cutting-edge AI weather models are becoming open-source and accessible on platforms like GitHub, a significant challenge persists for average users: the acquisition of initial conditions. These conditions, representing the current observed state of the weather, are essential for initiating any forecast model. This situation highlights that, despite the technical advancements and availability of sophisticated models, practical application still faces substantial hurdles, especially in terms of data accessibility. Addressing these challenges, innovative companies like Jua and Meteomatics are exploring novel solutions, including leveraging IoT and drones to actively gather atmospheric data.

1. Ensembles and Rapid Forecastingโ€‹

  • Ensemble Forecasts: With the acceleration of forecast capabilities in models like Huawei's Pangu-Weather and NVIDIA's FourCastNet, the adoption of large ensemble forecasts may increase. These ensembles are crucial for better predicting extreme weather events and for extending forecast ranges.
  • Real-Time Forecasting: Advancements in forecasting speed could lead to more frequent updates, potentially evolving towards continuous forecasting. This change highlights the importance of efficient data pipelines for integrating initial conditions. Insights from nowcasting could guide the development of this new forecasting paradigm.

2. Democratization of Forecastingโ€‹

  • Lightweight Models: Google's lightweight and fast models have the potential to democratize weather forecasting, particularly in less developed regions. However, this trend might be overshadowed by the movement towards open weather data policies by large inter-governmental institutions. With organizations like ECMWF, NOAA, and soon MeteoSwiss embracing open data policies, a global shift towards more accessible weather forecasting tools is likely.

At ClimaLinks, we find ourselves at the forefront of a transformative era in weather forecasting. We're not just witnessing the AI revolution in meteorology; we're an integral part of it. As accurate weather forecasts become more accessible and commonplace, we recognize that simply having forecasts isn't enough. The true game-changer lies in how we utilize this expanding pool of accurate data.

This is where ClimaLinks takes center stage. We're innovating the first-ever Weather Relationship Management (WRM) platform. Our aim? To revolutionize how weather data is used. As weather forecasts become more freely available and also precise, we're helping to shift the industry's focus. It's not just about predicting the weather anymore; it's about understanding and using this information to its fullest potential.

Our WRM tool is specifically designed to tackle this 'last-mile' challenge in weather forecasting. We're committed to helping our users not just access the best weather information but truly leverage it. At ClimaLinks, we envision a world where weather data becomes an indispensable tool for proactive decision-making, transforming the way individuals, businesses, and governments respond to meteorological changes.

In this new era, where the abundance of data is both an opportunity and a challenge, ClimaLinks is leading the way in making weather data a cornerstone of strategy and operations.

Take Actionโ€‹

Ready to take your weather strategy to the next level? At ClimaLinks, we're more than just forecasters; we're your partners in weather intelligence. Whether you're a business looking to optimize operations, a government agency aiming to enhance public safety, or just someone who wants to stay ahead of the weather, ClimaLinks has the tools and expertise you need.

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