Page 194 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

developed a foundational weather model with varying resolutions that can generate specialized
weather indicators for applications such as power energy, agricultural meteorology, and low-
altitude weather forecasting. This contributes to industry, innovation, and infrastructure goals.
Additionally, based on our comprehensive weather model, we have developed a reliable model
for regional renewable energy forecasting, effectively addressing the long-standing challenge
of unpredictability in renewable energy. This contributes to stable grid operation, aids in
increasing renewable energy consumption, and further alleviates climate change pressures.

Use Case Status: Operational

Partners: [N/A]

2�2 Benefits of use case

In this case, we utilize a Siamese MAE masked autoencoder based pretraining strategy to
provide better initialization and learn robust feature representations hidden within highly
volatile weather data, enabling precise weather insights. We have also independently
developed a foundational weather model with varying resolutions that can generate specialized
weather indicators for applications such as power energy, agricultural meteorology, and low-
altitude weather forecasting. This contributes to industry, innovation, and infrastructure goals.
Additionally, based on our comprehensive weather model, we have developed a reliable model
for regional renewable energy forecasting, effectively addressing the long-standing challenge
of unpredictability in renewable energy. This contributes to stable grid operation, aids in
increasing renewable energy consumption, and further alleviates climate change pressures.

2�3 Future Work

Leveraging our latest developed global-regional weather forecasting model, we aim to enhance
its functionality and broaden its use in two main areas:

Technical Development: We plan to refine our AI-driven weather forecasting model to better
address user challenges and societal needs. Our primary focus will be on developing more
precise algorithms to predict extreme weather events such as tropical cyclones, heavy rainfall,
and snowstorms. This will enhance our ability to forecast the timing and intensity of such
events, thereby mitigating their potential societal impact. In light of ongoing climate change,
we are also committed to extending our AI-powered weather model to improve forecasting
over longer time horizon. This includes building capabilities for climate predictions that cover
subseasonal, seasonal, and interannual forecasts. [3]

Applications: We aim to promote our AI-enabled global-regional meteorological model
both within China and internationally. This will involve offering weather forecasts with finer
spatiotemporal resolution to support various industries, including the renewable energy sector
and beyond. [4][5]

3 Use Case Requirements

• REQ-01: It's required to use global weather analysis data for global weather forecasting.
• REQ-02: It's required to use regional weather analysis data for regional weather
forecasting.
• REQ-03: It's required to use regional geography data to enhance model accuracy.

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