Artificial intelligence (AI) is rapidly transforming every sector, and the crucial field of weather forecasting is no exception. AI promises to provide increasingly accurate models that can have a significant impact on many aspects of our lives.
How does AI work for weather forecasts?
AI utilizes machine learning models that analyze vast amounts of data. These data can be of various types (temperature, precipitation, humidity, wind speed, atmospheric pressure, etc.) and can come from historical records or real-time sources.
Datasets are sourced from various channels such as satellites, radars, IoT sensors, and primarily from weather stations. AI processes this data to identify complex patterns and relationships that traditional weather models might not capture. This way, AI can generate more accurate and personalized forecasts for a specific geographical area.
The Benefits of AI for Weather Forecasting
The use of AI in forecasting offers significant advantages:
Greater Accuracy: AI can predict weather events more accurately, facilitating better planning.
Granular Forecasts: AI models can provide predictions for very specific areas, allowing management of events like hailstorms and snowfall.
Timeliness: AI model processing is significantly faster than traditional methods.
New Insights: AI helps meteorologists better understand the complex systems influencing weather, leading to the discovery of new relationships and patterns 1.
Applications of AI in Weather Forecasting
AI applied to weather data management can bring benefits in various fields:
Agriculture: Optimization and protection of crops and livestock, improving yield and reducing risks.
Aviation: More precise forecasts to monitor airline routes and enhance safety.
Energy: Optimization of energy production and distribution based on forecasts.
Extreme Weather Events: Broader advance warning for events like hurricanes, typhoons, and floods
GraphCast
GraphCast: An AI model developed by Google DeepMind and announced in November 2023, GraphCast has demonstrated an unprecedented ability to predict weather with remarkable precision. Leveraging a neural network, GraphCast analyzes relationships among various meteorological variables, providing forecasts that surpass the granularity and accuracy of traditional models.
The model relies on two data series: the weather state from 6 hours prior and the current weather state. Using these sets, GraphCast generates highly accurate predictions for the subsequent six hours. It further utilizes the latest forecasts and previous data to create new predictions
The result is a model that generates highly accurate forecasts up to 10 days. GraphCast is also a model that takes less than a minute to generate a 10-day forecast, with a 20% improvement compared to non-AI models, and it requires significantly less data than the competition
Bias and Conclusions
Like any AI model, GraphCast and other weather models currently in use (such as those by Nvidia and Huawei) are not immune to biases and issues. For Google’s deployed model, there has been some difficulty in accurately identifying the intensity of certain weather events. This challenge arises from the model’s tendency to align with data that represents average conditions.
However, AI models handling weather forecasting represent a revolutionary breakthrough. They provide a precise and timely tool to cope with sudden weather changes, which are becoming more frequent due to climate shifts.
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