Key takeaways
- Leverage AI models for improved accuracy in weather and disaster forecasting.
- Utilize Tomorrow.io's AI 'Gale' for localized weather insights across various industries.
- Adopt predictive models to enhance decision-making and operational efficiency.
- Recognize limitations of traditional forecasting methods in predicting weather phenomena.
- Explore new technologies like ChatGPT plugins for integrating weather forecasts.
Imagine a future where every possible weather catastrophe that rocks our world today can be averted. What if major weather changes and natural calamities can be predicted beforehand enabling us to dodge all the damage and destruction in its wake? While 100% predictive modelling for weather doesn’t exist today, companies are increasingly working towards models that might reach that level of accuracy.
With traditional forecasting methods having its limitations, predictive models that adopt AI for forecasting weather come with better interpretability and scalability. Here is a list of companies that are already in this space.
Tomorrow.io
Previously known as ClimaCell, Tomorrow.io uses AI and machine learning techniques for weather prediction. Their AI model ‘Gale’ analyses huge amounts of data, including radar, satellite imagery, atmospheric data, and other non-traditional sources to generate weather forecasts.
The company provides highly localised and precise weather information for various industries such as transportation, logistics, energy, and agriculture which helps these businesses to make informed decisions and optimise their operations based on reliable weather forecasts. The company has even launched the first predictive weather forecasting plugin in ChatGPT.
Atmo.io
Atmo.io builds hardware-software systems that solve weather predictions for cities, nations, defense organisations and businesses. Atmo combines Deep Neural Networks (DNN) and Numerical Weather Prediction (NWP) methods to create an innovative framework for weather forecasting. By harnessing the latest advancements in GPUs, the integration between NWP and DNN enhances Atmo’s forecast horizon and precision of spatial and temporal resolutions.
Atmo focuses on empowering the government by providing weather forecasting that can help them plan well ahead of disasters- reducing losses and damages in the process.
IBM- The Weather Company
IBM’s The Weather Company was named by ForecastWatch as the weather forecast provider with the highest likelihood of delivering the most accurate forecasts across all regions and time periods analysed. The model uses AI to combine information from almost 100 weather forecast models worldwide. The engine considers factors such as location, time, weather conditions and accuracy of recent forecasts for each model.
Jua.ai
Zurich-based company Jua, which launched an AI model for predicting weather, raised €2.5 million last year. Considered an advanced weather model, Jua uses deep neural network learning to deliver precise and accurate global weather forecasts.
In contrast to conventional weather models that combine regional models, the Jua model incorporates millions of data sources and provides forecasts at a high spatial resolution of 1km2. This helps the model achieve exceptional accuracy for predicting weather conditions up to 48 hours in advance. It also surpasses the capabilities of leading numerical models which provide accurate results for only up to 12 hours.
Pangu Weather
Huawei recently unveiled their latest AI model Pangu-Weather that is said to revolutionise the prediction of weekly weather patterns on a global scale. The model utilises deep learning techniques along with 43 years of historical data. The model’s prediction speed is 10,000 times faster than traditional methods which has the potential to reduce global weather prediction time to seconds.
NVIDIA Earth 2
NVIDIA Earth-2 is a comprehensible and accessible platform that speeds up climate and weather predictions through interactive simulations with high-resolution capabilities. It provides high-resolution climate and weather simulations with interactive visualisation.
The accelerated systems of Earth-2 can help climate scientists to generate climate simulations at a kilometer-scale resolution, perform extensive AI training and interface, and achieve real-time responsiveness with minimal delays.
