Imagine trying to predict the weather by looking at the air but ignoring the soil and plants beneath it. This project connects those two pieces, using AI to blend satellite data with ground conditions. It's like giving a weather app a high-resolution map of the earth's moisture and vegetation to make long-term forecasts much more accurate.
Current seasonal climate forecasts often lack consistency between land and atmosphere data, leading to inaccurate long-term predictions for water and agriculture. This creates financial risk for industries relying on precise environmental forecasting.
AI-powered observation operators and coupled land-atmosphere data assimilation systems. Concrete outputs include ERA6-Land and Arctic CARRA-Land-Pv1 prototype datasets.
If you are an agri-tech provider dealing with unpredictable crop yields due to soil moisture shifts — this project developed land-atmosphere coupled data assimilation that improves seasonal forecast skill. This allows for more precise planting and irrigation schedules based on better Earth system products.
If you are an insurer dealing with high payouts from unexpected droughts — this project developed reanalysis prototype datasets and seasonal prediction demonstrators. These tools provide a more consistent history of climate variables to better price risk and predict disaster events.
If you are a utility company dealing with reservoir depletion — this project developed diagnostic tools including integrated hydrological variables. This helps in predicting water availability over several months with higher accuracy than traditional skill scores.
Based on available project data, no pricing or cost information is provided as the project focuses on upgrading the C3S operational service.
The project is delivering proof-of-concept prototypes and demonstrators to show feasibility for integration into the core C3S operational service.
Based on available project data, specific licensing terms are not mentioned, but the outputs are intended for the evolution of the Copernicus Climate Change Service.
Integration is being tested through the delivery of reanalysis prototype datasets in pre-operational environments and seasonal prediction demonstrators in relevant environments.
The project runs from 2023-01-01 to 2026-12-31, with targeted progressive implementation of upgrades over these three years and beyond.
The consortium is heavily research-oriented, consisting of 12 partners across 9 countries. It is dominated by research organizations (8) and other entities (3), with only 1 industry partner and 1 SME, resulting in a low industry ratio of 8%. This suggests the project is primarily focused on scientific advancement and operational service upgrades rather than immediate commercial productization.
Contact the European Centre for Medium-Range Weather Forecasts (ECMWF) in the UK.
Contact us to explore how these C3S climate upgrades can improve your seasonal risk modeling.
