Estimating carbon pools in the European Shelf sea environment: replacing reanalysis by model-informed machine learning?
Researchers have developed a model-informed machine learning approach to estimate carbon pools in the European Shelf sea environment. This method utilizes a deep ensemble of neural networks trained on observable variables and a physics-biogeochemistry model. The approach offers a computationally cheaper alternative to traditional reanalyses, providing accurate predictions of carbon pools and their uncertainties, and can be driven by assimilated reanalysis data or direct observations. AI
IMPACT Offers a more efficient and cost-effective method for environmental monitoring and climate scenario analysis.