Maxime Beauchamp, Bertrand Bessagnet, Enrico Pisoni, Anthony Rey-Pommier, Alexander de Meij, Philippe Thunis
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引用次数: 0
Abstract
High-resolution air pollution datasets are crucial for exposure assessment and policy support but are computationally demanding to produce with traditional models. We present a machine learning-based framework for spatial downscaling of chemistry-transport model outputs from 6 km to 1 km horizontal resolution over a large European domain. The model is trained on base case simulations and validated across multiple emission scenarios and temporal scales. Results show good performance in reproducing both absolute concentrations and scenario-induced differences. Applying the model to monthly-averaged fields offers a computationally efficient solution, supporting rapid scenario analysis.
期刊介绍：
Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques.
We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.
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