King's College London (UK),  National Centre for Atmospheric Science (UK) & Department of Hydrology and Meteorology, Nepal (Nepal)

Accurate forecasts of weather are extremely important in the mountains – where conditions can regularly endanger lives and critical infrastructure. It is unfortunate, then, that numerical weather prediction (NWP) models can seldom be run at high-enough resolution to capture the fine details that can matter so much (e.g., whether rain instead of snow falls on a steep slope above a hydroelectric power station, or how high the wind speed is on an exposed climbing route).

The project addresses this challenge by developing a computational platform to leverage machine learning (ML) techniques to correct NWP at key sites using weather station observations – an example of so-called ‘Model Output Statistics’ (MOS). Using wind speeds on the summit of Mt. Everest as a target application, we demonstrate that simple ML algorithms can improve predictions of key variables at important sites. We anticipate that the such a lightweight and flexible approach to improving forecasts will facilitate at-scale MOS (AtsMOS) for mountain sites in the future. Our next steps are therefore to encourage wider application of the AtsMOS platform at other mountain sites worldwide.


Main outputs: 

The code base developed has been published in the form of a comprehensive Jupyter notebook on GitHub. The associated datasets and pre-trained machine-learning models are available on Zenodo. A paper is also in discussion at the journal Geoscientific Model Development. 

Contact: Dr. Tom Matthews, Senior Lecturer in Environmental Geography, King’s College London: tom.matthews [at]

Image from Tom Matthews

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