the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Calibration of sea ice drift forecasts using random forest algorithms
Malte Müller
Related authors
Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Sea ice forecasts are operationally produced using physical-based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique (also called calibration) using machine learning in order to improve the skill of short-term (up to 10 days) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows to reduce the errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.