Based on the principle “learn from past errors to correct current forecasts”, statistical postprocessing consists in optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-of-the-art tools for training statistical models and making predictions based on large datasets. In our study, ML-based solutions are developed to reduce forecast errors of 2m temperature and 10m wind speed of the ECMWF’s operational medium-range high-resolution forecasts produced with the Integrated Forecasting System (IFS). IFS forecasts and other spatio-temporal indicators are used as predictors after careful selection with the help of ML interpretability tools. Different ML approaches are tested: linear regression, random forest decision trees, and neural network. Statistical models of systematic and random errors are derived sequentially where the random error is defined as the residual error after bias correction. In terms of output, bias correction and forecast uncertainty prediction are made available at any point-locations around the world. All 3 ML methods show similar ability to capture situation-dependent biases leading to noteworthy performance improvements (between 10% and 15% improvement in terms of rootmean-square error for all lead times and variables), and similar ability to provide reliable uncertainty predictions.
BT - ECMWF Technical Memoranda DA - 04/2022 DO - 10.21957/vdcccja3f LA - eng M1 - 896 N2 -Based on the principle “learn from past errors to correct current forecasts”, statistical postprocessing consists in optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-of-the-art tools for training statistical models and making predictions based on large datasets. In our study, ML-based solutions are developed to reduce forecast errors of 2m temperature and 10m wind speed of the ECMWF’s operational medium-range high-resolution forecasts produced with the Integrated Forecasting System (IFS). IFS forecasts and other spatio-temporal indicators are used as predictors after careful selection with the help of ML interpretability tools. Different ML approaches are tested: linear regression, random forest decision trees, and neural network. Statistical models of systematic and random errors are derived sequentially where the random error is defined as the residual error after bias correction. In terms of output, bias correction and forecast uncertainty prediction are made available at any point-locations around the world. All 3 ML methods show similar ability to capture situation-dependent biases leading to noteworthy performance improvements (between 10% and 15% improvement in terms of rootmean-square error for all lead times and variables), and similar ability to provide reliable uncertainty predictions.
PB - ECMWF PY - 2022 T2 - ECMWF Technical Memoranda TI - Statistical modelling of 2m temperature and 10m wind speed forecast errors UR - https://www.ecmwf.int/node/20342 ER -