Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction

Since the number of available NWP forecasts is rapidly increasing, especially with the development of ensemble prediction systems, there is a need to develop innovative forecast products that provide a synthetic view of the weather situation and potential risks. A promising option is to identify different weather patterns in NWP outputs, that can then be used to delimit areas of interest, to...

The capability of machine learning to learn complex, non-linear behaviour from data offers many application areas across the numerical weather prediction workflow, including observation processing, data assimilation, the forecast model and the emulation of physical parametrisation schemes, as well as post-processing. This talk provides an overview on the activities at ECMWF to explore the...

The numerical weather predictions (NWPs) approach to forecasting of surface winds and their corresponding uncertainty in complex terrain remains an important challenge. Even for kilometer-scale NWP, many local topographical features remain unaccounted, often resulting in biased forecasts with respect to local weather conditions.

Through statistical postprocessing of NWP, such systematic...