Скачать или смотреть Machine Learning of Cloud Representations for Weather and Climate Models

ABSTRACT: Clouds are a major challenge for weather and climate models. The deficiencies in current parameterizations of cloud processes contribute to model uncertainty and forecasts failures. Machine learning methods hold the promise to overcomes these deficiencies and accelerate the model development in geosciences. This promise is twofold: First, machine learning methods can help us to find efficient and simplified approximations of the complicated nonlinear equations that govern the formation of clouds and precipitation. This can be done, for example, through a statistical emulations of reference simulations from detailed physically-based numerical models, which would be too expensive for weather and climate applications. Second, machine learning methods can assist us in the calibration and tuning of weather and climate models using observations. Current weather and climate models do need calibration and tuning because the representation of the physical processes on a given numerical grid is uncertain and inaccurate. Calibration and tuning are currently part of the model development process and remain tedious and to some extent subjective. Machine learning methods can in principle do this better and more objectively, but we have to overcome some obstacles to achieve this goal. In my presentation I will report on some efforts at the Deutscher Wetterdienst, which is the National Weather Service of Germany, to use machine learning methods in the development, calibration and tuning of the ICON model. The first part of the talk will focus on the parameterization of cloud microphysical processes based on detailed super-particle simulations. In the second part, I will report on an attempt to implement an online learning method to improve the cloud scheme of ICON based on satellite data.