EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Representing Microphysical Uncertainty in Convective-Scale Data Assimilation Using Additive Noise

Yuxuan Feng1,3, Tijana Janjic1, Yuefei Zeng1, Axel Seifert2, and Jinzhong Min3
Yuxuan Feng et al.
  • 1Ludwig Maximilians University Munich, Munich, Germany (
  • 2Deutscher Wetterdienst, Offenbach, Germany
  • 3Nanjing University of Information Science and Technology, Nanjing, China

For convective clouds and precipitation, model uncertainty in cloud microphysics is considered one of the most significant sources of model error. In our recent paper (Feng et al. 2021), samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two- and one-moment schemes during a one-month training period. The samples are then added to convective-scale ensemble data assimilation as additive noise and combined with large-scale additive noise based on samples from climatological atmospheric background error covariance. Two experiments, including the combination and large-scale error only, are conducted for a one-week convective period. The results reveal that the simulation with a two-moment scheme triggers more convection and has larger ice-phase precipitation particles, which produce a stronger signal in the melting layer. During data assimilation cycling, although more water is introduced to the model, it is shown that the combination performs better for both background and analysis and significantly improves short-term ensemble forecasts of radar reflectivity and hourly precipitation.

Feng, Y., Janjić, T., Zeng, Y., Seifert, A., & Min, J. (2021). Representing microphysical uncertainty in convective-scale data assimilation using additive noise. Journal of Advances in Modeling Earth Systems, 13, e2021MS002606.

How to cite: Feng, Y., Janjic, T., Zeng, Y., Seifert, A., and Min, J.: Representing Microphysical Uncertainty in Convective-Scale Data Assimilation Using Additive Noise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8198,, 2022.

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