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Impact of Radar Data Assimilation and Orography on Predictability of Deep Convection

20.05.2019

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Impact of Radar Data Assimilation and Orography on Predictability of Deep Convection

Deep convection represents a classic example of limited predictability on the convective scale. We investigate the potential impact of assimilating radar observations on the predictive skill of precipitation in short-term forecasts in an idealized COSMO setup. Additionally, the role of a mountain providing a source of predictability for the location of convection is examined with and without data assimilation.
We found a long-lasting beneficial impact of radar data assimilation throughout the entire forecast range and skillful forecasts on scales above 40km at lead times of 6h. Similarly, the presence of orography increases the predictability of precipitation in its proximity and in case no radar data are assimilated.
Our study demonstrates the potential impact that could be achieved for radar data assimilation if systematic model and operator deficiencies are reduced and highlights the effect of orography as a trigger for convection.


Bachmann, K, Keil, C, Weissmann, M. Impact of radar data assimilation and orography on predictability of deep convection. Q J R Meteorol Soc. 2019; 145: 117– 130. https://doi.org/10.1002/qj.3412