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| 001 | 820929 | ||
| 005 | 20210129224654.0 | ||
| 024 | 7 | _ | |a 10.1002/qj.2751 |2 doi |
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| 100 | 1 | _ | |a Bick, T. |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale |
| 260 | _ | _ | |a Weinheim [u.a.] |c 2016 |b Wiley |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Small-scale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase. |
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| 700 | 1 | _ | |a Simmer, C. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Trömel, S. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Wapler, K. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Hendricks-Franssen, Harrie-Jan |0 P:(DE-Juel1)138662 |b 4 |
| 700 | 1 | _ | |a Stephan, K. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Blahak, U. |0 P:(DE-HGF)0 |b 6 |
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| 700 | 1 | _ | |a Reich, H. |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Zeng, Y. |0 P:(DE-Juel1)145998 |b 9 |
| 700 | 1 | _ | |a Potthast, R. |0 P:(DE-HGF)0 |b 10 |
| 773 | _ | _ | |a 10.1002/qj.2751 |g Vol. 142, no. 696, p. 1490 - 1504 |0 PERI:(DE-600)2089168-4 |n 696 |p 1490 - 1504 |t Quarterly journal of the Royal Meteorological Society |v 142 |y 2016 |x 0035-9009 |
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