Details

Autor(en) / Beteiligte

• Schraff, C.
• Reich, H.
• Rhodin, A.
• Schomburg, A.
• Stephan, K.
• Periáñez, A.
• Potthast, R.

Titel

Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA)

Ist Teil von

• Quarterly journal of the Royal Meteorological Society, 2016-04, Vol.142 (696), p.1453-1472

Ort / Verlag

Chichester, UK: John Wiley & Sons, Ltd

Erscheinungsjahr

2016

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• An ensemble Kalman filter for convective‐scale data assimilation (KENDA) has been developed for the COnsortium for Small‐scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi‐operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.