1. Development of a 1D ensemble forecast System with variational assimilation Mathias D. Müller Institute of Meteorology, Climatology & Remote Sensing University of Basel, Switzerland Mathias.mueller@unibas.ch COST-722 MAY 2005, LARNAKA. Variational Assimilation Examples Advection Problem

2. variational assimilation B-matrices COBEL-NOAH PAFOG (future) Obser - vations 3D-Model runs post-processing Fog forecast period NMM-4 NMM-2 NMM-22 aLMo 3D - Forecast time Ensemble prediction system www.meteoblue.ch

3. Variational assimilation Background is taken from 3D-models (not 1D fog model): NMM-22, NMM-4, NMM-2, (ETA-22, ETA-4) and aLMo Radiosonde (about 150 km away) Surface observations of temperature and humidity at Zürich airport MTP-5 microwave temperature profiler Virtual profile of temperature and humidity from surface observations on nearby mountains

4. Assimilation example 28 Nov 2004 Zürich Kloten Airport 21 hour forecast of NMM-2

5. Write in incremental Form Introduce T and U transform to eliminate B from the cost function (physical space) (Control variable space) Cost function for variational assimilation

6. Error covariance matrix NMC-Method (use 3D models):

7. NMC estimates of B (winter season) NMM-4 1400 UTC large model and time dependence

8. Fog case - Observations CASE 1CASE 2

9. Without assimilation – CASE 1 12:00 27-28 Nov 2004 observed fog

10. With assimilation – CASE 1 12:00 27-28 Nov 2004 observed fog

11. Without assimilation – CASE 1 15:00 27-28 Nov 2004 observed fog

12. With assimilation – CASE 1 15:00 27-28 Nov 2004 observed fog

13. With assimilation – CASE 1 15:00 27-28 Nov 2004 observed fog

14. Fog case - Observations CASE 1CASE 2

15. Without assimilation – CASE 2 12:00 28-29 Nov 2004

16. With assimilation – CASE 2 12:00

17. Without assimilation – CASE 2 15:00 28-29 Nov 2004

18. With assimilation – CASE 2 15:00

19. T-Advection (stdv) Height Time

20. Advection statistics 1 December 2004 – 30 April 2005, all forecasthours and levels Deviation often stronger than signal

21. Conclusions Ensemble is based on members derived from different 3D model forecasts using variational assimilation Little information about humidity (except 00 and 12 UTC initialization with radiosonde) Assimilated ensemble still has remarkable spread Advection derived from 3D model is unreliable. Might be treated as stochastic process ?

22. T-Advection (mean)