1. Contents 1. Data assimilation in Russian Hydrometcentre at the end of 2003 - Tsyroulnikov M.D., Zaripov R.B., Tolstykh M.A., Bagrov A.N. 2. Development of data assimilation system in 2004 - Zaripov R.B., Bagrov A.N., Tsyroulnikov M.D., Tolstykh M.A. 3. Development of the INM RAS-Hydrometcentre semi- Lagrangian SL-AV model in 2004 - Tolstykh M.A. 4. The evaluation of forecast quality using observations - Bagrov A.N.

2. Presenter: Mikhail Tolstykh Institute of Numerical Mathematics Russian Academy of Sciences, and Russian Hydrometeorological Research Centre Moscow Russia

3. Data assimilation in Russian Hydrometcentre at the end of 2003 Tsyroulnikov M.D., Zaripov R.B., Bagrov A.N., Tolstykh M.A.

4. RHMC Data assimilation system - 1

6. RHMC Data assimilation system - 2 Sequential assimilation of different observation types : 1. Surface analysis 1.1 Surface pressure analysis 1.2 Temperature analysis at 2m (lowermost model levels are affected) 1.3 Surface temperature analysis using hypothesis that  T s =0.5  T 2m 1.4 Dew point temperature analysis at 2m 1.5 Snow water equivalent analysis

7. RHMC Data assimilation system - 3 Sequential assimilation of different observation types: 2. Upper-air analysis with twice coarser horizontal resolution 1.44x1.8 degrees lat/lon: 2.1 Multivariate 3D analysis for geopotential and wind fields at standard pressure levels 2.2 Univariate 2D analysis for dew point temperature at standard pressure fields

8. RHMC Data assimilation system - 4 Incremental preprocessing for upper-air fields to interpolate analysis increments from analysis grid (pressure levels and twice coarser horizontal grid) to model grid (sigma levels) Details in (M.D.Tsyroulnikov, M.A.Tolstykh, A.N.Bagrov, R.B.Zaripov, Russian Meteorology and Hydrology, 2003). Piecewise-constant interpolation in vertical (changed to linear in 2004).

9. The data assimilation system consists of following program units: Observations quality control; Surface data analysis; Upper-air analysis; Sea-surface temperature; Incremental preprocessnig; Atmospheric forecast model; Postprocessing.

10. First-guess errors for geopotential vs radiosondes: RMS (solid) and bias (dash)

11. First-guess errors for wind vs radiosondes: RMS (solid) and bias (dash)

12. You will not hear here that: M.D.Tsyroulnikov plans to work on 3D variational assimilation (3D-var) in collaboration with DWD; Unlike current OI scheme, 3D-var allows to assimilate indirect satellite measurements of radiances etc.; In 3D-var, all observations influence the analysis at any grid point, while the special hypotheses are introduced in the OI to select the number of influencing observations. This gives much smoother analyses

13. Development of data assimilation system in 2004 ( Zaripov R.B., Bagrov A.N., Tsyroulnikov M.D., Tolstykh M.A.) Operational implementation at RHMC on a 4- processor node of Itanium2 16-procs cluster, including retrieval of observations from new remote database containing much more data, and writing the resulting analyses and forecasts to the database on another computer. Increase of buffers size for observations handling Some corrections and improvements, including replacement of piecewise constant interpolation by linear one in incremental preprocessing.

14. Development of data assimilation system in 2004 (continued) Preparation of technology for variable resolution version of the model. Currently, it is launched using interpolation of analyses from the constant resolution version of the model. Later, full assimilation cycle is planned. Now the analyses and SL-AV model 5-days forecasts (constant resolution version) are available from Hydrometcentre ftp-server (ftp://ftp. hydromet.ru) for research purposes for free. Later, variable resolution version analyses and forecasts will be placed on this server.

15. Development of the INM RAS-Hydrometcentre semi-Lagrangian SL-AV model in 2004 Tolstykh M.A. Changes in dynamics, upgrade of parameterizations Parallel implementation and porting to different computer systems Variable resolution version with the horizontal resolution 30-35 above Russia

16. SL-AV model (semi-Lagrangian absolute vorticity) Shallow water constant-resolution version demonstrated the accuracy of a spectral model for most complicated tests from the standard test set (JCP 2002 v. 179, 180-200) 3D constant-resolution version (Russian Meteorology and Hydrology, 2001, N4) passed quasioperational tests at RHMC 3D dynamical core passed Held-Suarez test

18. Changes in dynamics in 2004 Implementation of the SETTLS scheme (Hortal, QJ 2003) with 2nd order uncentering instead of classical 2-time-level semi-Lagrangian scheme Change of some high-order differencing and averaging operators in the horizontal plane Additional orography filtering in some mountains (e.g. Alaska, Andes) Result: reduction of the false orographic resonance, possibility to reduce the horizontal diffusion coefficient for vorticity (contributing to cold bias reduction)

19. 500 hPa height field over Alaska(72h forecast from 26/10/03 (color isolines – old version, white isolines – new version)

20. Changes in parameterizations in 2004: Upgrade of the gravity-wave drag parameterization developed in Meteo-France Introduction of the mesospheric drag parameterization acting mainly at the uppermost vertical model level Result: Contribution to cold bias reduction, extended stability at the top of the model atmosphere

21. Averaged bias of 72h geopotential forecasts over Russia starting from 00 UTC (october 2003) (Blue line – old version, red line – new version)

22. Parallel implementation (MPI+OpenMP)

23. Parallel implementation for version 0.225ºх0.18ºх28

24. Parallel implementation (MPI+OpenMP) 2 Theoretical scalability is limited to N lat ; for future 0.25°x0.18°x60 version this gives 1000 processors High efficiency of the code in single CPU mode: 21% from peak performance on scalar Itanium 2 1.3GHz CPU; ~45-55% on modern vector machines For 0.9°x0.72°x28 version, 24h forecast takes 5.5 min on one 4-processor node of the Myrinet 16 Itanium2 processor Hydrometcentre’s cluster Successfully ported to SGI Altix, NEC SX6 and Cray X1

25. Extension to the case of variable resolution in latitude  Discrete coordinate transformation (given as a sequence of local map factors), subject to smoothness and ratio constraints. This requires very moderate changes in the constant resolution code (introduction of map factors in computation of gradients, semi-implicit scheme etc) and also allows to preserve all compact differencing and its properties intact.  Some changes in the semi-Lagrangian advection - interpolations and search of trajectories on a variable mesh.  Details in Tolstykh, Russian J. Num. An. & Math. Mod., 2003, V.18, N4, 347-361

26. Latitudinal resolution (in radians) vs. latitude (in degrees)

27. Averaged (January 2005) H500 RMS scores for 12 UTC forecasts over Russia: constant and variable resolution versions

28. Averaged (January 2005) MSLP RMS scores for 12 UTC forecasts over Russia: constant and variable resolution versions

29. Averaged (January 2005) T850 RMS scores for 12 UTC forecasts over Russia: constant and variable resolution versions

30. Averaged (January 2005) H500 anomaly correlation scores for 12 UTC forecasts over Russia: constant and variable resolution versions

31. The evaluation of forecast quality using observations data A.N. Bagrov

32. Brief characteristics of the method Radiosondes (TEMP) and near-surface observations (SYNOP) first pass quality check Root-mean-squared error (RMS) and tendencies correlation coefficient (RKT) are calculated fixing the number of observations used for forecasts evaluation Averaged monthly scores

33. The models compared: 1.EXE -Exeter, UKMO model 2. SMA - RHMC Eulerian spectral model; initial data from RHMC operational analyses 3. SLM – SL-AV model; initial data from assimilation system analyses described in parts 1-2

34. RMS scores of 500 hPa geopotential vs radiosondes. 00UTC forecasts. February 2005. Europe

35. RMS scores of MSLP field vs SYNOP data. 00UTC forecasts. February 2005. Europe

36. RMS scores of 850 hPa temperature vs radiosondes. 00UTC forecasts. February 2005. Europe

37. RMS scores of 250 hPa wind vs radiosondes. 00UTC forecasts. February 2005. Europe

38. Tendencies correlation for 500 hPa geopotential vs radiosondes. 00UTC forecasts. February 2005. Europe

39. RMS scores of 500 hPa geopotential vs radiosondes. 00UTC forecasts. February 2005. Central Russia

40. RMS scores of MSLP field vs SYNOP data. 00UTC forecasts. February 2005. Central Russia

41. RMS scores of 850 hPa temperature vs radiosondes. 00UTC forecasts. February 2005. Central Russia

42. RMS scores of 250 hPa wind vs radiosondes. 00UTC forecasts. February 2005. Central Russia

43. Future work Implementation of the reduced grid in the model and in the assimilation (see the poster of R. Fadeev) Implementation of the ISBA scheme developed in Meteo-France for soil parameterization and assimilation of soil variables Work on configuration with rotated poles Further plans to implement nonhydrostatic dynamical core