HIRLAM Regional 3D-VAR and MESAN downscaling re-analysis for the recent. 20 year period in the FP7 EURO4M project SMHI

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1 HIRLAM Regional 3D-VAR and MESAN downscaling re-analysis for the recent 20 year period in the FP7 EURO4M project Per Undén, Tomas Landelius Per Dahlgren, Per Kållberg Stefan Gollvik, Sébastien Villaume SMHI

2 SMHI EURO4M: 3D/2D reanalysis & validation ERA Interim ECMWF 78 km 3D: HIRLAM 2D: MESAN - ERA on the borders and as a large scale constraint - 60 vertical levels - HIRLAM as first guess - Surface parameters - 22 km horizontal resolution - 5 km grid

3 HIRLAM analysis and model configuration 6h cycling: analysis at 00, 06, 12 and 18UTC +48h forecasts every cycle, output +3,6,9,12,18,24,30,36,42,48h Lateral boundaries: ERA-Interim analyses every 6:th hour Each cycle: surface analysis, 3DVAR upper air analysis, forecast 3

4 Surface analysis SST and sea ice: interpolated from ERA-Interim T2m, RH2m and snow depth: OI analysis with SYNOP observations Albedo and snow corrected in Sahara and Greenland 4

5 Upper air analysis 3D-VAR Observations: SYNOP, SHIP, BUOY, TEMP, PILOT, AIRCRAFT FGAT First Guess at Appropriate Time h +4h +5h +6h +7h +8h +9h time Forecast length Observations valid at 09UTC are compared to the +3h forecast from the previous cycle, and so on. Large scale Era-Interim vorticity assimilated with Jk: 5

6 HIRLAM 3D-VAR Reanalysis so far: HIRLAM 3D-VAR run and data for archived in MARS at NSC(.liu.se) Monitoring and evaluation: Data extracted and evaluated by DWD (cloud, precip, rad, albedo, PWC). Also precipitation to Meteo-Swiss.

7 HIRLAM GRIB fields HIRLAM EURO4M parameters, More than 843 fields (of which 150 are diagnostic): Upper air parameters on 60 levels m parameters U-component of Wind V-component of Wind 2 m parameters Temperature Specific Humidity m Temperature over land m Spec Hum 2m over land 60 U-component of Wind 60 V-component of Wind 60 Temperature 60 Specific Humidity 60 Cloud Water Soil parameters 2D fields 60 Total Cloud Cover Mean sea level pressure, 60 Turbulent Kinetic Energy accumulated precip, radiation 60 Cloud ice fluxes e.g. Many diagnostic quantities 7

8 Parameters St ep s Levels MARS retrieve, class=re, model=hirlam, stream=da, expver=e4mh, levelist=0, levtype=105, type=fc, param=${par}.1, date=${date}, time=${hh}, step=24, 8

9 Validation

10 increments January 00Z increments January 12Z diff to ERA-Interim January 00Z diff to ERA-Interim January 12Z ggj

11 Annual cycle

12 HIRLAM total precipitation Nov 1996

13 78 km ERA-Interim interpolated total precipitation Nov

14 3D analysis Multivariate HIRLAM 3D-VAR 2D analysis Uni/bi-variate Optimum Interpolation MESAN An-istoropic correlations

15 Downscaling temperature Vertical and horizontal interpolation considering differences in orography, fraction of land and distance. Effect of anisotropic structure function.

16 Available t2m observations 3D reanalysis 2D reanalysis More observations needed for the 2D case!

17 Added detail; downscaling & analysis (t2m) HIRLAM 22 km Downscaled to 5 km MESAN 5 km

18 Downscaling precipitation Analysis in terms of % of hi-res climatology. Use first guess error field to simulate this. Annual precip divided by daily std isotropic. Bii= f(lat, oro, div, frland) Effect of normalization with the fg error.

19 MESAN analysis of daily precipitation (10 yrs) HIRLAM 22 km MESAN 5 km

20 MESAN Status and plans MESAN observation files merged and prepared MESAN 24h precipitation analysis ongoing MESAN t2m, rh2m, uv10m downscaling and analysis - ongoing Cooperation with MF on next gen MESAN/CANARI in EURO4M Replace OI with EnsVar in future MESCAN? Liu et al (2009): An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II, MWR vol 137, p : - Efficient implementation of localization shur(c,b). - Dimension of control vector: N_ens * N_eof (C)

21 UERRA Uncertainties in Ensembles of Regional Reanalyses 4 year FP7 project extending in many directions: Ensemble assimilation and uncertainties Multiple re-analyses Uncertainty estimates unified methodology against several data sets High resolution (11-12 km and 5 km for 2D-reanalysis) 50 year time periods (30 for ensemble) Data services Outreach and user interaction