HIRLAM Regional 3D-VAR and MESAN down-

scaling 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

SMHI EURO4M: 3D/2D reanalysis & validation

3D: HIRLAM

- ERA on the borders and as a large scale constraint

- 60 vertical levels

- 22 km horizontal resolution

ERA Interim 2D: MESAN

- HIRLAM as first guess

- Surface parameters

- 5 km grid78 km

ECMWF

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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

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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

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Upper air analysis

● 3D-VAR● Observations: SYNOP, SHIP, BUOY, TEMP, PILOT, AIRCRAFT● FGAT – First Guess at Appropriate Time

12 13 14 15111009 time

Forecast length+3h +4h +5h +6h +7h +8h +9h

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:

HIRLAM 3D-VAR Reanalysis so far:

HIRLAM 3D-VAR run and data for

– 1989 - 2002 archived in MARS at NSC(.liu.se)

– 2007-01-01 - 2010-12-31

Monitoring and evaluation: Data extracted and evaluated by DWD (cloud, precip, rad,

albedo, PWC). Also precipitation to Meteo-Swiss.

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HIRLAM GRIB fields

HIRLAM EURO4M parameters, More than 843 fields (of which 150 are diagnostic):

Upper air parameters on 60 levels 33 109 60 U-component of Wind 34 109 60 V-component of Wind 11 109 60 Temperature 51 109 60 Specific Humidity 76 109 60 Cloud Water 71 109 60 Total Cloud Cover 200 109 60 Turbulent Kinetic Energy 58 109 60 Cloud ice

10 m parameters 33 105 10 U-component of Wind

34 105 10 V-component of Wind

2 m parameters 11 105 2 Temperature 51 105 2 Specific Humidity 140 105 2 2m Temperature over

land 141 105 2 2m Spec Hum 2m over

land

Soil parameters – 2D fields Mean sea level pressure,accumulated precip, radiation fluxes e.g. Many diagnostic quantities

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Step

s

Le

vels

Parameters

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,

Validation

ggj

increments 1994-1996 January 00Z increments 1994-1996 January 12Z

diff to ERA-Interim 1994-1996 January 00Z diff to ERA-Interim 1994-1996 January 12Z

Annual cycle

HIRLAM total precipitation Nov 1996

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ERA-Interim interpolated total precipitation Nov 199678 km

2D analysisUni/bi-variateOptimum Interpolation

MESAN

3D analysis Multivariate

HIRLAM 3D-VAR

An-istoropic correlations

Downscaling temperature

Vertical and horizontalinterpolation considering differences in orography, fraction of land and distance.

Effect of anisotropic structure function.

3D reanalysis 2D reanalysis

Available t2m observations

More observations needed for the 2D case!

Added detail; downscaling & analysis (t2m)

HIRLAM 22 km Downscaled to 5 km MESAN 5 km

Downscaling precipitation

Bii= f(lat, oro, div, frland)

Analysis in terms of % of hi-res climatology.Use first guess error field to simulate this.

Annual precip divided by daily std – isotropic.

Effect of normalization with the fg error.

MESAN analysis of daily precipitation (10 yrs)

HIRLAM 22 km MESAN 5 km

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 1687-1704:- Efficient implementation of localization shur(C,B).- Dimension of control vector: N_ens * N_eof (C)

MESAN Status and plans

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