Experiences with SMHI local ALARO DA suite

LACE Data Assimilation Working Days, Budapest,

14-16 June, 2011

Magnus Lindskog, Ulf Andrae, Lisa Bengtsson, Lars Meuller, Karl-Ivar Ivarsson, Martin Ridal

Outline

• Introduction• SMHI local ALARO data assimilation set-up• Results from pre-operational system• SMHI data assimilation impact studies • Some recent general HARMONIE data

assimilation developments • Conclusions and future plans for SMHI ALARO

data assimilation

PRE-OPERATIONAL HARMONIE DOMAINS

2011

AEMETDMIFMI

KNMIMet Eirann

met.noSMHI

Veðurstofa

HARMONIE DOMAINS

AEMETDMIFMI

KNMIMet Eirann

met.noSMHI

Veðurstofa

3dvar/can/oim

NWP models at SMHI HIRLAM C22/C11 (4D-VAR)HIRLAM C22/C11 (4D-VAR)

HIRLAM E11 (3D-VAR)HIRLAM E11 (3D-VAR) HIRLAM G05 (3D-VAR)HIRLAM G05 (3D-VAR)

Operational

Pre-Operational

ALARO (3D-VAR)ALARO (3D-VAR) E05 (3D-VAR)E05 (3D-VAR)

AROME (Downscaling)AROME (Downscaling) Daily

SMHI Pre-operational ALARO system

General System Design

SMHI HARMONIE 2010:• 35h1.3• 5.5 km horisontal resolution• 60 vertical levels (HIRLAM definitions)• 3 hourly LBC from ECMWF fc• Forecast length: 0-36 h• ALARO with 2-L ISBA (not SURFEX)• Hydrostatic forecast model• Surface analysis and 3DVAR• IDFI

SMHI HARMONIE 2011:• 36h1.3• ALARO-0 physics with surfex scheme

General System Design

SMHI HARMONIE 2010:• 35h1.3• 5.5 km horisontal resolution• 60 vertical levels (HIRLAM definitions)• 3 hourly LBC from ECMWF fc• Forecast length: 0-36 h• ALARO with 2-L ISBA (not SURFEX)• Hydrostatic forecast model• Surface analysis and 3DVAR• IDFI

SMHI HARMONIE 2011:• 36h1.3• ALARO-0 physics with surfex scheme

SMHI ALARO is run under mini-SMS

system

Background error statistics • Background error statistics from ensemble of downscaled ECMWF 6h forecasts (20060920-2061031, 00UTC)• REDNMC=0.6• REDZONE=250 km• Background error statistics also derived also utilising ensemble DA (not used)• (Shiyu at DMI has derived background error statistics based on downscaling for different seasons and time of day (201001-201012))

• Background error statistics from ensemble of downscaled ECMWF 6h forecasts (20060920-2061031, 00UTC)• REDNMC=0.6• REDZONE=250 km• Background error statistics also derived also utilising ensemble DA (not used)• (Shiyu at DMI has derived background error statistics based on downscaling for different seasons and time of day (201001-201012))

Observation usage

• SYNOP/SHIP (Z)

• DRIBU (Z)

• AIREP/AMDAR (u,v,T)

• TEMP (u,v,T,q)

• PILOT (u,v)

• ATOVS AMSU-A (NOAA 18 and METOP) (Tb ch 6-10 and VarBC)

Spatialisation of screen level data (CANARI OI)

Surface data assimilation(SYNOP T2m H2m observations over land)

)()( 1 bTTba HxyRHBHBHxx

Surface data assimilation

11,wT

22 ,wT

OImain

mH

mT HTw 21211

mH

mT HTw 22222

mTT 21

22

2mTT

ECMWF SST, temperature over sea ice from surface temperature in boundary field, LST from FA file surface temperature climatology

Scores for verification against observations

April 2011

RMS/BIAS as function of forecast range

ALARO E11 E05 E05(7.3)Surface Pressure (hPa) T2m (K) 10 m Wind speed (m/s)

Scores for verification against observations

April 2011

T2m BIAS (K) averaged over forecast lengths

ALARO E05 E05(7.3)

00 UTC

12 UTC

Scores for verification

against observations

April 2011

ALARO E11 E05 E05(7.3)

RH (%)

Wind speed (m/s)

T (K)

RMS/BIAS averaged over forecasts length as function

of vertical level

Scores for verification against observations

February 2011

RMS/BIAS as function of forecast range

ALARO E11 E05 E05(7.3)Surface Pressure (hPa) T2m (K) 10 m Wind speed (m/s)

Scores for verification against observations

February 2011

T2m BIAS (K) averaged over forecast lengths

ALARO E05 E05(7.3)

00 UTC

12 UTC

00 UTC

12 UTC

Scores for verification

against observations

February 2011

ALARO E11 E05 E05(7.3)

RH (%)

Wind speed (m/s)

T (K)

RMS/BIAS averaged over forecasts length as function

of vertical level

Monitoring of satellite data

and VarBC

00 UTC 06 UTC 12 UTCco

vera

ge24

h u

pdat

e06

h u

pdat

e

A comparison of two off-line soil analysis schemes for

assimilation of screen level observations (Mahfof et al., 2009)

OI-equations

Table of coefficients

Conclusions

1/(2π)

1/(2) (ztiner in cactus.F90)

T2m BIAS/RMS (K) averaged over forecast lengths

ALARO 1/2

ALARO (1/2π)

RMS/BIAS T2M (K) as function o

forecast range

Scores for verification against observations

I month parallel exp, January 2010

ALARO (1/2π) ALARO 1/2

Experimental set-up

• 6 h intermittent data assimilation cycle

• 3 h intermittent data assimilation cycle

Lateral boundary conditions from 6 to 9 h old ECMWF forecasts and observations from ECMWF MARS archive

Two parallel exp. for July & August 2009 and January & Febr. 2010:

At 00 and 12 UTC 30 h forecasts were launched

• HH +/- 3 h for 6 h itermittent DA cycle

• HH +/- 1.5 h for 3 h intermittent DA cycle

Observation time window:

(no modifications of error statistics or IDFI settings

applied when modifying from 6 h to 3 h cycle)

20 Aug 2009 12 UTC sum6h mslpfc (black, hPa)

sum6h-sum3h psdiff (red, conint 1 hPa)

+6 h +12 h +18 h

20 August 2009 09 UTC (RUC 3h)sum3h mslpfc(black,hPa)

3h analysis incr.(red,conint 0.1hPa)

Scores for verification against observations

January, 2010, first 14 days

14 Day RMS/BIAS time series

4D-Var 3D-Var

500 Temperature (K) 500 hPa Wind Speed (m/s)

Timings 96 processor on SMHI Linux Clustre3D-Var:~925 s4D-Var: ~5500 s

30 h forecast: ~5200 s

(experiment for January and July ongoing)

RADAR radial wind DA experiments

Recent HARMONIE data assimilation developments

• Technical problems with initialisation of snow in SURFEX solved (Trygve Aspelien).

• Technical problems with syncronisation of snow between FA file and LFI file solved (Trygve Aspelien).

• Data assimilation of with empty pools in ODB is enabled (Sami Saarinen) arp/obs_preprocs/readoba.F90 &

odb/cma2odb/shuffle_odb.F90, export BASETIME=YYYYMMHHDD.

• Spectral mixing of large scale information from first lateral boundary file enabled through LSMIXBC option (first step towards Jk-large scale constraint) (Ole Vignes). (xmix(m,n,l)=wbcxbc(m,n,l)+(1-wbc)xown(m,n,l))

Conclusions and Future Plans

• Verification scores indicate that the qualiy of SMHI ALARO upper air forecasts are at least as good as the quality of SMHI HIRLAM upper air forecasts.

• SMHI ALARO 10 m wind speeds too low and winter time 2m-temperatures too warm.

• SMHI system will be updated to next HARMONIE version (36h1.4) with recent developments for handling ODB empty pools, improved handling of snow and option for LSBCMIX (to be tested in SMHI system).

• Further experiments with modified surface data assimilation in OImain to be carried out, utilising updated version.

• AMSU-B, radar radial winds and later on ground based GPS to be introduced and evaluated (follow work by others on IASI brightness temperatures and radar reflectivities).

• Sensitivity studies to VarBC settings and wide extension zone.• Potential application of EKF for surface data assimilation, Flake lake

model, RUC and 4D-Var are in the longer term plans. Follow DMI work on varying structure functions.

• Close co-operation with met.no towards common operational HARMONIE system in 2014.

Proposed domains for operational SMHI-met.no system in 2014

~5.5 km hor res (1212*1360 gp), ~65 vert levels ~2.5 km hor res (1134*1720 gp), ~65-90 vert levels