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, KarlIvar Ivarsson, Martin Ridal
Outline
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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
AEMET
DMI
FMI
KNMI
Met Eirann
met.no
SMHI
Veðurstofa
HARMONIE
DOMAINS
AEMET
DMI
FMI
KNMI
Met Eirann
met.no
SMHI
Veðurstofa
3dvar/can/oim
NWP models at
SMHI
Operational
HIRLAM C22/C11 (4D-VAR)
HIRLAM E11 (3D-VAR)
HIRLAM G05 (3D-VAR)
Pre-Operational
E05 (3D-VAR)
ALARO (3D-VAR)
Daily
AROME (Downscaling)
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
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))
Observation usage
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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)
Surface data assimilation
(SYNOP T2m H2m observations over land)
ECMWF SST, temperature over sea ice from surface temperature in
boundary field, LST from FA file surface temperature climatology
Spatialisation of screen level data
Surface data assimilation
(CANARI OI)
OImain
a b
T
T

1
b
x

x

BH
(
HBH

R
)
(
y

Hx
)
T1 T2m
T2m
T2 
2
T
H

w



T


H
1
1 2
m 1
2
m
T
H

w



T



H
2
2 2
m 2
2
m
T1 , w1
T2 , w2
Scores for verification against observations
April 2011
RMS/BIAS as function of forecast range
ALARO
Surface Pressure (hPa)
E11
E05
T2m (K)
E05(7.3)
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
T (K)
RMS/BIAS averaged over
forecasts length as function
of vertical level
Wind speed (m/s)
ALARO
E11
E05
E05(7.3)
RH (%)
Scores for verification against observations
February 2011
RMS/BIAS as function of forecast range
ALARO
Surface Pressure (hPa)
E11
E05
T2m (K)
E05(7.3)
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
12
UTC
UTC
Scores for verification
against observations
February 2011
T (K)
RMS/BIAS averaged over
forecasts length as function
of vertical level
Wind speed (m/s)
ALARO
E11
E05
E05(7.3)
RH (%)
Monitoring of satellite data
and VarBC
06 h update
24 h update
coverage
00 UTC
06 UTC
12 UTC
A comparison of two off-line soil analysis schemes for
assimilation of screen level observations (Mahfof et al., 2009)
OI-equations
1/(2π)
1/(2)
(ztiner in cactus.F90)
Table of
coefficients

Conclusions
Scores for verification against observations
I month parallel exp, January 2010
RMS/BIAS T2M (K)
as function o
forecast range
ALARO (1/2π)
ALARO 1/2
T2m BIAS/RMS (K) averaged over forecast lengths
ALARO (1/2π)
ALARO 1/2
Experimental set-up
Two parallel exp. for July & August 2009 and January & Febr. 2010:
•
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
Observation time window:
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HH +/- 3 h for 6 h itermittent DA cycle
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HH +/- 1.5 h for 3 h intermittent DA cycle
(no modifications of error statistics or IDFI settings
applied when modifying from 6 h to 3 h cycle)
At 00 and 12 UTC 30 h forecasts were launched
20 Aug 2009 12 UTC
sum6h mslpfc (black, hPa)
sum6h-sum3h psdiff (red, conint 1 hPa)
+6 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
(experiment for January and July ongoing)
4D-Var
3D-Var
14 Day RMS/BIAS time series
500 Temperature (K)
500 hPa Wind Speed (m/s)
Timings 96 processor on SMHI Linux Clustre
3D-Var:~925 s
4D-Var: ~5500 s
30 h forecast: ~5200 s
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
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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 2mtemperatures 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