Assimilation of GPS radio-occultation observations at Meteo-France - CNRM-GAME, Nathalie Saint-Ramond With contributions from Vincent Guidard ...
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Assimilation of GPS radio-occultation
observations at Meteo-France
Nathalie Saint-Ramond
With contributions from
Vincent Guidard
Pierre Brousseau
CNRM-GAME,
Météo-France and CNRS
OPAC-IROWG Sept. 2013, Leibnitz, Austria
Outline
GPS RO operational assimilation and recent change in the vertical
thinning
Test of GPSRO in the non-hydrostatic limited area model AROME
Impacts of different groups of observations
Summary and Outlook
2
GPS RO operational assimilation
Assimilated in the global 4DVAR : ARPEGE (collaboration with ECMWF)
and LAM 3DVAR : ALADIN
since september 2007
Bending angles
Rising and setting occultations
Up to 46 km
Accounting for tangent point drift
Screening tests
Horizontal thinning
No vertical thinning
T798 C2.4 L70
3
GPS RO operational assimilation 4
GPS RO operational assimilation
Change of
operationnal model
2 July 2013
5
GPS RO operational assimilation
AFTER July 2
AIRS No vertical thinning
ATOVS
0.82%
ATMS
AIRCRAFTS
AIRS
IASI
ATOVS
4.9%
IASI
CRIS
BEFORE July 2
With vertical thinning
6
GPSRO vertical thinning
Fraction of used observations:
L6
not used
used
L7
Before:
Only 1 obs per model level was
used
After:
All observations used
7
GPSRO vertical thinning
departure statistics of the bending angles
BIAS AFTER
RMS BEFORE
(o-b)/b
(o-a)/a
% %
Scores for the model T wrt ECMWF and radiosondes:
AFTER
T / RS T/ECMWF BEFORE
00H Analyses
(hPa)
00H
RMS
BIAS
(K) (K)
8
GPSRO impact on 24h forecast: FSO
With vertical thinning : BEFORE Without vertical thinning : AFTER
Impact depending on height
9
Outline
GPS RO operational assimilation and change in the vertical thinning
Test of GPSRO in the non-hydrostatic limited area model AROME
Impacts of different groups of observations
Summary and Outlook
10Tests in the non-hydrostatic model AROME
Model description:
-2.5 km horizontal resolution
-60 levels
-Non-hydrostatic
-3DVAR assimilation system :
-Same data available as for
ARPEGE
-Fine scale data from radar
network
ARPEGE 10km AROME 2.5 km
11Tests in the non-hydrostatic model AROME
Results on rain 24h forecasts:
Probability Of Detection
OPER TEST
0.5mm
2mm 5mm
10mm
False Alarm Rate
OPER TEST
10mm
2mm 5mm
0.5mm
12Outline
GPS RO operational assimilation and change in the vertical thinning
Test of GPSRO in the non-hydrostatic limited area model AROME
Impacts of different groups of observations
Summary and Outlook
13ARPEGE ANALYSIS Base Best config. to provide the forecast in time 14 Credit for Météo-France Dprévi/COMPAS
ARPEGE ANALYSIS Base Best config. to provide the forecast in time 15 Credit for Météo-France Dprévi/COMPAS
Objectives Estimate the impact of observations in the production cycle of 00 UTC Compare this estimation with other methods Answer to data providers (CNES, EUMETSAT, etc.) about the contribution of their data during situations with high stakes Study performed by Vincent Guidard 16
Experiments
Period : from 24 Jan. 2013 to 28 Feb. 2013
Context: ARPEGE operationnal model (cy37t1)
Experiments:
- Reference = OPER
- No Assim = no data
- Baseline = in situ data + satellite winds
- Baseline + GNSS RO + SCATT
- Baseline + AIRS + IASI
- Baseline + AMSU-A + AMSU-B/MHS
17Impact on temperature D+1 18
Impact on humidity D+1 19
Impact on geopotential height D+1 20
Summary of the different contributions
to D+1 forecast
Reduction of the Forecast error standard deviation, in percentage
Temperature Relative Humidity Geopotential Number
Baseline 55 % 50 % 40 % 20 %
Baseline 65 % 56 % 48 % 22 %
+ GNSS-RO + SCATT
Baseline 83 % 75 % 88 % 35 %
+ AMSUA + AMSUB
Baseline 71 % 64 % 52 % 76 %
+ AIRS + IASI
Reference 100 % 100 % 100 % 100 %
21Summary of the contributions to D+1
Reduction of the Forecast error
standard deviation, in percentage
Temperature Relative Humidity Geopotential FSO
Baseline 55 % 50 % 40 % 34 %
Baseline 65 % 56 % 48 % 43 %
+ GNSS-RO + SCATT
Baseline 83 % 75 % 88 % 68 %
+ AMSUA + AMSUB
Baseline 71 % 64 % 52 % 53 %
+ AIRS + IASI
Reference 100 % 100 % 100 % 100 %
22Summary of the contributions to D+1
Reduction of the Forecast error
standard deviation, in percentage
Temperature Relative Humidity Geopotential FSO
Baseline 55 % 50 % 40 % 34 %
Baseline 65 % 56 % 48 % 43 %
+ GNSS-RO + SCATT
Baseline 83 % 75 % 88 % 68 %
+ AMSUA + AMSUB
Baseline 71 % 64 % 52 % 53 %
+ AIRS + IASI
Reference 100 % 100 % 100 % 100 %
Good agreement !
23Summary of the contributions to D+1
Reduction of the Forecast error
standard deviation, in percentage
Temperature Relative Humidity Geopotential FSO
Baseline 55 % 50 % 40 % 34 %
Baseline 65 % 56 % 48 % 43 %
+ GNSS-RO + SCATT
Baseline 83 % 75 % 88 % 68 %
+ AMSUA + AMSUB
Baseline 71 % 64 % 52 % 53 %
+ AIRS + IASI
Reference 100 % 100 % 100 % 100 %
Under estimation of AMSU-A and
AMSU-B impact with the FSO …
24
calculation with dry energy !Impact on temperature D+1
AIRS+IASI degrade the
baseline
AMSUA+AMSUB have
a very variable impact
GNSSRO (+SCATT),
associated with the
baseline: better result
than the complete
system !!
25Summary and Outlook
GPS RO operationaly assimilated since 2007 in global and LA Models
1D + AD/TL from ROM-SAF
Removing the vertical thinnning has proven to have a good impact on
model performences with observation errors adjusted
Impact in the non-hydrostatic model AROME is not clear for the
moment.
Good impact on upper troposphere / lower stratosphere but also at
higher levels where few other observations are assimilated.
The assimilation of radiances (AIRS, IASI, AMSU) at high levels
needs some improvements: observation errors, bias correction,
chanel selection, need to be tuned
26Thank you
Other modifications in the new model
New observations:
CrIs: Infrared sounder on Suomi NPP
ATMS: microwave sounder on Suomi NPP
Metop-B instruments :
IASI (more WV channels, observation error reduced)
AMSU-A
MHS
GRAS
OSCAT on OceanSat-2: good impact on cyclone forecast over « La Réunion »
(observation error aslo modified)
Radiances from GOES-13 and GOES-15: 1 high tropospheric channel
More channels from MHS on NOAA-19
More ground GPS
Observation errors in the models were tuned in order to balance the
minimisation, using Desroziers’s diagnosis: modified by a factor
0.75 for SYNOP, AIRCRAFTS, SATWIND, BUOYS, RS, PILOT
1.35 for GPS RO
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