International Typhoon Workshop Tokyo 2009
Slide 1
Slide 1
Impact of increased satellite data density
in sensitive areas
Carla Cardinali, Peter Bauer, Roberto Buizza, Jean-Noël ThépautECMWF
Florian Harnisch and Martin WeissmannDLR
Many thanks to Fernando Prates
International Typhoon Workshop Tokyo 2009
Slide 2
Slide 2
Background• Thinning of data is applied to:
- reduce data volume- avoid the introduction of spatial observation error correlation that is
currently not accounted for in data assimilation algorithm • Thinning is performed statically on a fixed latitude/longitude grid.
Objective Evaluate impact of selective satellite observational data thinning on medium-range
NWP aiming at denser data in sensitive areas and less dense data in other areas trade-off between data impact and data volume.
Approach• Experiments with global data thinning:
- Change global latitude/longitude thinning grid.• Experiments with data thinning in selected regions:
- Increased density in sensitive areas and reduced density elsewhere using a Singular Vector based measure to identify areas from which forecast errors are growing fast (ECMWF 2007 QJ papers).
- Sensitive areas are computed for Southern Hemisphere
Study contents
International Typhoon Workshop Tokyo 2009
Slide 3
Slide 3
• More dense satellite data coverage on SV-areas in the Southern Hemisphere
• More dense data coverage in SV-areas in Typhoon areas
• Typhoon Track Forecast impact assessment
• Forecast sensitivity to evaluate the 24-hour forecast impact
Outline
International Typhoon Workshop Tokyo 2009
Slide 4
Slide 4
Southern Hemisphere Experiments
Selective data thinning• Thinn_Cntrl : ~ is 1.25o proxy for Thinn_1.25• Thinn_SV : ~ is 1.25o and 0.625o in SV areas.• Thinn_RD : ~ is 1.25o and 0.625o in randomly distributed areas.• Thinn_CSV : ~ is 1.25o and 0.625o in Climatological SV areas.• Thinn_0.625
Additional information• All experiments are run at T511L91 (12-hour 4D-Var) for 01/12/2008-
28/02/2009.• All experiments are verified with T799L91 operational model analyses (without
first 7 days (spin-up) i.e. 83 cases).• All SV/RD/CSV areas occupy same fraction (15%) of the Southern hemisphere.• The SV-based climatology derived from the mean 2007 SV-areas.
International Typhoon Workshop Tokyo 2009
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Slide 5
Data coverage: Single case01/01/2009 00 UTCdata density AMSU-A channel 9:
Singular Vectors:
Randomly distributed circular areas:
2007 Singular Vector climatology:
International Typhoon Workshop Tokyo 2009
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Slide 6
Data coverage: Average01-07/01/2009 00 and 12 UTC data density AMSU-A channel 9:
Singular Vectors:
Randomly distributed circular areas:
2007 Singular Vector climatology:
International Typhoon Workshop Tokyo 2009
Slide 7
Slide 7
Selective data thinning: DFSDecrease of DFS relative to the Thin_0.625 experiment
0 10 20 30 40 50 60 70 80 90 100
Thin_0.6
SV
RD
C_sv
Thin_1.2
DFS (%)0 10 20 30 40 50 60 70 80 90 100
Thin_0.6
SV
RD
C_sv
Thin_1.2
DFS (%)
Global Southern Hemisphere
International Typhoon Workshop Tokyo 2009
Slide 8
Slide 8
Selective data thinning: Forecast impact SV-CNTRLSouthern H. Normalized RMSE 95% confidence 83 cases
1000 hPa
500 hPa
200 hPa
0 1 2 3 4 5 6 7 8 Forecast Day
International Typhoon Workshop Tokyo 2009
Slide 9
Slide 9
Selective data thinning: Forecast impact SV-RDSouthern H. Normalized RMSE 95% confidence 83 cases
1000 hPa
500 hPa
200 hPa
0 1 2 3 4 5 6 7 8 Forecast Day
International Typhoon Workshop Tokyo 2009
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Slide 10
Selective data thinning: Forecast impact SV - CSVSouthern H. Normalized RMSE 95% confidence 83 cases
1000 hPa
500 hPa
200 hPa
0 1 2 3 4 5 6 7 8 Forecast Day
International Typhoon Workshop Tokyo 2009
Slide 11
Slide 11
Sinlaku: Track forecast between 00 UTC 09 - 19 Sept.last forecast verification time 12 UTC 20 Sept.(classified as extra-tropical in best track data from 00 UTC 21 Sept)
Hagupit: track forecast between 00 UTC 20 - 24 Sept.last forecast verification time 00 UTC 25 Sept.(dispersing over land, tropical depression from 00 UTC 25 Sept)
Jangmi: track forecast between 00 UTC 25 - 29 Sept.last forecast verification time 12 UTC 30 Sept.(classified as extra-tropical in best track data from 00 UTC 01 Oct)
Typhoons TPARK campaign Summer 2008
International Typhoon Workshop Tokyo 2009
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Slide 12
Targeting Typhoon season with extra-satellite data
Selective data thinning experiments
• Cntrl : 1.25o Global • SV-Sat: 1.25o Global and 0.625o in SV areas.• Drop : 1.25o Global +Targeted Dropsondes• SV-Sat-Drop: Targeted Dropsondes+ SV areas 0.625o
Additional information
• All experiments are run at T799TL95/159/255 L91 (12-hour 4D-Var) • 06-30 September 2008• Verification and SV-target region 10-50N, 110-180E• 20 Leading T95L62 SV• SVs area cover 20% of the target region
International Typhoon Workshop Tokyo 2009
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Slide 13
Targeting Typhoon season with extra-satellite data: SV-areas
International Typhoon Workshop Tokyo 2009
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Slide 14
09 + 1011 Sept
SV-S
at +
Dro
p
cntrl
Sinlaku 09-19 September: mean track error km
Dro
p
cntr
cntrl
SV -S
at
International Typhoon Workshop Tokyo 2009
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Slide 15
intensification
1
2
3
International Typhoon Workshop Tokyo 2009
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CNTRL very accurate track forecast : difficult to improve
Hagupit 20-24 September
International Typhoon Workshop Tokyo 2009
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Slide 17
cntrl
SV-S
at +
Dro
pSV
-Sat
cntrl
Dro
p
cntrl
Hagupit 20-24 September
International Typhoon Workshop Tokyo 2009
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Slide 18
Difficult to determine TC position over land
Jangmi 25-27 September
International Typhoon Workshop Tokyo 2009
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Slide 19
cntrl
SV-S
at+D
rop
Dro
p
cntrl
SV-S
at
cntrl
Jangmi 25-27 September
International Typhoon Workshop Tokyo 2009
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Slide 20
Forecast sensitivity to observation
The tool provides information on the observation type, subtype, variable and level responsible for the forecast error variation
, ,a
a
J JJ
xy y
y y x
Forecast error
KT
Observation departure
International Typhoon Workshop Tokyo 2009
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Slide 21
Forecast Sensitivity to Obs: SV-Sat+Drop
International Typhoon Workshop Tokyo 2009
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Slide 22
Forecast Sensitivity to Obs: SV-Sat+Drop
Forecast error andVerifying analysis
International Typhoon Workshop Tokyo 2009
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Slide 23
ConclusionsSelective data thinning• Forecast scores are best for experiment with increased data density in SV-based
areas that are updated for each analysis.• 40% loss of DFS by increasing the data density over SV areas instead than
globally.
Targeting Typhoon with extra satellite data• Limited statistical sample• Extra-satellite data gave a more consistent impact due to homogeneous coverage
and data diversity (moist, temperature, cloud, precipitation and surface wind)
Forecast Sensitivity To Observation (FSO)• The forecast value per Observation shows that dropsondes are more beneficial
that extra-radiances• Strong impact per dropsonde produces more extreme beneficial/detrimental
impact• Computation of forecast error by using observation instead of analysis field is
likely to shows larger dropsonde impact on typhoon.
International Typhoon Workshop Tokyo 2009
Slide 24
Slide 24
Forecast sensitivity to observation: Equations and Solution
a b bx = x + K(y - Hx )Analysis solution
Analysis sensitivity to observation and background
a
a
J J
x
y y x
J is a measure of the forecast error: energy norm Forecast error sensitivity to the analysis
a
Jx
( )bJ JJ
y y Hxy y
1
a
J J
R HA
y x
The tool providesinformation on the observation type, subtype, variable and level responsible for the forecast error variation
Rabier F, et al. 1996.
Solve the linear system:Compute the δJ
1Ta
x K R HAy