adjoint sensitivity tool applied to satellite observations over land sangwon joo visiting scientist...
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Adjoint sensitivity tool applied to satellite observations over land
Sangwon JooVisiting Scientist at Met-office from Korea Meteorological Administration
Thanks to Richard Marriott, Ed Pavelin, James Cameron, Brett Candy, and John Eyre
Motivation and purposeContribution of radiance data on the forecast error reduction
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100101_qu00 100101_qu06 100101_qu12 100101_qu18
Date
Perc
enta
ge[%
]
TEMP ATOVS IASI Radiance
Observation Impact 100101 qu00-qu18
-2.5
-2
-1.5
-1
-0.5
0To
tal I
mpa
ct[J
/kg]
IASI_LAND IASI_SEA ATOVS_LAND ATOVS_SEA TEMP
Met Office IASI channel selection
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0 500 1000 1500 2000 2500
Wave number(cm-1)
Pea
k T
Jaco
bian
(hP
a)
All Channels Land Reject Chennals
• Radiance data contributes more in reducing forecast error than TEMP globally but the radiance data is not effective over land because most low peaking channels are not used with the difficulties of specifying the surface conditions accurately
• A new land surface emissivity has developed to make use of the low level peaking channels over land at Met Office(Ed Pavelin) and it is necessary to identify which information is improving the forecast accuracy and which is not for further use of the land radiance data.
With the help of adjoint sensitivity, the contribution of land satellite data is investigated quantitative depending on channels and area.
Relative observation Impact
LandTEMP
Radiance
Land Surface Emissivity
Fji
nch
jj
Fi FA
1
: SSE functional SpectraFji : Eigen vector jF
Training Data Set: UCSB MODIS surface emissivity database
Select 12 leading PCs to represent SSE
Background from Atlas
(Reference : Zhou et al.(2010) and Ed Pavelin)
Retrieval from 1dVar xHyxHyxxxxJ T
bT
b 11 OB
SSE is included as a background and retrieved with other state variables
,
Observation Impact
fbtw
Tfbtwfa
tw
TfatwJ CC
Penalty Function of J = Decrease of the energy norm error due to analysis
t
fatxfbtx
atx
fatw fb
tw
Observation impact calculate an aspect of forecast error reduction due to analysis
TT
o
TooTo
w
J
y
wyyyimpactobs
ˆ fbt
fat www
(Reference : VSDP 63)
Negative value of observation impact implies error reduction of forecast and it is referred as a positive observation impact in this presentation
0tht 6
atx 0
btx 0
Experiment Design
Name Land emissivity Channels Purpose
Cntl 0.98 (operation) Operation Reproduce Operation
Exp1 New SSE for IASI IASI window Ch at land Iasi Impact over land
Experiment Period: 2010.6.1.18UTC ~ 2010. 6. 7. 12UTC(6 hourly)
Experiments Name:
Met Office IASI channel selection
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Wave number(cm-1)
Pea
k T
Jac
ob
ian
(hP
a)
All Channels Land Reject Chennals
Observation Impact: 24 hour forecast error reduction of the tropospheric global dry energy norm by mass[J/kg]
SSE 146
• Surface emissivity for window channel is decreased over the desert area.
• Large variation over the Sahara, Arabian desert, the Himalaya and Australia.
• Low emissivity area is slightly shifted northward over Australia
http://geology.com/records/sahara-desert-map-1.gif
Observation Impact of each observation
Land
Sea
• Satellite data shows strong positive impact (negative value) over land and sea in Exp1 except ATOVS data over land.
• The new emissivity is used to simulate IASI data over land only. But it is assumed other satellite data also has a benefit from better background caused by better use of IASI data over land.
IASI=-1.420J/kg
AIRS
IASIAIRS
IASI AIRS
IASI=-0.975J/kg
AIRS
ATOVS
Cntl Exp1
Percentage contribution of observation
• Satellite data covers 59% of observation impact in the Exp1 and 57% in the Cntl.
• Radiance data contribution over ocean increases from 38% to 41%.
• However satellite contribution over land is slightly decreased from Cntl(8.7%) to Exp1(8.6% ) and it is mainly by ATOVS (6.0% 4.6%).
Exp1 Total Impact Ratio
23%
11%
7%5%3%1%5%2%1%1%1%1%0%0%
16%
10%
10% 3%
1%
0%
0%
Sea_ATOVS
Sea_MetOp2_(A)_IASI
Sea_EOS2_AIRS_AIRS
Land_ATOVS
Land_MetOp2_(A)_IASI
Land_EOS2_AIRS_AIRS
GOES
ASCAT
MSG
F16_SSMIS
ESA
JMA
WINDSAT
ERS
SYNOP
TEMP
Aircraft
BUOY
PILOT
SHIP
BOGUS
Cntl Total Impact Ratio
23%
9%
6%6%
2%
1%
5%2%1%1%1%0%0%0%
19%
10%
9%
3%
1%
1%
0%
Sea_ATOVS
Sea_MetOp2_(A)_IASI
Sea_EOS2_AIRS_AIRS
Land_ATOVS
Land_MetOp2_(A)_IASI
Land_EOS2_AIRS_AIRS
GOES
ASCAT
MSG
ESA
JMA
WINDSAT
F16_SSMIS
ERS
SYNOP
TEMP
Aircraft
BUOY
PILOT
SHIP
BOGUS
57%
59%38% 41%
8%8%
Why the ATOVS contribution is deceased over land?
Cntl
The observation impact of ATOVS over land at the Cntl is strikingly large ( 9 times larger than nomal) at 18UTC 5 June.
The large observation impact of the land ATOVS located at a few point of the edge of Antarctica
It makes the observation impact at the Cntl larger than Exp1 and it results in reduction of the observation impact of ATOVS at Exp1 run
2010060518
Exp1
Mean Observation Sensitivity(110E-120E)
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latitude
Se
ns
itiv
ity
[J/k
g/o
bs
un
it] AMSUA 6 AMSUA 7 SYNOP
Super-Sensitivity
Assimilated Data Records(110E-120E)
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latitude
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er
AMSUA 6 AMSUA 7 SYNOP
Baker and Daley(2000)“Specifically, the observation sensitivity is maximized when the length-scale of the analysis sensitivity gradient is similar to the background-error correlation length-scale, and the observations are assumed to be accurate relative to the background. Under these conditions, when the observation density is low or there is an abrupt change in observation density, the magnitudes of the observation and/or background sensitivities may
greatly exceed the analysis sensitivity. We have defined this phenomenon as ‘super-sensitivity” quoted from Baker and Daley(200)
How to deal with the super-sensitivity?
• Super-sensitivity depends on case such as data density, the ratio between length scales of analysis sensitivity and the background error correlation length.
• In application of the adjoint sensitivity tool, the super sensitivity is shown sometimes at coast regions and not easy to interpret it properly because only a few observations dominate all the other observations.
• When the super-sensitivity data is ignored, the land ATOVS observation shows similar between Exp1 and Cntl run.
Cntl Exp1
ATOVS ATOVS
Land
IASIAIRS
IASIAIRS
Forecast Error ReductionTime Series of Energy Norm Error Reduction
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-2.6
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-1.8
60118 60218 60318 60418 60518 60618
Date
J(J
/kg
)
Exp1_J Cntl_J
Exp1_J=-2.30178, Cntl_J=-2.29978
24 hour forecast error reduction is slightly better in the Exp1 than the Cntl.
RMS O-B
Level Cntl Exp1Sfc-850 TempT 1.3860(80189) 1.3945 (80176)
850-700 TempT 1.1077 (53133) 1.1109(53131)
700-500 TempT 1.0123 (59577) 1.0141(59584)
500-250 TempT 1.1118 (77356) 1.0141(77361)
250-100 TempT 1.8147 (66607) 1.8153(66607)
100-50 TempT 2.3951(28420) 2.3924 (28414)
50- TempT 3.9849 (38317) 3.9916(38329)
Synop T 1.9704 (394118) 1.9740 (394129)
• Obviously far more IASI data is used over the land with positive impact but no improvement of O-B fit is shown even in the lower level temperature.
• The IASI data may play a less significant role in analysis near RAOB points and it is useful to check O-B fit for the area where no conventional data exists.
A-O(1dVar) IASI Window channel Exp1Cntl
STDV
BIAS
IASI retrievals fit well to the IASI observation in Exp1 and it can improve the surface temperature analysis where there is no in-situ observation such as the Sahara desert.
2.0 1.0
A-O(1dVar) IASI Window channel
STDV
BIAS
Exp1Cntl
• STDV is reduced mostly. However it is still large over the Asia.
• There is negative bias in Asia and positive bias in Africa. However the values are much reduced in Exp1
A-B(1dVar) of IASI TskinExp1Cntl
BIAS
• IASI retrieved skin temperature shows large positive bias compared to the background in Exp1 and it is not reduced during the experiment period.
• IASI pushes to increase the surface temperature with the decreased emissivity in Exp1 but the skin temperature is not affected by the IASI information
• It might be caused by the large observation error over the land relative to the background error for IASI window channels(0.38 in 1dVar, 1.0 in 4dVar).
3.00.5
A-B(1dVar) of IASI TskinExp1
BIAS
• The Exp1 shows positive bias mostly and large positive area coincides well to the desert.
• If the IASI land data used 4dVar with reduced observation error, it can increase the skin temperature over the desert areas.
• It is necessary to check if the model surface temperature has a cold bias.
Cntl
http://geology.com/records/sahara-desert-map-1.gif
Most channels added in the Exp1 contribute to reduce forecast error but window channels degrade the impact.
Contribution of IASI channels over land
Window
Observation Impact of Increased IASI channels in Exp1
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MetDB Channel Number
Pea
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Good Impact Bad Impact
Low level peaking
Exp1Cntl
Water vapour
Adjustment period is needed with the new data
TS of IASI Window ch Total Obs Impact
-6.E-03
-4.E-03
-2.E-03
0.E+00
2.E-03
4.E-03
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Date
To
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t(J/
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Exp1
The window channels degrade the impact at the begging of the experiment but after 4 days cycles it adjust to improve the observation impact.
Observation impact to West Pacific
0-40N, 130-180E
Calculate the observation sensitivity to the forecast error over the West Pacific to see the impact of satellite data over land with new emissivity for North Pacific High development which affect the onset and duration of summer monsoon over the East Asia.
IASI_LandAIRS_Land
ATOVS_Land
• Land satellite radiance data shows almost negligible impact on reducing forecast error over the area of the North Pacific High.
• It might be necessary to extend the forecast hours more than 48 hours to see the impact properly.
Summary• Adjoint based observation impact tool is applied successfully to
evaluate the impact of a satellite data to UM.– Geographic and spectroscopic impact of a satellite data can be assessed
quantitatively. (It can help monitoring and QC)• Satellite data over land reduces short term global forecast error with
improved surface emissivity.– The observation impact of the satellite radiance is increased(57->59%) but the
impact of ATOVS land is decreased and it is assumed to be caused by super-sensitivity.
– Even the new emissivity is applied only for IASI land, it improves the impact over sea and other instrument also.
– The main contribution of the land IASI improvement is from low level peaking channels except window channels, but window channls show positive results after 3 days of the cycle.
• Super-sensitivity should be considered properly to see the impact of each observation.
– Need more works to see the reason of large impact at a few point over coastal area
• It is necessary to adjust error in 4dVar to put the IASI information properly
– The IASI land information is properly affect the 4dvar analysis
Future Works
Applying other forecast aspects
- Humidity norm, Extended forecast hours
Reasoning the negative contribution of IASI land data to forecast error reduction over the Asia
To enhance the impact of IASI data to 4dVar over land for window channels.
Applying the adjoint sensitivity tool for the evaluation of other satellite such as COMS AMV
Investigating how to deal with the super-sensitivity
Thank you for you attention