Adjoint sensitivity tool applied to satellite
observations over land
Sangwon Joo
Visiting Scientist at Met-office from Korea Meteorological Administration
(sangwon.joo@metoffice.gov.uk)
Thanks to Richard Marriott, Ed Pavelin, James Cameron, Brett Candy,
and John Eyre
Motivation and purpose
Contribution
of radiance
data on the forecast
error reduction
Relative
observation
Impact
ATOVS
IASI
80
Percentage[%]
60
All Channels
Radiance
TEMP
Land
-2.5
50
40
30
20
10
Land Reject Chennals
10
110
210
-2
Total Impact[J/kg]
70
Met Office IASI channel selection
Observation Impact 100101 qu00-qu18
Radiance
Peak T Jacobian(hPa)
TEMP
-1.5
-1
310
410
510
610
710
810
-0.5
910
0
100101_qu00
100101_qu06
100101_qu12
-10
Date
100101_qu18
1010
0
IASI_LAND
0
IASI_SEA
ATOVS_LAND
ATOVS_SEA
TEMP
500
1000
1500
2000
Wave number(cm-1)
• 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.
2500
Land Surface Emissivity
Background from Atlas
Training Data Set: UCSB MODIS surface emissivity database
Select 12 leading PCs to represent SSE
nch
AiF   F j   Fji , F j   : SSE functional Spectra
j 1
 Fji : Eigen vector
Retrieval from 1dVar
J  x  xb  B1 x  xb    y  H x O1  y  H x
T
T
SSE is included as a background and retrieved with other state variables
(Reference : Zhou et al.(2010) and Ed Pavelin)
Observation Impact
Observation impact calculate an aspect of forecast error reduction due to analysis
xta
xta0
xtfa
xtb0
wtfa
wtfb
xt fb
t  6h
t0
t
Penalty Function of J = Decrease of the energy norm error due to analysis
T
T
fa
fa
fb
fb
J   w  Cw   w  Cw
t
t
 t 
 t 
obs impact  y

o T
yˆ o  y

o T
T
 w   J 
 o  


y


w


 
T
w  wtfa  wtfb
Negative value of observation impact implies error reduction of forecast and it is
referred as a positive observation impact in this presentation
(Reference : VSDP 63)
Experiment Design
Observation Impact: 24 hour forecast error reduction of the tropospheric
global dry energy norm by mass[J/kg]
Experiment Period: 2010.6.1.18UTC ~ 2010. 6. 7. 12UTC(6 hourly)
Experiments Name:
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
Met Office IASI channel selection
All Channels
Land Reject Chennals
10
110
Peak T Jacobian(hPa)
210
310
410
510
610
710
810
910
1010
0
500
1000
1500
Wave number(cm-1)
2000
2500
SSE 146
http://geology.com/records/sahara-desert-map-1
• 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
Observation Impact of each observation
Cntl
Exp1
AIRS
Land
AIRS
IASI=-0.975J/kg
IASI=-1.420J/kg
ATOVS
Sea
IASI
AIRS
AIRS
IASI
• 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.
Percentage contribution of observation
Cntl Total Impact Ratio
Sea_ATOVS
Sea_MetOp2_(A)_IASI
Sea_MetOp2_(A)_IASI
1%
Land_ATOVS
Land_MetOp2_(A)_IASI
Land_MetOp2_(A)_IASI
1%
ASCAT
9%
10%
MSG
23%
10%
ESA
9%
19%
6%
6%
5%
2%
1%
Land_EOS2_AIRS_AIRS
0%
GOES
3%
Sea_EOS2_AIRS_AIRS
Land_ATOVS
Land_EOS2_AIRS_AIRS
1%
0%
0%
0%
1%
1%
1%2%
Exp1 Total Impact Ratio
Sea_EOS2_AIRS_AIRS
0%
57%
Sea_ATOVS
GOES
0%
ASCAT
3%
F16_SSMIS
10%
JMA
ESA
WINDSAT
JMA
F16_SSMIS
38% ERS
SYNOP
11%
59%
16%
0%
0%
1%
1%
1%
1%2%
5%
1% 3%
5%
7%
TEMP
8%
MSG
23%
ERS
SYNOP
TEMP
Aircraft
BUOY
41%
WINDSAT
Aircraft
8%
BUOY
PILOT
PILOT
SHIP
SHIP
BOGUS
BOGUS
• 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%).
Why the ATOVS contribution is
deceased over land?
Cntl
Exp1
2010060518
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
Super-Sensitivity
Mean Observation Sensitivity(110E-120E)
AMSUA 6
AMSUA 7
SYNOP
Sensitivity[J/kg/obs unit]
0.06
0.04
0.02
0
-0.02
-80
-78
-75
-73
-70
-68
-65
-63
-60
-58
-55
-53
-50
-0.04
-0.06
-0.08
-0.1
latitude
Assimilated Data Records(110E-120E)
AMSUA 6
AMSUA 7
SYNOP
91
81
Number
71
61
51
41
31
21
11
1
-80
-77.5
-75
-72.5
-70
-67.5
-65
-62.5
latitude
-60
-57.5
-55
-52.5
-50
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 supersensitivity?
Cntl
Exp1
Land
AIRS
IASI
ATOVS
AIRS
IASI
ATOVS
• 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.
Forecast Error Reduction
Time Series of Energy Norm Error Reduction
Exp1_J
Cntl_J
-3
Exp1_J=-2.30178, Cntl_J=-2.29978
-2.8
J(J/kg)
-2.6
-2.4
-2.2
-2
-1.8
60118
60218
60318
60418
60518
60618
Date
24 hour forecast error reduction is slightly better in the Exp1 than the Cntl.
RMS O-B
Level
Cntl
Exp1
Sfc-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
Exp1
Cntl
2.0
1.0
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.
A-O(1dVar) IASI Window channel
Cntl
Exp1
STDV
BIAS
•
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 Tskin
Exp1
Cntl
0.5
3.0
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).
A-B(1dVar) of IASI Tskin
Cntl
Exp1
BIAS
http://geology.com/records/sahara-desert-map-1.gif
•
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.
Contribution of IASI channels over land
Observation Impact of Increased IASI channels in Exp1
Good Impact
Exp1
110
210
Peak T Jacobian(hPa)
Cntl
Bad Impact
10
310
410
510
610
710
810
910
1010
50
100
150
200
250
300
MetDB Channel Number
Low level
peaking
Window
Water vapour
Most channels added in the Exp1
contribute to reduce forecast error
but window channels degrade the
impact.
Adjustment period is needed with
the new data
TS of IASI Window ch Total Obs Impact
Total Obs Impact(J/kg)
4.E-03
2.E-03
0.E+00
-2.E-03
-4.E-03
Exp1
60
60
6
60
61
8
60
70
6
60
41
8
60
50
6
60
51
8
60
30
6
60
31
8
60
40
6
60
11
8
60
20
6
60
21
8
-6.E-03
Date
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
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.
0-40N, 130-180E
ATOVS_Land
IASI_Land
AIRS_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 supersensitivity.
– 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
To enhance the impact of IASI data to 4dVar over land for window channels.
Reasoning the negative contribution of IASI land data to forecast error reduction
over the Asia
Investigating how to deal with the super-sensitivity
Applying other forecast aspects
- Humidity norm, Extended forecast hours
Applying the adjoint sensitivity tool for the evaluation of other satellite such as
COMS AMV
Thank you for you attention