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Adjoint sensitivity tool applied to satellite observations over land

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.

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Adjoint sensitivity tool applied to satellite observations over land

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  1. 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

  2. Motivation and purpose Relative observation Impact Land Radiance TEMP • 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.

  3. Land Surface Emissivity Background from Atlas Training Data Set: UCSB MODIS surface emissivity database Select 12 leading PCs to represent SSE : Eigen vector , : SSE functional Spectra Retrieval from 1dVar SSE is included as a background and retrieved with other state variables (Reference : Zhou et al.(2010) and Ed Pavelin)

  4. Observation Impact Observation impact calculate an aspect of forecast error reduction due to analysis Penalty Function of J = Decrease of the energy norm error due to analysis 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)

  5. 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:

  6. http://geology.com/records/sahara-desert-map-1.gif 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

  7. Observation Impact of each observation Cntl Exp1 AIRS AIRS Land IASI=-0.975J/kg IASI=-1.420J/kg ATOVS Sea AIRS AIRS IASI 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.

  8. 57% Percentage contribution of observation 38% 59% 41% 8% 8% • 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%).

  9. Exp1 Why the ATOVS contribution is deceased over land? Cntl 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

  10. Baker and Daley(2000) Super-Sensitivity “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)

  11. How to deal with the super-sensitivity? Cntl Exp1 Land AIRS AIRS IASI IASI ATOVS 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.

  12. Forecast Error Reduction Exp1_J=-2.30178, Cntl_J=-2.29978 24 hour forecast error reduction is slightly better in the Exp1 than the Cntl.

  13. RMS O-B • 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.

  14. 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.

  15. A-O(1dVar) IASI Window channel Exp1 Cntl 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

  16. A-B(1dVar) of IASI Tskin Exp1 Cntl 3.0 0.5 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).

  17. http://geology.com/records/sahara-desert-map-1.gif A-B(1dVar) of IASI Tskin Exp1 Cntl 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.

  18. Contribution of IASI channels over land Cntl Exp1 Low level peaking Window Water vapour Most channels added in the Exp1 contribute to reduce forecast error but window channels degrade the impact.

  19. Adjustment period is needed with the new data The window channels degrade the impact at the begging of the experiment but after 4 days cycles it adjust to improve the observation impact.

  20. 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.

  21. 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

  22. 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

  23. Thank you for you attention

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