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ADEOS-II

ADEOS-II. 2. Stratospheric aerosol and cloud characterization from ILAS observations (extended). Sergey Oshchepkov Yasuhiro Sasano Tatsuya Yokota Hideaki Nakajima Satellite Remote Sensing Research Team, National Institute for Environmental Studies , Tsukuba, JAPAN

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ADEOS-II

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  1. ADEOS-II

  2. 2 Stratospheric aerosol and cloud characterization from ILAS observations(extended) Sergey Oshchepkov Yasuhiro Sasano Tatsuya Yokota Hideaki Nakajima Satellite Remote Sensing Research Team, National Institute for Environmental Studies, Tsukuba, JAPAN FUJITSU FIP Corporation, Tokyo, JAPAN Limb Workshop, Bremen, 14-16 April 2003

  3. 3 What is the main importance of the aerosol retrievals for the ILAS? • First of all, For the accurate gas retrievals The point is that Infrared transmission spectra are affected by aerosol components which is especially true in the presence of PSC • On the other hand, The aerosol information is of great interest in itself in studying various heterogeneous reactions related to ozone depletion, etc. Limb Workshop, Bremen, 14-16 April 2003

  4. 4 Sketch of the talk Very short ILAS / ILAS-II overview (toward to simultaneous gas and aerosol retrievals) Classification of aerosol retrievals for the ILAS / ILAS-II data processing Short description of each method Representative results Limb Workshop, Bremen, 14-16 April 2003

  5. Short ILAS/ILAS-II overview (time period and main targets) 5 Both apparatus operate in the Solar occultation mode - ILAS … was on board ADEOS (satellite) for the period from November 1996 to June 1997 - ILAS-II is currently on board ADEOS-II and has been launched in December 14th, 2002) The main target is to derive vertical profiles of O3, HNO3, CH4, H2O, N2O, NO2, CFC-11, CFC-12, ClONO2, aerosol (related to ozone depletion) Limb Workshop, Bremen, 14-16 April 2003

  6. Short ILAS/ILAS-II overview 6 (Observation Region) N: 57-730 , S: 64-900 Expected ILAS-II 2D map in a day

  7. Short ILAS/ILAS-II overview (spectral coverage) 7 ILAS ILAS-II has two additional channels Spectral coverage: Ch.1:   6.21 - 11.76 µm (44) Ch.2:    753-  784 nm Altitude resolution:1 km Altitude observation:10-60 km Spectral coverage: Ch.1:   6.21 - 11.76 µm (44)Ch.2:3.0-  5.7 µm(22) Ch.3:12.78-  12.85 µm Ch.2:    753-  784 nm Limb Workshop, Bremen, 14-16 April 2003

  8. 8 Gas Optical Depth for layer of 20km The main advantage of ILAS-IIversusILASdue to additional IR spectral channels from 3.0 to 5.7 µm is CO2CO Additional retrievals! N2O H2OCH4 Retrieval improvements!

  9. 9 Classification of aerosol correction or retrievals using ILAS/ ILAS-II observations(both in the operational data processing and for the future algorithms) • Linear interpolation technique • Non linear interpolation using smoothness constraints • Simultaneous gas and aerosol retrievals using aerosol physical modeling - Simple gas Window Channel analysis for aerosol retrievals - All channels analysis for simultaneous gas and aerosol retrievals

  10. 10 Linear interpolation technique(the simplest way to take aerosol contribution into account) To realize the technique 1. The aerosol optical depth is estimated at the gas window channels; 2. Climatological gas data set is used to subtract the remainder gas contribution; 3. Then, linear interpolation is utilized to estimate the optical depth over all channels. The main problem is The broken line is not representative enough to describe real aerosol spectra

  11. 11 In this method,gas concentrationsandaerosol extinction coefficient spectraare subjects of simultaneous retrievals Non linear “interpolation” method The Chi square minimization procedure is extended here by additional term of transmission data term of smoothness constrains on desired aerosol spectra The good point: there is no need to make aerosol physical modeling The bad point: it is quitedifficult to choose appropriate level of smoothness constraints for low spectral resolution

  12. 12 Method of simultaneous gas and aerosol retrievals using aerosol physical modeling(set up for the inverse problem) In both techniques the AEC is presented through linear combination of the basic aerosol components: Then, subject of the retrievals is aerosol volume density profile at a given specific spectrum for each component The main question is how to select representative aerosol component the inverse modeling? 1. representative & 2. retrievable

  13. 13 Method of simultaneous gas and aerosol retrievals using aerosol physical modeling(component selection) 220K Supercooled Ternary Solution (0.075-1µm) 190K Nitric Acid Trihydrate (0.5-2µm) Nitric Acid Dihydrate (0.5-2µm) (Rm) Water Ice (1-5 µm) All of these component are retrieved simultaneously in rather a wide range of particle sizes There are some physical assumptions like temperature dependence of size and component composition for both internal and external mixture

  14. 14 12 Basic aerosol spectral functions used in the inverse modeling The simplest explanation to recognize between selected components is their different spectral features

  15. 15 Aerosol retrievals using aerosol physical modeling(Window Channel Analysis) Using these base function, the numerical procedure constitutes weighted least square minimization procedure with smoothness and positive constraints on the desired aerosol volume vertical profiles The good point: is very rapid data processing with small amount of aerosol look up table The bad point: is the window channel data might be affected by gas contribution and limited … that is the reason

  16. 16 Method of simultaneous gas and aerosol retrievals from the total transmission measurements is priority!!! In this method, a set up for the inverse problem involves simultaneous retrievals of 14 GASES and 4 types of AEROSOL O3 HNO3 CH4 H2O, N2O NO2 CFC-11 CFC-12 ClONO2 N2O5 CO2 CO OCS N2O5 Supercooled Ternary Solution (weights of Nitric & Sulfuric acid are available) Nitric Acid Trihydrate, Nitric Acid Dihydrate, Water Ice Among other features, the numerical solution involves: - Levenberg-Marquardt iteration procedure - Positive constraints for both gas and aerosol profiles (through penalty functions) - Onion peeling technique - HITRAN database for gas cross sections

  17. 17 Aerosol retrievals from Window Channel Data(An example of background aerosol) The retrieval volume density profiles (black lines) are supported here by error bars and independent equilibrium thermodynamic predictions (red symbols) using Carslaw modeling. … this is the first time such agreement has been obtained.

  18. 18 Aerosol retrievalsfrom Window Channel Data(Two typical examples of PSC state) First one represents mostly NAD In the second, no preferable component One of the reason for the multicomponent composition is that the occultation measurement has rather a long path length and PSC are subject to to high horizontal chemical inhomogeneity. As to the comparison with thermodynamic predictions, the agreement could be acceptable since we have to be careful to apply thermodynamic equilibriums to PSC.

  19. 19 Gas retrievals from all IR Channels (PSC state) - Denotations - All gases are affected by PSC - H2O For the simultaneous gas and aerosol … Jacobean (onion peeling …)

  20. 20 CONCLUSSIONS • Three methods have been developed for aerosol retrievals using the ILAS / ILAS-II data • The methods are shown to be very important both for accurate gas retrievals and aerosol characterization Limb Workshop, Bremen, 14-16 April 2003

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