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NCEO Carbon Land Meeting 2012 Greenhouse Gas Satellite Remote Sensing from GOSAT Robert Parker, Austin Cogan and Hartmut Boesch EOS Group, University of.

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Presentation on theme: "NCEO Carbon Land Meeting 2012 Greenhouse Gas Satellite Remote Sensing from GOSAT Robert Parker, Austin Cogan and Hartmut Boesch EOS Group, University of."— Presentation transcript:

1 NCEO Carbon Land Meeting 2012 Greenhouse Gas Satellite Remote Sensing from GOSAT Robert Parker, Austin Cogan and Hartmut Boesch EOS Group, University of Leicester Thanks to Annmarie Fraser and Liang Feng and Paul Palmer School of GeoSciences, University of Edinburgh

2 GOSAT  First dedicated Greenhouse Gas measuring satellite  Launched on 23 January 2009 by JAXA  Payload carries 2 instruments:  TANSO Fourier Transform Spectrometer (FTS): Provides spectrally- resolved radiances for 4 shortwave-IR (polarized) and thermal-IR bandsProvides spectrally- resolved radiances for 4 shortwave-IR (polarized) and thermal-IR bands Covers several absorption bands of CO 2, CH 4, H 2 O and O 2Covers several absorption bands of CO 2, CH 4, H 2 O and O 2 Cloud aerosol imager (CAI): 4 broadband channels from UV to SWIR with high spatial resolution that provide cloud information4 broadband channels from UV to SWIR with high spatial resolution that provide cloud information

3  UoL FP Algorithm: OCO (ACOS) Full Physics Optimal Estimation algorithm (O’Dell et al., 2011, Boesch et al.,2011) – similar to ACOS algorithm  CO 2 and CH 4 Full Physics Retrieval:  Fit to O 2 A Band, 2.06μm CO 2 Band and 1.61 μm CO 2 or 1.65 μm CH 4 Band  CO 2 / CH 4 profile and scaling factors for CH 4, H 2 O  Extinction profiles of 2 aerosol types and 1 cirrus type  Surface pressure (Surface Pressure renormalisation is applied)  O 2 A Band zero level offset (fluorescence + non-linearity correction)  CH 4 Retrieval (CH 4 /CO 2 proxy):  Fit to CO 2 Band at 1.61μm μm CH 4 Band  Retrieves CO 2 and CH 4 profiles  Modelled aerosol optical depth CH 4 OE Retrieval: Spectral Fit to CH 4 and CO 2 Bands Cloud Screening: O 2 A Band retrieval (Micro window): surface pressure difference < 20 hPa Post-filtering: Quality of Fit (χ 2 ) A Posterior Error Number of Diverging Steps Post-filtering: Quality of Fit (χ 2 ) A Posterior Error Number of Diverging Steps Pre-filtering: SNR > 50, land surface UoL FP Retrieval Post-filtering: Quality of Fit (χ 2 ) A Posterior Error Surface Pressure Bias Aerosol/Cirrus Optical Depth Number of Diverging Steps and a few more Post-filtering: Quality of Fit (χ 2 ) A Posterior Error Surface Pressure Bias Aerosol/Cirrus Optical Depth Number of Diverging Steps and a few more CO 2 (or CH 4 ) OE Retrieval: Simultaneous Spectral Fit to O 2 and two CO 2 (CH 4 ) Bands

4 The “Proxy” Retrieval  Clouds and aerosols can significantly change the light-path and hence the inferred concentrations by scattering light  The proxy approach performs a retrieval of an additional species which travels the same light-path  This ratios out majority of scattering effects  CO 2 is used as proxy gas as it varies far less than methane and is spectrally close For accurate retrieval of CO 2 we need to describe:  Multiple-scattering  Aerosols and Clouds  Polarization  Spherical Geometry  Surface properties  Instrument properties  Solar flux  Gas absorption  Spectroscopy (incl. line- mixing) [Courtesy to C. O’Dell, CSU]  Relies on model CO 2 to convert ratio back to VMR

5 Validation against ground-based TCCON  TCCON (Total carbon column observing network):  Network of ground-based Fourier Transform Spectrometers  Provide precise and accurate total columns of CO 2, CH 4 and other gases  Columns are calibrated against aircraft in-situ profiles (by applying spectroscopic correction factor)  Validation uses all cloud-free GOSAT overpasses within +/-5 o and 2 hours Calibration against in-situ profiles (Wunch et al., 2010)

6 Validation of GOSAT Proxy XCH 4  Correlations typically between 0.4 and 0.7  Estimated single-sounding precision between ppb  Site-dependent bias between -0.6 and 8 ppb

7 Global Annual GOSAT X CH4

8 Global Monthly GOSAT X CH4 (Proxy)  Key features  India/China – September – Rice paddies  Alaska/Boreal Asia – NH Summer – Wetlands/Wildfires  Africa/S. America – Biomass burning Updated version of Parker et al., 2011 GRL

9 Regional comparison of GOSAT with GEOS-Chem Updated version of Parker et al., 2011 GRL

10 Validation of GOSAT X CO2 Cogan et al., Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite: Comparison with ground-based TCCON observations and model comparison, ACP, Paper in prep.

11 Regional GOSAT-Model Comparisons GOSAT H + M Gain data included

12 Seasonal GOSAT-Model Comparisons

13 Seasonal GOSAT-Model Comparisons with Bias Correction Bias-correction based on regression against pseudo-observations >25 o S (similar to Wunch et al., 2011)

14  Global multi-year GOSAT CH 4 (proxy method) and CO 2 retrieved at Leicester. Full physics CH 4 retrieval are underway  From validation against TCCON we find  Bias of % with precision of % for proxy CH 4  Bias of % with precision of % for CO 2  Comparisons to GEOS-Chem models show  Very high consistency for methane (r=0.85)  Good agreement (r=0.65) for CO 2 but some apparent biases over Sahara and central Asia which can be reduced by bias-correction methods  Flux inversions (Edinburgh) of GOSAT data underway  Further improvements are expected from implementation of new aerosol scheme for our CO 2 and CH 4 FP retrievals and recent updates to spectroscopy (JPL) and new L1B data (v1.50 data)  Collaborations with several NCEO partners (Edinburgh, RAL, Leeds, KCL) and involvement in number of internat. projects: ESA CCI, FP7 MACC-II, FP7 InGOSSummary

15  Further develop and improve the GHG retrieval (CO 2 and CH 4 and CO 2 /CH 4 ratio) for generation of long-time series from GOSAT.  Continued characterization and validation of the retrieval and interact closely with inverse modelling and land surface modelling groups on the use of the datasets  Expand the retrieval for upcoming (OCO-2 or S5P) and proposed missions (Carbonsat, GOSAT-2, Tansat, TCM) for generation of new datasets and inter- calibration of data from different missions  Challenge land surface models (such as Jules or SDGVM) with regional Name- based modelling studies (currently under development)  Exploit additional capabilities of GOSAT/OCO-like sensor: plant fluorescence, aerosol vertical distribution, H 2 O profiles over land Phase 2 Activity


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