Summary of the Final Report of Research Results1). Title of the proposed research
Algorithm developments for SWIR FTS GOSAT data processing with regard to atmospheric horizontal
inhomogeneity2). Principal Investigator (PI) and Co-Investigators (Co-Is)
PI:
Dr. Sergey Oshchepkov
Co-I:
Dr. Tatsuya Yokota
Dr. Shamil Maksyutov
Dr. Andrey BrilDr. Igor Polonsky
Dr. Ryu Saito
3). PI’s affiliation
National Institute for Environmental Studies, Tsukuba, Japan4). Summary

In this report we present two basic topics that have been studied in the connection of atmospherichorizontal inhomogeneity when processing GOSAT L1B data.
First topic concerns cloud inhomogeneity within the GOSAT field of view. The GOSAT single
scans that contain thin broken clouds are very difficult for the processing because all GOSAT fullphysics algorithms assume atmospheric horizontal homogeneity when using radiative transfer
modeling for theoretical radiance spectra. To study this phenomenon, we apply PPDF-D method toidentify GOSAT single scans “contaminated” by the clouds over the ocean. Then, we look at theimprovements of the gas retrievals after removing the detected scans. This method was comparedwith TANSO-CAI spatial coherence test that is utilized in NIES operational algorithms. We haveshown that PPDF-based selection provides reliable filtering regarding the number of selected single
scans as well as substantial reduction of column averaged gas amount negative bias and standard
deviations. As a case study, Figure 1 illustrates this statement for July 2009. PPDF-D selection is seenfrom Figure 1 substantially reduce negative bias and standard deviation (right-hand panel) and hassome advantages over TANSO-CAI spatial coherence test with respect to the number of scans
available for the processing and standard deviation (bars).

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PPDF
CAI Spat. Coher. Test

No selection
after CAI
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test

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Figure 1. Counts and latitudinal distribution of XCO2 retrievals with DOAS-based algorithm from CO2absorption bands after CAI spatial coherence test (red) and PPDF-D selection from O2 absorption band (blue).
Green color corresponds to XCO2 transport model. GOSAT data are over sea from July 2009. Black and cyan
symbols shows XCO2 average values within latitude bin.

Second topic of this research concerns improvement of the GOSAT data processing using multi-pixel analysis.
As a first step, we correct a set of GOSAT single scan PPDF-S retrievals that fall into a certain region over sites
from the Total Carbon Column Observing Network (TCCON) using TCCON data and multivariate analysis ofvariance (MANOVA). MANOVA tests for the difference in two or more vectors of means. The MANOVA statistics
was applied to the GOSAT raw data using reference TCCON measurements. These 12 sites were in the Northernhemisphere: Bialystok, Poland (53.2°N, 23.1°E); Bremen, Germany (53.1°N, 8.85°E); Garmisch, Germany (47.5°N,11.1°E); Lamont, USA (36.6°N, 97.5°W); Orleans, France (48.0°N, 2.11°E); Park Falls, USA (45.9°N, 90.3°W);
Sodankyla, Finland (67.4°N, 26.6°E); Tsukuba, Japan (36.0°N, 140.2°E); and from the Southern hemisphere:
Darwin, Australia (12.4°S, 130.9°E); Lauder, New Zealand (45.0°S, 169.7°E); Wollongong, Australia (34.4°S,150.9°E). GOSAT single scans were selected within a 5° radius latitude/longitude circle centered over each TCCON
station, TCCON data were mean values measured within plus/minus 1 hour of the GOSAT overpass time, and 43
months of GOSAT operation from June 2009 we considered. Figure 1 represents correlation diagram between
GOSAT and TCCON measurements of XCO2. A posteriori bias correction (black symbols) noticeable improves theGOSAT-TCCON agreement. This can be seen from the insets in each panel of Figure 2.

2(GOSAT XCO2 (ppm)405

PPDF-S XCO2 Raw Data vs. TCCON

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PPDF-S XCO2 Modified Data vs. TCCON

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Figure 2. Correlation diagrams between GOSAT and TCCON measurements of XCO2 for raw PPDF-based data(left-hand panel, grey symbols) and after post-processing correction (right-hand panel). Red lines correspond to thebest fit and the green line representing one-to-one correspondence.
5). List of publications
We plan to submit the material from second section of the report to Journal Geophysical Research.

6). Further recommended research
Further improvement of data quality is expected from new version of the PPDF-S algorithm (under development)that explicitly accounts for the strong XCO2 correlations between temporally and spatially adjacent observations
through joint variance-covariance matrix. To implement this approach we assume similar CO2 vertical profiles for
adjacent observations and retrieve scaling coefficients to allow for spatial and temporal variations. This approach isexpected to provide additional information (as compared with scan-by-scan retrievals) for the estimation of thePPDF parameters.

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