Evaluation of bias correction methods for GOSAT SWIR XH2O using TCCON data

  • This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5° × 0.5° latitude × longitude box centered at each TCCON site matched with TCCON XH2O data averaged over ±15 min of the GOSAT overpass time. ConsideringThis study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5° × 0.5° latitude × longitude box centered at each TCCON site matched with TCCON XH2O data averaged over ±15 min of the GOSAT overpass time. Considering land data, the global bias changed from −1.3 ± 9.3% to −2.2 ± 8.5%, and station bias from −2.3 ± 9.0% to −1.7 ± 8.4%. In mixed land and ocean data, global bias and station bias changed from −0.3 ± 7.6% and −1.9 ± 7.1% to −0.8 ± 7.2% and −2.3 ± 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods.show moreshow less

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Author:Tran Thi Ngoc Trieu, Isamu Morino, Hirofumi Ohyama, Osamu Uchino, Ralf SussmannORCiDGND, Thorsten Warneke, Christof Petri, Rigel Kivi, Frank Hase, David F. Pollard, Nicholas M. Deutscher, Voltaire A. Velazco, Laura T. Iraci, James R. Podolske, Manvendra K. Dubey
URN:urn:nbn:de:bvb:384-opus4-1203422
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/120342
ISSN:2072-4292OPAC
Parent Title (English):Remote Sensing
Publisher:MDPI
Type:Article
Language:English
Year of first Publication:2019
Publishing Institution:Universität Augsburg
Release Date:2025/03/15
Volume:11
Issue:3
First Page:290
DOI:https://doi.org/10.3390/rs11030290
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Physische Geographie mit Schwerpunkt Klimaforschung
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)