Satellite and Ground-based Measurements of X&amp;lt;sub&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;&amp;lt;/sub&amp;gt; in a Remote Semi-Arid Region of Australia

Velazco, Voltaire A.; Deutscher, Nicholas M.; Morino, Isamu; Uchino, Osamu; Bukosa, Beata; Ajiro, Masataka; Kamei, Atsushi; Jones, Nicholas; Paton-Walsh, Clare; Griffith, David

doi:10.5194/essd-2018-161

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…found some EM27/SUN biases to TCCON as large as 0.14% (∼ 0.56 ppm), but also found statistically significant variability amongst TCCON sites up to 0.3 ppm, suggesting that the site-to-site biases amongst TCCON sites may be of similar size to biases between EM27/SUN FTS and TCCON observed 10 by Hedelius et al (2016). Velazco et al (2018) found an average offset to TCCON of approximately 0.46% (∼ 1.84 ppm) when comparing two years of co-located observations between an EM27/SUN FTS and the TCCON site at the University of Wollongong. While some of these biases are large enough to produce significantly different results when choosing the EM27/SUN FTS or TCCON for ground-based validation of satellite-based X CO2 retrievals, these biases tend to be systematic in nature and can be corrected to achieve acceptable agreement with TCCON through regular calibration measurements.…”

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confidence: 76%

“…All three instruments record a solar infrared spectrum that can be used to retrieve X CO2 . Several recent studies have compared EM27/SUN observations to TCCON (Hedelius et al, 2016Velazco et al, 2018;Frey et al, 2019). This paper uses similar retrieval methods for EM27/SUN retrievals of X CO2 as Hedelius et al (2016), , and Velazco et al (2018) by implementing the GGG2014 5 retrieval algorithm coupled with the EM27/SUN GGG interferogram processing suite (EGI) .…”

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confidence: 99%

“…Several recent studies have compared EM27/SUN observations to TCCON (Hedelius et al, 2016Velazco et al, 2018;Frey et al, 2019). This paper uses similar retrieval methods for EM27/SUN retrievals of X CO2 as Hedelius et al (2016), , and Velazco et al (2018) by implementing the GGG2014 5 retrieval algorithm coupled with the EM27/SUN GGG interferogram processing suite (EGI) . Hedelius et al (2016) observed a 0.03% ±0.08% (∼ 0.12 ± 0.32 ppm) offset when comparing four EM27/SUN spectrometers to co-located observations at the Caltech TCCON site.…”

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confidence: 99%

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Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Jacobs

Simpson

Wunch

et al. 2020

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Abstract. Seasonal CO2 exchange in the Boreal Forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2. Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize Boreal Forest seasonal cycles across longitudes with a spatially and temporally rich dataset, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry-air mole fractions of CO2 (XCO2) in Boreal Forest regions. In addition to the standard quality control filters recommended for ACOS B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to Boreal Forest observations (Boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada and Sodankylä, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN FTS at Fairbanks, Alaska, United States. EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50° N than the B8 QC, and the Boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While Boreal QC results in a substantial increase in passable retrievals this is accompanied by increases in the standard deviations in biases at Boreal Forest sites from ∼ 1.4 ppm with B9 QC to ∼ 1.6 ppm with Boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at the TCCON sites than when comparing to EM27/SUN observations in Fairbanks. Monthly average biases generally varied between −1 ppm and +1 ppm at the three sites considered, with more negative biases in spring (MAM) and autumn (SO), but more positive biases in summer months (JJA). Monthly standard deviations in biases ranged from approximately 1.0 ppm to 2.0 ppm and do not exhibit strong seasonal dependence apart from exceptionally high standard deviation observed with all three QC methods at Sodankylä in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of airmass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of XCO2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these Boreal Forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

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Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Jacobs

Simpson

Wunch

et al. 2020

Preprint

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“…However, because insufficient ocean data and gain-M data were available, we only analyzed land data and mixed data acquired with gain-H amplification mode. Although described in Suto et al [34], band 1 (0.76 µ m), band 2 (1.6 µ m), and band 3 (2.0 µ m) of SWIR could be measured with three gains including high (H), medium (M), and low (L), GOSAT uses gain-H for most soundings over land and ocean and gain-M for soundings over bright surfaces in the SWIR as deserts and semi-arid regions (e.g., Sahara, Nevada, and central Australia) [35].…”

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confidence: 99%

Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data

et al. 2019

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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. 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.

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Satellite and Ground-based Measurements of X<sub>CO<sub>2</sub></sub> in a Remote Semi-Arid Region of Australia

Cited by 2 publications

References 17 publications

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data

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Satellite and Ground-based Measurements of X&lt;sub&gt;CO&lt;sub&gt;2&lt;/sub&gt;&lt;/sub&gt; in a Remote Semi-Arid Region of Australia

Cited by 2 publications

References 17 publications

Quality controls, bias, and seasonality of CO&lt;sub&gt;2&lt;/sub&gt; columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Quality controls, bias, and seasonality of CO&lt;sub&gt;2&lt;/sub&gt; columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data

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Satellite and Ground-based Measurements of X<sub>CO<sub>2</sub></sub> in a Remote Semi-Arid Region of Australia

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the Boreal Forest with OCO-2, TCCON, and EM27/SUN measurements