Evaluation of MOPITT Version 7 joint TIR–NIR X&amp;lt;sub&amp;gt;CO&amp;lt;/sub&amp;gt;  retrievals with TCCON

Hedelius, Jacob K.; He, Tai‐Long; Jones, Dylan B. A.; Baier, Bianca C.; Buchholz, Rebecca R.; Mazière, Martine De; Deutscher, Nicholas M.; Dubey, Manvendra K.; Feist, Dietrich G.; Griffith, David; Hase, Frank; Iraci, L. T.; Jeseck, Pascal; Kiel, Matthäus; Kivi, Rigel; Liu, Cheng; Morino, Isamu; Notholt, Justus; Oh, Youngsuk; Ohyama, Hirofumi; Pollard, David F.; Rettinger, Markus; Roche, Sébastien; Roehl, Coleen M.; Schneider, Mat­thias; Shiomi, Kei; Strong, Kimberly; Sussmann, Ralf; Sweeney, Colm; Té, Yao; Uchino, Osamu; Velazco, Voltaire A.; Wang, Wei; Warneke, Thorsten; Wennberg, P. O.; Worden, H. M.; Wunch, Debra

doi:10.5194/amt-12-5547-2019

Cited by 27 publications

(28 citation statements)

References 76 publications

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“…The urban regions often have different surface parameters (e.g., surface temperature and emissivity) and usually but not always have higher CO concentrations than non-urban regions. However, the surface parameters are unlikely to impact the ultimate quality of MOPITT retrieval products (Pan et al, 1998;Ho et al, 2005). The goal of this study is to understand if MOPITT retrievals are able to represent conditions over urban regions given sampling and cloud cover.…”

Section: Aircraft Measurements Used For Comparisonsmentioning

confidence: 99%

Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

et al. 2020

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Abstract. The Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions have not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we compare MOPITT products over urban and non-urban regions with aircraft measurements from the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ – 2011–2014), Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS – 2013), Air Chemistry Research In Asia (ARIAs – 2016), A-FORCE (2009, 2013), and Korea United States Air Quality (KORUS-AQ – 2016) campaigns. In general, MOPITT agrees reasonably well with the in situ profiles, over both urban and non-urban regions. Version 8 multispectral product (V8J) biases vary from −0.7 % to 0.0 % and version 8 thermal-infrared product (TIR) biases vary from 2.0 % to 3.5 %. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than those over urban regions with smaller biases and higher correlation coefficients. We find that the agreement of MOPITT V8J and V8T with aircraft measurements at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily polluted scenes. We test the sensitivities of the agreements between MOPITT and in situ profiles to assumptions and data filters applied during the comparisons of MOPITT retrievals and in situ profiles. The results at the surface layer are insensitive to the model-based profile extension (required due to aircraft altitude limitations), whereas the results at levels with limited aircraft observations (e.g., the 600 hPa layer) are more sensitive to the model-based profile extension. The results are insensitive to the maximum allowed time difference criterion for co-location (12, 6, 3, and 1 h) and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km), and hence few MOPITT retrievals are included in the comparison. Daytime MOPITT products have smaller overall biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in situ profiles, likely due to the reduced number of MOPITT retrievals for comparison. Comparisons of MOPITT retrievals and in situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate that some of the errors are due to CO representativeness with these sensitivity tests, but further quantification of representativeness errors due to CO variability within the MOPITT footprint will require future work.

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Section: Aircraft Measurements Used For Comparisonsmentioning

confidence: 99%

Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

et al. 2020

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“…In addition to the different instrument sensitivities, however, there is also a known problem in the TCCON X CO scaling, wherein the TCCON data were scaled down by ≈6.7% to match older aircraft data. There is now less confidence placed in this value given that it has changed as more recent in situ profiles have been added for comparison [62], and thus it is believed that this scaling factor also contributes to the observed discrepancy between the NUCAPS and TCCON CO. Figure 6. Box-whisker robust error statistics (%) of total column SNPP NUCAPS trace gas retrievals (QA-accepted cases including trace gas QA) versus means at individual collocated (∆r ≤ 125 km and ±2 h) TCCON stations, ordered from south to north: (left) CO, (center) CH 4 , and (right) CO 2 .…”

Section: Statistical Analysis Versus Tccon Baselinementioning

confidence: 99%

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

et al. 2020

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This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

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“…6. The KORUS-AQ campaign provides a unique test bed for comparing surface and aircraft in situ observations with ground-based and satellite-based remote sensing (Herman et al, 2018), particularly important for targeted short-lived species such as formaldehyde (CH 2 O) and nitrogen dioxide (NO 2 ). Miyazaki et al (2019a) showed that the background Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

et al. 2020

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Abstract. Global coupled chemistry–climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea–United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the control run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O3 datasets and observationally constrained box model simulations of OH and HO2. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO2, and 27 % for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O3, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.

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Evaluation of MOPITT Version 7 joint TIR–NIR X<sub>CO</sub> retrievals with TCCON

Cited by 27 publications

References 76 publications

Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

Assessing Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals over urban versus non-urban regions

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

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