Tropospheric NO2 columns retrieved from satellite instruments are useful to infer NOx pollution, NOx emissions and atmospheric chemistry. Current satellite products are subject to limitations in assumptions of aerosol optical effects, surface reflectance anisotropy, vertical profiles of NO2, and cloud optical properties.
\r\nHere we develop an improved Peking University Ozone Monitoring Instrument NO2 product (POMINO) for China and surrounding areas. As of 2018/09/20, there are two versions available: POMINO v1 and POMINO v2.
\r\nAlgorithm
\r\nPOMINO v1 (Lin et al., 2014; Lin et al., 2015):
\r\nThis is the original "POMINO" algorithm.
\r\nPOMINO v1 explicitly accounts for aerosol optical effects, angular dependence of surface reflectance, and dynamically varying atmospheric profiles of air pressure, air temperature and NO2 at a high horizontal resolution (25-50 km). The daily AOD data are simulated by nested GEOS-Chem and further constrained by MODIS (C5.1) data on a monthly basis. The daily BRDF data are from MCD43C2 (C5.1).
\r\nPrior to the NO2 retrieval, we retrieve cloud top pressure and cloud fraction using consistent assumptions about the states of the atmosphere and surface.
\r\nFor our NO2 and cloud retrievals, we adopt from KNMI (via www.temis.nl) the SCDs of tropospheric NO2 (DOMINO v2) and O2-O2 dimer (OMCLDO2 v1.1.1.3), the TOA reflectance, and some other ancillary information.
\r\nWe develop the AMFv6 code for air mass factor calculation, based on the radiative transfer model LIDORT v3.6. The AMFv6 code improves upon the code developed by Paul Palmer, Randall Martin et al., with various aforementioned capability added/extended to accommodate the calculation here. With AMFv6, radiative transfer is calculated explicitly for each satellite pixel with no need to use a look-up table. The calculation of AMFv6 is parallelized and is sufficiently fast so that one day of retrieval with global coverage would only take about three hours using 16 CPU cores.
\r\nPOMINO v2 (Liu et al., 2019):
\r\nOn top of POMINO v1, POMINO v2 further constrains the vertical profile of aerosol extinction by monthly climatology from CALIOP, uses the SCD data from QA4ECV, and updates to MODIS (C6) merged AOD and MCD43C2 (C6) daily BRDF.
\r\nValidation
\r\nThe POMINO v1 product is consistent with MAX-DOAS NO2 data in China, with a R^2 of 0.96 as compared to the value at 0.72 for DOMINO v2. The improved consistency is related to explicit pixel-by-pixel radiative transfer calculation (instead of using a look-up table), consistent treatments of all parameters in retrieving clouds and NO2, explicit consideration of aerosol optical effects (rather than adjusting ‘effective’ clouds to implicitly account for aerosols), and consideration of surface reflectance anisotropy.
\r\nThe POMINO v1 product is able to capture the high pollution situations (e.g., high aerosol and NO2 concentrations), in addition to the modest and low population situations.
\r\nThe POMINO v2 product further reduces the bias against MAX-DOAS data, while maintaining the high correlation.
\r\nAMFv6 Code
\r\nOur AMFv6 code is available for public use. Currently, AMFv6 also allows users to (some of them may need further customization):
\r\nThis is the original "POMINO" product.
\r\nAs of 2018/09/20, data are available from 2004 through 2016. For newer data, see our POMINO v2 product below.
\r\nPOMINO animation of monthly mean NO2 VCD maps (0.25 x 0.25 degree): 2004/10-2016/12
\r\nLevel-3 data
\r\nPOMINO Monthly or Daily Level-3 Data Download
\r\nBoth daily and monthly Level-3 NO2 tropospheric VCD products are on a 0.25 x 0.25 degree grid, spatially aggregated from the Level-2 data.
\r\nIncluded in the Level-3 data are tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\nThe file "readme_POMINO_level3.txt" in the link above provides an introduction, including example reading programs in IDL and Fortran.
\r\nLevel-2 data
\r\n \r\n\t\t\tIncluded are pixel-specific NO2 tropospheric VCD product and ancillary data.
\r\n\t\t\tEach tar.gz file contains a month worth of data files. Each data file contains Level-2 data for tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\n\t\t\tThe file "readme_POMINO_level2.txt" in the link above provides an introduction, including example reading programs in IDL and Fortran.
\r\n\t\t\t\r\n\t\t\t
This product is added on 2018/09/20.
\r\n\t\t\tAs of 2019/05/13, data are available from 2004 through 2018.
\r\n\t\t\tPOMINO v2 animation of monthly mean NO2 VCD maps (0.25 x 0.25 degree): 2004/10-2017/12
\r\nLevel-3 data
\r\n\t\t\tPOMINO v2 Monthly or Daily Level-3 Data Download
\r\n\t\t\tBoth daily and monthly Level-3 NO2 tropospheric VCD products are on a 0.25 x 0.25 degree grid, spatially aggregated from the Level-2 data.
\r\n\t\t\tIncluded in the Level-3 data are tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\n\t\t\tSee user guide for brief documentation of the variables included (NO2 VCD, AMF, AOD, SSA, etc.), as well as how to read the Level-3 data.
\r\n\t\t\tLevel-2 data
\r\n\t\t\tPOMINO v2 Level-2 Data Download
\r\n\t\t\tSee user guide for brief documentation of the variables included (NO2 VCD, AMF, AOD, SSA, etc.).
\r\n\t\t\tExamples to read the Level-2 data in IDL and Fortran are provided in the link above.
\r\n\t\t\t"},"citation_zh:描述":{"dsDescription_zh:文本":"
Tropospheric NO2 columns retrieved from satellite instruments are useful to infer NOx pollution, NOx emissions and atmospheric chemistry. Current satellite products are subject to limitations in assumptions of aerosol optical effects, surface reflectance anisotropy, vertical profiles of NO2, and cloud optical properties.
\r\nHere we develop an improved Peking University Ozone Monitoring Instrument NO2 product (POMINO) for China and surrounding areas. As of 2018/09/20, there are two versions available: POMINO v1 and POMINO v2.
\r\nAlgorithm
\r\nPOMINO v1 (Lin et al., 2014; Lin et al., 2015):
\r\nThis is the original "POMINO" algorithm.
\r\nPOMINO v1 explicitly accounts for aerosol optical effects, angular dependence of surface reflectance, and dynamically varying atmospheric profiles of air pressure, air temperature and NO2 at a high horizontal resolution (25-50 km). The daily AOD data are simulated by nested GEOS-Chem and further constrained by MODIS (C5.1) data on a monthly basis. The daily BRDF data are from MCD43C2 (C5.1).
\r\nPrior to the NO2 retrieval, we retrieve cloud top pressure and cloud fraction using consistent assumptions about the states of the atmosphere and surface.
\r\nFor our NO2 and cloud retrievals, we adopt from KNMI (via www.temis.nl) the SCDs of tropospheric NO2 (DOMINO v2) and O2-O2 dimer (OMCLDO2 v1.1.1.3), the TOA reflectance, and some other ancillary information.
\r\nWe develop the AMFv6 code for air mass factor calculation, based on the radiative transfer model LIDORT v3.6. The AMFv6 code improves upon the code developed by Paul Palmer, Randall Martin et al., with various aforementioned capability added/extended to accommodate the calculation here. With AMFv6, radiative transfer is calculated explicitly for each satellite pixel with no need to use a look-up table. The calculation of AMFv6 is parallelized and is sufficiently fast so that one day of retrieval with global coverage would only take about three hours using 16 CPU cores.
\r\nPOMINO v2 (Liu et al., 2019):
\r\nOn top of POMINO v1, POMINO v2 further constrains the vertical profile of aerosol extinction by monthly climatology from CALIOP, uses the SCD data from QA4ECV, and updates to MODIS (C6) merged AOD and MCD43C2 (C6) daily BRDF.
\r\nValidation
\r\nThe POMINO v1 product is consistent with MAX-DOAS NO2 data in China, with a R^2 of 0.96 as compared to the value at 0.72 for DOMINO v2. The improved consistency is related to explicit pixel-by-pixel radiative transfer calculation (instead of using a look-up table), consistent treatments of all parameters in retrieving clouds and NO2, explicit consideration of aerosol optical effects (rather than adjusting ‘effective’ clouds to implicitly account for aerosols), and consideration of surface reflectance anisotropy.
\r\nThe POMINO v1 product is able to capture the high pollution situations (e.g., high aerosol and NO2 concentrations), in addition to the modest and low population situations.
\r\nThe POMINO v2 product further reduces the bias against MAX-DOAS data, while maintaining the high correlation.
\r\nAMFv6 Code
\r\nOur AMFv6 code is available for public use. Currently, AMFv6 also allows users to (some of them may need further customization):
\r\nThis is the original "POMINO" product.
\r\nAs of 2018/09/20, data are available from 2004 through 2016. For newer data, see our POMINO v2 product below.
\r\nPOMINO animation of monthly mean NO2 VCD maps (0.25 x 0.25 degree): 2004/10-2016/12
\r\nLevel-3 data
\r\nPOMINO Monthly or Daily Level-3 Data Download
\r\nBoth daily and monthly Level-3 NO2 tropospheric VCD products are on a 0.25 x 0.25 degree grid, spatially aggregated from the Level-2 data.
\r\nIncluded in the Level-3 data are tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\nThe file "readme_POMINO_level3.txt" in the link above provides an introduction, including example reading programs in IDL and Fortran.
\r\nLevel-2 data
\r\n \r\n\t\t\tIncluded are pixel-specific NO2 tropospheric VCD product and ancillary data.
\r\n\t\t\tEach tar.gz file contains a month worth of data files. Each data file contains Level-2 data for tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\n\t\t\tThe file "readme_POMINO_level2.txt" in the link above provides an introduction, including example reading programs in IDL and Fortran.
\r\n\t\t\t\r\n\t\t\t
This product is added on 2018/09/20.
\r\n\t\t\tAs of 2019/05/13, data are available from 2004 through 2018.
\r\n\t\t\tPOMINO v2 animation of monthly mean NO2 VCD maps (0.25 x 0.25 degree): 2004/10-2017/12
\r\nLevel-3 data
\r\n\t\t\tPOMINO v2 Monthly or Daily Level-3 Data Download
\r\n\t\t\tBoth daily and monthly Level-3 NO2 tropospheric VCD products are on a 0.25 x 0.25 degree grid, spatially aggregated from the Level-2 data.
\r\n\t\t\tIncluded in the Level-3 data are tropospheric NO2 AMF, tropospheric NO2 VCD, AOD at 550 nm, SSA at 550 nm, and other ancillary parameters.
\r\n\t\t\tSee user guide for brief documentation of the variables included (NO2 VCD, AMF, AOD, SSA, etc.), as well as how to read the Level-3 data.
\r\n\t\t\tLevel-2 data
\r\n\t\t\tPOMINO v2 Level-2 Data Download
\r\n\t\t\tSee user guide for brief documentation of the variables included (NO2 VCD, AMF, AOD, SSA, etc.).
\r\n\t\t\tExamples to read the Level-2 data in IDL and Fortran are provided in the link above.
\r\n\t\t\t"},"citation_zh:学科":"地球与环境科学","citation:Subject":"Earth and Environmental Sciences","citation:Keyword":[{"keyword:Term":"Remote-sensing","keyword:Vocabulary":"LCSH"},{"keyword:Term":"Atmospheric chemistry","keyword:Vocabulary":"LCSH"}],"citation_zh:语言":"英语","citation_zh:数据种类":"卫星遥感","citation:Time Period Covered":{"timePeriodCovered:Start":"2004-10-01"},"citation_zh:覆盖的时间段":{"timePeriodCovered_zh:开始":"2004-10-01"},"citation_zh:资助信息":{"grantNumber_zh:资助机构":"NSFC"},"citation_zh:相关出版物":[{"publication_zh:相关类型":"References","publication_zh:引用信息":"Lin, J.-T. *, R. V. Martin, K. F. Boersma, M. Sneep, P. Stammes, R. Spurr, P. Wang, M. Van Roozendael, K. Clémer, and H. Irie: Retrieving tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: Effects of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen dioxide, Atmos. Chem. Phys., 14, 1441-1461, doi:10.5194/acp-14-1441-2014, 2014","publication_zh:ID类型":"doi","publication_zh:ID数值":"10.5194/acp-14-1441-2014","publication_zh:URL":"http://www.phy.cqjj8.com/~acm/publications/Lin%20JT%202014%20ACP_1.pdf"},{"publication_zh:相关类型":"References","publication_zh:引用信息":"Lin, J.-T. *, Liu, M.-Y., Xin, J.-Y., Boersma, K. F., Spurr, R., Martin, R., and Zhang, Q.: Influence of aerosols and surface reflectance on satellite NO2 retrieval: seasonal and spatial characteristics and implications for NOx emission constraints, Atmospheric Chemistry and Physics, 15, 11217-11241, doi:10.5194/acp-15-11217-2015, 2015","publication_zh:ID类型":"doi","publication_zh:ID数值":"10.5194/acp-15-11217-2015","publication_zh:URL":"http://www.phy.cqjj8.com/~acm/publications/Lin%20JT%202015%20ACP_1.pdf"},{"publication_zh:相关类型":"References","publication_zh:引用信息":"Liu, M.-Y., Lin, J.-T. * , Boersma, K. F. *, Pinardi, G., Wang, Y., Chimot, J., Wagner, T., Xie, P., Eskes, H., Van Roozendael, M., Hendrick, F., Wang, P., Wang, T., Yan, Y.-Y., Chen, L.-L., and Ni, R.-J.: Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile, Atmospheric Measurement Techniques, 12, 1-21, doi:10.5194/amt-12-1-2019, 2019","publication_zh:ID类型":"doi","publication_zh:ID数值":"10.5194/amt-12-1-2019","publication_zh:URL":"http://www.phy.cqjj8.com/~acm/publications/Liu%20MY%202019%20AMT_1.pdf"}],"citation_zh:相关数据集":{"relatedDatasets_zh:相关类型":"IsderivedFrom","relatedDatasets_zh:引用信息":"Boersma, K.F., H.J. Eskes, R. J. Dirksen, R. J. van der A, J. P. Veefkind, P. Stammes, V. Huijnen, Q. L. Kleipool, M. Sneep, J. Claas, J. Leitao, A. Richter, Y. Zhou, and D. Brunner, An improved retrieval of tropospheric NO2 columns from the Ozone Monitoring Instrument, Atmos. Meas. Tech. , 4, 1905-1928, 2011","relatedDatasets_zh:URL":"http://www.temis.nl/airpollution/no2.html"},"@id":"doi:10.18170/DVN/0ELDHP","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.1","schema:datePublished":"2020-01-17","schema:name":"Peking University Ozone Monitoring Instrument NO2 product (POMINO v1 \\ v2)","schema:dateModified":"2020-01-17 18:13:08.269","schema:license":"http://creativecommons.org/publicdomain/zero/1.0/","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":false},"schema:includedInDataCatalog":"Dataverse for Lingroup","ore:aggregates":[],"schema:hasPart":[]},"@context":{"author":"http://opendata.cqjj8.com/schema/citation/author#","author_zh":"http://opendata.cqjj8.com/schema/citation_zh/author_zh#","citation":"http://opendata.cqjj8.com/schema/citation#","citation_zh":"http://opendata.cqjj8.com/schema/citation_zh#","contributor_zh":"http://opendata.cqjj8.com/schema/citation_zh/contributor_zh#","datasetContact":"http://opendata.cqjj8.com/schema/citation/datasetContact#","datasetContact_zh":"http://opendata.cqjj8.com/schema/citation_zh/datasetContact_zh#","dcterms":"http://purl.org/dc/terms/","dsDescription":"http://opendata.cqjj8.com/schema/citation/dsDescription#","dsDescription_zh":"http://opendata.cqjj8.com/schema/citation_zh/dsDescription_zh#","dvcore":"http://dataverse.org/schema/core#","grantNumber_zh":"http://opendata.cqjj8.com/schema/citation_zh/grantNumber_zh#","keyword":"http://opendata.cqjj8.com/schema/citation/keyword#","ore":"http://www.openarchives.org/ore/terms/","publication_zh":"http://opendata.cqjj8.com/schema/citation_zh/publication_zh#","relatedDatasets_zh":"http://opendata.cqjj8.com/schema/citation_zh/relatedDatasets_zh#","schema":"http://schema.org/","timePeriodCovered":"http://opendata.cqjj8.com/schema/citation/timePeriodCovered#","timePeriodCovered_zh":"http://opendata.cqjj8.com/schema/citation_zh/timePeriodCovered_zh#"}}