Articles | Volume 7, issue 2
https://doi.org/10.5194/amt-7-451-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-451-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 Feb 2014
Research article |
| 10 Feb 2014
A novel gridding algorithm to create regional trace gas maps from satellite observations
G. Kuhlmann
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
A. Hartl
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
H. M. Cheung
Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
Y. F. Lam
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
M. O. Wenig
Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany
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Cited
27 citations as recorded by crossref.
- Mapping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign G. Kuhlmann et al. https://doi.org/10.5194/amt-15-1609-2022
- A physics-based approach to oversample multi-satellite, multispecies observations to a common grid K. Sun et al. https://doi.org/10.5194/amt-11-6679-2018
- Prediction of Vertical Profile of NO₂ Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing S. Zhang et al. https://doi.org/10.1109/TGRS.2021.3061476
- Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements C. Zhao et al. https://doi.org/10.3390/rs14020419
- Measurement report: Validation of multi-satellite remote sensing products and potential source apportionment of BrO and IO in the Arctic using ship-based DOAS Q. Zhang et al. https://doi.org/10.5194/acp-26-8387-2026
- Mobile MAX-DOAS measurements and source analysis of NO2, HCHO, and HONO during the Chengdu 2023 FISU world university games Q. Zhang et al. https://doi.org/10.1016/j.jes.2025.04.051
- How big is an OMI pixel? M. de Graaf et al. https://doi.org/10.5194/amt-9-3607-2016
- The Berkeley High Resolution Tropospheric NO2 product J. Laughner et al. https://doi.org/10.5194/essd-10-2069-2018
- Space- and ground-based CO2 measurements: A review T. Yue et al. https://doi.org/10.1007/s11430-015-0239-7
- Ship-based MAX-DOAS measurements of tropospheric NO2, SO2, and HCHO distribution along the Yangtze River Q. Hong et al. https://doi.org/10.5194/acp-18-5931-2018
- Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. https://doi.org/10.3390/rs13091798
- Observations of tropospheric NO 2 using ground based MAX-DOAS and OMI measurements during the Shanghai World Expo 2010 K. Chan et al. https://doi.org/10.1016/j.atmosenv.2015.08.041
- Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan S. Araki et al. https://doi.org/10.1016/j.scitotenv.2018.03.324
- Global association between satellite-derived nitrogen dioxide (NO2) and lockdown policies under the COVID-19 pandemic H. Zhang et al. https://doi.org/10.1016/j.scitotenv.2020.144148
- Observations of atmospheric trace gases in China using a compact LED long path DOAS system N. Zheng et al. https://doi.org/10.1016/j.apr.2017.10.004
- Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model G. Kuhlmann et al. https://doi.org/10.5194/acp-15-5627-2015
- Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign A. Souri et al. https://doi.org/10.1016/j.atmosenv.2016.02.020
- Observations of tropospheric aerosols and NO2 in Hong Kong over 5 years using ground based MAX-DOAS K. Chan et al. https://doi.org/10.1016/j.scitotenv.2017.10.153
- In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data H. Sihler et al. https://doi.org/10.5194/amt-10-881-2017
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. https://doi.org/10.5194/amt-17-5221-2024
- Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning M. Kim et al. https://doi.org/10.1016/j.rse.2021.112573
- The version 3 OMI NO2 standard product N. Krotkov et al. https://doi.org/10.5194/amt-10-3133-2017
- A Comparative Study of Ground-Gridded and Satellite-Derived Formaldehyde during Ozone Episodes in the Chinese Greater Bay Area Y. Zhao et al. https://doi.org/10.3390/rs15163998
- Estimates of lightning NOx production based on high-resolution OMI NO2 retrievals over the continental US X. Zhang et al. https://doi.org/10.5194/amt-13-1709-2020
- Comparative assessments and insights of data openness of 50 smart cities in air quality aspects H. Mak & Y. Lam https://doi.org/10.1016/j.scs.2021.102868
- Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns K. Chan et al. https://doi.org/10.5194/essd-15-1831-2023
- Estimation of winter time NOx emissions in Hefei, a typical inland city of China, using mobile MAX-DOAS observations W. Tan et al. https://doi.org/10.1016/j.atmosenv.2018.12.009
27 citations as recorded by crossref.
- Mapping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign G. Kuhlmann et al. https://doi.org/10.5194/amt-15-1609-2022
- A physics-based approach to oversample multi-satellite, multispecies observations to a common grid K. Sun et al. https://doi.org/10.5194/amt-11-6679-2018
- Prediction of Vertical Profile of NO₂ Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing S. Zhang et al. https://doi.org/10.1109/TGRS.2021.3061476
- Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements C. Zhao et al. https://doi.org/10.3390/rs14020419
- Measurement report: Validation of multi-satellite remote sensing products and potential source apportionment of BrO and IO in the Arctic using ship-based DOAS Q. Zhang et al. https://doi.org/10.5194/acp-26-8387-2026
- Mobile MAX-DOAS measurements and source analysis of NO2, HCHO, and HONO during the Chengdu 2023 FISU world university games Q. Zhang et al. https://doi.org/10.1016/j.jes.2025.04.051
- How big is an OMI pixel? M. de Graaf et al. https://doi.org/10.5194/amt-9-3607-2016
- The Berkeley High Resolution Tropospheric NO2 product J. Laughner et al. https://doi.org/10.5194/essd-10-2069-2018
- Space- and ground-based CO2 measurements: A review T. Yue et al. https://doi.org/10.1007/s11430-015-0239-7
- Ship-based MAX-DOAS measurements of tropospheric NO2, SO2, and HCHO distribution along the Yangtze River Q. Hong et al. https://doi.org/10.5194/acp-18-5931-2018
- Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. https://doi.org/10.3390/rs13091798
- Observations of tropospheric NO 2 using ground based MAX-DOAS and OMI measurements during the Shanghai World Expo 2010 K. Chan et al. https://doi.org/10.1016/j.atmosenv.2015.08.041
- Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan S. Araki et al. https://doi.org/10.1016/j.scitotenv.2018.03.324
- Global association between satellite-derived nitrogen dioxide (NO2) and lockdown policies under the COVID-19 pandemic H. Zhang et al. https://doi.org/10.1016/j.scitotenv.2020.144148
- Observations of atmospheric trace gases in China using a compact LED long path DOAS system N. Zheng et al. https://doi.org/10.1016/j.apr.2017.10.004
- Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model G. Kuhlmann et al. https://doi.org/10.5194/acp-15-5627-2015
- Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign A. Souri et al. https://doi.org/10.1016/j.atmosenv.2016.02.020
- Observations of tropospheric aerosols and NO2 in Hong Kong over 5 years using ground based MAX-DOAS K. Chan et al. https://doi.org/10.1016/j.scitotenv.2017.10.153
- In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data H. Sihler et al. https://doi.org/10.5194/amt-10-881-2017
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. https://doi.org/10.5194/amt-17-5221-2024
- Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning M. Kim et al. https://doi.org/10.1016/j.rse.2021.112573
- The version 3 OMI NO2 standard product N. Krotkov et al. https://doi.org/10.5194/amt-10-3133-2017
- A Comparative Study of Ground-Gridded and Satellite-Derived Formaldehyde during Ozone Episodes in the Chinese Greater Bay Area Y. Zhao et al. https://doi.org/10.3390/rs15163998
- Estimates of lightning NOx production based on high-resolution OMI NO2 retrievals over the continental US X. Zhang et al. https://doi.org/10.5194/amt-13-1709-2020
- Comparative assessments and insights of data openness of 50 smart cities in air quality aspects H. Mak & Y. Lam https://doi.org/10.1016/j.scs.2021.102868
- Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns K. Chan et al. https://doi.org/10.5194/essd-15-1831-2023
- Estimation of winter time NOx emissions in Hefei, a typical inland city of China, using mobile MAX-DOAS observations W. Tan et al. https://doi.org/10.1016/j.atmosenv.2018.12.009
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