Atmospheric Composition Modeling and Analysis Program

Kaku, K.C., et al. (2018), Assessing the Challenges of Surface-Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States, J. Geophys. Res., 123, 7530-7562, doi:10.1029/2017JD028074.

Peng, X., et al. (2018), Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP), Q. J. R. Meteorol. Soc., 30, 8, doi:10.1002/qj.3497.

Shi, Y.R., et al. (2019), Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals, Atmos. Chem. Phys., 19, 259-274, doi:10.5194/acp-19-259-2019.

Toth, T.D., et al. (2018), Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products, Atmos. Meas. Tech., 11, 499-514, doi:10.5194/amt-11-499-2018.

Toth, T.D., et al. (2019), A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations, Atmos. Meas. Tech., 12, 1739-1754, doi:10.5194/amt-12-1739-2019.

Zhang, J., et al. (2019), Characterization and application of artificial light sources for nighttime aerosol optical depth retrievals using the VIIRS Day/Night Band, Atmos. Meas. Tech., doi:10.5194/amt-2018-424.

Sun, K., et al. (2017), Deriving the slit functions from OMI solar observations and its implications for ozone-profile retrieval, Atmos. Meas. Tech., 10, 3677-3695, doi:10.5194/amt-10-3677-2017.

Judd, L.M., et al. (2018), The Dawn of Geostationary Air Quality Monitoring: Case Studies From Seoul and Los Angeles, Front. Environ. Sci., 6, 85, doi:10.3389/fenvs.2018.00085.

Sun, K., et al. (2018), A physics-based approach to oversample multi-satellite, multispecies observations to a common grid, Atmos. Meas. Tech., 11, 6679-6701, doi:10.5194/amt-11-6679-2018.