Publications for Terra- MISR

Publication Citation
Kokhanovsky, A.A., A.B. Davis, B. Cairns, O. Dubovik, O.P. Hasekamp, I. Sano, S. Mukai, V.V. Rozanov, P. Litvinov, T. Lapyonok, I.S. Kolomiets, Y.A. Oberemok, S. Savenkov, W. Martin, A. Wasilewski, A. Di Noia, F.A. Sap, J. Rietjens, F. Xu, V. Natraj, M. Duan, T. Cheng, and R. Munro (2015), Space-based remote sensing of atmospheric aerosols: The multi-angle spectro-polarimetric frontier, Earth-Science Reviews, 85-116, doi:10.1016/j.earscirev.2015.01.012.
Lallart, P., R. Kahn, and D. Tanré (2008), POLDER2/ADEOSII, MISR, and MODIS/Terra reflectance comparisons, J. Geophys. Res., 113, D14S02, doi:10.1029/2007JD009656.
Lee, B., L. Di Girolamo, G. Zhao, and Y. Zhan (2018), Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer, doi:10.3390/rs10111858.
Lee, H., O.V. Kalashnikova, K. Suzuki, A. Braverman, M.J. Garay, and R.A. Kahn (2016), Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models, Atmos. Chem. Phys., 16, 6627-6640, doi:10.5194/acp-16-6627-2016.
Levis, A., A.B. Davis, and Y.Y. Schechner (2017), Multiple-Scattering Microphysics Tomography, IEEE Conference on Computer Vision and Pattern Recognition (CVPR17), 5797-5806.
Li, J., B.E. Carlson, and A.A. Lacis (2014), Application of spectral analysis techniques in the intercomparison of aerosol data. Part II: Using maximum covariance analysis to effectively compare spatiotemporal variability of satellite and AERONET measured aerosol optical depth, J. Geophys. Res., 119, 153-166, doi:10.1002/2013JD020537.
Li, J., B.E. Carlson, and A.A. Lacis (2014), Application of spectral analysis techniques in the intercomparison of aerosol data: Part III. Using combined PCA to compare spatiotemporal variability of MODIS, MISR, and OMI aerosol optical depth, J. Geophys. Res., 119, 4017-4042, doi:10.1002/2013JD020538.
Li, J., B.E. Carlson, and A.A. Lacis (2014), Application of spectral analysis techniques to the intercomparison of aerosol data - Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis, Atmos. Meas. Tech., 7, 2531-2549, doi:10.5194/amt-7-2531-2014.
Li, J., B.E. Carlson, and A.A. Lacis (2015), How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States?, Atmos. Environ., 102, 260-273, doi:10.1016/j.atmosenv.2014.12.010.
Li, J., X. Li, B.E. Carlson, R.A. Kahn, A.A. Lacis, O. Dubovik, and T. Nakajima (2016), Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations, J. Geophys. Res., 121, doi:10.1002/2016JD025469.
Li, J., X. Li, B.E. Carlson, R.A. Kahn, A.A. Lacis, O. Dubovik, and T. Nakajima (2017), Reducing multisensor monthly mean aerosol optical depth uncertainty: 2. Optimal locations for potential ground observation deployments, J. Geophys. Res., 122, doi:10.1002/2016JD026308.
Li, J., R.A. Kahn, J. Wei, B.E. Carlson, A.A. Lacis, Z. Li, X. Li, O. Dubovik, and T. Nakajima (2020), Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach, J. Geophys. Res., 125, 1-17, doi:10.1029/2019JD031884.
Li, S., R. Khan, M. Chin, M.J. Garay, and Y. Liu (2015), Improving satellite-retrieved aerosol microphysical properties using GOCART data, Atmos. Meas. Tech., 8, 1157-1171, doi:10.5194/amt-8-1157-2015.
Li, Y., D.Q. Tong, F. Ngan, M.D. Cohen, A.F. Stein, S. Kondragunta, X. Zhang, C. Ichoku, E.J. Hyer, and R.A. Kahn (2020), Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model, J. Geophys. Res., 125, e2020JD032768, doi:10.1029/2020JD032768.
Li, Y., D. Tong, S. Ma, S.R. Freitas, R. Ahmadov, M. Sofiev, X. Zhang, S. Kondragunta, R.A. Kahn, Y. Tang, B. Baker, P. Campbell, R. Saylor, I. Stajner, and G. Grell (2022), Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: A comparison of three schemes (Briggs, Freitas, and Sofiev), Atmos. Chem. Phys., 23, 3083-3101, doi:10.5194/acp-23-3083-2023.
Liang, L., L. Di Girolamo, and W. Sun (2015), Bias in MODIS cloud drop effective radius for oceanic water clouds as deduced from optical thickness variability across scattering angles, J. Geophys. Res., 120, 7661-7681, doi:10.1002/2015JD023256.
Limbacher, J.A., and R.A. Khan (2014), MISR research-aerosol-algorithm refinements for dark water retrievals, Atmos. Meas. Tech., 7, 3989-4007, doi:10.5194/amt-7-3989-2014.
Limbacher, J.A., and R.A. Khan (2015), MISR empirical stray light corrections in high-contrast scenes, Atmos. Meas. Tech., 8, 2927-2943, doi:10.5194/amt-8-2927-2015.
Limbacher, J.A., and R.A. Kahn (2015), MISR empirical stray light corrections in high-contrast scenes, Atmos. Meas. Tech., 8, 1-17, doi:10.5194/amt-8-1-2015.
Limbacher, J.A., and R.A. Kahn (2017), Updated MISR dark water research aerosol retrieval algorithm – Part 1: Coupled 1.1 km ocean surface chlorophyll a retrievals with empirical calibration corrections, Atmos. Meas. Tech., 10, 1539-1555, doi:10.5194/amt-10-1539-2017.