Organization:
NASA Goddard Institute for Space Studies
Columbia University
Business Address:
Department of Applied Physics and Applied Mathematics
New York, NY 10025
United StatesFirst Author Publications:
- Alexandrov, M. D., et al. (2022), Markovian Statistical Model of Cloud Optical Thickness. Part I: Theory and Examples, J. Atmos. Sci., 79, 3315-3332, doi:10.1175/JAS-D-22-0125.1.
- Alexandrov, M. D., et al. (2020), Vertical profiles of droplet size distributions derived from cloud-side T observations by the research scanning polarimeter: Tests on simulated data ⁎, Atmos. Res., 239, 104924, doi:10.1016/j.atmosres.2020.104924.
- Alexandrov, M. D., et al. (2018), Retrievals of cloud droplet size from the research scanning polarimeter data: T Validation using in situ measurements, Remote Sensing of Environment, 210, 76-95, doi:10.1016/j.rse.2018.03.005.
- Alexandrov, M. D., and M. Mishchenko (2017), Information content of bistatic lidar observations of aerosols from space, Optics Express, 25, A134-A150.
- Alexandrov, M. D., and A. Marshak (2017), Cellular Statistical Models of Broken Cloud Fields. Part III: Markovian Properties, J. Atmos. Sci., 74, 2921-2935, doi:10.1175/JAS-D-17-0075.1.
- Alexandrov, M. D., et al. (2016), Polarized view of supercooled liquid water clouds, Remote Sensing of Environment, 181, 96-110, doi:10.1016/j.rse.2016.04.002.
- Alexandrov, M. D., et al. (2016), Derivation of cumulus cloud dimensions and shape from the airborne measurements by the Research Scanning Polarimeter, Remote Sensing of Environment, 177, 144-152, doi:10.1016/j.rse.2016.02.032.
- Alexandrov, M. D., et al. (2016), New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean*, J. Atmos. Sci., 73, 821-837, doi:10.1175/JAS-D-15-0130.1.
- Alexandrov, M. D., et al. (2015), Liquid water cloud properties during the Polarimeter Definition Experiment (PODEX), Remote Sensing of Environment, 169, 20-36, doi:10.1016/j.rse.2015.07.029.
- Alexandrov, M. D., et al. (2012), Accuracy assessments of cloud droplet size retrievals from polarized reflectance measurements by the research scanning polarimeter, Remote Sensing of Environment, 125, 92-111, doi:10.1016/j.rse.2012.07.012.
- Alexandrov, M. D., B. Cairns, and M. Mishchenko (2012), Rainbow Fourier transform, J. Quant. Spectrosc. Radiat. Transfer, 113, 2521-2535, doi:10.1016/j.jqsrt.2012.03.025.
- Alexandrov, M. D., A. Marshak, and A. S. Ackerman (2010), Cellular Statistical Models of Broken Cloud Fields. Part I: Theory, J. Atmos. Sci., 67, 2125-2151, doi:10.1175/2010JAS3364.1.
- Alexandrov, M. D., A. S. Ackerman, and A. Marshak (2010), Cellular Statistical Models of Broken Cloud Fields. Part II: Comparison with a Dynamical Model and Statistics of Diverse Ensembles, J. Atmos. Sci., 67, 2152-2170, doi:10.1175/2010JAS3365.1.
- Alexandrov, M. D., et al. (2009), Columnar water vapor retrievals from multifilter rotating shadowband radiometer data, J. Geophys. Res., 114, D02306, doi:10.1029/2008JD010543.
- Alexandrov, M. D., et al. (2008), Characterization of atmospheric aerosols using MFRSR measurements, J. Geophys. Res., 113, D08204, doi:10.1029/2007JD009388.
- Alexandrov, M. D., et al. (2007), Optical depth measurements by shadow-band radiometers and their uncertainties, Appl. Opt., 46, 8027-8038.
- Alexandrov, M. D., et al. (2005), Separation of fine and coarse aerosol modes in MFRSR data sets, J. Geophys. Res., 110, D13204, doi:10.1029/2004JD005226.
- Alexandrov, M. D., et al. (2004), Automated cloud screening algorithm for MFRSR data, Geophys. Res. Lett., 31, L04118, doi:10.1029/2003GL019105.
- Alexandrov, M. D., et al. (2004), Scaling Properties of Aerosol Optical Thickness Retrieved from Ground-Based Measurements, J. Atmos. Sci., 61, 1024-1039.
- Alexandrov, M. D., et al. (2002), Remote Sensing of Atmospheric Aerosols and Trace Gases by Means of Multifilter Rotating Shadowband Radiometer. Part II: Climatological Applications, J. Atmos. Sci., 59, 544-566.
- Alexandrov, M. D., et al. (2002), Remote Sensing of Atmospheric Aerosols and Trace Gases by Means of Multifilter Rotating Shadowband Radiometer. Part I: Retrieval Algorithm, J. Atmos. Sci., 59, 524-543.
- Alexandrov, M. D., and A. Lacis (2000), A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function, Applied Mathematics and Computation, 116, 153-165.
Co-Authored Publications:
- Sorooshian, A., et al. (2023), Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset, Earth Syst. Sci. Data, 15, 3419-3472, doi:10.5194/essd-15-3419-2023.
- Fu, D., et al. (2022), An evaluation of the liquid cloud droplet effective radius derived from MODIS, airborne remote sensing, and in situ measurements from CAMP2 Ex, Atmos. Chem. Phys., doi:10.5194/acp-22-8259-2022.
- Sinclair, K., et al. (2021), Inference of Precipitation in Warm Stratiform Clouds Using Remotely Sensed Observations of the Cloud Top Droplet Size Distribution, Geophys. Res. Lett..
- Miller, D. J., et al. (2020), Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm, Atmos. Meas. Tech., 13, 3447-3470, doi:10.5194/amt-13-3447-2020.
- Sinclair, K., et al. (2020), Observations of Aerosol‐Cloud Interactions During the North Atlantic Aerosol and Marine Ecosystem Study, Geophys. Res. Lett., 47, 1-10, doi:10.1029/2019GL085851.
- Sinclair, K., et al. (2019), Polarimetric retrievals of cloud droplet number concentrations T a,b,⁎ b,c b b,c, Remote Sensing of Environment, 228, 227-240, doi:10.1016/j.rse.2019.04.008.
- Segal-Rozenhaimer, M., et al. (2018), Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations, J. Quant. Spectrosc. Radiat. Transfer, 220, 39-51, doi:10.1016/j.jqsrt.2018.08.030.
- Xu, F., et al. (2018), Coupled Retrieval of Liquid Water Cloud and Above-Cloud Aerosol Properties Using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), J. Geophys. Res., 123, 3175-3204, doi:10.1002/2017JD027926.
- Mishchenko, M., et al. (2016), Multistatic aerosol–cloud lidar in space: A theoretical perspective, J. Quant. Spectrosc. Radiat. Transfer, 184, 180-192, doi:10.1016/j.jqsrt.2016.07.015.
- Geogdzhayev, I., et al. (2013), Statistical analysis of single-track instrument sampling in spaceborne aerosol remote sensing for climate research, J. Quant. Spectrosc. Radiat. Transfer, 121, 69-77, doi:10.1016/j.jqsrt.2013.02.003.
- Knobelspiesse, K., et al. (2012), Analysis of fine-mode aerosol retrieval capabilities by different passive remote sensing instrument designs, Opt. Express, 20, 21457-21484.
- Knobelspiesse, K., et al. (2012), Analysis of fine-mode aerosol retrieval capabilities by different passive remote sensing instrument designs , Optics Express, 20, 21457-21484.
- Zaveri, R. A., et al. (2012), Overview of the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES), Atmos. Chem. Phys., 12, 7647-7687, doi:10.5194/acp-12-7647-2012.
- Kassianov, E., et al. (2010), Retrieval of aerosol optical depth in vicinity of broken clouds from reflectance ratios: case study, Atmos. Meas. Tech., 3, 1333-1349, doi:10.5194/amt-3-1333-2010.
Note: Only publications that have been uploaded to the
ESD Publications database are listed here.