Publication Citation
Deng, M., J. Mace, and Z. Wang (2016), Anvil Productivities of Tropical Deep Convective Clusters and Their Regional Differences, J. Atmos. Sci., 73, 3467-3487, doi:10.1175/JAS-D-15-0239.1.
Desmons, M., et al. (2013), Improved information about the vertical location and extent of monolayer clouds from POLDER3 measurements in the oxygen A-band, Atmos. Meas. Tech., 6, 2221-2238, doi:10.5194/amt-6-2221-2013.
Devasthale, A., and M. A. Thomas (2012), Sensitivity of Cloud Liquid Water Content Estimates to the Temperature-Dependent Thermodynamic Phase: A Global Study Using CloudSat Data, J. Climate, 25, 7297-7307, doi:10.1175/JCLI-D-11-00521.1.
Di Giuseppe, F., and A. M. Tompkins (2015), Generalizing Cloud Overlap Treatment to Include the Effect of Wind Shear, J. Atmos. Sci., 72, 2865-2876, doi:10.1175/JAS-D-14-0277.1.
Di Michele, S., et al. (2012), Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties, Q. J. R. Meteorol. Soc., 138, 2047-2065.
Díaz, J. P., et al. (2015), WRF multi-physics simulation of clouds in the African region, Q. J. R. Meteorol. Soc., 141, 2737-2749, doi:10.1002/qj.2560.
Doan, K., et al. (2014), Performance Comparison of Big-Data Technologies in Locating Intersections in Satellite Ground Tracks, Conference, Harvard University, December, 2014, 14-16.
Dodson, J. B., D. A. Randall, and K. Suzuki (2013), Comparison of observed and simulated tropical cumuliform clouds by CloudSat and NICAM, J. Geophys. Res., 118, 1852-1867, doi:10.1002/jgrd.50121.
Dolinar, E. K., et al. (2015), Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations, Clim. Dyn., 44, 2229-2247, doi:10.1007/s00382-014-2158-9.
Dongmei, X., T. Auligné, and X. Huang (2015), A Validation of the Multivariate and Minimum Residual Method for Cloud Retrieval Using Radiance from Multiple Satellites, Advances In Atmospheric Sciences, 32, 349-362.
Draft--, -., et al. (2016), Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of In-Situ Observations, Satellite Active Remote Sensing Retrievals and Global Climate Model Simulations, J. Appl. Meteor. Climat..
Eastman, R., and R. Wood (2016), Factors Controlling Low-Cloud Evolution over the Eastern Subtropical Oceans: A Lagrangian Perspective Using the A-Train Satellites, J. Atmos. Sci., 73, 331-351, doi:10.1175/JAS-D-15-0193.1.
Eberhard, G., S. D’Amico, and O. Montenbruck (2007), Autonomous Formation Flying for the PRISMA mission, Journal of Spacecraft and Rockets, 44, 671-681, doi:10.2514/1.23015.
Efon, E., et al. (2016), Cloud properties during active and break spells of the West African summer monsoon from CloudSat–CALIPSO measurements, Journal of Atmospheric and Solar-Terrestrial Physics, 145, 1-11.
Ekström, M., and P. Eriksson (2008), Altitude resolved ice-fraction in the uppermost tropical troposphere, Geophys. Res. Lett., 35, L13822, doi:10.1029/2008GL034305.
Eliasson, S., et al. (2013), Systematic and random errors between collocated satellite ice water path observations, J. Geophys. Res., 118, 2629-2642, doi:10.1029/2012JD018381.
Ellis, T. D., et al. (2009), How often does it rain over the global oceans? The perspective from CloudSat, Geophys. Res. Lett., 36, L03815, doi:10.1029/2008GL036728.
Elsaesser, G., et al. (2017), An improved convective ice parameterization for the NASA GISS Global Climate Model and impacts on cloud ice simulation, J. Clim., 30, 317-336, doi:10.1175/JCLI-D-16-0346.1.
English, J. M., et al. (2014), Contributions of Clouds, Surface Albedos, and Mixed-Phase Ice Nucleation Schemes to Arctic Radiation Biases in CAM5, J. Climate, 27, 5174-5197, doi:10.1175/JCLI-D-13-00608.1.
Eriksson, P., et al. (2008), Comparison between early Odin-SMR, Aura MLS and CloudSat retrievals of cloud ice mass in the upper tropical troposphere, Atmos. Chem. Phys., 8, 1937-1948, doi:10.5194/acp-8-1937-2008.

Pages