Publication Citation
Wu, L., H. Su, and J. H. Jiang (2011), Regional simulations of deep convection and biomass burning over South America: 1. Model evaluations using multiple satellite data sets, J. Geophys. Res., 116, D17208, doi:10.1029/2011JD016105.
Wyant, M. C., et al. (2015), Global and regional modeling of clouds and aerosols in the marine boundary layer during VOCALS: the VOCA intercomparison, Atmos. Chem. Phys., 15, 153-172, doi:10.5194/acp-15-153-2015.
Yan, H., et al. (2014), Long-term aerosol-mediated changes in cloud radiative forcing of deep clouds at the top and bottom of the atmosphere over the Southern Great Plains, Atmos. Chem. Phys., 14, 7113-7124, doi:10.5194/acp-14-7113-2014.
Yang, D., and P. Wang (2010), Spatial Distributions of Atmospheric Radiative Fluxes and Heating Rates over China during Summer, Atmospheric and Oceanic Science Letters, 05, doi:10.1080/16742834.2010.11446877.
Yang, Q., Q. Fu, and Y. Hu (2010), Radiative impacts of clouds in the tropical tropopause layer, J. Geophys. Res., 115, D00H12, doi:10.1029/2009JD012393.
Yang, S., and X. Zou (2012), Assessments of cloud liquid water contributions to GPS radio occultation refractivity using measurements from COSMIC and CloudSat, J. Geophys. Res., 117, D06219, doi:10.1029/2011JD016452.
Yao, Z., et al. (2010), Synergetic use of POLDER and MODIS for multilayered cloud identification, Remote Sensing of Environment, 114, 1910-1923, doi:10.1016/j.rse.2010.03.014.
Yao, Z., et al. (2013), Evaluation of single field-of-view cloud top height retrievals from hyperspectral infrared sounder radiances with CloudSat and CALIPSO measurements, J. Geophys. Res., 118, 9182-9190, doi:10.1002/jgrd.50681.
Yoo, H., and Z. Li (2012), Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products Hyelim Yoo • Zhanqing Li, Clim. Dyn., 39, 2769-2787, doi:10.1007/s00382-012-1430-0.
Yoshida, R., et al. (2010), Global analysis of cloud phase and ice crystal orientation from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data using attenuated backscattering and depolarization ratio, J. Geophys. Res., 115, D00H32, doi:10.1029/2009JD012334.
Yost, C., et al. (2009), Parameterization of cirrus microphysical property profiles using GOES, CloudSat, and CALIPSO data. Eos Trans., AGU, 90, 14-17.
Young, A. H. (2016), The characterization of deep convection in the tropical tropopause layer using active and passive satellite observations, Ph.D. Thesis, School of Earth and Atmospheric Science, Georgia Institute of Technology.
Young, A. H., J. J. Bates, and J. A. Curry (2012), Complementary use of passive and active remote sensing for detection of penetrating convection from CloudSat, CALIPSO, and Aqua MODIS, J. Geophys. Res., 117, D13205, doi:10.1029/2011JD016749.
Young, A. H., J. J. Bates, and J. A. Curry (2013), Application of cloud vertical structure from CloudSat to investigate MODIS-derived cloud properties of cirriform, anvil, and deep convective clouds, J. Geophys. Res., 118, 4689-4699, doi:10.1002/jgrd.50306.
Yuan, R., Z. Wang, and D. Zhang (2015), Quantifying the Hygroscopic Growth of Marine Boundary Layer Aerosols by Satellite-Based and Buoy Observations TAO LUO Department of Atmospheric Science, J. Atmos. Sci., 72, 1063-1074, doi:10.1175/JAS-D-14-0170.1.
Yuan, T., and L. Oreopoulos (2013), On the global character of overlap between low and high clouds, Geophys. Res. Lett., 40, 5320-5326, doi:10.1002/grl.50871.
Yue, Q., et al. (2016), Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train, J. Climate, 29, 2023-2040, doi:10.1175/JCLI-D-15-0257.1.
Zamora, L., and R. Kahn (2020), Saharan dust aerosols change deep convective cloud prevalence, possibly by inhibiting marine new particle formation, J. Climate, 33, 9467-9477, doi:10.1175/JCLI-D-20-0083.1.
Zeng, S., et al. (2014), Study of global cloud droplet number concentration with A-Train satellites, Atmos. Chem. Phys., 14, 7125-7134, doi:10.5194/acp-14-7125-2014.
Zhai, C., J. H. Jiang, and H. Su (2015), Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity, Geophys. Res. Lett., 42, 8729-8737, doi:10.1002/2015GL065911.

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