Published (ACROS members in bold)


78. Zheng, J.Zhang, Z.DeSouza-Machado, S.Ryder, C. L.Garnier, A.Di Biagio, C., et al. (2024). Assessment of dust size retrievals based on AERONET: A case study of radiative closure from visible-near-infrared to thermal infraredGeophysical Research Letters51, e2023GL106808.

77. Digby, R. A. R., Gillett, N. P., Monahan, A. H., von Salzen, K., Gkikas, A., Song, Q., and Zhang, Z. (2024): How well do Earth system models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study, Atmos. Chem. Phys., 24, 2077–2097,


76. X. Zheng, Y. Zhang, S. A. Klein, M. Zhang, Z. Zhang, M. Deng, J.Tian, C. Terai, B. Geerts, P. Caldwell, P. A. Bogenschutz, Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global2 Storm-Resolving Simulations 2023 (submitted to GRL)

75. Adeleke S. Ademakinwa, Zahid H. Tushar, Jianyu Zheng, Chenxi Wang, Sanjay Purushotham, Jianwu Wang, Kerry G. Meyer, Tamas Várnai, and Zhibo Zhang. Radiative Closure Studies of How Cloud Property Retrieval Errors due to three-dimensional radiative effects Influence Our Understanding of Broadband Cloud Radiative Effects 2023 (submitted to ACP)

74. S. Rozenhaimer, M.; Nukrai, D.; Che, H.; Wood, R.; Zhang, Z. Cloud Mesoscale Cellular Classification and Diurnal Cycle Using a Convolutional Neural Network (CNN). Remote Sens. 202315, 1607.

73. Zheng, J., Zhang, Z., Yu, H., Garnier, A., Song, Q., Wang, C., Di Biagio, C., Kok, J. F., Derimian, Y., and Ryder, C.: Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations, Atmos. Chem. Phys., 23, 8271–8304,, 2023.


72. Huang, H., Qian, Y., Liu, Y., He, C., Zheng, J., Zhang, Z., and Gkikas, A.: Where does the dust deposited over the Sierra Nevada snow come from?, Atmos. Chem. Phys., 22, 15469–15488,, 2022.

71. Song, Q., Zhang, Z., Yu, H., Kok, J. F., Biagio, C. D., Albani, S., et al. (2022). Size-Resolved Dust Direct Radiative Effect Efficiency Derived from Satellite Observations. Atmospheric Chemistry and Physics, 2022, 1–44.

70. Wang, H., Wang, M., Zhang, Z., Larson, V. E., Griffin, B. M., Guo, Z., et al. (2022). Improving the treatment of subgrid cloud variability in warm rain simulation in CESM2. Journal of Advances in Modeling Earth Systems.

69. Oreopoulos, L., Cho, N., Lee, D., Lebsock, M., & Zhang, Z. (2022). Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction. Journal of Atmospheric and Oceanic Technology.

68. Zhang, Z., Oreopoulos, L., Lebsock, M. D., Mechem, D. B., & Covert, J. (2022). Understanding the microphysical control and spatial-temporal variability of warm rain probability using CloudSat and MODIS observations. Geophysical Research Letters.

67. Ma, P.-L., Harrop, B. E., Larson, V. E., Neale, R. B., Gettelman, A., Morrison, H., et al. (2022). Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1. Geoscientific Model Development, 15(7), 2881–2916.

66. Covert, J. A., Mechem, D. B., & Zhang, Z. (2021). Subgrid-scale horizontal and vertical variation of cloud water in stratocumulus clouds: a case study based on LES and comparisons with in situ observations. Atmospheric Chemistry and Physics, 22(2), 1159–1174.

65: Zheng, J., Zhang, Z., Garnier, A., Yu, H., Song, Q., Wang, C., et al. (2022). The thermal infrared optical depth of mineral dust retrieved from integrated CALIOP and IIR observations. Remote Sensing of Environment, 270, 112841.


64. Zheng, Jianyu, Xin Huang, Supriya Sangondimath, Jianwu Wang, and Zhibo Zhang. (2021). “Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework” Remote Sensing 13, no. 17: 3541.

63: Wang, J., Wood, R., Jensen, M. P., Chiu, J. C., Liu, Y., Lamer, K., et al. (2021). Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Bulletin of the American Meteorological Society, 1–51.

62: Song, Q., Zhang, Z., Yu, H., Ginoux, P., and Shen, J. (2021): Global dust optical depth climatology derived from CALIOP and MODIS aerosol retrievals on decadal timescales: regional and interannual variability, Atmos. Chem. Phys., 21, 13369–13395,, [link to the Global Dust Optical Depth database]

61: Yu, H., Tan, Q., Zhou, L., Zhou, Y., Bian, H., Chin, M., Ryder, C. L., Levy, R. C., Pradhan, Y., Shi, Y., Song, Q., Zhang, Z., Colarco, P. R., Kim, D., Remer, L. A., Yuan, T., Mayol-Bracero, O., and Holben, B. N.: Observation and modeling of a historic African dust intrusion into the Caribbean Basin and the southern U.S. in June 2020, Atmos. Chem. Phys. Discuss. [accepted, in print],, in review, 2021.

60: Lee, Jangho; Shi, Yingxi R.; Cai, Changjie; Ciren, Pubu; Wang, Jianwu; Gangopadhyay, Aryya; Zhang, Zhibo. 2021. “Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation” Remote Sens. 13, no. 3: 456.

59: Zhang, Z., Song, Q., Mechem, D. B., Larson, V. E., Wang, J., Liu, Y., Witte, M. K., Dong, X., and Wu, P.: Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models, Atmos. Chem. Phys., 21, 3103–3121,, 2021.

58: Ming et al. Assessing the influence of COVID-19 on the shortwave radiative fluxes over the East Asian Marginal Seas Geophysical Research Letters,.


57. Teng, S., Liu, C., Zhang, Z., Wang, Y., Sohn, B.‐J., & Yung, Y. L. (2020). Retrieval of ice‐over‐water cloud microphysical and optical properties using passive radiometers. Geophysical Research Letters, 47, e2020GL088941.

56. Wang, C., S. Platnick, K. Meyer, Z. Zhang, and Y. Zhou (2020), A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations, AMT, 13(5), 2257–2277, doi:10.5194/amt-13-2257-2020.

55. Rajapakshe, C., and Z. Zhang (2020), Using polarimetric observations to detect and quantify the three-dimensional radiative transfer effects in passive satellite cloud property retrievals: Theoretical framework and feasibility study, Journal of Quantitative Spectroscopy and Radiative Transfer, 246, 106920, doi:10.1016/j.jqsrt.2020.106920.

54. Alexandrov, M. D., D. J. Miller, C. Rajapakshe, A. Fridlind, B. van Diedenhoven, B. Cairns, A. S. Ackerman, and Z. Zhang (2020), Vertical profiles of droplet size distributions derived from cloud-side observations by the research scanning polarimeter: Tests on simulated data, Atmospheric Research, 239, 104924, doi:10.1016/j.atmosres.2020.104924.

53. Bai, H.Wang, M.Zhang, Z., & Liu, Y. ( 2020). Synergetic satellite trend analysis of aerosol and warm cloud properties over ocean and its implication for aerosol‐cloud interactionsJournal of Geophysical Research: Atmospheres125, e2019JD031598.


52. Song, H., Tian, J., Huang, J., Guo, P., Zhang, Z., Wang, J. (2019). Hybrid Causality Analysis of ENSO’s Global Impacts on Climate Variables Based on Data-Driven Analytics and Climate Model Simulation Frontiers in Earth Science 7(), 233.

51. Yu, H. et al. (2019), Estimates of African Dust Deposition Along the Trans‐Atlantic Transit Using the Decadelong Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI, Journal of Geophysical Research-Atmospheres, 380(4), 112, https://doi:10.1029/2019JD030574.

50. Wang, Z., S. Cui, Z. Zhang*, J. Yang, H. Gao, and F. Zhang (2019), Theoretical extension of universal forward and backward Monte Carlo radiative transfer modeling for passive and active polarization observation simulations, Journal of Quantitative Spectroscopy and Radiative Transfer, 235, 81–94, https://doi:10.1016/j.jqsrt.2019.06.025.

49. Di Noia, A., Hasekamp, O. P., van Diedenhoven, B., and Zhang, Z. *(2019): Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach, Atmos. Meas. Tech., 12, 1697-1716,

48. Zhang, Z*., H. Song, P.-L. Ma, V. E. Larson, M. Wang, X. Dong, and J. Wang (2019), Subgrid variations of the cloud water and droplet number concentration over the tropical ocean: satellite observations and implications for warm rain simulations in climate models, Atmospheric Chemistry and Physics, 19(2), 1077–1096, https://doi:10.5194/acp-19-1077-2019.

47. Wang, C., S. Platnick, T. Fauchez, K. Meyer, Z. Zhang*, H. Iwabuchi, and B. H. Kahn (2019), An assessment of the impacts of cloud vertical heterogeneity on global ice cloud data records from passive satellite retrievals, Journal of Geophysical Research-Atmospheres, https://doi:10.1029/2018JD029681.


46. Wu, P., B. Xi, X. Dong, and Z. Zhang* (2018), Evaluation of autoconversion and accretion enhancement factors in general circulation model warm-rain parameterizations using ground-based measurements over the Azores, Atmospheric Chemistry and Physics18(23), 17405–17420, doi:10.5194/acp-18-17405-2018.

45.Werner, F., Z. Zhang*, G. Wind, D. J. Miller, S. Platnick, and L. Di Girolamo (2018), Improving cloud optical property retrievals for partly cloudy pixels using coincident higher‐resolution single band measurements: A feasibility study using ASTER observations, Journal of Geophysical Research-Atmospheres, doi:10.1029/2018JD028902. (Highlighted by AGU News Link )

44. Song, Q.*Z. Zhang*, H. Yu, S. Kato, P. Yang, P. Colarco, L. A. Remer, and C. L. Ryder (2018), Net radiative effects of dust in the tropical North Atlantic based on integrated satellite observations and in situ measurements, Atmospheric Chemistry and Physics18(15), 11303–11322, doi:10.5194/acp-18-11303-2018.

43. Song, H.*, Zhang, Z.*, Ma, P.-L., Ghan, S., and Wang, M. (2018): The Importance of Considering Sub-grid Cloud Variability When Using Satellite Observations to Evaluate the Cloud and Precipitation Simulations in Climate Models, Geosci. Model Dev. .,

42. Miller, D. J.*, Z. Zhang*, S. Platnick, A. S. Ackerman, F. Werner, C. Cornet, and K. Knobelspiesse (2018), Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator, Atmos. Meas. Tech. Discuss.11(6), 3689–3715, doi:10.5194/amt-11-3689-2018.

41. Grosvenor, D. P. et al. (2018), Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Reviews of Geophysics56(2), 409–453, doi:10.1029/2017RG000593.

40. Werner, F.*, Z. Zhang*, G. Wind, D. J. Miller, and S. Platnick (2018), Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High‐Resolution ASTER Observations, Journal of Geophysical Research-Atmospheres123(8), 4239–4258, doi:10.1002/2017JD027916.

39. Lu, Z., X. Liu, Z. Zhang*, C. Zhao, K. Meyer, C. Rajapakshe*, C. Wu, Z. Yang, and J. E. Penner (2018), Biomass smoke from southern Africa can significantly enhance the brightness of stratocumulus over the southeastern Atlantic Ocean, PNAS115(12), 201713703–2929, doi:10.1073/pnas.1713703115.

38. Bai, H., C. Gong, M. Wang, Z. Zhang*, and T. L’Ecuyer (2018), Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites, Atmospheric Chemistry and Physics18(3), 1763–1783, doi:10.5194/acp-18-1763-2018.

37. Song, H.*, Z. Zhang*, P.-L. Ma, S. J. Ghan, M. Wang, and H. Song (2018), An Evaluation of Marine Boundary Layer Cloud Property Simulations in the Community Atmosphere Model Using Satellite Observations: Conventional Subgrid Parameterization versus CLUBB, Journal of Climate31(6), 2299–2320, doi:10.1175/JCLI-D-17-0277.1.


36. Hui Xu, Jianping Guo, Yuan Wang, Chuanfeng Zhao, Zhibo Zhang*, Min Min, Yucong Miao, Huan Liu, Jing He, Shunwu Zhou, Panmao Zhai (2017), Warming effect of dust aerosols modulated by overlapping clouds below, Atmospheric Environment, Available online 21 July 2017, ISSN 1352-2310, (link)

35. Rajapakshe, C.*, Z. Zhang, J. E. Yorks, H. Yu, Q. Tan, K. Meyer, S. Platnick, and D. M. Winker (2017), Seasonally transported aerosol layers over southeast Atlantic are closer to underlying clouds than previously reported, Geophys. Res. Lett., 44, doi:10.1002/2017GL073559.

34. Zhen Wang*, Shengcheng Cui, Jun Yang, Haiyang Gao, Chao Liu, Zhibo Zhang* (2017) A novel hybrid scattering order-dependent variance reduction method for Monte Carlo simulations of radiative transfer in cloudy atmosphere. JQSRT

33. Zhang, Z.*, X. Dong, B. Xi, H. Song, P.-L. Ma, S. J. Ghan, S. Platnick, and P. Minnis (2017), Intercomparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products, J. Geophys. Res. Atmos., 122, 2351–2365, doi:10.1002/2016JD025763.

32. Steven Platnick, Kerry G. Meyer, Michael D. King, Galina Wind, Nandana Amarasinghe, Benjamin Marchant, G. Thomas Arnold, Zhibo Zhang*, Paul A. Hubanks, Robert E. Holz, Ping Yang, William L. Ridgway, and Jérôme Riedi (2017) The MODIS cloud optical and microphysical products: Updates for Collection 6 and examples from Terra and Aqua, IEEE Transactions on Geoscience and Remote Sensing


31. Werner, F.*, Wind, G., Zhang, Z., Platnick, S., Di Girolamo, L., Zhao, G., Amarasinghe, N., and Meyer, K. (2016): Marine boundary layer cloud property retrievals from high–resolution ASTER observations: Case studies and comparison with Terra–MODIS, Atmos. Meas. Tech. doi:10.5194/amt-2016-265

30. Zhang, Z.*, F. Werner, H. M. Cho, G. Wind, S. Platnick, A. S. Ackerman, L. Di Girolamo, A. Marshak, and K. Meyer (2016), A framework based on 2-D Taylor expansion for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method, Journal of Geophysical Research-Atmospheres, 2016JD024837, doi:10.1002/2016JD024837 (highlighted by EOS of AGU)

29. Wang, C., S. Platnick, Z. Zhang, K. Meyer, and P. Yang (2016), Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content, Journal of Geophysical Research-Atmospheres, 121(10), 5809–5826, doi:10.1002/2015JD024526.

28. Wang, C., S. Platnick, Z. Zhang, K. Meyer, G. Wind, and P. Yang (2016), Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation, Journal of Geophysical Research-Atmospheres, 121(10), 5827–5845, doi:10.1002/2015JD024528.

27. Miller, D. J*., Z. Zhang*, A. S. Ackerman, S. Platnick, and B. A. Baum (2016), The impact of cloud vertical profile on liquid water path retrieval based on the bispectral method: A theoretical study based on large‐eddy simulations of shallow marine boundary layer clouds, Journal of Geophysical Research-Atmospheres, 121(8), 4122–4141, doi:10.1002/2015JD024322.

26. Zhang, Z.*, K. Meyer, H. Yu, S. Platnick, P. Colarco, Z. Liu, and L. Oreopoulos (2016), Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations, ACP, 16(5), 2877–2900, doi:10.5194/acpd-15-26357-2015.


25. H-M Cho*, Z. Zhang, Kerry Meyer, Matthew Lebsock, Steven Platnick, Andrew S. Ackerman, Larry Di Girolamo, Laurent C.-Labonnote, Céline Cornet, Jerome Riedi , Robert Holz: (2015) Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans A comprehensive analysis using A-Train observations (DOI 10.1002/2015JD023161

24. Huang, J., J. Guo, F. Wang, Z. Liu, M.-J. Jeong, H. Yu, and Z. Zhang, (2015) CALIPSO Inferred Most Probable Heights of Global Dust and Smoke Layers, Journal of Geophysical Research-Atmospheres, n/a–n/a, doi:10.1002/2014JD022898.

23. Yu, H., Chin, M., Yuan, T., Bian, H., Remer, L. A., Prospero, J. M. Ali Omar. D. Winker, Y.Yang, Y. Zhang, Z. Zhang and C. Zhao: (2015) The Fertilizing Role of African Dust in the Amazon Rainforest: A First Multiyear Assessment Based on CALIPSO Lidar Observations Geophysical Research Letter. DOI 10.1002/2015GL063040

22. Kerry Meyer, Steve Platnick, Zhibo Zhang: (2015) Simultaneously inferring above-cloud absorbing aerosol optical thickness and the optical and microphysical properties of underlying liquid phase clouds using multiple MODIS spectral channels from the visible to shortwave infrared, JGR

21. Yu, H., Chin, M., Bian, H., Yuan, T., Prospero, J. M., Omar, A. H., L A. Remer, D. M. Winker, Y.Yang, Y. Zhang, Z. Zhang (2015) Quantification of trans-Atlantic dust transport from seven-year (2007–2013) record of CALIPSO lidar measurements. Remote Sensing of Environment. doi:10.1016/j.rse.2014.12.010


20. Min Min* and Z. Zhang, (2014)On the influence of cloud fraction diurnal cycle and sub-grid cloud optical thickness variability on all-sky direct aerosol radiative forcing, JQSRT,

19. Zhang, Z., Meyer, K., Platnick, S., Oreopoulos, L., Lee, D., and Yu, H. (2014).: A novel method for estimating shortwave direct radiative effect of above-cloud aerosols using CALIOP and MODIS data, Atmos. Meas. Tech. Discuss., 6, 9993-10020, doi:10.5194/amtd-6-9993-2013,


18. Zhang, Z. (2013), On the sensitivity of cloud effective radius retrieval based on spectral method to bi-modal droplet size distribution: A semi-analytical model, Journal of Quantitative Spectroscopy and Radiative Transfer VL -, (0 SP – EP – PY – T2 -), doi:10.1016/j.jqsrt.2013.05.033.

17. Yu, H., and Z. Zhang (2013) New Directions: Emerging satellite observations of above-cloud aerosols and direct radiative forcing. Atmos. Environ, 72, 36-40. doi:10.1016/j.atmosenv.2013.02.017


16. Zhang, Z., A. S. Ackerman, G. Feingold, S. Platnick, R. Pincus, and H. Xue (2012), (2012) Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations, J Geophys Res, 117(D19), D19208–, doi:10.1029/2012JD017655.

15. Wang, C., P. Yang, S. Platnick, A. Heidinger, B. Baum, T. Greenwald, Z. Zhang, and R. Holz, (2012) Retrieval of ice cloud properties from AIRS observations based on a fast high-spectral-resolution radiative transfer model. J. Appl. Meteor. Climatol., 52, 710–726

2011 and older

14. Zhang, Z., and S. Platnick, (2011) An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands, J. Geophys. Res., 116, D20215, doi:10.1029/2011JD016216.

13. Baum, B. A., P. Yang, A. J. Heymsfield, C. Schmitt, Y. Xie, A. Bansemer, Y. X. Hu, and Z. Zhang (2011): Improvements to shortwave bulk scattering and absorption models for the remote sensing of ice clouds. Journal of Applied Meteorology and Climatology, 50, 1037-1056

12. Zhang, Z., S. Platnick, P. Yang, A. K. Heidinger, and J. M. Comstock (2010): Effects of ice particle size vertical inhomogeneity on the passive remote sensing of ice clouds, J. Geophysical. Research, 115(D17), D17203.

11. Zhang, Z., P. Yang, G. Kattawar, J. Riedi, L. C.-Labonnote, B. Baum, S. Platnick, and H.-L. Huang (2009): Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison, Atmospheric Chemistry and Physics, 9, 7115-7129.

10. Dessler, A. E., Z. Zhang, and P. Yang: (2008) Water-vapor climate feedback inferred from climate fluctuations, 2003-2008. Geophysical. Research Letters, 35.

9. Dessler, A. E., P. Yang, J. Lee, J. Solbrig, Z. Zhang, K. Minschwaner, N. M. Tech, and N. M. (2008) Socorro: An analysis of the dependence of clear-sky top-of-atmosphere outgoing longwave radiation on atmospheric temperature and water vapor. J. Geophysical. Research, 113, doi:10.1029/2008JD010137.

8. Yang, P., Z. Zhang, G. W. Kattawar, S. G. Warren, B. A. Baum, H.-L. Huang, Y. X. Hu, D. Winker, and J. Iaquinta (2008): Effect of Cavities on the Optical Properties of Bullet Rosettes: Implications for Active and Passive Remote Sensing of Ice Cloud Properties. Journal of Applied Meteorology and Climatology, 47, 2311-2330.

7. Zhang, Z., P. Yang, G. Kattawar, H. L. Huang, T. Greenwald, J. Li, B. A. Baum, D. K. Zhou, and Y. Hu: (2007) A fast infrared radiative transfer model based on the adding-doubling method for hyperspectral remote-sensing applications. Journal of Quantitative Spectroscopy & Radiative Transfer, 105, 243-263.

6. Zhang, Z., P. Yang, G. W. Kattawar, and W. J. Wiscombe (2007). Single-scattering properties of Platonic solids in geometrical-optics regime. Journal of Quantitative Spectroscopy & Radiative Transfer, 106, 595-603.

5. Zhang, Z., P. Yang, G. W. Kattawar, S.-C. Tsay, B. A. Baum, Y. Hu, A. J. Heymsfiel, and J. Reichardt: (2004) Geometrical-optics solution to light scattering by droxtal ice crystals. Applied Optics, 43, 2490-2499.

4. Yang, P., Z. Zhang, B. A. Baum, H. L. Huang, and Y. Hu: (2004) A new look at anomalous diffraction theory (ADT): Algorithm in cumulative projected-area distribution domain and modified ADT. Journal of Quantitative Spectroscopy & Radiative Transfer, 89, 421-442.

Books Chapters

3 Z. Zhang, Steven Platnick, Andrew S. Ackerman, Hyoun-Myoung Cho*: Spectral dependence of MODIS cloud droplet effective radius retrievals for marine boundary layer clouds. (Invited Book Chapter in Light Scattering Review 9: Light Scattering and Radiative Transfer, edited by Alexander Kokhanovsky, Springer-Verlag Berlin Heidelberg)

Conference and Proceedings

2. T. Fauchez, S. Platnick, K. Meyer, O. Sourdeval, C. Cornet, Z. Zhang, F. Szczap (2016) Cirrus heterogeneity effects on cloud optical properties retrieved with an optimal estimation method from MODIS VIS to TIR channels. Proceeding for 2016 International Radiation Symposium, April, New Zealand.

1. Z. Zhang, F. Werner, H.-M. Cho, G. Wind, S. Platnick, A. S. Ackerman, L. Di Girolamo, and A. Marshak, Kerry Meyer (2016) A framework for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method. Proceeding for 2016 International Radiation Symposium, April, New Zealand.