Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-3093-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-3093-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Mar 2024
Research article |
| 12 Mar 2024
| 12 Mar 2024
Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect
Adeleke S. Ademakinwa
Department of Physics, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Zahid H. Tushar
Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Jianyu Zheng
Department of Physics, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Chenxi Wang
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory Code 613, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Sanjay Purushotham
Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Jianwu Wang
Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Kerry G. Meyer
Climate and Radiation Laboratory Code 613, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Tamas Várnai
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory Code 613, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Department of Physics, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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Cited articles
Ademakinwa, A.: Dataset for manuscript “Influence of Cloud Retrieval Errors Due to Three Dimensional Radiative Effects on Calculations of Broadband Shortwave Cloud Radiative Effect”, Version v1, Zenodo [data set], https://doi.org/10.5281/zenodo.10511732, 2024.
ARM LASSO Bundle Browser: LASSO LES data, ARM [data set], https://archive.arm.gov/lassobrowser, last access: 19 May 2023.
Barker, H. W., Jerg, M. P., Wehr, T., Kato, S., Donovan, D. P., and Hogan, R. J.: A 3D cloud-construction algorithm for the EarthCARE satellite mission, Q. J. Roy. Meteor. Soc., 137, 1042–1058, https://doi.org/10.1002/qj.824, 2011.
Barker, H. W., Kato, S., and Wehr, T.: Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data, Surv. Geophys., 33, 657–676, https://doi.org/10.1007/s10712-011-9164-9, 2012.
Cahalan, R. F., Oreopoulos, L., Marshak, A., Evans, K. F., Davis, A. B., Pincus, R., Yetzer, K. H., Mayer, B., Davies, R., Ackerman, T. P., Barker, H. W., Clothiaux, E. E., Ellingson, R. G., Garay, M. J., Kassianov, E., Kinne, S., Macke, A., O'Hirok, W., Partain, P. T., Prigarin, S. M., Rublev, A. N., Stephens, G. L., Szczap, F., Takara, E. E., Várnai, T., Wen, G., and Zhuravleva, T. B.: THE I3RC: Bringing Together the Most Advanced Radiative Transfer Tools for Cloudy Atmospheres, B. Am. Meteorol. Soc., 86, 1275–1294, https://doi.org/10.1175/BAMS-86-9-1275, 2005.
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., Di Girolamo, L., C.-Labonnote, L., Cornet, C., Riedi, J., and Holz, R. E.: Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans, J. Geophys. Res.-Atmos., 120, 4132–4154, https://doi.org/10.1002/2015JD023161, 2015.
Coddington, O., Pilewskie, P., Schmidt, K. S., McBride, P. J., and Vukicevic, T.: Characterizing a New Surface-Based Shortwave Cloud Retrieval Technique, Based on Transmitted Radiance for Soil and Vegetated Surface Types, Atmosphere, 4, 48–71, https://doi.org/10.3390/atmos4010048, 2013.
Davis, A. B. and Marshak, A.: Multiple Scattering in Clouds: Insights from Three-Dimensional Diffusion/P1 Theory, Nucl. Sci. Eng., 137, 251–280, https://doi.org/10.13182/NSE01-A2190, 2001.
Di Giuseppe, F. and Tompkins, A. M.: Three-dimensional radiative transfer in tropical deep convective clouds, J. Geophys. Res.-Atmos., 108, 4741, https://doi.org/10.1029/2003JD003392, 2003.
Evans, K. F.: The Spherical Harmonics Discrete Ordinate Method for Three-Dimensional Atmospheric Radiative Transfer, J. Atmos. Sci., 55, 429–446, https://doi.org/10.1175/1520-0469(1998)055<0429:TSHDOM>2.0.CO;2, 1998 (code available at: https://coloradolinux.com/shdom/, last access: 20 December 2022).
Gristey, J. J., Feingold, G., Glenn, I. B., Schmidt, K. S., and Chen, H.: Surface Solar Irradiance in Continental Shallow Cumulus Fields: Observations and Large-Eddy Simulation, J. Atmos. Sci., 77, 1065–1080, https://doi.org/10.1175/JAS-D-19-0261.1, 2020.
Gristey, J. J., Feingold, G., Schmidt, K. S., and Chen, H.: Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance, J. Geophys. Res.-Atmos., 127, e2022JD036822, https://doi.org/10.1029/2022JD036822, 2022.
Gustafson, W. I., Vogelmann, A. M., Li, Z., Cheng, X., Dumas, K. K., Endo, S., Johnson, K. L., Krishna, B., Fairless, T., and Xiao, H.: The Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Activity for Continental Shallow Convection, B. Am. Meteorol. Soc., 101, E462–E479, https://doi.org/10.1175/BAMS-D-19-0065.1, 2020.
Hogan, R. J., Fielding, M. D., Barker, H. W., Villefranque, N., and Schäfer, S. A. K.: Entrapment: An Important Mechanism to Explain the Shortwave 3D Radiative Effect of Clouds, J. Atmos. Sci., 76, 2123–2141, https://doi.org/10.1175/JAS-D-18-0366.1, 2019.
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A., Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M., Charlock, T. P., Stackhouse Jr., P. W., Xu, K.-M., and Collins, W. D.: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J. Geophys. Res.-Atmos., 116, D19209, https://doi.org/10.1029/2011JD016050, 2011.
Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.: Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators, J. Climate, 25, 5190–5207, https://doi.org/10.1175/JCLI-D-11-00469.1, 2012.
Kiehl, J. T. and Trenberth, K. E.: Earth's Annual Global Mean Energy Budget, B. Am. Meteorol. Soc., 78, 197–208, https://doi.org/10.1175/1520-0477(1997)078<0197:EAGMEB>2.0.CO;2, 1997.
Levis, A., Schechner, Y. Y., Aides, A., and Davis, A. B.: Airborne Three-Dimensional Cloud Tomography, in: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015, IEEE, 3379–3387, https://doi.org/10.1109/ICCV.2015.386, 2015.
Liou, K. N.: Radiation and cloud processes in the atmosphere: Theory, observation and modeling, Oxford University Press, 487 pp., ISBN 9780195049107, 1992.
Loeb, N. G. and Manalo-Smith, N.: Top-of-Atmosphere Direct Radiative Effect of Aerosols over Global Oceans from Merged CERES and MODIS Observations, J. Climate, 18, 3506–3526, https://doi.org/10.1175/JCLI3504.1, 2005.
Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation, Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, 2023.
Marshak, A. and Davis, A. B. (Eds.): Horizontal Fluxes and Radiative Smoothing, in: 3D Radiative Transfer in Cloudy Atmospheres, Springer Berlin Heidelberg, Berlin, Heidelberg, 543–586, https://doi.org/10.1007/3-540-28519-9_12, 2005.
Marshak, A., Platnick, S., Várnai, T., Wen, G., and Cahalan, R. F.: Impact of three-dimensional radiative effects on satellite retrievals of cloud droplet sizes, J. Geophys. Res.-Atmos., 111, D09207, https://doi.org/10.1029/2005JD006686, 2006.
Masuda, R., Iwabuchi, H., Schmidt, K. S., Damiani, A., and Kudo, R.: Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer, Remote Sens.-Basel, 11, 1962, https://doi.org/10.3390/rs11171962, 2019.
Miller, D. J., Zhang, Z., Ackerman, A. S., Platnick, S., and Baum, B. A.: 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, J. Geophys. Res.-Atmos., 121, 4122–4141, https://doi.org/10.1002/2015JD024322, 2016.
Miller, D. J., Zhang, Z., Platnick, S., Ackerman, A. S., Werner, F., Cornet, C., and Knobelspiesse, K.: Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator, Atmos. Meas. Tech., 11, 3689–3715, https://doi.org/10.5194/amt-11-3689-2018, 2018.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests, J. Climate, 21, 3642–3659, 2008.
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Nam, C., Bony, S., Dufresne, J. L., and Chepfer, H.: The 'too few, too bright' tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012.
Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network, Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, 2022.
Okamura, R., Iwabuchi, H., and Schmidt, K. S.: Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning, Atmos. Meas. Tech., 10, 4747–4759, https://doi.org/10.5194/amt-10-4747-2017, 2017.
Okata, M., Nakajima, T., Suzuki, K., Inoue, T., Nakajima, T. Y., and Okamoto, H.: A study on radiative transfer effects in 3-D cloudy atmosphere using satellite data, J. Geophys. Res.-Atmos., 122, 443–468, https://doi.org/10.1002/2016JD025441, 2017.
Oreopoulos, L., Cho, N., Lee, D., and Kato, S.: Radiative effects of global MODIS cloud regimes, J. Geophys. Res.-Atmos., 121, 2299–2317, https://doi.org/10.1002/2015JD024502, 2016.
O'Hirok, W. and Gautier, C.: A Three-Dimensional Radiative Transfer Model to Investigate the Solar Radiation within a Cloudy Atmosphere. Part I: Spatial Effects, J. Atmos. Sci., 55, 2162–2179, https://doi.org/10.1175/1520-0469(1998)055<2162:ATDRTM>2.0.CO;2, 1998.
Pincus, R.: The I3RC Community Monte Carlo Radiative Transfer Model, GitHub [code], https://github.com/RobertPincus/i3rc-monte-carlo-model (last access: 1 March 2024), 2009.
Pincus, R. and Evans, K. F.: Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131–3146, https://doi.org/10.1175/2009JAS3137.1, 2009.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., and Holz, R. E.: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, 2016.
Rajapakshe, C. and Zhang, Z.: Using polarimetric observations to detect and quantify the three-dimensional radiative transfer effects in passive satellite cloud property retrievals: Theoretical framework and feasibility study, J. Quant. Spectrosc. Ra., 246, 106920, https://doi.org/10.1016/j.jqsrt.2020.106920, 2020.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2288, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Singer, C. E., Lopez-Gomez, I., Zhang, X., and Schneider, T.: Top-of-Atmosphere Albedo Bias from Neglecting Three-Dimensional Cloud Radiative Effects, J. Atmos. Sci., 78, 4053–4069, https://doi.org/10.1175/JAS-D-21-0032.1, 2021.
Song, H., Zhang, Z., Ma, P.-L., Ghan, S. J., and Wang, M.: An Evaluation of Marine Boundary Layer Cloud Property Simulations in the Community Atmosphere Model Using Satellite Observations: Conventional Subgrid Parameterization versus CLUBB, J. Climate, 31, 2299–2320, https://doi.org/10.1175/JCLI-D-17-0277.1, 2018.
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M. (Eds.): IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., ISBN 9781107661820, 2013.
Tompkins, A. M. and Di Giuseppe, F.: Generalizing Cloud Overlap Treatment to Include Solar Zenith Angle Effects on Cloud Geometry, J. Atmos. Sci., 64, 2116–2125, https://doi.org/10.1175/JAS3925.1, 2007.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1, 2009.
Vardavas, I. and Taylor, F.: Radiation and Climate: Atmospheric energy budget from satellite remote sensing, International Series of Monographs on Physics, 138, Oxford University Press, Oxford, UK, 512 pp., ISBN 9780199697144, 2011.
Várnai, T.: Influence of Three-Dimensional Radiative Effects on the Spatial Distribution of Shortwave Cloud Reflection, J. Atmos. Sci., 57, 216–229, https://doi.org/10.1175/1520-0469(2000)057<0216:IOTDRE>2.0.CO;2, 2000.
Várnai, T. and Davies, R.: Effects of Cloud Heterogeneities on Shortwave Radiation: Comparison of Cloud-Top Variability and Internal Heterogeneity, J. Atmos. Sci., 56, 4206–4224, https://doi.org/10.1175/1520-0469(1999)056<4206:EOCHOS>2.0.CO;2, 1999.
Várnai, T. and Marshak, A.: Statistical Analysis of the Uncertainties in Cloud Optical Depth Retrievals Caused by Three-Dimensional Radiative Effects, J. Atmos. Sci., 58, 1540–1548, https://doi.org/10.1175/1520-0469(2001)058<1540:SAOTUI>2.0.CO;2, 2001.
Várnai, T. and Marshak, A.: Observations of Three-Dimensional Radiative Effects that Influence MODIS Cloud Optical Thickness Retrievals, J. Atmos. Sci., 59, 1607–1618, https://doi.org/10.1175/1520-0469(2002)059<1607:OOTDRE>2.0.CO;2, 2002.
Várnai, T. and Marshak, A.: View angle dependence of cloud optical thicknesses retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS), J. Geophys. Res.-Atmos., 112, D06203, https://doi.org/10.1029/2005JD006912, 2007.
Welch, R. M. and Wielicki, B. A.: Stratocumulus Cloud Field Reflected Fluxes: The Effect of Cloud Shape, J. Atmos. Sci., 41, 3085–3103, https://doi.org/10.1175/1520-0469(1984)041<3085:SCFRFT>2.0.CO;2, 1984.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Smith, G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment, B. Am. Meteorol. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J. Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012.
Zhang, Z. and Platnick, S.: An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands, J. Geophys. Res.-Atmos., 116, D20215, https://doi.org/10.1029/2011JD016216, 2011.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue, H.: 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.-Atmos., 117, D19208, https://doi.org/10.1029/2012JD017655, 2012.
Zhang, Z., Werner, F., Cho, H. M., Wind, G., Platnick, S., Ackerman, A. S., Di Girolamo, L., Marshak, A., and Meyer, K.: A framework based on 2-D Taylor expansion for quantifying the impacts of subpixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bispectral method, J. Geophys. Res.-Atmos., 121, 7007–7025, https://doi.org/10.1002/2016JD024837, 2016.
Zhang, Z., Dong, X., Xi, B., Song, H., Ma, P.-L., Ghan, S. J., Platnick, S., and Minnis, P.: 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, https://doi.org/10.1002/2016JD025763, 2017.
Zhuravleva, T. B., Kabanov, D. M., Sakerin, S. M., and Firsov, K. M.: Simulation of aerosol direct radiative forcing under typical summer conditions of Siberia. Part 1. Method of calculation and choice of input parameters, Atmospheric and Oceanic Optics, 22, 63–73, https://doi.org/10.1134/S1024856009010102, 2009.
Short summary
Clouds play a critical role in our climate system. At present and in the near future, satellite-based remote sensing is the only means to obtain regional and global observations of cloud properties. The current satellite remote sensing algorithms are mostly based on the so-called 1D radiative transfer. This deviation from the 3D world reality can lead to large errors. In this study we investigate how this error affects our estimation of cloud radiative effects.
Clouds play a critical role in our climate system. At present and in the near future,...
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