Articles | Volume 18, issue 13
https://doi.org/10.5194/amt-18-3009-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3009-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jul 2025
Research article |
| 10 Jul 2025
| 10 Jul 2025
Errors in stereoscopic retrievals of cloud top height for single-layer clouds
Jesse Loveridge
CORRESPONDING AUTHOR
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
now at: Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80523, USA
Larry Di Girolamo
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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Cloud droplet temperature and lifetime impact cloud microphysical processes such as the activation of ice-nucleating particles. We investigate the thermal and radial evolution of supercooled cloud droplets and their surrounding environments with an aim to better understand observed enhanced ice formation at supercooled cloud edges. This analysis shows that the magnitude of droplet cooling during evaporation is greater than estimated from past studies, especially for drier environments.
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Genevieve Rose Lorenzo, Avelino F. Arellano, Maria Obiminda Cambaliza, Christopher Castro, Melliza Templonuevo Cruz, Larry Di Girolamo, Glenn Franco Gacal, Miguel Ricardo A. Hilario, Nofel Lagrosas, Hans Jarett Ong, James Bernard Simpas, Sherdon Niño Uy, and Armin Sorooshian
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Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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Rose Marie Miller, Robert M. Rauber, Larry Di Girolamo, Matthew Rilloraza, Dongwei Fu, Greg M. McFarquhar, Stephen W. Nesbitt, Luke D. Ziemba, Sarah Woods, and Kenneth Lee Thornhill
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Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
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Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Cited articles
Aerenson, T., Marchand, R., Chepfer, H., and Medeiros, B.: When Will MISR Detect Rising High Clouds?, J. Geophys. Res.-Atmos., 127, e2021JD035865, https://doi.org/10.1029/2021JD035865, 2022.
Albrecht, B., Ghate, V., Mohrmann, J., Wood, R., Zuidema, P., Bretherton, C., Schwartz, C., Eloranta, E., Glienke, S., Donaher, S., Sarkar, M., McGibbon, J., Nugent, A. D., Shaw, R. A., Fugal, J., Minnis, P., Paliknoda, R., Lussier, L., Jensen, J., Vivekanandan, J., Ellis, S., Tsai, P., Rilling, R., Haggerty, J., Campos, T., Stell, M., Reeves, M., Beaton, S., Allison, J., Stossmeister, G., Hall, S., and Schmidt, S.: Cloud System Evolution in the Trades (CSET): Following the Evolution of Boundary Layer Cloud Systems with the NSF–NCAR GV, B. Am. Meteorol. Soc., 100, 93–121, https://doi.org/10.1175/BAMS-D-17-0180.1, 2019.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL atmospheric constituent profiles (0.120km), Air Force Geophys. Lab., Environ. Res. Papers, Tech. Rep. AFGL-TR-86-0110, 1986.
Barnes, W. L., Xiong, X., Guenther, B. W., and Salomonson, V.: Development, characterization, and performance of the EOS MODIS sensors, in: Earth Observing Systems VIII, International Society for Optics and Photonics, 337–345, https://doi.org/10.1117/12.504818, 2003.
Battaglia, A., Kollias, P., Dhillon, R., Lamer, K., Khairoutdinov, M., and Watters, D.: Mind the gap – Part 2: Improving quantitative estimates of cloud and rain water path in oceanic warm rain using spaceborne radars, Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, 2020.
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012.
Beals, M. A., Fugal, J. P., Shaw, R. A., Lu, J., Spuler, S. M., and Stith, J. L.: Holographic measurements of inhomogeneous cloud mixing at the centimeter scale, Science, 350, 87–90, https://doi.org/10.1126/science.aab0751, 2015.
Beekmans, C., Schneider, J., Läbe, T., Lennefer, M., Stachniss, C., and Simmer, C.: Cloud photogrammetry with dense stereo for fisheye cameras, Atmos. Chem. Phys., 16, 14231–14248, https://doi.org/10.5194/acp-16-14231-2016, 2016.
Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data, Earth Space Sci., 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.
Bleyer, M. and Breiteneder, C.: Stereo Matching–State-of-the-Art and Research Challenges, in: Advanced Topics in Computer Vision, edited by: Farinella, G. M., Battiato, S., and Cipolla, R., Springer, London, 143–179, https://doi.org/10.1007/978-1-4471-5520-1_6, 2013.
Boers, R., Spinhirne, J. D., and Hart, W. D.: Lidar Observations of the Fine-Scale Variability of Marine Stratocumulus Clouds, J. Appl. Meteorol. Clim., 27, 797–810, https://doi.org/10.1175/1520-0450(1988)027<0797:LOOTFS>2.0.CO;2, 1988.
Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.: Spatial variability of liquid cloud and rain: observations and microphysical effects, Q. J. Roy. Meteor. Soc., 140, 583–594, https://doi.org/10.1002/qj.2140, 2014.
Braun, S., Stephens, G. L., Berndt, E., Blanchard, Y., Blanchet, J.-P., Carmichael, G., da Silva, A., Ferrare, R., Ivanco, M., Kacenelenbogen, M., Kirschbaum, D., Libois, Q., Mace, G., Omar, A., Petersen, W., Redemann, J., Seidel, F., van den Heever, S., Waliser, D., and Winker, D.: Aerosol, Cloud, Convection, and Precipitation (ACCP) Science & Applications, https://aos.gsfc.nasa.gov/docs/ACCP_Science_Narrative-(Mar2022).pdf (last accessed: 7 July 2025), 2022.
Burns, D., Kollias, P., Tatarevic, A., Battaglia, A., and Tanelli, S.: The performance of the EarthCARE Cloud Profiling Radar in marine stratiform clouds, J. Geophys. Res.-Atmos., 121, 14525–14537, https://doi.org/10.1002/2016JD025090, 2016.
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.
Castro, E., Ishida, T., Takahashi, Y., Kubota, H., Perez, G. J., and Marciano, J. S.: Determination of Cloud-top Height through Three-dimensional Cloud Reconstruction using DIWATA-1 Data, Sci. Rep., 10, 7570, https://doi.org/10.1038/s41598-020-64274-z, 2020.
Chambers, L. H., Wielicki, B. A., and Evans, K. F.: Accuracy of the independent pixel approximation for satellite estimates of oceanic boundary layer cloud optical depth, J. Geophys. Res.-Atmos., 102, 1779–1794, https://doi.org/10.1029/96JD02995, 1997.
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.
Considine, G., Curry, J. A., and Wielicki, B.: Modeling cloud fraction and horizontal variability in marine boundary layer clouds, J. Geophys. Res.-Atmos., 102, 13517–13525, https://doi.org/10.1029/97JD00261, 1997.
Dandini, P., Cornet, C., Binet, R., Fenouil, L., Holodovsky, V., Y. Schechner, Y., Ricard, D., and Rosenfeld, D.: 3D cloud envelope and cloud development velocity from simulated CLOUD (C3IEL) stereo images, Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, 2022.
Davies, R.: The Effect of Finite Geometry on the Three-Dimensional Transfer of Solar Irradiance in Clouds, J. Atmos. Sci., 35, 1712–1725, https://doi.org/10.1175/1520-0469(1978)035<1712:TEOFGO>2.0.CO;2, 1978.
Davies, R., Jovanovic, V. M., and Moroney, C. M.: Cloud heights measured by MISR from 2000 to 2015, J. Geophys. Res.-Atmos., 122, 3975–3986, https://doi.org/10.1002/2017JD026456, 2017.
Davis, A., Marshak, A., Cahalan, R., and Wiscombe, W.: The Landsat Scale Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect: Implications for Cloud Remote Sensing, J. Atmos. Sci., 54, 241–260, https://doi.org/10.1175/1520-0469(1997)054<0241:TLSBIS>2.0.CO;2, 1997.
Davis, A. B., Marshak, A., Gerber, H., and Wiscombe, W. J.: Horizontal structure of marine boundary layer clouds from centimeter to kilometer scales, J. Geophys. Res.-Atmos., 104, 6123–6144, https://doi.org/10.1029/1998JD200078, 1999.
Davis, A. B., Forster, L., Diner, D. J., and Mayer, B.: Toward Cloud Tomography from Space using MISR and MODIS: The Physics of Image Formation for Opaque Convective Clouds, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14537, 27 July 2021a.
Davis, A. B., Forster, L., Diner, D. J., and Mayer, B.: Toward Cloud Tomography from Space using MISR and MODIS: The Physics of Image Formation for Opaque Convective Clouds, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14537, 27 July 2021b.
De Vera, M. V., Di Girolamo, L., Zhao, G., Rauber, R. M., Nesbitt, S. W., and McFarquhar, G. M.: Observations of the macrophysical properties of cumulus cloud fields over the tropical western Pacific and their connection to meteorological variables, Atmos. Chem. Phys., 24, 5603–5623, https://doi.org/10.5194/acp-24-5603-2024, 2024.
Di Girolamo, L. and Davies, R.: Cloud fraction errors caused by finite resolution measurements, J. Geophys. Res.-Atmos., 102, 1739–1756, https://doi.org/10.1029/96JD02663, 1997.
Di Giuseppe, F. and Tompkins, A. M.: Effect of Spatial Organization on Solar Radiative Transfer in Three-Dimensional Idealized Stratocumulus Cloud Fields, J. Atmos. Sci., 60, 1774–1794, https://doi.org/10.1175/1520-0469(2003)060<1774:EOSOOS>2.0.CO;2, 2003.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic retrieval algorithms. II: Adjoint method, J. Quant. Spectrosc Ra., 285, 108177, https://doi.org/10.1016/j.jqsrt.2022.108177, 2022.
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.
Ewald, F., Zinner, T., Kölling, T., and Mayer, B.: Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization, Atmos. Meas. Tech., 12, 1183–1206, https://doi.org/10.5194/amt-12-1183-2019, 2019.
Facciolo, G., de Franchis, C., and Meinhardt, E.: MGM: A Significantly More Global Matching for Stereovision, in: Procedings of the British Machine Vision Conference 2015, 7–10 September 2015, Swansea, UK, edited by: Xianghua Xie, X., Jones, M. W., and Tam, G. K. L., BMVA Press, 90.1–90.12, https://doi.org/10.5244/C.29.90, 2015.
Fielding, M. D., Chiu, J. C., Hogan, R. J., and Feingold, G.: A novel ensemble method for retrieving properties of warm cloud in 3-D using ground-based scanning radar and zenith radiances, J. Geophys. Res.-Atmos., 119, 10912–10930, https://doi.org/10.1002/2014JD021742, 2014.
Fisher, D., Poulsen, C. A., Thomas, G. E., and Muller, J.-P.: Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR, Atmos. Meas. Tech., 9, 909–928, https://doi.org/10.5194/amt-9-909-2016, 2016.
Foley, S. R., Knobelspiesse, K. D., Sayer, A. M., Gao, M., Hays, J., and Hoffman, J.: 3D cloud masking across a broad swath using multi-angle polarimetry and deep learning, Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, 2024.
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R., and Wang, X.: Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5, J. Atmos. Ocean. Tech., 25, 1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008.
Fu, D., Di Girolamo, L., Rauber, R. M., McFarquhar, G. M., Nesbitt, S. W., Loveridge, J., Hong, Y., van Diedenhoven, B., Cairns, B., Alexandrov, M. D., Lawson, P., Woods, S., Tanelli, S., Schmidt, S., Hostetler, C., and Scarino, A. J.: An evaluation of the liquid cloud droplet effective radius derived from MODIS, airborne remote sensing, and in situ measurements from CAMP2Ex, Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, 2022.
Garrett, T. J., Glenn, I. B., and Krueger, S. K.: Thermodynamic Constraints on the Size Distributions of Tropical Clouds, J. Geophys. Res.-Atmos., 123, 8832–8849, https://doi.org/10.1029/2018JD028803, 2018.
Gasteiger, J., Emde, C., Mayer, B., Buras, R., Buehler, S. A., and Lemke, O.: Representative wavelengths absorption parameterization applied to satellite channels and spectral bands, J. Quant. Spectrosc. Ra., 148, 99–115, https://doi.org/10.1016/j.jqsrt.2014.06.024, 2014.
Goldbergs, G., Maier, S. W., Levick, S. R., and Edwards, A.: Limitations of high resolution satellite stereo imagery for estimating canopy height in Australian tropical savannas, Int. J. Appl. Earth Obs., 75, 83–95, https://doi.org/10.1016/j.jag.2018.10.021, 2019.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D. T., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and Clouds: The Software Package OPAC, B. Am. Meteorol. Soc., 79, 831–844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998.
Holz, R. E., Ackerman, S. A., Nagle, F. W., Frey, R., Dutcher, S., Kuehn, R. E., Vaughan, M. A., and Baum, B.: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res.-Atmos., 112, D11205, https://doi.org/10.1029/2008JD009837, 2008.
Horváth, Á. and Davies, R.: Simultaneous retrieval of cloud motion and height from polar-orbiter multiangle measurements, Geophys. Res. Lett., 28, 2915–2918, https://doi.org/10.1029/2001GL012951, 2001.
Iwabuchi, H. and Hayasaka, T.: A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data, Remote Sens. Environ., 88, 294–308, https://doi.org/10.1016/j.rse.2003.08.005, 2003.
Kahn, R. A., Li, W.-H., Moroney, C., Diner, D. J., Martonchik, J. V., and Fishbein, E.: Aerosol source plume physical characteristics from space-based multiangle imaging, J. Geophys. Res.-Atmos., 112, D11205, https://doi.org/10.1029/2006JD007647, 2007.
Kazil, J., Narenpitak, P., Yamaguchi, T., and Feingold, G.: On Climate Change and Trade Cumulus Organization, J. Adv. Model. Earth Sy., 16, e2023MS004057, https://doi.org/10.1029/2023MS004057, 2024.
Killen, R. M. and Ellingson, R. G.: The Effect of Shape and Spatial Distribution of Cumulus Clouds on Longwave Irradiance, J. Atmos. Sci., 51, 2123–2136, https://doi.org/10.1175/1520-0469(1994)051<2123:TEOSAS>2.0.CO;2, 1994.
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review, Surv. Geophys., 38, 1307–1329, https://doi.org/10.1007/s10712-017-9433-3, 2017.
Kobayashi, T.: Reflected Solar Flux for Horizontally Inhomogeneous Atmospheres, J. Atmos. Sci., 48, 2436–2447, https://doi.org/10.1175/1520-0469(1991)048<2436:RSFFHI>2.0.CO;2, 1991.
Kokhanovsky, A.: Optical properties of terrestrial clouds, Earth-Sci. Rev., 64, 189–241, https://doi.org/10.1016/S0012-8252(03)00042-4, 2004.
Kölling, T., Zinner, T., and Mayer, B.: Aircraft-based stereographic reconstruction of 3-D cloud geometry, Atmos. Meas. Tech., 12, 1155–1166, https://doi.org/10.5194/amt-12-1155-2019, 2019.
Koren, I., Oreopoulos, L., Feingold, G., Remer, L. A., and Altaratz, O.: How small is a small cloud?, Atmos. Chem. Phys., 8, 3855–3864, https://doi.org/10.5194/acp-8-3855-2008, 2008.
Lamer, K., Kollias, P., Battaglia, A., and Preval, S.: Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars, Atmos. Meas. Tech., 13, 2363–2379, https://doi.org/10.5194/amt-13-2363-2020, 2020.
Lean, P., Migliorini, S., and Kelly, G.: Understanding Atmospheric Motion Vector Vertical Representativity Using a Simulation Study and First-Guess Departure Statistics, J. Appl. Meteorol. Clim., 54, 2479–2500, https://doi.org/10.1175/JAMC-D-15-0030.1, 2015.
Lehmann, K., Siebert, H., and Shaw, R. A.: Homogeneous and Inhomogeneous Mixing in Cumulus Clouds: Dependence on Local Turbulence Structure, J. Atmos. Sci., 66, 3641–3659, https://doi.org/10.1175/2009JAS3012.1, 2009.
Levis, A., Schechner, Y. Y., Davis, A. B., and Loveridge, J.: Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size, Remote Sensing, 12, 2831, https://doi.org/10.3390/rs12172831, 2020.
Loeb, N. G., Várnai, T., and Winker, D. M.: Influence of Subpixel-Scale Cloud-Top Structure on Reflectances from Overcast Stratiform Cloud Layers, J. Atmos. Sci., 55, 2960–2973, https://doi.org/10.1175/1520-0469(1998)055<2960:IOSSCT>2.0.CO;2, 1998.
Loveridge, J.: Simulated Stereo Cloud Top Height Retrievals, Version v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.14509808, 2024.
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, 2023a.
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 2: Local optimization, Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, 2023b.
Loveridge, J. R. and Di Girolamo, L.: Do Subsampling Strategies Reduce the Confounding Effect of Errors in Bispectral Retrievals on Estimates of Aerosol Cloud Interactions?, J. Geophys. Res.-Atmos., 129, e2023JD040189, https://doi.org/10.1029/2023JD040189, 2024.
Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res.-Atmos., 119, 9441–9462, https://doi.org/10.1002/2013JD021374, 2014.
Marchand, R. T., Ackerman, T. P., and Moroney, C.: An assessment of Multiangle Imaging Spectroradiometer (MISR) stereo-derived cloud top heights and cloud top winds using ground-based radar, lidar, and microwave radiometers, J. Geophys. Res.-Atmos., 112, D06204, https://doi.org/10.1029/2006JD007091, 2007.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products, Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, 2020.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative smoothing in fractal clouds, J. Geophys. Res.-Atmos., 100, 26247–26261, https://doi.org/10.1029/95JD02895, 1995.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Scale Invariance in Liquid Water Distributions in Marine Stratocumulus. Part II: Multifractal Properties and Intermittency Issues, J. Atmos. Sci., 54, 1423–1444, https://doi.org/10.1175/1520-0469(1997)054<1423:SIILWD>2.0.CO;2, 1997.
Martins, J. P. A., Teixeira, J., Soares, P. M. M., Miranda, P. M. A., Kahn, B. H., Dang, V. T., Irion, F. W., Fetzer, E. J., and Fishbein, E.: Infrared sounding of the trade-wind boundary layer: AIRS and the RICO experiment, Geophys. Res. Lett., 37, L24806, https://doi.org/10.1029/2010GL045902, 2010.
Mason, S. L., Barker, H. W., Cole, J. N. S., Docter, N., Donovan, D. P., Hogan, R. J., Hünerbein, A., Kollias, P., Puigdomènech Treserras, B., Qu, Z., Wandinger, U., and van Zadelhoff, G.-J.: An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products, Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, 2024.
McKim, B., Bony, S., and Dufresne, J.-L.: Weak anvil cloud area feedback suggested by physical and observational constraints, Nat. Geosci., 17, 392–397, https://doi.org/10.1038/s41561-024-01414-4, 2024.
Mitra, A., Di Girolamo, L., Hong, Y., Zhan, Y., and Mueller, K. J.: Assessment and Error Analysis of Terra-MODIS and MISR Cloud-Top Heights Through Comparison With ISS-CATS Lidar, J. Geophys. Res.-Atmos., 126, e2020JD034281, https://doi.org/10.1029/2020JD034281, 2021.
Mitra, A., Loveridge, J. R., and Di Girolamo, L.: Fusion of MISR Stereo Cloud Heights and Terra-MODIS Thermal Infrared Radiances to Estimate Two-Layered Cloud Properties, J. Geophys. Res.-Atmos., 128, e2022JD038135, https://doi.org/10.1029/2022JD038135, 2023.
Mueller, K. J., Wu, D. L., Horváth, Á., Jovanovic, V. M., Muller, J.-P., Di Girolamo, L., Garay, M. J., Diner, D. J., Moroney, C. M., and Wanzong, S.: Assessment of MISR Cloud Motion Vectors (CMVs) Relative to GOES and MODIS Atmospheric Motion Vectors (AMVs), J. Appl. Meteorol. Clim., 56, 555–572, https://doi.org/10.1175/JAMC-D-16-0112.1, 2017.
Muller, J. -P., Denis, M. -A., Dundas, R. D., Mitchell, K. L., Naud, C., and Mannstein, H.: Stereo cloud-top heights and cloud fraction retrieval from ATSR-2, Int. J. Remote Sens., 28, 1921–1938, https://doi.org/10.1080/01431160601030975, 2007.
Muller, J.-P., Mandanayake, A., Moroney, C., Davies, R., Diner, D. J., and Paradise, S.: MISR stereoscopic image matchers: techniques and results, IEEE T. Geosci. Remote, 40, 1547–1559, https://doi.org/10.1109/TGRS.2002.801160, 2002.
Naud, C., Muller, J.-P., Haeffelin, M., Morille, Y., and Delaval, A.: Assessment of MISR and MODIS cloud top heights through inter-comparison with a back-scattering lidar at SIRTA, Geophys. Res. Lett., 31, L04114, https://doi.org/10.1029/2003GL018976, 2004.
Naud, C. M., Muller, J.-P., Clothiaux, E. E., Baum, B. A., and Menzel, W. P.: Intercomparison of multiple years of MODIS, MISR and radar cloud-top heights, Ann. Geophys., 23, 2415–2424, https://doi.org/10.5194/angeo-23-2415-2005, 2005.
Neggers, R. A. J., Griewank, P. J., and Heus, T.: Power-Law Scaling in the Internal Variability of Cumulus Cloud Size Distributions due to Subsampling and Spatial Organization, J. Atmos. Sci., 76, 1489–1503, https://doi.org/10.1175/JAS-D-18-0194.1, 2019.
Niclòs, R., Valor, E., Caselles, V., Coll, C., and Sánchez, J. M.: In situ angular measurements of thermal infrared sea surface emissivity–Validation of models, Remote Sens. Environ., 94, 83–93, https://doi.org/10.1016/j.rse.2004.09.002, 2005.
Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and Limperis, T.: Geometrical considerations and nomenclature for reflectance, NBS Monograph 160, https://doi.org/10.6028/NBS.MONO.160, 1977.
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.
Ohring, G., Wielicki, B., Spencer, R., Emery, B., and Datla, R.: Satellite Instrument Calibration for Measuring Global Climate Change: Report of a Workshop, B. Am. Meteorol. Soc., 86, 1303–1314, https://doi.org/10.1175/BAMS-86-9-1303, 2005.
Peng, Z., Yu, D., Huang, D., Heiser, J., Yoo, S., and Kalb, P.: 3D cloud detection and tracking system for solar forecast using multiple sky imagers, Sol. Energy, 118, 496–519, https://doi.org/10.1016/j.solener.2015.05.037, 2015.
Peters, J. M., Morrison, H., Varble, A. C., Hannah, W. M., and Giangrande, S. E.: Thermal Chains and Entrainment in Cumulus Updrafts. Part II: Analysis of Idealized Simulations, J. Atmos. Sci., 77, 3661–3681, https://doi.org/10.1175/JAS-D-19-0244.1, 2020.
Peters, J. M., Morrison, H., Zhang, G. J., and Powell, S. W.: Improving the Physical Basis for Updraft Dynamics in Deep Convection Parameterizations, J. Adv. Model. Earth Sy., 13, e2020MS002282, https://doi.org/10.1029/2020MS002282, 2021.
Platnick, S.: Approximations for horizontal photon transport in cloud remote sensing problems, J. Quant. Spectrosc. Ra., 68, 75–99, https://doi.org/10.1016/S0022-4073(00)00016-9, 2001.
Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, 2015.
Prasad, A. A. and Davies, R.: Detecting tropical thin cirrus using Multiangle Imaging SpectroRadiometer's oblique cameras and modeled outgoing longwave radiation, J. Geophys. Res.-Atmos., 117, D06208, https://doi.org/10.1029/2011JD016798, 2012.
Prasad, A. A. and Davies, R.: An assessment of cirrus heights from MISR oblique stereo using ground-based radar and lidar at the Tropical Western Pacific ARM sites, J. Geophys. Res.-Atmos., 118, 5588–5599, https://doi.org/10.1002/jgrd.50454, 2013.
Radtke, J., Vogel, R., Ament, F., and Naumann, A. K.: Spatial Organisation Affects the Pathway to Precipitation in Simulated Trade-Wind Convection, Geophys. Res. Lett., 50, e2023GL103579, https://doi.org/10.1029/2023GL103579, 2023.
Reid, J. S., Maring, H. B., Narisma, G. T., Heever, S. van den, Girolamo, L. D., Ferrare, R., Lawson, P., Mace, G. G., Simpas, J. B., Tanelli, S., Ziemba, L., Diedenhoven, B. van, Bruintjes, R., Bucholtz, A., Cairns, B., Cambaliza, M. O., Chen, G., Diskin, G. S., Flynn, J. H., Hostetler, C. A., Holz, R. E., Lang, T. J., Schmidt, K. S., Smith, G., Sorooshian, A., Thompson, E. J., Thornhill, K. L., Trepte, C., Wang, J., Woods, S., Yoon, S., Alexandrov, M., Alvarez, S., Amiot, C. G., Bennett, J. R., M., B., Burton, S. P., Cayanan, E., Chen, H., Collow, A., Crosbie, E., DaSilva, A., DiGangi, J. P., Flagg, D. D., Freeman, S. W., Fu, D., Fukada, E., Hilario, M. R. A., Hong, Y., Hristova-Veleva, S. M., Kuehn, R., Kowch, R. S., Leung, G. R., Loveridge, J., Meyer, K., Miller, R. M., Montes, M. J., Moum, J. N., Nenes, T., Nesbitt, S. W., Norgren, M., Nowottnick, E. P., Rauber, R. M., Reid, E. A., Rutledge, S., Schlosser, J. S., Sekiyama, T. T., Shook, M. A., Sokolowsky, G. A., Stamnes, S. A., Tanaka, T. Y., Wasilewski, A., Xian, P., Xiao, Q., Xu, Z., and Zavaleta, J.: The coupling between tropical meteorology, aerosol lifecycle, convection, and radiation, during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex), B. Am. Meteorol. Soc., 104, E1179–E1205, https://doi.org/10.1175/BAMS-D-21-0285.1, 2023.
Smalley, K. M. and Rapp, A. D.: A-Train estimates of the sensitivity of the cloud-to-rainwater ratio to cloud size, relative humidity, and aerosols, Atmos. Chem. Phys., 21, 2765–2779, https://doi.org/10.5194/acp-21-2765-2021, 2021.
Stein, M.: Large Sample Properties of Simulations Using Latin Hypercube Sampling, Technometrics, 29, 143–151, https://doi.org/10.1080/00401706.1987.10488205, 1987.
Tan, Z., Kaul, C. M., Pressel, K. G., Cohen, Y., Schneider, T., and Teixeira, J.: An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified Representation of Subgrid-Scale Turbulence and Convection, J. Adv. Model. Earth Sy., 10, 770–800, https://doi.org/10.1002/2017MS001162, 2018.
Tao, H., Sawhney, H. S., and Kumar, R.: A global matching framework for stereo computation, in: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada, 7–14 July 2001, IEEE, 1, 532–539, https://doi.org/10.1109/ICCV.2001.937562, 2001.
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.
Vaughan, M. A., Powell, K. A., Winker, D. M., Hostetler, C. A., Kuehn, R. E., Hunt, W. H., Getzewich, B. J., Young, S. A., Liu, Z., and McGill, M. J.: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements, J. Atmos. Ocean. Tech., 26, 2034–2050, https://doi.org/10.1175/2009JTECHA1228.1, 2009.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., and Morcette, J.-J.: Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview, IEEE T. Geosci. Remote, 35, 675–686, https://doi.org/10.1109/36.581987, 1997.
Vial, J., Bony, S., Stevens, B., and Vogel, R.: Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review, Surv. Geophys., 38, 1331–1353, https://doi.org/10.1007/s10712-017-9418-2, 2017.
Vial, J., Albright, A. L., Vogel, R., Musat, I., and Bony, S.: Cloud transition across the daily cycle illuminates model responses of trade cumuli to warming, P. Natl. Acad. Sci. USA, 120, e2209805120, https://doi.org/10.1073/pnas.2209805120, 2023.
Volkmer, L., Kölling, T., Zinner, T., and Mayer, B.: Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2024-19, 2024a.
Volkmer, L., Pörtge, V., Jakub, F., and Mayer, B.: Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS, Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, 2024b.
Wielicki, B. A. and Parker, L.: On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution, J. Geophys. Res.-Atmos., 97, 12799–12823, https://doi.org/10.1029/92JD01061, 1992.
Yang, Y. and Di Girolamo, L.: Impacts of 3-D radiative effects on satellite cloud detection and their consequences on cloud fraction and aerosol optical depth retrievals, J. Geophys. Res.-Atmos., 113, D04213, https://doi.org/10.1029/2007JD009095, 2008.
Yin, B., Min, Q., Morgan, E., Yang, Y., Marshak, A., and Davis, A. B.: Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations, Atmos. Meas. Tech., 13, 5259–5275, https://doi.org/10.5194/amt-13-5259-2020, 2020.
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.
Zhao, G. and Di Girolamo, L.: Statistics on the macrophysical properties of trade wind cumuli over the tropical western Atlantic, J. Geophys. Res.-Atmos., 112, D10204, https://doi.org/10.1029/2006JD007371, 2007.
Zhao, M. and Austin, P. H.: Life Cycle of Numerically Simulated Shallow Cumulus Clouds. Part I: Transport, J. Atmos. Sci., 62, 1269–1290, https://doi.org/10.1175/JAS3414.1, 2005.
Zhao, M., Golaz, J.-C., Held, I. M., Ramaswamy, V., Lin, S.-J., Ming, Y., Ginoux, P., Wyman, B., Donner, L. J., Paynter, D., and Guo, H.: Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics, J. Climate, 29, 543–560, https://doi.org/10.1175/JCLI-D-15-0191.1, 2016.
Short summary
Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and often have tenuous boundaries. We evaluate the effect of this on stereoscopy using numerical simulations. Stereoscopic techniques retrieve a cloud boundary that is ~100 m interior to the true boundary and is smoother, depending on the cloud shape and resolution of the instrument. This error is similar across views, demonstrating the strength of stereoscopy for detecting changes in cloud geometry.
Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and...
