Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-6951-2026
© Author(s) 2026. 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-26-6951-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2026
Research article |
| 22 May 2026
| 22 May 2026
Toward less subjective metrics for quantifying the shape and organization of clouds
Thomas D. DeWitt
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E Rm 819, Salt Lake City, UT 84112, USA
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E Rm 819, Salt Lake City, UT 84112, USA
Karlie N. Rees
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E Rm 819, Salt Lake City, UT 84112, USA
Related authors
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Suzanne Crumeyrolle, Quentin Coopman, Eric Bourrianne, Clara Lapointe, Eloise Delbarre, Elise Devigne, Olivier Pujol, Romain De Filippi, and Timothy J. Garrett
EGUsphere, https://doi.org/10.5194/egusphere-2026-706, https://doi.org/10.5194/egusphere-2026-706, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This study presents a novel methodology to improve our understanding and the estimation of aerosol–cloud interactions (ACI). Its innovation lies in the synergy between in-situ measurements and remote sensing from ground based and satellite observations. This approach aims to refine the characterization of cloud properties susceptibilities to aerosol burden as a function of particle physicochemical properties, offering new insights for climate modeling and atmospheric process understanding.
Timothy J. Garrett and Matheus R. Grasselli
EGUsphere, https://doi.org/10.5194/egusphere-2026-1304, https://doi.org/10.5194/egusphere-2026-1304, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Widely used Integrated Assessment Model (IAM) predictions of the impact of climate change on the economy do not directly consider inflation. Here we introduce an economic identity we term Wealth – a metric of the entirety of civilization – that permits inflationary forces within IAMs, but reveals implausibly modest levels at odds with historical evidence. Within a more physics-based framework, we show global hyperinflationary shocks will quite plausibly emerge within the next half century.
Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak
Atmos. Chem. Phys., 25, 16729–16746, https://doi.org/10.5194/acp-25-16729-2025, https://doi.org/10.5194/acp-25-16729-2025, 2025
Short summary
Short summary
Accurate snowfall prediction requires quantifying how snowflakes interact with atmospheric turbulence. Using field-based imaging techniques, we directly measured the mass, size, density, and fall speed of snowflakes in surface-layer turbulence. We found that turbulence and microstructure jointly modulate fall speed, often deviating from the terminal velocity in still air. These results inform new parameterizations for numerical weather and climate models.
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, https://doi.org/10.5194/amt-17-4581-2024, 2024
Short summary
Short summary
Accurate measurements of the properties of snowflakes are challenging to make. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), is shown to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density. The method exploits the rate of heat transfer during the melting of a snowflake on a hotplate.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Timothy J. Garrett, Matheus R. Grasselli, and Stephen Keen
Earth Syst. Dynam., 13, 1021–1028, https://doi.org/10.5194/esd-13-1021-2022, https://doi.org/10.5194/esd-13-1021-2022, 2022
Short summary
Short summary
Current world economic production is rising relative to energy consumption. This increase in
production efficiencysuggests that carbon dioxide emissions can be decoupled from economic activity through technological change. We show instead a nearly fixed relationship between energy consumption and a new economic quantity, historically cumulative economic production. The strong link to the past implies inertia may play a more dominant role in societal evolution than is generally assumed.
Karlie N. Rees and Timothy J. Garrett
Atmos. Meas. Tech., 14, 7681–7691, https://doi.org/10.5194/amt-14-7681-2021, https://doi.org/10.5194/amt-14-7681-2021, 2021
Short summary
Short summary
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties according to World Meteorological Organization standards. Measurement accuracy depends on instrument sampling area, time interval, and precipitation rate. Simulations are compared with field measurements taken by an emerging hotplate precipitation sensor. We find that the current collection area is sufficient for light rain, but a larger collection area is required to detect moderate to heavy rain.
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Cited articles
Ackerman, S. A., Strabala, K., Menzel, W., Frey, R., Moeller, C., and Gumley, L.: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141–32157, 1998. a
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P., Trewin, B., Achuta Rao, K., Adhikary, B., Allan, R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J., Cassou, C., Cherchi, A., Collins, W., Collins, W., Connors, S., Corti, S., Cruz, F., Dentener, F., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes, F., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E., Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J., Gillett, N., Goldfarb, L., Gorodetskaya, I., Gutierrez, J., Hamdi, R., Hawkins, E., Hewitt, H., Hope, P., Islam, A., Jones, C., Kaufman, D., Kopp, R., Kosaka, Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock, T., Meinshausen, M., Min, S.-K., Monteiro, P., Ngo-Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A., Ruiz, L., Sallée, J.-B., Samset, B., Sathyendranath, S., Seneviratne, S., Sörensson, A., Szopa, S., Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R., von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 33–144, https://doi.org/10.1017/9781009157896.002, 2021. a
Bazell, D. and Desert, F. X.: Fractal Structure of Interstellar Cirrus, Astrophys. J., 333, 353, https://doi.org/10.1086/166751, 1988. a, b
Benner, T. C. and Curry, J. A.: Characteristics of small tropical cumulus clouds and their impact on the environment, J. Geophys. Res.-Atmos., 103, 28753–28767, 1998. a
Bock, L. and Lauer, A.: Cloud properties and their projected changes in CMIP models with low to high climate sensitivity, Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, 2024. a
Bony, S., Schulz, H., Vial, J., and Stevens, B.: Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions, Geophys. Res. Lett., 47, e2019GL085988, https://doi.org/10.1029/2019GL085988, 2020. a, b
Brinkhoff, L., von Savigny, C., Randall, C., and Burrows, J.: The fractal perimeter dimension of noctilucent clouds: Sensitivity analysis of the area–perimeter method and results on the seasonal and hemispheric dependence of the fractal dimension, J. Atmos. Sol.-Terr. Phys., 127, 66–72, https://doi.org/10.1016/j.jastp.2014.06.005, 2015. a, b, c, d
Carvalho, L. M. V. and Dias, M. A. F. S.: An Application of Fractal Box Dimension to the Recognition of Mesoscale Cloud Patterns in Infrared Satellite Images, J. Appl. Meteorol., 37, 1265–1282, https://doi.org/10.1175/1520-0450(1998)037<1265:AAOFBD>2.0.CO;2, 1998. a, b
Ceppi, P., Brient, F., Zelinka, M. D., and Hartmann, D. L.: Cloud feedback mechanisms and their representation in global climate models, WIREs Clim. Change, 8, e465, https://doi.org/10.1002/wcc.465, 2017. a
Cheraghalizadeh, J., Luković, M., and Najafi, M. N.: Simulating cumulus clouds based on self-organized criticality, Phys. A, 636, 129553, https://doi.org/10.1016/j.physa.2024.129553, 2024. a
DeWitt, T.: objscale: Object-based analysis functions for fractal dimensions and size distributions, Zenodo [code and data set], https://doi.org/10.5281/zenodo.16114657, 2025. a, b
Gifford, F. A.: The Shape of Large Tropospheric Clouds, or “Very Like a Whale”, B. Am. Meteorol. Soc., 70, 468–475, https://doi.org/10.1175/1520-0477(1989)070<0468:TSOLTC>2.0.CO;2, 1989. a, b, c
Gotoh, K. and Fujii, Y.: A Fractal Dimensional Analysis on the Cloud Shape Parameters of Cumulus over Land, J. Appl. Meteorol., 37, 1283–1292, https://doi.org/10.1175/1520-0450(1998)037<1283:AFDAOT>2.0.CO;2, 1998. a, b
Grassberger, P. and Procaccia, I.: Characterization of strange attractors, Phys. Rev. Lett., 50, 346, https://doi.org/10.1103/PhysRevLett.50.346, 1983. a
Hentschel, H. G. E. and Procaccia, I.: Relative diffusion in turbulent media: The fractal dimension of clouds, Phys. Rev. A, 29, 1461–1470, https://doi.org/10.1103/PhysRevA.29.1461, 1984. a, b
Howard, L.: On the Modification of Clouds, Printed for the author, available at Met Office Digital Library & Archive, https://www.metoffice.gov.uk/research/library-and-archive/archive-hidden-treasures/on-the-modification-of-clouds-1803 (last access: 1 November 2024), 1803. a
Jayanthi, N., Gupta, A., and SELVAM, A. M.: The fractal geometry of winter monsoon clouds over the Indian region, MAUSAM, 41, 69–72, https://doi.org/10.54302/mausam.v41i4.2787, 1990. a, b
Kuo, K.-S., Welch, R. M., Weger, R. C., Engelstad, M. A., and Sengupta, S.: The three-dimensional structure of cumulus clouds over the ocean: 1. Structural analysis, J. Geophys. Res.-Atmos., 98, 20685–20711, 1993. a
Lovejoy, S. and Schertzer, D.: Multifractals, universality classes and satellite and radar measurements of cloud and rain fields, J. Geophys. Res.-Atmos., 95, 2021–2034, https://doi.org/10.1029/JD095iD03p02021, 1990. a
Lovejoy, S. and Schertzer, D.: Towards a new synthesis for atmospheric dynamics: Space–time cascades, Atmos. Res., 96, 1–52, https://doi.org/10.1016/j.atmosres.2010.01.004, 2010. a
Lovejoy, S. and Schertzer, D.: The weather and climate: emergent laws and multifractal cascades, Cambridge University Press, ISBN 9781139612326, 2013. a
Lovejoy, S., Schertzer, D., and Tsonis, A. A.: Functional Box-Counting and Multiple Elliptical Dimensions in Rain, Science, 235, 1036–1038, https://doi.org/10.1126/science.235.4792.1036, 1987. a, b, c
Luo, Z. and Liu, C.: A validation of the fractal dimension of cloud boundaries, Geophys. Res. Lett., 34, https://doi.org/10.1029/2006GL028472, 2007. a, b
Mandelbrot, B.: How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension, Science, 156, 636–638, https://doi.org/10.1126/science.156.3775.636, 1967. a
Müller, S. K. and Hohenegger, C.: Self-Aggregation of Convection in Spatially Varying Sea Surface Temperatures, J. Adv. Model. Earth Sy., 12, e2019MS001698, https://doi.org/10.1029/2019MS001698, 2020. a
Nastrom, G., Gage, K. S., and Jasperson, W.: Kinetic energy spectrum of large-and mesoscale atmospheric processes, Nature, 310, 36–38, 1984. a
Noether, E.: Invariante Variationsprobleme, Springer Berlin Heidelberg, Berlin, Heidelberg, 231–239, https://doi.org/10.1007/978-3-642-39990-9_13, 1918. a
Raghunathan, G. N., Blossey, P., Boeing, S., Denby, L., Ghazayel, S., Heus, T., Kazil, J., and Neggers, R.: Flower-Type Organized Trade-Wind Cumulus: A Multi-Day Lagrangian Large Eddy Simulation Intercomparison Study, J. Adv. Model. Earth Sy., 17, e2024MS004864, https://doi.org/10.1029/2024MS004864, 2025. a
Rees, K. N., Garrett, T. J., DeWitt, T. D., Bois, C., Krueger, S. K., and Riedi, J. C.: A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales, Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, 2024. a, b, c
Sengupta, S. K., Welch, R. M., Navar, M. S., Berendes, T. A., and Chen, D. W.: Cumulus Cloud Field Morphology and Spatial Patterns Derived from High Spatial Resolution Landsat Imagery, J. Appl. Meteorol. Clim., 29, 1245–1267, https://doi.org/10.1175/1520-0450(1990)029<1245:CCFMAS>2.0.CO;2, 1990. a, b, c
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
Shukla, J., Hagedorn, R., Hoskins, B., Kinter, J., Marotzke, J., Miller, M., Palmer, T. N., and Slingo, J.: Revolution in Climate Prediction is both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction, B. Am. Meteorol. Soc., 90, 175–178, https://doi.org/10.1175/2008BAMS2759.1, 2009. a
Stephens, G. L., O'Brien, D., Webster, P. J., Pilewski, P., Kato, S., and Li, J.-l.: The albedo of Earth, Rev. Geophys., 53, 141–163, https://doi.org/10.1002/2014RG000449, 2015. a
Stevens, B., Bony, S., Brogniez, H., Hentgen, L., Hohenegger, C., Kiemle, C., L'Ecuyer, T. S., Naumann, A. K., Schulz, H., Siebesma, P. A., Vial, J., Winker, D. M., and Zuidema, P.: Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds, Q. J. Roy. Meteor. Soc., 146, 141–152, https://doi.org/10.1002/qj.3662, 2020. a, b, c
Strogatz, S. H.: Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering, CRC press, https://doi.org/10.1201/9780429398490, 2018. a, b, c
Stumpf, M. P. H. and Porter, M. A.: Critical Truths About Power Laws, Science, 335, 665–666, https://doi.org/10.1126/science.1216142, 2012. a, b
von Savigny, C., Brinkhoff, L. A., Bailey, S. M., Randall, C. E., and Russell III, J. M.: First determination of the fractal perimeter dimension of noctilucent clouds, Geophys. Res. Lett., 38, https://doi.org/10.1029/2010GL045834, 2011. a, b, c, d
Wood, R. and Field, P. R.: The distribution of cloud horizontal sizes, J. Climate, 24, 4800–4816, 2011. a
Yamaguchi, T. and Feingold, G.: On the size distribution of cloud holes in stratocumulus and their relationship to cloud-top entrainment, Geophys. Res. Lett., 40, 2450–2454, 2013. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Short summary
Clouds appear chaotic, but they in fact follow fractal mathematical patterns similar to coastlines. We measured their fractal properties using satellite images and found two key numbers that describe cloud shapes: one for how rough individual cloud edges are, and another for how clouds of different sizes organize together. We recommend methodology that provides objective ways to verify whether climate models accurately simulate real clouds.
Clouds appear chaotic, but they in fact follow fractal mathematical patterns similar to...
Altmetrics
Final-revised paper
Preprint