Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-497-2024
© 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/npg-31-497-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
21 Oct 2024
Research article | Highlight paper |
| 21 Oct 2024
| 21 Oct 2024
A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales
Karlie N. Rees
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
Thomas D. DeWitt
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112, USA
Corey Bois
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112, USA
Steven K. Krueger
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, Rm 819, Salt Lake City, UT 84112, USA
Jérôme C. Riedi
LOA – Laboratoire d'Optique Atmosphérique, CNRS, UMR 8518, University of Lille, 59000 Lille, France
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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
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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.
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
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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.
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
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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.
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
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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
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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.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
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In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
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
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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.
Xavier Ceamanos, Bruno Six, Suman Moparthy, Dominique Carrer, Adèle Georgeot, Josef Gasteiger, Jérôme Riedi, Jean-Luc Attié, Alexei Lyapustin, and Iosif Katsev
Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, https://doi.org/10.5194/amt-16-2575-2023, 2023
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A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean from geostationary meteorological satellites is proposed and successfully evaluated with reference ground-based and satellite data. The high-temporal-resolution aerosol observations that are obtained from the EUMETSAT Meteosat Second Generation mission are unprecedented and open the door to studies that cannot be conducted with the once-a-day observations available from low-Earth-orbit satellites.
Huazhe Shang, Souichiro Hioki, Guillaume Penide, Céline Cornet, Husi Letu, and Jérôme Riedi
Atmos. Chem. Phys., 23, 2729–2746, https://doi.org/10.5194/acp-23-2729-2023, https://doi.org/10.5194/acp-23-2729-2023, 2023
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We find that cloud profiles can be divided into four prominent patterns, and the frequency of these four patterns is related to intensities of cloud-top entrainment and precipitation. Based on these analyses, we further propose a cloud profile parameterization scheme allowing us to represent these patterns. Our results shed light on how to facilitate the representation of cloud profiles and how to link them to cloud entrainment or precipitating status in future remote-sensing applications.
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
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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
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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
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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
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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.
Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali
Atmos. Meas. Tech., 14, 1801–1816, https://doi.org/10.5194/amt-14-1801-2021, https://doi.org/10.5194/amt-14-1801-2021, 2021
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This research estimates the magnitude of a motion-induced error in the measurement of polarimetric state of light by a planned instrument on a future satellite. We discovered that the motion-induced error can not be cancelled out by spatiotemporal averaging, but it can be predicted from the along-track change of the intensity of light. With the estimated statistics and the simulation model, this research paves a way to provide pixel-level quality information in the future satellite products.
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, https://doi.org/10.5194/amt-14-1127-2021, 2021
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Snow measurements are very sensitive to wind. Here, we compare airflow and snowfall simulations to Arctic observations for a Multi-Angle Snowflake Camera to show that measurements of fall speed, orientation, and size are accurate only with a double wind fence and winds below 5 m s−1. In this case, snowflakes tend to fall with a nearly horizontal orientation; the largest flakes are as much as 5 times more likely to be observed. Adjustments are needed for snow falling in naturally turbulent air.
Steven K. Krueger
Atmos. Chem. Phys., 20, 7895–7909, https://doi.org/10.5194/acp-20-7895-2020, https://doi.org/10.5194/acp-20-7895-2020, 2020
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When CCN are injected into a turbulent cloud chamber at a constant rate, and the rate of droplet activation is balanced by the rate of droplet fallout, a steady-state droplet size distribution (DSD) can be achieved. Analytic DSDs and PDFs of droplet radius were derived for such conditions when there is uniform supersaturation. Given the chamber height, the analytic PDF is determined by the supersaturation alone. This could allow one to infer the supersaturation that produced a measured PDF.
Céline Cornet, Laurent C.-Labonnote, Fabien Waquet, Frédéric Szczap, Lucia Deaconu, Frédéric Parol, Claudine Vanbauce, François Thieuleux, and Jérôme Riédi
Atmos. Meas. Tech., 11, 3627–3643, https://doi.org/10.5194/amt-11-3627-2018, https://doi.org/10.5194/amt-11-3627-2018, 2018
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Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo of the cloudy scenes, effective radius and variance of the cloud droplet size distribution, cloud top pressure and aerosol above cloud are analyzed.
Mathias Gergely, Steven J. Cooper, and Timothy J. Garrett
Atmos. Chem. Phys., 17, 12011–12030, https://doi.org/10.5194/acp-17-12011-2017, https://doi.org/10.5194/acp-17-12011-2017, 2017
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This study investigates the importance of snowflake surface-area-to-volume ratio (SAV) for the interpretation of snowfall triple-frequency radar signatures. The results indicate that snowflake SAV has a strong impact on modeled snowfall radar signatures and therefore may be used to further constrain (the large variety and high natural variability of) snowflake shape for snowfall remote sensing, e.g., to distinguish graupel snow from snowfall characterized by large aggregate snowflakes.
Guillaume Merlin, Jérôme Riedi, Laurent C. Labonnote, Céline Cornet, Anthony B. Davis, Phillipe Dubuisson, Marine Desmons, Nicolas Ferlay, and Frédéric Parol
Atmos. Meas. Tech., 9, 4977–4995, https://doi.org/10.5194/amt-9-4977-2016, https://doi.org/10.5194/amt-9-4977-2016, 2016
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The vertical distribution of cloud cover has a significant impact on a large number of meteorological and climatic processes. Cloud top altitude (CTOP) and cloud geometrical thickness (CGT) are essential for understanding these processes. Previous studies established the possibility of retrieving those parameters from multi-angular oxygen A-band measurements. Here we perform a study and comparison of the performance of future instruments.
Husi Letu, Hiroshi Ishimoto, Jerome Riedi, Takashi Y. Nakajima, Laurent C.-Labonnote, Anthony J. Baran, Takashi M. Nagao, and Miho Sekiguchi
Atmos. Chem. Phys., 16, 12287–12303, https://doi.org/10.5194/acp-16-12287-2016, https://doi.org/10.5194/acp-16-12287-2016, 2016
Souichiro Hioki, Ping Yang, Bryan A. Baum, Steven Platnick, Kerry G. Meyer, Michael D. King, and Jerome Riedi
Atmos. Chem. Phys., 16, 7545–7558, https://doi.org/10.5194/acp-16-7545-2016, https://doi.org/10.5194/acp-16-7545-2016, 2016
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The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multi-directional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed, which provides a more noise-resilient roughness estimate than the conventional approach. A global one-month data sample shows the use and the limit of a severely roughened ice habit to simulate the polarized reflectivity.
Quentin Coopman, Timothy J. Garrett, Jérôme Riedi, Sabine Eckhardt, and Andreas Stohl
Atmos. Chem. Phys., 16, 4661–4674, https://doi.org/10.5194/acp-16-4661-2016, https://doi.org/10.5194/acp-16-4661-2016, 2016
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We analyze interactions of Arctic clouds with pollution plumes that have been transported long distances from midlatitudes. Constraining for meteorological state, we find that pollution decreases cloud-droplet effective radius and increases cloud optical depth. The impact is highest when the atmosphere is particularly humid and/or stable suggesting that aerosol–cloud interactions depend on the Arctic's climate.
Benjamin Marchant, Steven Platnick, Kerry Meyer, G. Thomas Arnold, and Jérôme Riedi
Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, https://doi.org/10.5194/amt-9-1587-2016, 2016
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The current paper presents the new MODIS Collection 6 (C6) cloud thermodynamic phase classification algorithm. To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
T. J. Garrett
Earth Syst. Dynam., 6, 673–688, https://doi.org/10.5194/esd-6-673-2015, https://doi.org/10.5194/esd-6-673-2015, 2015
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GCMs and economic models are often coupled for climate scenarios. Here, what is examined is how well a simple non-equilibrium thermodynamic model can represent the multi-decadal growth of global civilization. Initialized with growth trends from the 1950s, the model attains high skill at hindcasting how fast the GDP and energy consumption grew during the 2000s. This opens treating the coupled economy and climate as a physically deterministic response to available flows of energy and matter.
S. Zeng, J. Riedi, C. R. Trepte, D. M. Winker, and Y.-X. Hu
Atmos. Chem. Phys., 14, 7125–7134, https://doi.org/10.5194/acp-14-7125-2014, https://doi.org/10.5194/acp-14-7125-2014, 2014
B. H. Cole, P. Yang, B. A. Baum, J. Riedi, and L. C.-Labonnote
Atmos. Chem. Phys., 14, 3739–3750, https://doi.org/10.5194/acp-14-3739-2014, https://doi.org/10.5194/acp-14-3739-2014, 2014
F. Waquet, F. Peers, P. Goloub, F. Ducos, F. Thieuleux, Y. Derimian, J. Riedi, M. Chami, and D. Tanré
Atmos. Chem. Phys., 14, 1755–1768, https://doi.org/10.5194/acp-14-1755-2014, https://doi.org/10.5194/acp-14-1755-2014, 2014
S. Zeng, J. Riedi, F. Parol, C. Cornet, and F. Thieuleux
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-6-8371-2013, https://doi.org/10.5194/amtd-6-8371-2013, 2013
Revised manuscript has not been submitted
A. K. Kochanski, M. A. Jenkins, J. Mandel, J. D. Beezley, C. B. Clements, and S. Krueger
Geosci. Model Dev., 6, 1109–1126, https://doi.org/10.5194/gmd-6-1109-2013, https://doi.org/10.5194/gmd-6-1109-2013, 2013
T. J. Garrett and C. Zhao
Atmos. Meas. Tech., 6, 1227–1243, https://doi.org/10.5194/amt-6-1227-2013, https://doi.org/10.5194/amt-6-1227-2013, 2013
F. Waquet, C. Cornet, J.-L. Deuzé, O. Dubovik, F. Ducos, P. Goloub, M. Herman, T. Lapyonok, L. C. Labonnote, J. Riedi, D. Tanré, F. Thieuleux, and C. Vanbauce
Atmos. Meas. Tech., 6, 991–1016, https://doi.org/10.5194/amt-6-991-2013, https://doi.org/10.5194/amt-6-991-2013, 2013
B. van Diedenhoven, B. Cairns, A. M. Fridlind, A. S. Ackerman, and T. J. Garrett
Atmos. Chem. Phys., 13, 3185–3203, https://doi.org/10.5194/acp-13-3185-2013, https://doi.org/10.5194/acp-13-3185-2013, 2013
Related subject area
Subject: Scaling, multifractals, turbulence, complex systems, self-organized criticality | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Part 1: Multifractal analysis of wind turbine power and the associated biases
Part 2: Joint multifractal analysis of available wind power and rain intensity from an operational wind farm
Stieltjes functions and spectral analysis in the physics of sea ice
Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud?
Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality
Characteristics of intrinsic non-stationarity and its effect on eddy-covariance measurements of CO2 fluxes
How many modes are needed to predict climate bifurcations? Lessons from an experiment
Non-linear hydrologic organization
The impact of entrained air on ocean waves
Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data
Jerry Jose, Auguste Gires, Yelva Roustan, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-5, https://doi.org/10.5194/npg-2024-5, 2024
Revised manuscript accepted for NPG
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Wind energy exhibits extreme variability in space and time. However, they also show scaling properties (properties that remain similar across different time and space of measurement), this can be quantified using appropriate statistical tools. In this line, the scaling properties of power from a wind farm are analyzed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit in the data. This bias is identified here using data and numerical simulations.
Jerry Jose, Auguste Gires, Ernani Schnorenberger, Yelva Roustan, Daniel Schertzer, and Ioulia Tchiguirinskaia
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-6, https://doi.org/10.5194/npg-2024-6, 2024
Revised manuscript accepted for NPG
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To understand the influence of rainfall on wind power production, turbine power and rainfall were simultaneously measured in an operational wind farm and subjected to analysis. The correlation between wind, wind power, air density and other fields was obtained across various temporal scales during rain and dry conditions. An increase in correlation was observed with an increase in rain; rain also influenced the correspondence between actual and expected values of power at various velocities.
Kenneth M. Golden, N. Benjamin Murphy, Daniel Hallman, and Elena Cherkaev
Nonlin. Processes Geophys., 30, 527–552, https://doi.org/10.5194/npg-30-527-2023, https://doi.org/10.5194/npg-30-527-2023, 2023
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Our paper tours powerful methods of finding the effective behavior of complex systems, which can be applied well beyond the initial setting of sea ice. Applications include transport properties of porous and polycrystalline media, such as rocks and glacial ice, and advection diffusion processes that arise throughout geophysics. Connections to random matrix theory establish unexpected parallels of these geophysical problems with semiconductor physics and Anderson localization phenomena.
Shaun Lovejoy
Nonlin. Processes Geophys., 30, 311–374, https://doi.org/10.5194/npg-30-311-2023, https://doi.org/10.5194/npg-30-311-2023, 2023
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How big is a cloud?and
How long does the weather last?require scaling to answer. We review the advances in scaling that have occurred over the last 4 decades: (a) intermittency (multifractality) and (b) stratified and rotating scaling notions (generalized scale invariance). Although scaling theory and the data are now voluminous, atmospheric phenomena are too often viewed through an outdated scalebound lens, and turbulence remains confined to isotropic theories of little relevance.
Hans von Storch
Nonlin. Processes Geophys., 30, 31–36, https://doi.org/10.5194/npg-30-31-2023, https://doi.org/10.5194/npg-30-31-2023, 2023
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Climate science is, as all sciences, a social process and as such conditioned by the zeitgeist of the time. It has an old history and has attained different political significances. Today, it is the challenge of anthropogenic climate change – and societies want answers about how to deal with it. In earlier times, it was mostly the ideology of climate determinism which led people to construct superiority and eventually colonialism.
Lei Liu, Yu Shi, and Fei Hu
Nonlin. Processes Geophys., 29, 123–131, https://doi.org/10.5194/npg-29-123-2022, https://doi.org/10.5194/npg-29-123-2022, 2022
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We find a new kind of non-stationarity. This new kind of non-stationarity is caused by the intrinsic randomness. Results show that the new kind of non-stationarity is widespread in small-scale variations of CO2 turbulent fluxes. This finding reminds us that we need to handle the short-term averaged turbulent fluxes carefully, and we also need to re-screen the existing non-stationarity diagnosis methods because they could make a wrong diagnosis due to this new kind of non-stationarity.
Bérengère Dubrulle, François Daviaud, Davide Faranda, Louis Marié, and Brice Saint-Michel
Nonlin. Processes Geophys., 29, 17–35, https://doi.org/10.5194/npg-29-17-2022, https://doi.org/10.5194/npg-29-17-2022, 2022
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Present climate models discuss climate change but show no sign of bifurcation in the future. Is this because there is none or because they are in essence too simplified to be able to capture them? To get elements of an answer, we ran a laboratory experiment and discovered that the answer is not so simple.
Allen Hunt, Boris Faybishenko, and Behzad Ghanbarian
Nonlin. Processes Geophys., 28, 599–614, https://doi.org/10.5194/npg-28-599-2021, https://doi.org/10.5194/npg-28-599-2021, 2021
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The same power law we previously used to quantify growth of tree roots in time describes equally the assemblage of river networks in time. Even the basic length scale of both networks is the same. The one difference is that the basic time scale is ca. 10 times shorter for drainage networks than for tree roots, since the relevant flow rate is 10 times faster. This result overturns the understanding of drainage networks and forms a basis to organize thoughts about surface and subsurface hydrology.
Juan M. Restrepo, Alex Ayet, and Luigi Cavaleri
Nonlin. Processes Geophys., 28, 285–293, https://doi.org/10.5194/npg-28-285-2021, https://doi.org/10.5194/npg-28-285-2021, 2021
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A homogenization of Navier–Stokes to wave scales allows us to determine that air bubbles suspended near the ocean surface modify the momentum equation, specifically enhancing the vorticity in the flow. A model was derived that relates the rain rate to the production of air bubbles near the ocean surface. At wave scales, the air bubbles enhance the wave dissipation for small gravity or capillary waves.
Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer
Nonlin. Processes Geophys., 27, 133–145, https://doi.org/10.5194/npg-27-133-2020, https://doi.org/10.5194/npg-27-133-2020, 2020
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This paper aims to analyse and simulate correlations between two fields in a scale-invariant framework. It starts by theoretically assessing and numerically confirming the behaviour of renormalized multiplicative power law combinations of two fields with known scale-invariant properties. Then a new indicator of correlation is suggested and tested on rainfall data to study the correlation between the common rain rate and drop size distribution features.
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, 32 141–157, 1998. a
Alder, B. and Wainwright, T.: Decay of the velocity autocorrelation function, Phys. Rev. A, 1, 18, https://doi.org/10.1103/PhysRevA.1.18, 1970. a
Batista-Tomás, A., Díaz, O., Batista-Leyva, A., and Altshuler, E.: Classification and dynamics of tropical clouds by their fractal dimension, Q. J. Roy. Meteorol. Soc., 142, 983–988, 2016. a
Bolgiano Jr., R.: Turbulent spectra in a stably stratified atmosphere, J. Geophys. Res., 64, 2226–2229, 1959. a
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. Phy., 127, 66–72, 2015. a
Ceamanos, X., Six, B., and Riedi, J.: Quasi-Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS-GEO, J. Geophys. Res.-Atmos., 126, e2021JD034906, https://doi.org/10.1029/2021JD034906, 2021. a, b
Celani, A., Cencini, M., Mazzino, A., and Vergassola, M.: Active versus Passive Scalar Turbulence, Phys. Rev. Lett., 89, 234502, https://doi.org/10.1103/PhysRevLett.89.234502, 2002. a
Clauset, A., Shalizi, C. R., and Newman, M. E.: Power-law distributions in empirical data, SIAM Revi., 51, 661–703, 2009. a
Derrien, M. and Gléau, H. L.: MSG/SEVIRI cloud mask and type from SAFNWC, Int. J. Remote Sens., 26, 4707–4732, https://doi.org/10.1080/01431160500166128, 2005. a, b, c, d
Derrien, M. and Gléau, H. L.: Improvement of cloud detection near sunrise and sunset by temporal-differencing and region-growing techniques with real-time SEVIRI, Int. J. Remote Sens., 31, 1765–1780, https://doi.org/10.1080/01431160902926632, 2010. a, b, c
DeWitt, T. D. and Garrett, T. J.: Finite domains cause bias in measured and modeled distributions of cloud sizes, Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, 2024. a
Di Girolamo, L. and Davies, R.: Cloud fraction errors caused by finite resolution measurements, J. Geophys. Res.-Atmos., 102, 1739–1756, 1997. a
Fielding, M. D., Schäfer, S. A. K., Hogan, R. J., and Forbes, R. M.: Parametrizing cloud geometry and its application in a subgrid cloud-edge erosion scheme, Q. J. Roy. Meteor. Soc., 146, 1651–1667, https://doi.org/10.1002/qj.3758, 2020. a
Gage, K. S. and Nastrom, G. D.: Theoretical interpretation of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft during GASP, J. Atmos. Sci., 43, 729–740, 1986. a
Gifford, F.: The shape of large tropospheric clouds, or “very like a whale”, B. Am. Meteorol. Soc., 70, 468–475, 1989. a
Heus, T., Van Dijk, G., Jonker, H. J., and Van den Akker, H. E.: Mixing in shallow cumulus clouds studied by Lagrangian particle tracking, J. Atmos. Sci., 65, 2581–2597, 2008. a
ICARE: ICARE Data and Services Center, ICARE [data set], https://www.icare.univ-lille.fr/, last access: 1 March 2023. a
Imre, A.: Problems of measuring the fractal dimension by the slit-island method, Scripta Metall. Mater., 27, 1713–1716, 1992. a
Kantha, L. and Hocking, W.: Dissipation rates of turbulence kinetic energy in the free atmosphere: MST radar and radiosondes, J. Atmos. Sol.-Terr. Phy., 73, 1043–1051, 2011. a
Khairoutdinov, M. F., Krueger, S. K., Moeng, C.-H., Bogenschutz, P. A., and Randall, D. A.: Large-eddy simulation of maritime deep tropical convection, J. Adv. Model. Earth Sy., 1, 15, https://doi.org/10.3894/JAMES.2009.1.15, 2009. a
Kopp, T. J., Thomas, W., Heidinger, A. K., Botambekov, D., Frey, R. A., Hutchison, K. D., Iisager, B. D., Brueske, K., and Reed, B.: The VIIRS Cloud Mask: Progress in the first year of S-NPP toward a common cloud detection scheme, J. Geophys. Res.-Atmos., 119, 2441–2456, https://doi.org/10.1002/2013JD020458, 2014. a, b
Korčák, J.: Deux types fondamentaux de distribution statistique, Bull. Inst. Int. Stat. III, 30, 295–299, 1938. a
Lovejoy, S.: CloudSat Cloud Length, Thickness Distributions Again Confirm the 23/9 (2.55 D) Scaling, Stratified, Turbulence Model, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 11–16 July 2021, Brussels, Belgium, 1727–1730, IEEE, 2021. a
Lovejoy, S.: Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud?, Nonlin. Processes Geophys., 30, 311–374, https://doi.org/10.5194/npg-30-311-2023, 2023. a, b, c
Lovejoy, S. and Schertzer, D.: Generalized scale invariance in the atmosphere and fractal models of rain, Water Resour. Res., 21, 1233–1250, 1985. 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, 1990. a
Lovejoy, S. and Schertzer, D.: Multifractal analysis techniques and the rain and cloud fields from 10−3 to 106 m, Non-linear variability in geophysics: Scaling and fractals, Springer, Dordrecht, 111–144, https://doi.org/10.1007/978-94-009-2147-4_8, 1991. a, b, c
Lovejoy, S. and Schertzer, D.: Multifractals, cloud radiances and rain, J. Hydrol., 322, 59–88, 2006. a
Lovejoy, S. and Schertzer, D.: Haar wavelets, fluctuations and structure functions: convenient choices for geophysics, Nonlin. Processes Geophys., 19, 513–527, https://doi.org/10.5194/npg-19-513-2012, 2012. a
Lovejoy, S., Schertzer, D., Silas, P., Tessier, Y., and Lavallée, D.: The unified scaling model of atmospheric dynamics and systematic analysis of scale invariance in cloud radiances, Ann. Geophys., 11, 119–127, 1993. a
Lovejoy, S., Schertzer, D., and Stanway, J.: Direct evidence of multifractal atmospheric cascades from planetary scales down to 1 km, Phys. Rev. Lett., 86, 5200, https://doi.org/10.1103/PhysRevLett.86.5200, 2001. a
Lovejoy, S., Tuck, A. F., Schertzer, D., and Hovde, S. J.: Reinterpreting aircraft measurements in anisotropic scaling turbulence, Atmos. Chem. Phys., 9, 5007–5025, https://doi.org/10.5194/acp-9-5007-2009, 2009. a
Mandelbrot, B. B.: Self-affine fractals and fractal dimension, Phys. Scripta, 32, 257, https://doi.org/10.1088/0031-8949/32/4/001, 1985. a
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative smoothing in fractal clouds, J. Geophys. Res.-Atmos., 100, 26247–26261, 1995. a
Mieslinger, T., Horváth, Á., Buehler, S. A., and Sakradzija, M.: The Dependence of Shallow Cumulus Macrophysical Properties on Large-Scale Meteorology as Observed in ASTER Imagery, J. Geophys. Res.-Atmos., 124, 11477–11505, https://doi.org/10.1029/2019JD030768, 2019. a
NASA: NASA Earthdata, NASA [data set], https://www.earthdata.nasa.gov/, last access: 1 March 2023. a
Obukhov, A.: Description of turbulence in terms of Lagrangian variables, Adv. Geophys., 6, 113–116, 1959. a
Peters, O., Neelin, J. D., and Nesbitt, S. W.: Mesoscale convective systems and critical clusters, J. Atmos. Sci., 66, 2913–2924, 2009. a
Pressel, K. G. and Collins, W. D.: First-order structure function analysis of statistical scale invariance in the AIRS-observed water vapor field, J. Climate, 25, 5538–5555, 2012. a
Pressel, K. G., Collins, W. D., and Desai, A. R.: The spatial scale dependence of water vapor variability inferred from observations from a very tall tower, J. Geophys. Res.-Atmos., 119, 9822–9837, 2014. a
Read, P. L., Barstow, J., Charnay, B., Chelvaniththilan, S., Irwin, P. G. J., Knight, S., Lebonnois, S., Lewis, S. R., Mendonça, J., and Montabone, L.: Global energy budgets and “Trenberth diagrams” for the climates of terrestrial and gas giant planets, Q. J. Roy. Meteorol. Soc., 142, 703–720, 2016. a
Richardson, L. F.: The problem of contiguity: An appendix to statistics of deadly quarrels, General systems yearbook, 6, 139–187, 1961. a
Sagan, C.: Pale Blue Dot: A Vision of the Human Future in Space, New York, Random House, Inc, ISBN 0-679-43841-6, 1994. a
Schertzer, D. and Lovejoy, S.: On the dimension of atmospheric motions, paper presented at IUTAM Symp. on turbulence and chaotic phenomena in fluids, Kyoto, Japan, 141–144, 1983. a
Schertzer, D. and Lovejoy, S.: Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes, J. Geophys. Res.-Atmos., 92, 9693–9714, 1987. a
Schwertz, F. and Brow, J. E.: Diffusivity of water vapor in some common gases, J. Chem. Phys., 19, 640–646, 1951. a
Siebesma, A. and Jonker, H.: Anomalous scaling of cumulus cloud boundaries, Phys. Rev. Lett., 85, 214, https://doi.org/10.1103/PhysRevLett.85.214, 2000. a, b
Tennekes, H. and Lumley, J. L.: A first course in turbulence, MIT press, ISBN 0262200198, 1972. a
Wood, R. and Field, P. R.: The distribution of cloud horizontal sizes, J. Climate, 24, 4800–4816, 2011. a
Yang, Y., Meyer, K., Wind, G., Zhou, Y., Marshak, A., Platnick, S., Min, Q., Davis, A. B., Joiner, J., Vasilkov, A., Duda, D., and Su, W.: Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation, Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, 2019. a, b
Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating Climate Models' Cloud Feedbacks Against Expert Judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021JD035198, 2022. a
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. a
Zhao, M. and Austin, P. H.: Life cycle of numerically simulated shallow cumulus clouds. Part II: Mixing dynamics, J. Atmos. Sci., 62, 1291–1310, 2005. a
Executive editor
This is an interesting contribution to the long-running debate on the anisotropic scaling of atmospheric dynamics, confirming that it is neither 3D nor 2D, but rather 23/9 D. This is of interest to the whole atmospheric community.
This is an interesting contribution to the long-running debate on the anisotropic scaling of...
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.
The shapes of clouds viewed from space reflect vertical and horizontal motions in the...
