Articles | Volume 20, issue 21
https://doi.org/10.5194/acp-20-13217-2020
© Author(s) 2020. 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-20-13217-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Nov 2020
Research article |
| 10 Nov 2020
| 10 Nov 2020
Environmental sensitivities of shallow-cumulus dilution – Part 1: Selected thermodynamic conditions
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC, Canada
Daniel J. Kirshbaum
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC, Canada
Pavlos Kollias
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Related authors
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 21, 14039–14058, https://doi.org/10.5194/acp-21-14039-2021, https://doi.org/10.5194/acp-21-14039-2021, 2021
Short summary
Short summary
This numerical study provides insights into the sensitivity of shallow-cumulus dilution to geostrophic vertical wind profile. The cumulus dilution is strongly sensitive to vertical wind shear in the cloud layer, with shallow cumuli being more diluted in sheared environments. On the other hand, wind shear in the subcloud layer leads to less diluted cumuli. The sensitivities are explained by jointly considering the impacts of vertical velocity and the properties of the entrained air.
Susmitha Sasikumar, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-3573, https://doi.org/10.5194/egusphere-2025-3573, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The study present a method to estimate how much the radar signal is weakened as it passes through rain or clouds, designed to implement in the new EarthCARE satellite cloud profiling radar data. The approach builds on the method used in the CloudSat mission, with key improvements that make it robust under non-ideal instrument conditions in the early mission phase. This leads to more reliable retrieval of clouds and rainfall during initial satellite operations.
Zackary Mages, Pavlos Kollias, Bernat Puigdomènech Treserras, Paloma Borque, and Mariko Oue
Atmos. Chem. Phys., 25, 6025–6045, https://doi.org/10.5194/acp-25-6025-2025, https://doi.org/10.5194/acp-25-6025-2025, 2025
Short summary
Short summary
Convective clouds are a key component of the climate system. Using remote sensing observations during two field experiments in Houston, Texas, we identify four diurnal patterns of shallow convective clouds. We find areas more frequently experiencing shallow convective clouds, and we find areas where the vertical extent of shallow convective clouds is higher and where they are more likely to precipitate. This provides insight into the complicated environment that forms these clouds in Houston.
Francesco Manconi, Alessandro Battaglia, and Pavlos Kollias
Atmos. Meas. Tech., 18, 2295–2310, https://doi.org/10.5194/amt-18-2295-2025, https://doi.org/10.5194/amt-18-2295-2025, 2025
Short summary
Short summary
The paper aims to study the ground reflection, or clutter, of the signal from a spaceborne radar in the context of ESA's WIVERN (WInd VElocity Radar Nephoscop) mission, which will observe in-cloud winds. Using topography and land type data, with a model of the satellite orbit and rotating antenna, simulations of scans have been run over the Piedmont region of Italy. These measurements cover the full range of the ground clutter over land for WIVERN and have allowed for analyses of the precision and accuracy of velocity observations.
Jialin Yan, Mariko Oue, Pavlos Kollias, Edward Luke, and Fan Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2149, https://doi.org/10.5194/egusphere-2025-2149, 2025
Short summary
Short summary
In this study, we analyzed over six years of ground-based radar and weather balloon data collected in northern Alaska. We found that ice particle changes depend strongly on temperature, humidity conditions and turbulence. We also found that turbulence and the presence of supercooled liquid water often occur together, and when they do, ice particle growth is especially strong. These findings help scientists to improve weather models.
Aida Galfione, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-1914, https://doi.org/10.5194/egusphere-2025-1914, 2025
Short summary
Short summary
Convection drives atmospheric circulation but is difficult to observe and model. EarthCARE's radar provides the first space-based vertical wind data, capturing updrafts and downdrafts. Combined with satellite imagery from other sensors, it offers a broader view of convective storms. While resolution limits detail, cloud-top cooling helps track storm development. This combined approach improves understanding and modeling of convection.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Sarah Wugofski, Matthew R. Kumjian, Mariko Oue, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-671, https://doi.org/10.5194/egusphere-2025-671, 2025
Short summary
Short summary
Doppler spectral data inform on how particles of varying vertical velocities contribute to total backscattered power observed. Through examining three case studies, consistent features in radar moment data were found to be characteristic of multi-modal spectra. We quantified how spectrum width and mean Doppler velocity can be used to determine whether or not a layer is multi-modal. The identification criteria and methods are described in Part 1 and assessed in Part 2.
Marco Coppola, Alessandro Battaglia, Frederic Tridon, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-416, https://doi.org/10.5194/egusphere-2025-416, 2025
Short summary
Short summary
The WIVERN conically scanning Doppler W-band radar, has the potential, for the first time, to map the mesoscale and synoptic variability of cloud dynamics, and precipitation microphysics. This study shows that the oblique angle of incidence will be advantageous compared to standard nadir-looking radars due to substantial clutter suppression over ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Short summary
The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Robin J. Hogan, Anthony J. Illingworth, Pavlos Kollias, Hajime Okamoto, and Ulla Wandinger
Atmos. Meas. Tech., 17, 3081–3083, https://doi.org/10.5194/amt-17-3081-2024, https://doi.org/10.5194/amt-17-3081-2024, 2024
Kristofer S. Tuftedal, Bernat Puigdomènech Treserras, Mariko Oue, and Pavlos Kollias
Atmos. Chem. Phys., 24, 5637–5657, https://doi.org/10.5194/acp-24-5637-2024, https://doi.org/10.5194/acp-24-5637-2024, 2024
Short summary
Short summary
This study analyzed coastal convective cells from June through September 2018–2021. The cells were classified and their lifecycles were analyzed to better understand their characteristics. Features such as convective-core growth, for example, are shown. The study found differences in the initiation location of shallow convection and in the aerosol loading in deep convective environments. This work provides a foundation for future analyses of convection or other tracked events elsewhere.
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
Short summary
Short summary
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.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
David P. Donovan, Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5327–5356, https://doi.org/10.5194/amt-16-5327-2023, https://doi.org/10.5194/amt-16-5327-2023, 2023
Short summary
Short summary
The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation-oriented satellite for climate and weather applications. For this satellite mission to be successful, the development and implementation of new techniques for turning the measured raw signals into useful data is required. This paper describes how atmospheric model data were used as the basis for creating realistic high-resolution simulated data sets to facilitate this process.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
Short summary
Short summary
CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
Short summary
Short summary
We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
Short summary
Short summary
We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Abdanour Irbah, Julien Delanoë, Gerd-Jan van Zadelhoff, David P. Donovan, Pavlos Kollias, Bernat Puigdomènech Treserras, Shannon Mason, Robin J. Hogan, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, https://doi.org/10.5194/amt-16-2795-2023, 2023
Short summary
Short summary
The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are used to probe the Earth's atmosphere by measuring cloud and aerosol profiles. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is the synergy of the measurements of these two instruments that allows a better classification of the atmospheric targets and the description of the associated products, which are the subject of this paper.
Pavlos Kollias, Bernat Puidgomènech Treserras, Alessandro Battaglia, Paloma C. Borque, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, https://doi.org/10.5194/amt-16-1901-2023, 2023
Short summary
Short summary
The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission developed by the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) features the first spaceborne 94 GHz Doppler cloud-profiling radar (CPR) with Doppler capability. Here, we describe the post-processing algorithms that apply quality control and corrections to CPR measurements and derive key geophysical variables such as hydrometeor locations and best estimates of particle sedimentation fall velocities.
Zackary Mages, Pavlos Kollias, Zeen Zhu, and Edward P. Luke
Atmos. Chem. Phys., 23, 3561–3574, https://doi.org/10.5194/acp-23-3561-2023, https://doi.org/10.5194/acp-23-3561-2023, 2023
Short summary
Short summary
Cold-air outbreaks (when cold air is advected over warm water and creates low-level convection) are a dominant cloud regime in the Arctic, and we capitalized on ground-based observations, which did not previously exist, from the COMBLE field campaign to study them. We characterized the extent and strength of the convection and turbulence and found evidence of secondary ice production. This information is useful for model intercomparison studies that will represent cold-air outbreak processes.
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
Short summary
Short summary
This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
Short summary
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
Alessandro Battaglia, Paolo Martire, Eric Caubet, Laurent Phalippou, Fabrizio Stesina, Pavlos Kollias, and Anthony Illingworth
Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, https://doi.org/10.5194/amt-15-3011-2022, 2022
Short summary
Short summary
We present an instrument simulator for a new sensor, WIVERN (WInd VElocity Radar Nephoscope), a conically scanning radar payload with Doppler capabilities, recently down-selected as one of the four candidates for the European Space Agency Earth Explorer 11 program. The mission aims at measuring horizontal winds in cloudy areas. The simulator is instrumental in the definition and consolidation of the mission requirements and the evaluation of mission performances.
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 21, 14039–14058, https://doi.org/10.5194/acp-21-14039-2021, https://doi.org/10.5194/acp-21-14039-2021, 2021
Short summary
Short summary
This numerical study provides insights into the sensitivity of shallow-cumulus dilution to geostrophic vertical wind profile. The cumulus dilution is strongly sensitive to vertical wind shear in the cloud layer, with shallow cumuli being more diluted in sheared environments. On the other hand, wind shear in the subcloud layer leads to less diluted cumuli. The sensitivities are explained by jointly considering the impacts of vertical velocity and the properties of the entrained air.
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Short summary
Multi-wavelength radar measurements provide capabilities to identify ice particle types and growth processes in clouds beyond the capabilities of single-frequency radar measurements. This study introduces Doppler velocity and polarimetric radar observables into the multi-wavelength radar reflectivity measurement to improve identification analysis. The analysis clearly discerns snowflake aggregation and riming processes and even early stages of riming.
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
Short summary
Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
Short summary
Short summary
We compare clouds in different cloud-resolving atmosphere simulations with airborne remote sensing observations. The focus is on warm shallow clouds in the Atlantic trade wind region. Those clouds are climatologically important but challenging for climate models. We use forward operators to apply instrument-specific thresholds for cloud detection to model outputs. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Alessandro Battaglia, Pavlos Kollias, Ranvir Dhillon, Katia Lamer, Marat Khairoutdinov, and Daniel Watters
Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, https://doi.org/10.5194/amt-13-4865-2020, 2020
Short summary
Short summary
Warm rain accounts for slightly more than 30 % of the total rain amount and 70 % of the total rain area in the tropical belt and usually appears in kilometer-size cells. Spaceborne radars adopting millimeter wavelengths are excellent tools for detecting such precipitation types and for separating between the cloud and rain components. Our work highlights the benefits of operating multifrequency radars and discusses the impact of antenna footprints in quantitative estimates of liquid water paths.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Cited articles
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteorol. Soc., 134, 1337–1351,
https://doi.org/10.1002/qj.289, 2008. a, b
Betts, A. K.: Parametric Interpretation of Trade-Wind Cumulus Budget Studies,
J. Atmos. Sci., 32, 1934–1945,
https://doi.org/10.1175/1520-0469(1975)032<1934:PIOTWC>2.0.CO;2, 1975. a, b
Brown, A. R.: Large-Eddy Simulation and Parametrization of the Effects of
Shear on Shallow Cumulus Convection, Bound.-Lay. Meteorol., 91, 65–80,
https://doi.org/10.1023/A:1001836612775, 1999. a
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J.-C.,
Khairoutdinov, J. M., Lewellen, D. C., Lock, A. P., Macvean, M. K., Moeng,
C.-H., Neggers, R. A. J., Siebesma, A. P., and Stevens, B.: Large-eddy
simulation of the diurnal cycle of shallow cumulus convection over land,
Q. J. Roy. Meteorol. Soc., 128, 1075–1094,
https://doi.org/10.1256/003590002320373210, 2002. a, b, c, d, e, f
Brown, R. G. and Zhang, C.: Variability of Midtropospheric Moisture and Its
Effect on Cloud-Top Height Distribution during TOGA COARE*, J. Atmos. Sci., 54, 2760–2774, https://doi.org/10.1175/1520-0469(1997)054<2760:VOMMAI>2.0.CO;2,
1997. a
Bryan, G. H. and Fritsch, J. M.: A Benchmark Simulation for Moist
Nonhydrostatic Numerical Models, Mon. Weather Rev., 130, 2917–2928,
https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2, 2002. a
Dawe, J. T. and Austin, P. H.: Interpolation of LES Cloud Surfaces for Use in
Direct Calculations of Entrainment and Detrainment, Mon. Weather Rev., 139,
444–456, https://doi.org/10.1175/2010MWR3473.1, 2011. a
Dawe, J. T. and Austin, P. H.: Statistical analysis of an LES shallow cumulus
cloud ensemble using a cloud tracking algorithm, Atmos. Chem. Phys., 12,
1101–1119, https://doi.org/10.5194/acp-12-1101-2012, 2012. a, b, c, d
de Rooy, W. C. and Siebesma, A. P.: A Simple Parameterization for Detrainment
in Shallow Cumulus, Mon. Weather Rev., 136, 560–576,
https://doi.org/10.1175/2007MWR2201.1, 2008. a
Del Genio, A. D.: Representing the Sensitivity of Convective Cloud Systems to
Tropospheric Humidity in General Circulation Models, Surv. Geophys., 33,
637–656, https://doi.org/10.1007/s10712-011-9148-9, 2012. a
Del Genio, A. D. and Wu, J.: The Role of Entrainment in the Diurnal Cycle of
Continental Convection, J. Clim., 23, 2722–2738,
https://doi.org/10.1175/2009JCLI3340.1, 2010. a, b, c
Derbyshire, S. H., Beau, I., Bechtold, P., Grandpeix, J.-Y., Piriou, J.-M.,
Redelsperger, J. L., and Soares, P. M. M.: Sensitivity of moist convection
to environmental humidity, Q. J. Roy. Meteorol. Soc., 130, 3055–3079,
https://doi.org/10.1256/qj.03.130, 2004. a, b, c, d
Derbyshire, S. H., Maidens, A. V., Milton, S. F., Stratton, R. A., and Willett,
M. R.: Adaptive detrainment in a convective parametrization, Q. J. Roy. Meteorol. Soc., 137, 1856–1871, https://doi.org/10.1002/qj.875, 2011. a, b
Endo, S., Fridlind, A. M., Lin, W., Vogelmann, A. M., Toto, T., Ackerman,
A. S., McFarquhar, G. M., Jackson, R. C., Jonsson, H. H., and Liu, Y.:
RACORO continental boundary layer cloud investigation: 2. Large-eddy
simulations of cumulus clouds and evaluation with in situ and ground-based
observation, J. Geophys. Res.-Atmos., 120, 5993–6014,
https://doi.org/10.1002/2014JD022525, 2015. a
Gerber, H. E., Frick, G. M., Jensen, J. B., and Hudson, J. G.: Entrainment,
Mixing and Microphysics in Trade-Wind Cumulus, J. Meteorol. Soc. Jpn. A,
86, 87–106, https://doi.org/10.2151/jmsj.86A.87, 2008. a, b
Grant, A. L. M.: Precipitating convection in cold air: Virtual potential
temperature structure, Q. J. Roy. Meteorol. Soc., 133, 25–36,
https://doi.org/10.1002/qj.2, 2007. a
Grant, A. L. M. and Brown, A. R.: A similarity hypothesis for shallow-cumulus
transports, Q. J. Roy. Meteorol. Soc., 125, 1913–1936,
https://doi.org/10.1002/qj.49712555802, 1999. a, b, c
Gregory, D.: Estimation of entrainment rate in simple models of convective
clouds, Q. J. Roy. Meteorol. Soc., 127, 53–72,
https://doi.org/10.1002/qj.49712757104, 2001. a
Hanley, K. E., Plant, R. S., Stein, T. H. M., Hogan, R. J., Nicol, J. C., Lean,
H. W., Halliwel, C., and Clark, P. A.: Mixing‐length controls on
high‐resolution simulations of convective storm, Q. J. Roy. Meteorol. Soc., 141, 272–284, https://doi.org/10.1002/qj.2356, 2015. a
Hannah, W. M.: Entrainment versus Dilution in Tropical Deep Convection, J. Atmos. Sci., 74, 3725–3747, https://doi.org/10.1175/JAS-D-16-0169.1, 2017. a, b
Heus, T. and Junker, H. J. J.: Subsiding Shells around Shallow Cumulus
Clouds, J. Atmos. Sci., 65, 1003–1018, https://doi.org/10.1175/2007JAS2322.1, 2008. a
Holland, J. Z. and Rasmusson, E. M.: Measurement of Atmospheric Mass, Energy,
and Momentum Budgets Over a 500-Kilometer Square of Tropical Ocean, Mon. Weather Rev., 101, 44–55,
https://doi.org/10.1175/1520-0493(1973)101<0044:MOTAME>2.3.CO;2, 1973. a
Holloway, C. E. and Neelin, J. D.: Moisture Vertical Structure, Column Water
Vapor, and Tropical Deep Convection, J. Atmos. Sci., 66, 1665–1683,
https://doi.org/10.1175/2008JAS2806.1, 2009. a
Houghton, H. G. and Cramer, H. E.: A Theory of Entrainment in Convective
Currents, J. Meteorol., 8, 95–102,
https://doi.org/10.1175/1520-0469(1951)008<0095:ATOEIC>2.0.CO;2, 1951. a
Jiang, H., Xue, H., Teller, A., Feingold, G., and Levin, Z.: Aerosol effects
on the lifetime of shallow cumulus, Geophys. Res. Lett., 33, L14806,
https://doi.org/10.1029/2006GL026024, 2006. a
Kain, J. S. and Fritsch, J. M.: A One-Dimensional Entraining/Detraining Plume
Model and Its Application in Convective Parameterization, J. Atmos. Sci.,
47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990. a
Klocke, D., Pincus, R., and Quaas, J.: On Constraining Estimates of Climate
Sensitivity with Present-Day Observations through Model Weighting, J. Clim., 24, 6092–6099, https://doi.org/10.1175/2011JCLI4193.1, 2011. a
Knight, C. G., Knight, S. H. E., Massey, N., Aina, T., Christensen, C., Frame,
D. J., Kettleborough, J. A., Martin, A., Pascoe, S., Sanderson, B.,
Stainforth, D. A., and Allen, M. R.: Association of parameter, software, and
hardware variation with large-scale behavior across 57,000 climate models,
P. Natl. Acad. Sci. USA, 104, 12259–12264,
https://doi.org/10.1073/pnas.0608144104, 2007. a
Krueger, S. K.: Fine-scale modeling of entrainment and mixing of cloudy and clear air, 15th International Conference on Clouds and Precipitation, Cancun, Mexico, 2008. a
Lamer, K. and Kollias, P.: Observations of fair-weather cumuli over land:
Dynamical factors controlling cloud size and cover, Geophys. Res. Lett.,
42, 8693–8701, https://doi.org/10.1002/2015GL064534, 2015. a
Lin, C.: Some Bulk Properties of Cumulus Ensembles Simulated by a
Cloud-Resolving Model. Part II: Entrainment Profiles, J. Atmos. Sci., 56,
3736–3748, https://doi.org/10.1175/1520-0469(1999)056<3736:SBPOCE>2.0.CO;2, 1999. a
Lu, C., Niu, S., Liu, Y., and Vogelmann, A. M.: Empirical relationship between
entrainment rate and microphysics in cumulus clouds, Geophys. Res. Lett.,
40, 2333–2338, https://doi.org/10.1002/grl.50445, 2013. a
Lu, C., Liu, Y., Zhang, G., Wu, X., Endo, S., Cao, L., Li, Y., and Guo, X.:
Improving Parameterization of Entrainment Rate for Shallow Convection with
Aircraft Measurements and Large-Eddy Simulation, J. Atmos. Sci., 73,
761–773, https://doi.org/10.1175/JAS-D-15-0050.1, 2016. a
Lu, C., Sun, C., Liu, Y., Zhang, G., Lin, Y., Gao, W., Niu, S., Yin, Y., Qiu,
Y., and Jin, L.: Observational Relationship Between Entrainment Rate
Environmental Relative Humidity and Implications for Convection
Parameterization, Geophys. Res. Lett., 45, 13495–13504,
https://doi.org/10.1029/2018GL080264, 2018. a, b, c, d, e
Murphy, J. M., Sexton, D. H., Barnett, D. N., Jones, G. S., Webb, M. J.,
Collins, M., and Stainforth, D. A.: Quantification of modelling
uncertainties in a large ensemble of climate change simulations, Nature,
430, 768–772, 2004. a
Neggers, R. A. J., Siebesma, A. P., and Jonker, H. J. J.: A Multiparcel Model
for Shallow Cumulus Convection, J. Atmos. Sci., 59, 1655–1668,
https://doi.org/10.1175/1520-0469(2002)059<1655:AMMFSC>2.0.CO;2, 2002. a, b
Neggers, R. A. J., Duynkerke, P. G., and Rodts, S. M. A.: Shallow cumulus
convection: A validation of large-eddy simulation against aircraft and
Landsat observations, Q. J. Roy. Meteorol. Soc., 129, 2671–2696,
https://doi.org/10.1256/qj.02.93, 2003a. a
Neggers, R. A. J., Jonker, H. J. J., and Siebesma, A. P.: Size Statistics of
Cumulus Cloud Populations in Large-Eddy Simulations, J. Atmos. Sci., 60,
1060–1074, https://doi.org/10.1175/1520-0469(2003)60<1060:SSOCCP>2.0.CO;2,
2003b. a
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. a
Norris, J. R.: Low Cloud Type over the Ocean from Surface Observations. Part
II: Geographical and Seasonal Variations, J. Clim,, 11, 383–403,
https://doi.org/10.1175/1520-0442(1998)011<0383:LCTOTO>2.0.CO;2, 1998. a
Raga, G. B., Jensen, J. B., and Baker, M. B.: Characteristics of Cumulus Band
Clouds off the Coast of Hawaii, J. Atmos. Sci., 47, 338–355,
https://doi.org/10.1175/1520-0469(1990)047<0338:COCBCO>2.0.CO;2, 1990. a
Rauber, R., Stevens, B., III, H. T. O., Knight, C., Albrecht, B. A., Blyth, A.,
Fairall, C., Jensen, J. B., Lasher-Trapp, S. G., Mayol-Bracero, O. L., Vali,
G., Anderson, J. R., Baker, B. A., Bandy, A. R., Burnet, F., Brenguier,
J.-L., Brewer, W. A., Brown, P. R. A., Chuang, P., Cotton, W. R., Girolamo,
L. D., Geerts, B., Gerber, H., Göke, S., Gomes, L., Heikes, B. G., Hudson,
J. G., Kollias, P., Lawson, R. P., Krueger, S. K., Lenschow, D. H., Nuijens,
L., O'Sullivan, D. W., Rilling, R. A., Rogers, D. C., Siebesma, A. P.,
Snodgrass, E., Stith, J. L., Thornton, D., Tucker, S., Twohy, C. H., and
Zuidema, P.: Rain in shallow cumulus over the ocean – the RICO campaign,
Bull. Am. Meteorol. Soc., 88, 1912–1928, https://doi.org/10.1175/BAMS-88-12-1912,
2007. a
Raymond, D. J. and Blyth, A. M.: A Stochastic Mixing Model for
Nonprecipitating Cumulus Clouds, J. Atmos. Sci., 43, 2708–2718,
https://doi.org/10.1175/1520-0469(1986)043<2708:ASMMFN>2.0.CO;2, 1986. a
Rieck, M., Hohenegger, C., and van Heerwaarden, C. C.: The Influence of Land
Surface Heterogeneities on Cloud Size Development, Mon. Weather Rev., 142,
3830–3846, https://doi.org/10.1175/MWR-D-13-00354.1, 2014. a, b, c
Romps, D. M.: A Direct Measure of Entrainment, J. Atmos. Sci., 67,
1908–1927, https://doi.org/10.1175/2010JAS3371.1, 2010. a
Romps, D. M. and Kuang, Z.: Nature versus Nurture in Shallow Convection, J. Atmos. Sci., 67, 1655–1666,
https://doi.org/10.1175/2009JAS3307.1, 2010. a, b
Rougier, J., Sexton, D. M. H., Murphy, J. M., and Stainforth, D.: Analyzing
the Climate Sensitivity of the HadSM3 Climate Model Using Ensembles from
Different but Related Experiments, J. Clim., 22, 3540–3557,
https://doi.org/10.1175/2008JCLI2533.1, 2009. a
Sanderson, B. M., Piani, C., Ingram, W. J., Stone, D. A., and Allen, M. R.:
Towards contraining climate sensitivity by linear analysis of feedback
patterns in thousands of perturbed-physics GCM simulations, Clim. Dynam., 30,
175–190, https://doi.org/10.1007/s00382-007-0280-7, 2008. a
Seigel, R. B.: Shallow Cumulus Mixing and Subcloud-Layer Responses to
Variations in Aerosol Loading, J. Atmos. Sci., 71, 2581–2603,
https://doi.org/10.1175/JAS-D-13-0352.1, 2014. a
Sherwood, S. C.: Convective Precursors and Predictability in the Tropical
Western Pacific, Mon. Weather Rev., 127, 2977–2991,
https://doi.org/10.1175/1520-0493(1999)127<2977:CPAPIT>2.0.CO;2, 1999. a
Siebesma, A. P.: Shallow cumulus convection, in: Buoyant Convection in
Geophysical Flows, edited by: Plate, E., Fedorovich, E., Viegas, D., and
Wyngaard, J., Springer, the Netherlands, 441–486, 1998. a
Siebesma, A. P. and Cuijpers, J. W. M.: Parametric Assumptions for Shallow
Cumulus Convection, J. Atmos. Sci., 52, 650–666,
https://doi.org/10.1175/1520-0469(1995)052<0650:EOPAFS>2.0.CO;2, 1995. a
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J.,
Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H.,
Sanchez, E., Stevens, B., and Stevens, D. E.: A Large Eddy Simulation
Intercomparison Study of Shallow Cumulus Convection, J. Atmos. Sci., 60,
1201–1248, 2003. a, b, c
Small, J. D., Chuang, P. Y., Feingold, G., and Jiang, H.: Can aerosol decrease
cloud lifetime?, Geophys. Res. Lett., 36, L16806,
https://doi.org/10.1029/2009GL038888, 2009. a
Squires, P. and Turner, J. S.: An entraining jet model for cumulo-nimbus
updraughts, Tellus, 14, 422–434, https://doi.org/10.3402/tellusa.v14i4.9569, 1962. a
Stevens, B., Ackerman, A. S., Albrecht, B. A., Brown, A. R., Chlond, A.,
Cuxart, J., Duynkerke, P. G., Lewellen, D. C., Macvean, M. K., Neggers, R.
A. J., Sanchez, E., Siebesma, A. P., and Stevens, D. E.: Simulations of
Trade Wind Cumuli under a Strong Inversion, J. Atmos. Sci., 58,
1870–1891, https://doi.org/10.1175/1520-0469(2001)058<1870:SOTWCU>2.0.CO;2, 2001. a
Stommel, H.: Entrainment of air into a cumulus cloud, J. Meteorol., 4, 91–94,
https://doi.org/10.1175/1520-0469(1947)004<0091:EOAIAC>2.0.CO;2, 1947. a
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 Syst., 10, 770–800, https://doi.org/10.1002/2017MS001162, 2018. a, b, c
Tang, S. L. and Kirshbaum, D. J.: On the sensitivity of deep-convection
initiation to horizontal grid resolution, Q. J. Roy. Meteorol. Soc.,
146, 1085–1105, https://doi.org/10.1002/qj.3726, 2020. a, b, c
Tian, Y. and Kuang, Z.: Dependence of entrainment in shallow cumulus
convection on vertical velocity and distance to cloud edge, Geophys. Res. Lett., 43, 4056–4065, https://doi.org/10.1002/2016GL069005, 2016. a, b
vanZanten, M. C., Stevens, B., Nuijens, L., Siebesma, A. P., Ackerman, A. S.,
Burnet, F., Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M., Kogan, Y.,
Lewellen, D. C., Mechem, D., Nakamura, K., Noda, A., Shipway, B. J.,
Slawinska, J., Wang, S., and Wyszogrodzki, A.: Controls on precipitation and
cloudiness in simulations of trade-wind cumulus as observed during RICO, J. Adv. Model. Earth Syst., 3, 1–99, https://doi.org/10.1029/2011MS000056, 2011.
a, b, c
Vogelmann, A. M., McFarquhar, G. M., Ogren, J. A., Turner, D. D., Comstock,
J. M., Feingold, G., Long, C. N., Jonsson, H. H., Bucholtz, A., Collins,
D. R., Diskin, G. S., Gerber, H., Lawson, R. P., Woods, R. K., Andrews, E.,
Yang, H.-J., Chiu, J. C., Hartsock, D., Hubbe, J. M., Lo, C., Marshak, A.,
Monroe, J. W., McFarlane, S. A., Schmid, B., Tomlinson, J. M., and Toto, T.:
RACORO Extended-Term Aircraft Observations of Boundary Layer Clouds, Bull. Am. Meteorol. Soc., 93, 861–878, https://doi.org/10.1175/BAMS-D-11-00189.1, 2012. a
von Salzen, K. and McFarlane, N. A.: Parameterization of the Bulk Effects of
Lateral and Cloud-Top Entrainment in Transient Shallow Cumulus Clouds, J. Atmos. Sci., 59, 1405–1430,
https://doi.org/10.1175/1520-0469(2002)059<1405:POTBEO>2.0.CO;2, 2002. a
Wang, H. and McFarquhar, G. M.: Modeling aerosol effects on shallow cumulus
convection under various meteorological conditions observed over the Indian
Ocean and implications for development of mass-flux parameterizations for
climate models, J. Geophys. Res., 113, D20201,
https://doi.org/10.1029/2008JD009914, 2008. a, b, c, d, e, f
Webb, M. J., A. P. Lock, C. S. B., Bony, S., Cole, J. N. S., Idelkadi, A.,
Kang, S. M., Koshiro, T., Kawai, H., Ogura, T., Roehrig, T., Shin, Y.,
Mauritsen, T., Sherwood, S. C., Vial, J., Watanabe, M., Woelfle, M. D., and
Zhao, M.: The impact of parametrized convection on cloud feedback, Philos. T. R. Soc. A, 373, 20140414, https://doi.org/10.1098/rsta.2014.0414, 2015. a
Zhang, G. J., Wu, X., Zeng, X., and Motivski, T.: Estimation of convective
entrainment properties from a cloud-resolving model simulation during
TWP-ICE, Clim. Dynam., 47, 2177–2192, https://doi.org/10.1007/s00382-015-2957-7,
2016. a
Zhao, G. and Di Girolamo, L.: Statistics on the macrophysical properties of
trade wind cumuli over the tropical western Atlantic, J. Geophys. Res.,
112, D10204, https://doi.org/10.1029/2006JD007371, 2007. a
Short summary
This numerical study provides insights into selected environmental sensitivities of shallow-cumulus dilution. Among the parameters under consideration, the dilution of the cloud cores is strongly sensitive to continentality and cloud-layer relative humidity and weakly sensitive to subcloud- and cloud-layer depths. The impacts of all four parameters are interpreted using a similarity theory of shallow cumulus and buoyancy-sorting arguments.
This numerical study provides insights into selected environmental sensitivities of...
Altmetrics
Final-revised paper
Preprint