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Journal articles on the topic 'Cloud models'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2025

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1

Tjernström, Michael, Joseph Sedlar, and Matthew D. Shupe. "How Well Do Regional Climate Models Reproduce Radiation and Clouds in the Arctic? An Evaluation of ARCMIP Simulations." Journal of Applied Meteorology and Climatology 47, no. 9 (2008): 2405–22. http://dx.doi.org/10.1175/2008jamc1845.1.

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Abstract Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties w

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Grabowski, Wojciech W. "Representation of Turbulent Mixing and Buoyancy Reversal in Bulk Cloud Models." Journal of the Atmospheric Sciences 64, no. 10 (2007): 3666–80. http://dx.doi.org/10.1175/jas4047.1.

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Abstract This paper discusses the representation of subgrid-scale turbulent mixing in bulk models of warm (ice free) clouds, which assume instantaneous adjustment to grid-scale saturation. This is a reasonable assumption for condensation of water vapor because supersaturations inside clouds are typically small (∼0.1% or smaller), except near cloud bases where about an order of magnitude larger supersaturations are anticipated. For the cloud evaporation, however, instantaneous adjustment to grid-scale saturation is questionable, especially when evaporation occurs as a result of turbulent mixing

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Chou, Ming-Dah, Kyu-Tae Lee, Si-Chee Tsay, and Qiang Fu. "Parameterization for Cloud Longwave Scattering for Use in Atmospheric Models." Journal of Climate 12, no. 1 (1999): 159–69. http://dx.doi.org/10.1175/1520-0442-12.1.159.

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Abstract A parameterization for the scattering of thermal infrared (longwave) radiation by clouds has been developed based on discrete-ordinate multiple-scattering calculations. The effect of backscattering is folded into the emission of an atmospheric layer and the absorption between levels by scaling the cloud optical thickness. The scaling is a function of the single-scattering albedo and asymmetry factor. For wide ranges of cloud particle size, optical thickness, height, and atmospheric conditions, flux errors induced by the parameterization are small. They are &lt;4 W m−2 (2%) in the

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Yu, Shanshan, Xiaozhou Xin, Hailong Zhang, Li Li, Lin Zhu, and Qinhuo Liu. "A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data." Remote Sensing 15, no. 23 (2023): 5531. http://dx.doi.org/10.3390/rs15235531.

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Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found t

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Mieslinger, Theresa, Bjorn Stevens, Tobias Kölling, Manfred Brath, Martin Wirth, and Stefan A. Buehler. "Optically thin clouds in the trades." Atmospheric Chemistry and Physics 22, no. 10 (2022): 6879–98. http://dx.doi.org/10.5194/acp-22-6879-2022.

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Abstract. We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models and large eddy simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds significantly contribute to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well, especially how they change in a future climate, we have to know how cloudy it is. In this study we develop a method to quanti

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Zhu, Ping, James J. Hack, and Jeffrey T. Kiehl. "Diagnosing Cloud Feedbacks in General Circulation Models." Journal of Climate 20, no. 11 (2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.

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Abstract In this study, it is shown that the NCAR and GFDL GCMs exhibit a marked difference in climate sensitivity of clouds and radiative fluxes in response to doubled CO2 and ±2-K SST perturbations. The GFDL model predicted a substantial decrease in cloud amount and an increase in cloud condensate in the warmer climate, but produced a much weaker change in net cloud radiative forcing (CRF) than the NCAR model. Using a multiple linear regression (MLR) method, the full-sky radiative flux change at the top of the atmosphere was successfully decomposed into individual components associated with

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Yuan, T. "Cloud macroscopic organization: order emerging from randomness." Atmospheric Chemistry and Physics 11, no. 15 (2011): 7483–90. http://dx.doi.org/10.5194/acp-11-7483-2011.

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Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate thi

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Schulte, Richard M., Matthew D. Lebsock, and John M. Haynes. "What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations." Atmospheric Measurement Techniques 16, no. 14 (2023): 3531–46. http://dx.doi.org/10.5194/amt-16-3531-2023.

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Abstract. Single-layer nonprecipitating warm clouds are integral to Earth's climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud-profiling capabilities. However, a significant fraction of clouds is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 %

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Yuan, T. "Cloud macroscopic organization: order emerging from randomness." Atmospheric Chemistry and Physics Discussions 11, no. 1 (2011): 1105–19. http://dx.doi.org/10.5194/acpd-11-1105-2011.

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Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds and it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self

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10

Wolf, Kevin, Evelyn Jäkel, André Ehrlich, et al. "Impact of stratiform liquid water clouds on vegetation albedo quantified by coupling an atmosphere and a vegetation radiative transfer model." Biogeosciences 22, no. 12 (2025): 2909–33. https://doi.org/10.5194/bg-22-2909-2025.

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Abstract. This paper investigates the influence of clouds on vegetation albedo. For this purpose, we use coupled atmosphere–vegetation radiative transfer (RT) simulations combining the library for Radiative Transfer (libRadtran) and the vegetation Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE2.0) model. Both models are iteratively linked to more realistically simulate cloud–vegetation–radiation interactions above three types of canopy, represented by the spherical, erectophile, and planophile leaf angle distributions. The coupled models are applied to simulate solar, spect

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11

Hall, Samuel R., Kirk Ullmann, Michael J. Prather, et al. "Cloud impacts on photochemistry: building a climatology of photolysis rates from the Atmospheric Tomography mission." Atmospheric Chemistry and Physics 18, no. 22 (2018): 16809–28. http://dx.doi.org/10.5194/acp-18-16809-2018.

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Abstract. Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flig

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Lin, Jia-Lin, Taotao Qian, and Toshiaki Shinoda. "Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models." Journal of Climate 27, no. 8 (2014): 3000–3022. http://dx.doi.org/10.1175/jcli-d-13-00376.1.

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Abstract This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and ass

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Taylor, Patrick C., Robyn C. Boeke, Ying Li, and David W. J. Thompson. "Arctic cloud annual cycle biases in climate models." Atmospheric Chemistry and Physics 19, no. 13 (2019): 8759–82. http://dx.doi.org/10.5194/acp-19-8759-2019.

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Abstract. Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that

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Czerkawski, Mikolaj, Robert Atkinson, Craig Michie, and Christos Tachtatzis. "SatelliteCloudGenerator: Controllable Cloud and Shadow Synthesis for Multi-Spectral Optical Satellite Images." Remote Sensing 15, no. 17 (2023): 4138. http://dx.doi.org/10.3390/rs15174138.

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Optical satellite images of Earth frequently contain cloud cover and shadows. This requires processing pipelines to recognize the presence, location, and features of the cloud-affected regions. Models that make predictions about the ground behind the clouds face the challenge of lacking ground truth information, i.e., the exact state of Earth’s surface. Currently, the solution to that is to either (i) create pairs from samples acquired at different times or (ii) simulate cloudy data based on a clear acquisition. This work follows the second approach and proposes an open-source simulation tool

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15

Stevens, Robin G., Katharina Loewe, Christopher Dearden, et al. "A model intercomparison of CCN-limited tenuous clouds in the high Arctic." Atmospheric Chemistry and Physics 18, no. 15 (2018): 11041–71. http://dx.doi.org/10.5194/acp-18-11041-2018.

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Abstract. We perform a model intercomparison of summertime high Arctic (> 80∘ N) clouds observed during the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign, when observed cloud condensation nuclei (CCN) concentrations fell below 1 cm−3. Previous analyses have suggested that at these low CCN concentrations the liquid water content (LWC) and radiative properties of the clouds are determined primarily by the CCN concentrations, conditions that have previously been referred to as the tenuous cloud regime. The intercomparison includes results from three large eddy simulation models (UCLALE

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16

Wall, Casey J., Tsubasa Kohyama, and Dennis L. Hartmann. "Low-Cloud, Boundary Layer, and Sea Ice Interactions over the Southern Ocean during Winter." Journal of Climate 30, no. 13 (2017): 4857–71. http://dx.doi.org/10.1175/jcli-d-16-0483.1.

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During austral winter, a sharp contrast in low-cloud fraction and boundary layer structure across the Antarctic sea ice edge is seen in ship-based measurements and in active satellite retrievals from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO), which provide an unprecedented view of polar clouds during winter. Sea ice inhibits heat and moisture transport from the ocean to the atmosphere, and, as a result, the boundary layer is cold, stable, and clear over sea ice and warm, moist, well mixed, and cloudy over open water. The mean low-cloud fraction observed by C

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17

Lacour, A., H. Chepfer, N. B. Miller, et al. "How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet." Journal of Climate 31, no. 22 (2018): 9293–312. http://dx.doi.org/10.1175/jcli-d-18-0023.1.

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Using lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let d

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Zelinka, Mark D., Li-Wei Chao, Timothy A. Myers, Yi Qin, and Stephen A. Klein. "Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels." Atmospheric Chemistry and Physics 25, no. 3 (2025): 1477–95. https://doi.org/10.5194/acp-25-1477-2025.

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Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we p

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Pincus, Robert, Steven Platnick, Steven A. Ackerman, Richard S. Hemler, and Robert J. Patrick Hofmann. "Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators." Journal of Climate 25, no. 13 (2012): 4699–720. http://dx.doi.org/10.1175/jcli-d-11-00267.1.

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Abstract The properties of clouds that may be observed by satellite instruments, such as optical thickness and cloud-top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through “instrument simulators,” diagnostic tools that map the model representation to synthetic observations so that differences can be interpreted as model error. But simulators may themselves be restricted by limited information or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essentia

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20

Sun, J., H. Leighton, M. K. Yau, and P. Ariya. "Numerical evidence for cloud droplet nucleation at the cloud-environment interface." Atmospheric Chemistry and Physics Discussions 12, no. 7 (2012): 17723–42. http://dx.doi.org/10.5194/acpd-12-17723-2012.

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Abstract. Cumulus clouds have long been recognized as being the results of ascending moist air from below the cloud base. Cloud droplet nucleation is understood to take place near the cloud base and inside accelerating rising cloudy air. Here we describe circumstances under which cloud droplet nucleation takes place at the interface of ascending cloudy air and clear air. Evaporation is normally expected to occur at this interface. However, continuity of moving air requires cloud-free air above the boundary of rising cloudy air to move upwards in response to the gradient force of perturbation p

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Sun, J., H. Leighton, M. K. Yau, and P. Ariya. "Numerical evidence for cloud droplet nucleation at the cloud-environment interface." Atmospheric Chemistry and Physics 12, no. 24 (2012): 12155–64. http://dx.doi.org/10.5194/acp-12-12155-2012.

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Abstract. Cumulus clouds have long been recognized as being the results of ascending moist air from below the cloud base. Cloud droplet nucleation is understood to take place near the cloud base and inside accelerating rising cloudy air. Here we describe circumstances under which cloud droplet nucleation takes place at the interface of ascending cloudy air and clear air. Evaporation is normally expected to occur at this interface. However, continuity of moving air requires cloud-free air above the boundary of rising cloudy air to move upwards in response to the gradient force of perturbation p

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22

Walsh, John E., William L. Chapman, and Diane H. Portis. "Arctic Cloud Fraction and Radiative Fluxes in Atmospheric Reanalyses." Journal of Climate 22, no. 9 (2009): 2316–34. http://dx.doi.org/10.1175/2008jcli2213.1.

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Abstract Arctic radiative fluxes, cloud fraction, and cloud radiative forcing are evaluated from four currently available reanalysis models using data from the North Slope of Alaska (NSA) Barrow site of the Atmospheric Radiation Measurement Program (ARM). A primary objective of the ARM–NSA program is to provide a high-resolution dataset of direct measurements of Arctic clouds and radiation so that global climate models can better parameterize high-latitude cloud radiative processes. The four reanalysis models used in this study are the 1) NCEP–NCAR global reanalysis, 2) 40-yr ECMWF Re-Analysis

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Robbins-Blanch, Nina, Tiffany Kataria, Natasha E. Batalha, and Danica J. Adams. "Cloudy and Cloud-free Thermal Phase Curves with PICASO: Applications to WASP-43b." Astrophysical Journal 930, no. 1 (2022): 93. http://dx.doi.org/10.3847/1538-4357/ac658c.

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Abstract We present new functionality within PICASO, a state-of-the-art radiative transfer model for exoplanet and brown dwarf atmospheres, by developing a new pipeline that computes phase-resolved thermal emission (thermal phase curves) from three-dimensional (3D) models. Because PICASO is coupled to Virga, an open-source cloud code, we are able to produce cloudy phase curves with different sedimentation efficiencies (f sed) and cloud condensate species. We present the first application of this new algorithm to hot Jupiter WASP-43b. Previous studies of the thermal emission of WASP-43b from Ka

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Plant, R. S. "Statistical properties of cloud lifecycles in cloud-resolving models." Atmospheric Chemistry and Physics Discussions 8, no. 6 (2008): 20537–64. http://dx.doi.org/10.5194/acpd-8-20537-2008.

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Abstract. A new technique is described for the analysis of cloud-resolving model simulations, which allows one to investigate the statistics of the lifecycles of cumulus clouds. Clouds are tracked from timestep-to-timestep within the model run. This allows for a very simple method of tracking, but one which is both comprehensive and robust. An approach for handling cloud splits and mergers is described which allows clouds with simple and complicated time histories to be compared within a single framework. This is found to be important for the analysis of an idealized simulation of radiative-co

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Plant, R. S. "Statistical properties of cloud lifecycles in cloud-resolving models." Atmospheric Chemistry and Physics 9, no. 6 (2009): 2195–205. http://dx.doi.org/10.5194/acp-9-2195-2009.

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Abstract. A new technique is described for the analysis of cloud-resolving model simulations, which allows one to investigate the statistics of the lifecycles of cumulus clouds. Clouds are tracked from timestep to timestep within the model run. This allows for a very simple method of tracking, but one which is both comprehensive and robust. An approach for handling cloud splits and mergers is described which allows clouds with simple and complicated time histories to be compared within a single framework. This is found to be important for the analysis of an idealized simulation of radiative-co

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26

Rangno, Arthur L. "How Good Are Our Conceptual Models of Orographic Cloud Seeding?" Meteorological Monographs 43 (December 1, 1986): 115. http://dx.doi.org/10.1175/0065-9401-21.43.115.

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Abstract Some of the complexities of clouds and precipitation that have been encountered in field projects are reviewed. These complexities highlight areas of cloud microstructure and precipitation development that need to be better understood before adequate conceptual or numerical models of orographic cloud seeding can be developed. Some concerns about cloud sampling with regard to the evolutionary behavior of supercooled clouds from water to ice are also discussed.

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Alexandrov, Mikhail D., Alexander Marshak, and Andrew S. Ackerman. "Cellular Statistical Models of Broken Cloud Fields. Part I: Theory." Journal of the Atmospheric Sciences 67, no. 7 (2010): 2125–51. http://dx.doi.org/10.1175/2010jas3364.1.

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Abstract A new analytical statistical model describing the structure of broken cloud fields is presented. It depends on two parameters (cell size and occupancy probability) and provides chord distributions of clouds and gaps between them by length, as well as the cloud fraction distribution. This approach is based on the assumption that the structure of a cloud field is determined by a semiregular grid of cells (an abstraction of the atmospheric convective cells), which are filled with cloud with some probability. First, a simple discrete model is introduced, where clouds and gaps can occupy a

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Tarafdar, S. P., S. K. Ghosh, K. R. Heere, and S. S. Prasad. "Some Salient Features of Evolving Models of Interstellar Clouds." Highlights of Astronomy 8 (1989): 345–55. http://dx.doi.org/10.1017/s1539299600007978.

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ABSTRACTDifficulties faced by various models of interstellar clouds have been discussed. A new evolutionary model which uses energy equation instead of empirical temperature-density relation used in earlier models has been presented. This calculation shows that for a given initial density, the collapsing cloud has a minimum mass which is significantly smaller than the Jean's mass. The clouds with larger mass than the critical mass continue collapsing and physical and chemical evolution remain similar to earlier evolving models. Clouds with mass smaller than the critical mass initially collapse

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Bangert, M., C. Kottmeier, B. Vogel, and H. Vogel. "Regional scale effects of the aerosol cloud interaction simulated with an online coupled comprehensive chemistry model." Atmospheric Chemistry and Physics Discussions 11, no. 1 (2011): 1–37. http://dx.doi.org/10.5194/acpd-11-1-2011.

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Abstract. We have extended the coupled mesoscale atmosphere and chemistry model COSMO-ART to account for the transformation of aerosol particles into cloud condensation nuclei and to quantify their interaction with warm cloud microphysics on the regional scale. The new model system aims to fill the gap between cloud resolving models and global scale models. It represents the very complex microscale aerosol and cloud physics as detailed as possible, whereas the continental domain size and efficient codes will allow for both studying weather and regional climate. The model system is applied in a

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Mang, James, Caroline V. Morley, Tyler D. Robinson, and Peter Gao. "Microphysical Prescriptions for Parameterized Water Cloud Formation on Ultra-cool Substellar Objects." Astrophysical Journal 974, no. 2 (2024): 190. http://dx.doi.org/10.3847/1538-4357/ad6c4c.

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Abstract Water must condense into ice clouds in the coldest brown dwarfs and exoplanets. When they form, these icy clouds change the emergent spectra, temperature structure, and albedo of the substellar atmosphere. The properties of clouds are governed by complex microphysics but these complexities are often not captured by the simpler parameterized cloud models used in climate models or retrieval models. Here, we combine microphysical cloud modeling and 1D climate modeling to incorporate insights from microphysical models into a self-consistent, parameterized cloud model. Using the 1D Communi

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Henderson, David S., Jason A. Otkin, and John R. Mecikalski. "Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures." Monthly Weather Review 149, no. 4 (2021): 1153–72. http://dx.doi.org/10.1175/mwr-d-20-0272.1.

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AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The result

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Várnai, Tamás, and Alexander Marshak. "Analysis of Near-Cloud Changes in Atmospheric Aerosols Using Satellite Observations and Global Model Simulations." Remote Sensing 13, no. 6 (2021): 1151. http://dx.doi.org/10.3390/rs13061151.

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This paper examines cloud-related variations of atmospheric aerosols that occur in partly cloudy regions containing low-altitude clouds. The goal is to better understand aerosol behaviors and to help better represent the radiative effects of aerosols on climate. For this, the paper presents a statistical analysis of a multi-month global dataset that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite instruments with data from the Modern-Era Retrospective analysis for Research and Applications, Ver

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Hamill, Colin D., Alexandria V. Johnson, Natasha Batalha, et al. "Reflected-light Phase Curves with PICASO: A Kepler-7b Case Study." Astrophysical Journal 976, no. 2 (2024): 181. http://dx.doi.org/10.3847/1538-4357/ad7de6.

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Abstract Examining reflected light from exoplanets aids in our understanding of the scattering properties of their atmospheres and will be a primary task of future flagship space- and ground-based telescopes. We introduce an enhanced capability of Planetary Intensity Code for Atmospheric Scattering Observations (PICASO), an open-source radiative transfer model used for exoplanet and brown dwarf atmospheres, to produce reflected light phase curves from three-dimensional atmospheric models. Since PICASO is coupled to the cloud code Virga, we produce phase curves for different cloud condensate sp

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Črnivec, Nina, and Bernhard Mayer. "Quantifying the bias of radiative heating rates in numerical weather prediction models for shallow cumulus clouds." Atmospheric Chemistry and Physics 19, no. 12 (2019): 8083–100. http://dx.doi.org/10.5194/acp-19-8083-2019.

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Abstract. The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP) due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large the NWP radiative bias is for shallow cumulus clouds and how it scales with various input parameters of radiation schemes, such as solar zenith angle, surface albedo, cloud cover and liquid water path. A set of radiative transfer calculations was carried out for a realistically evolving shallow cumulus clou

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Slobodda, J., A. Hünerbein, R. Lindstrot, R. Preusker, K. Ebell, and J. Fischer. "Multichannel analysis of correlation length of SEVIRI images around ground-based cloud observatories to determine their representativeness." Atmospheric Measurement Techniques 8, no. 2 (2015): 567–78. http://dx.doi.org/10.5194/amt-8-567-2015.

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Abstract. Images of measured radiance in different channels of the geostationary Meteosat-9 SEVIRI instrument are analysed with respect to the representativeness of the observations of eight cloud observatories in Europe (e.g. measurements from cloud radars or microwave radiometers). Cloudy situations are selected to get a time series for every pixel in a 300 km × 300 km area centred around each ground station. Then a cross correlation of each time series to the pixel nearest to the corresponding ground site is calculated. In the end a correlation length is calculated to define the representat

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Liu, Qun, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis. "SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models." Geoscientific Model Development 14, no. 5 (2021): 2801–26. http://dx.doi.org/10.5194/gmd-14-2801-2021.

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Abstract. A simple diagnostic cloud scheme (SimCloud) for general circulation models (GCMs), which has a modest level of complexity and is transparent in describing its dependence on tunable parameters, is proposed in this study. The large-scale clouds, which form the core of the scheme, are diagnosed from relative humidity. In addition, the marine low stratus clouds, typically found off the west coast of continents over subtropical oceans, are determined largely as a function of inversion strength. A “freeze-dry” adjustment based on a simple function of specific humidity is also available to

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Li, Yang, Fanchen Peng, Feng Dou, Yao Xiao, and Yi Li. "PCCDiff: Point Cloud Completion with Conditional Denoising Diffusion Probabilistic Models." Symmetry 16, no. 12 (2024): 1680. https://doi.org/10.3390/sym16121680.

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Point clouds obtained from laser scanners or other devices often exhibit incompleteness, which poses a challenge for subsequent point cloud processing. Therefore, accurately predicting the complete shape from partial observations has paramount significance. In this paper, we introduce PCCDiff, a probabilistic model inspired by Denoising Diffusion Probabilistic Models (DDPMs), designed for point cloud completion tasks. Our model aims to predict missing parts in incomplete 3D shapes by learning the reverse diffusion process, transforming a 3D Gaussian noise distribution into the desired shape di

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Mollière, P., T. Stolker, S. Lacour, et al. "Retrieving scattering clouds and disequilibrium chemistry in the atmosphere of HR 8799e." Astronomy & Astrophysics 640 (August 2020): A131. http://dx.doi.org/10.1051/0004-6361/202038325.

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Context. Clouds are ubiquitous in exoplanet atmospheres and they represent a challenge for the model interpretation of their spectra. When generating a large number of model spectra, complex cloud models often prove too costly numerically, whereas more efficient models may be overly simplified. Aims. We aim to constrain the atmospheric properties of the directly imaged planet HR 8799e with a free retrieval approach. Methods. We used our radiative transfer code petitRADTRANS for generating the spectra, which we coupled to the PyMultiNest tool. We added the effect of multiple scattering which is

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Kuma, Peter, Frida A. M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland. "Machine learning of cloud types in satellite observations and climate models." Atmospheric Chemistry and Physics 23, no. 1 (2023): 523–49. http://dx.doi.org/10.5194/acp-23-523-2023.

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Abstract. Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud type

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Kuma, Peter, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland. "Machine learning of cloud types in satellite observations and climate models." Atmospheric Chemistry and Physics 23, no. 1 (2023): 523–49. https://doi.org/10.5281/zenodo.7533870.

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Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low

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Medeiros, Brian, and Louise Nuijens. "Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models." Proceedings of the National Academy of Sciences 113, no. 22 (2016): E3062—E3070. http://dx.doi.org/10.1073/pnas.1521494113.

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Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados reg

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Zhang, Qi, Yi Yu, Weimin Zhang, Tengling Luo, and Xiang Wang. "Cloud Detection from FY-4A’s Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches." Remote Sensing 11, no. 24 (2019): 3035. http://dx.doi.org/10.3390/rs11243035.

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FengYun-4A (FY-4A)’s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection data with high temporal and spectral resolution. As clouds have complex spectral characteristics, and the retrieval of atmospheric profiles incorporating clouds is a significant problem, it is often necessary to undertake cloud detection before further processing procedures for cloud pixels when infrared hyperspectral data is entered into assimilation system. In this study, we proposed machine-l

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Bernet, Leonie, Francisco Navas-Guzmán, and Niklaus Kämpfer. "The effect of cloud liquid water on tropospheric temperature retrievals from microwave measurements." Atmospheric Measurement Techniques 10, no. 11 (2017): 4421–37. http://dx.doi.org/10.5194/amt-10-4421-2017.

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Abstract. Microwave radiometry is a suitable technique to measure atmospheric temperature profiles with high temporal resolution during clear sky and cloudy conditions. In this study, we included cloud models in the inversion algorithm of the microwave radiometer TEMPERA (TEMPErature RAdiometer) to determine the effect of cloud liquid water on the temperature retrievals. The cloud models were built based on measurements of cloud base altitude and integrated liquid water (ILW), all performed at the aerological station (MeteoSwiss) in Payerne (Switzerland). Cloud base altitudes were detected usi

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Črnivec, Nina, та Bernhard Mayer. "The incorporation of the Tripleclouds concept into the <i>δ</i>-Eddington two-stream radiation scheme: solver characterization and its application to shallow cumulus clouds". Atmospheric Chemistry and Physics 20, № 17 (2020): 10733–55. http://dx.doi.org/10.5194/acp-20-10733-2020.

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Abstract. The treatment of unresolved cloud–radiation interactions in weather and climate models has considerably improved over the recent years, compared to conventional plane-parallel radiation schemes, which previously persisted in these models for multiple decades. One such improvement is the state-of-the-art Tripleclouds radiative solver, which has one cloud-free and two cloudy regions in each vertical model layer and is thereby capable of representing cloud horizontal inhomogeneity. Inspired by the Tripleclouds concept, primarily introduced by Shonk and Hogan (2008), we incorporated a se

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Cesana, G., D. E. Waliser, D. Henderson, T. S. L’Ecuyer, X. Jiang, and J. L. F. Li. "The Vertical Structure of Radiative Heating Rates: A Multimodel Evaluation Using A-Train Satellite Observations." Journal of Climate 32, no. 5 (2019): 1573–90. http://dx.doi.org/10.1175/jcli-d-17-0136.1.

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Abstract We assess the vertical distribution of radiative heating rates (RHRs) in climate models using a multimodel experiment and A-Train satellite observations, for the first time. As RHRs rely on the representation of cloud amount and properties, we first compare the modeled vertical distribution of clouds directly against lidar–radar combined cloud observations (i.e., without simulators). On a near-global scale (50°S–50°N), two systematic differences arise: an excess of high-level clouds around 200 hPa in the tropics, and a general lack of mid- and low-level clouds compared to the observat

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Bangert, M., C. Kottmeier, B. Vogel, and H. Vogel. "Regional scale effects of the aerosol cloud interaction simulated with an online coupled comprehensive chemistry model." Atmospheric Chemistry and Physics 11, no. 9 (2011): 4411–23. http://dx.doi.org/10.5194/acp-11-4411-2011.

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Abstract. We have extended the coupled mesoscale atmosphere and chemistry model COSMO-ART to account for the transformation of aerosol particles into cloud condensation nuclei and to quantify their interaction with warm cloud microphysics on the regional scale. The new model system aims to fill the gap between cloud resolving models and global scale models. It represents the very complex microscale aerosol and cloud physics as detailed as possible, whereas the continental domain size and efficient codes will allow for both studying weather and regional climate. The model system is applied in a

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Zhang, Kun, Shiquan Qiao, Xiaohong Wang, Yongtao Yang, and Yongqiang Zhang. "Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models." Applied Sciences 9, no. 10 (2019): 2130. http://dx.doi.org/10.3390/app9102130.

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With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to

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Jiang, Zonglin. "The Clouds Observation and Classification Based on Satellites Data." Theoretical and Natural Science 83, no. 1 (2025): 157–66. https://doi.org/10.54254/2753-8818/2025.20201.

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Recently, there has been a strong focus on understanding how greenhouse gases contribute to rising global temperatures. However, greenhouse gases are not the only element influencing climate; clouds also play a crucial role in regulating global temperatures. Clouds affect the wave path of different wavelengths of radiation, which in turn impacts temperature and weather patterns. Additionally, the complex microphysics within clouds, such as interactions between water droplets and ice crystals, make cloud modeling extremely challenging. This passage will share some basic models of clouds, explor

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Errico, Ronald M., George Ohring, Fuzhong Weng, et al. "Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection." Journal of the Atmospheric Sciences 64, no. 11 (2007): 3737–41. http://dx.doi.org/10.1175/2007jas2622.1.

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Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collect

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Perkins, R. J., N. A. Malik, and J. C. H. Fung. "Cloud dispersion models." Applied Scientific Research 51, no. 1-2 (1993): 539–45. http://dx.doi.org/10.1007/bf01082588.

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