A Modeling Case Study of Post-frontal Mixed-phase Clouds in the Marine Boundary Layer Over the Southern Ocean in MARCUS PDF Download
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Author: Yishi Hu
Publisher:
ISBN:
Category : Boundary layer (Meteorology)
Languages : en
Pages : 50
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Book Description
A multi-day period (February 23-26, 2018) of post-frontal shallow convective mixed-phase clouds observed during the shipborne Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) field campaign is studied using the Weather Research and Forecast (WRF) model with the aim of understanding ice production as well as model sensitivity to ice process parameterizations. The Cloud-resolving model Radar SIMulator (CRSIM) is firstly used in this study to convert WRF S-band output into W-band radar observables. Comparisons between the observations and simulations suggest that the model captures the observed synoptic pattern and shallow convective nature of the mixed-phase clouds. The simulated clouds are mostly precipitating and liquid dominated. Interestingly, the control simulation significantly underestimates the ice content and overestimates the supercooled liquid water, which is contrary to the bias common in global climate models. Sensitivity simulations targeted at ice production processes suggest that the rime splintering process is not a primary contributor and that the simulated clouds show negligible sensitivity to cloud droplet number concentrations. Higher number concentrations of ice nuclei do not guarantee more ice production overall. However, the simulated mixed-phase clouds are found to be highly sensitive to the implementation of immersion freezing and condensation/deposition freezing. By increasing immersion freezing of cloud droplets or relaxing thresholds for condensation/deposition freezing, the model significantly improves its performance in producing ice. The key results of this work call for an increase in observations of ice nuclei, especially over the remote Southern Ocean and at relatively high temperatures.
Author: Yishi Hu
Publisher:
ISBN:
Category : Boundary layer (Meteorology)
Languages : en
Pages : 50
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Book Description
A multi-day period (February 23-26, 2018) of post-frontal shallow convective mixed-phase clouds observed during the shipborne Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) field campaign is studied using the Weather Research and Forecast (WRF) model with the aim of understanding ice production as well as model sensitivity to ice process parameterizations. The Cloud-resolving model Radar SIMulator (CRSIM) is firstly used in this study to convert WRF S-band output into W-band radar observables. Comparisons between the observations and simulations suggest that the model captures the observed synoptic pattern and shallow convective nature of the mixed-phase clouds. The simulated clouds are mostly precipitating and liquid dominated. Interestingly, the control simulation significantly underestimates the ice content and overestimates the supercooled liquid water, which is contrary to the bias common in global climate models. Sensitivity simulations targeted at ice production processes suggest that the rime splintering process is not a primary contributor and that the simulated clouds show negligible sensitivity to cloud droplet number concentrations. Higher number concentrations of ice nuclei do not guarantee more ice production overall. However, the simulated mixed-phase clouds are found to be highly sensitive to the implementation of immersion freezing and condensation/deposition freezing. By increasing immersion freezing of cloud droplets or relaxing thresholds for condensation/deposition freezing, the model significantly improves its performance in producing ice. The key results of this work call for an increase in observations of ice nuclei, especially over the remote Southern Ocean and at relatively high temperatures.
Author: Constantin Andronache
Publisher: Elsevier
ISBN: 012810550X
Category : Science
Languages : en
Pages : 302
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Book Description
Mixed-Phase Clouds: Observations and Modeling presents advanced research topics on mixed-phase clouds. As the societal impacts of extreme weather and its forecasting grow, there is a continuous need to refine atmospheric observations, techniques and numerical models. Understanding the role of clouds in the atmosphere is increasingly vital for current applications, such as prediction and prevention of aircraft icing, weather modification, and the assessment of the effects of cloud phase partition in climate models. This book provides the essential information needed to address these problems with a focus on current observations, simulations and applications. - Provides in-depth knowledge and simulation of mixed-phase clouds over many regions of Earth, explaining their role in weather and climate - Features current research examples and case studies, including those on advanced research methods from authors with experience in both academia and the industry - Discusses the latest advances in this subject area, providing the reader with access to best practices for remote sensing and numerical modeling
Author: Yazhe Hu
Publisher:
ISBN:
Category : Boundary layer (Meteorology)
Languages : en
Pages : 53
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Book Description
A study of the vertical structure of post-frontal mesoscale cellular convection in the marine boundary layer over the Southern Ocean is presented. The central question of this two-part study regards cloud phase (liquid/ice) and associated precipitation growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-hour case with continuous sea-surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are lightly-precipitating and shallow (tops mostly ~ 2 km MSL), with weak up- anddowndrafts, and with cloud top temperatures generally around –10C to –18C. A cloud phase algorithm is presented that uses radiometer liquid water path, radar-derived ice water path, and lidar depolarization ratio. This algorithm classifies the vast majority (80% - 85%) of cloud profiles as liquid or liquid-dominated. This finding applies to all periods of postfrontal shallow mesoscale cellular convection in MARCUS, specifically 12 cases amounting to a total of 277 hours. The best predictors of ice presence are cloud top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment.
Author: Thomas A. Guinn
Publisher:
ISBN:
Category : Boundary layer (Meteorology)
Languages : en
Pages : 63
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Book Description
A coupled convective-radiative, boundary-layer model of marine stratocumulus clouds is presented. The model, which slightly generalized Lilly's (1968) cloud-topped mixed-layer model, has as dependent variables the cloud-top height, the cloud-base height, mixed-layer equivalent potential temperature and total water mixing ratio, the turbulent fluxes of equivalent potential temperature, total water mixing ratio, and virtual potential temperature, the cloud-top jumps of equivalent potential temperature and total water mixing ratio, the cloud-top temperature, and the net radiative flux divergence at cloud top and in the mixed layer. Keywords: Stratosphere. (EG).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Book Description
Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud top temperature of -15 C. The observed liquid water path of around 160 g m−2 was about two-thirds of the adiabatic value and much greater than the mass of ice crystal precipitation which when integrated from the surface to cloud top was around 15 g m−2. The simulations were performed by seventeen single-column models (SCMs) and nine cloud-resolving models (CRMs). While the simulated ice water path is generally consistent with the observed values, the median SCM and CRM liquid water path is a factor of three smaller than observed. Results from a sensitivity study in which models removed ice microphysics indicate that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this general underestimate, the simulated liquid and ice water paths of several models are consistent with the observed values. Furthermore, there is some evidence that models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter is also present. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics. This case study, which has been well observed from both aircraft and ground-based remote sensors, could be a benchmark for model simulations of mixed-phase clouds.
Author: Rachel Atlas
Publisher:
ISBN:
Category :
Languages : en
Pages : 61
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Book Description
Southern Ocean boundary layer clouds affect global albedo and oceanic heat uptake. Most climate models and reanalyses underestimate cloudiness in the Southern Ocean, which biases seas surface temperatures and tropospheric winds, and likely influences the global atmospheric circulation and oceanic heat uptake. This robust and persistent model bias reveals gaps in our understanding of the physical controls on the formation and evolution of low clouds in the Southern Ocean, compared to more well-studied regions. The physics of Southern Ocean boundary layer clouds are uncertain due, in part, to a lack of in-situ observations in the region. Here, I use recent state-of-the-art measurements from the SOCRATES aircraft campaign and cloud resolving simulations, to investigate the influence of synoptic dynamics, boundary layer structure and microphysical properties on Southern Ocean boundary layer clouds. I developed a technique for simulating boundary layer clouds in the synoptically active Southern Ocean with a large eddy simulation (LES) and I set up five modelling case studies from SOCRATES observations. I find that the LES realistically represents diverse boundary layer structures but produces clouds with persistently low liquid water paths. CAM6 persistently underestimates droplet concentrations and cloud driven turbulence.
Author: Paul Brian Krummel
Publisher:
ISBN:
Category : Aerosols
Languages : en
Pages : 152
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Book Description
Author: Johannes Karel Christiaan Mohrmann
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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Book Description
Marine low clouds are an important feature of the climate system, cooling the planet due to their high albedo and warm temperatures. They display a variety of different mesoscale organizations, which are tied to the varying environmental conditions in which they occur. This dissertation explores the drivers of marine low cloud variability using an observational perspective that draws upon aircraft, satellite, and reanalysis data, and uses the application of a number of machine learning techniques. The focus is largely though not exclusively on mesoscale organization. In the first part of this work, data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r=0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (-31%averaged over all cases), MBL depth (1560 m), estimated inversion strength (EIS; 22.2K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc-Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics. The remaining work focuses on the mesoscale organization of marine low clouds. A convolutional neural network (CNN) model is trained to classify 128 km by 128 km scenes of marine low clouds into six categories: stratus, open-cellular mesoscale cellular convection (MCC), closed-cellular MCC, disorganized MCC, clustered cumulus, and suppressed cumulus. Overall model test accuracy was approximately 90%. This model is applied to three years of data in the southeast Pacific, as well as the 2015 northeast Pacific summer for comparison with the CSET campaign. Meteorological variables related to marine low cloud processes are composited by mesoscale cloud type, allowing for the identification of distinct meteorological regimes. Presentation of MCC is largely consistent with previous literature, both in terms of geographic distribution boundary layer structure, and cloud-controlling factors. The two more novel types, clustered and suppressed cumulus, are examined in more detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the presentation of marine shallow mesoscale convective self-aggregation found in previous large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud regimes are found to be well-separated by variables associated with a low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Divergence is consistent with near-surface inflow into clustered regimes and outflow from suppressed regimes. To further understand the dependencies of mesoscale cloud type on environmental factors, a second classification model is built. This uses a random forest of decision trees to predict cloud type, but instead of using an image of a cloud scene, mesoscale averages of meteorological variables are used as inputs. The model uses the three-year dataset output from the CNN model for training, and overall accuracy is approximately 50%. Rotated principal component analysis of the meteorological variables is used to create a set of decorrelated features on which to train the model, allowing for the application of certain statistical analyses which rely on uncorrelated data. Permutation feature importance is used to quantify which variables are most important for correct prediction of cloud mesoscale organization. Overall, temperature and stability are approximately equally important; for correctly distinguishing between open-MCC and closed-MCC, stability is the most important feature, and for correctly distinguishing between suppressed and clustered cumulus, surface divergence is the most important variable. Partial dependence analysis is used to show the relationship between each input variable and the likelihood of observing each cloud type, and 2-dimensional partial dependence analysis shows bimodal distributions of MCC types, consistent with their subtropical and midlatitude incarnations. The random forest model is able to reproduce the geographic distributions of cloud type occurrences.
Author: Katharina Loewe
Publisher:
ISBN: 9781013281204
Category : Science
Languages : en
Pages : 160
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Book Description
This work provides new insights into macro- and microphysical properties of Arctic mixed-phase clouds: first, by comparing semi-idealized large eddy simulations with observations; second, by dissecting the influences of different surface types and boundary layer structures on Arctic mixed- phase clouds; third, by elucidating the dissipation process; and finally by analyzing the main microphysical processes inside Arctic mixed-phase clouds. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Author: Anthony S. Stender
Publisher:
ISBN:
Category : Atmospheric nucleation
Languages : en
Pages : 154
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Book Description
