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DOE OSTI2016

Regime dependence of cloud condensate variability observed at the Atmospheric Radiation Measurement Sites

Microphysical processes and cloud–radiation interaction occur on spatial scales of variability smaller than those represented explicitly in global weather forecasting and climate models. It is therefore necessary to parametrize the unresolved heterogeneity of humidity and cloud condensate in order to predict process rates accurately. Ground‐based observations from the Atmospheric Radiation Measurement sites located in various climatic regions of the world provide a source of high‐temporal‐resolution observations of cloud condensate. A number of different retrieval products for cloud condensate are assessed for the different geographical regions, years and seasons. The retrieval reliability varies with cloud type, but for cloud categories largely unaffected by precipitation a comparison across sites and longer time periods is possible. These observations confirm previously documented variability behaviour as a function of cloud fraction, but also reveal a systematic regime dependence that is not captured by existing parametrizations. Condensate variability measured as a fractional standard deviation (FSD) in warm boundary‐layer clouds is greater in the Tropics than in mid and high latitudes for scenes with comparable cloud type and fraction, with the observed FSD varying from 1.2 in the Tropics to 0.4 in the Arctic. A parametrization of the FSD of cloud liquid condensate based on the grid‐box mean total water amount and cloud fraction is formulated and shown to capture the observed range of FSD values better across different geographical sites and different seasons. The regime dependence of FSD for cirrus cloud is less pronounced than that for liquid clouds and is found largely to agree with FSD values previously derived from satellite observations.

Ahlgrimm, Maike↗

DOE OSTI2015

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme. This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Model computational expense is estimated, and sensitivity to the number of subcolumns is investigated. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.

58 GEOSCIENCES↗

DOE OSTI2018

Shallow Cumulus (SHALLOWCUMULUS) Value-Added Product Report

Periods of shallow cumulus clouds are identified automatically at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory in order to help determine times of interest for Large-Eddy Simulation (LES) and Observation (LASSO) modeling efforts. This value-added data product (VAP) uses input from the Cloud Type (CLDTYPE) VAP (with cloud types determined from Active Remotely Sensed Cloud Locations [ARSCL] VAP cloud boundaries), the ceilometer, and the total sky imager (TSI). The data set uses an hourly temporal resolution and only classifies events as shallow cumulus when low clouds are detected for at least 1.5 hours and meet specified criteria for cloud occurrence and cloud fraction. In addition to time periods that contain just shallow cumulus, cases with overlying cirrus and a number of transitional cases (transitions to/from cirrus, stratus, and altocumulus/altostratus) are also identified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Regime-specific Cloud Vertical Overlap Characteristics from Radar and Lidar Observations at the ARM sites

Climate and numerical weather prediction models require assumptions to represent the vertical distribution of subgrid-scale clouds, which have radiative transfer implications. In this study, nearly 25 years of ground-based radar and lidar observations of vertical cloud profiles at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site are utilized to derive cloud vertical overlap characteristics from the Cloud Type (CLDTYPE) data product. Here, the cloud vertical overlap characteristics are further separated by cloud regime by considering seven cloud types (i.e., low cloud, congestus, deep convection, altocumulus, altostratus, cirrostratus, and cirrus) as well as periods of shallow cumulus. The decorrelation length scale (i.e., exponential transition from maximum to random overlap with layer separation) is found to vary by cloud regime, ranging between 0.04 km for cirrostratus paired with cirrus to 4.58 km for low cloud paired with cirrus at SGP. Cloud vertical overlap characteristics are also considered for other ARM sites including the Tropical Western Pacific (TWP), North Slope of Alaska (NSA), and Eastern North Atlantic (ENA) sites amongst other shorter term ARM deployments globally. The decorrelation length scale ranged globally from 1.03 km in the Arctic Ocean to 3.06 km in Manacapuru, Brazil. Globally, the decorrelation length scale by cloud regime exhibited similarities (e.g., for cirrus paired with cirrus) and differences (e.g., congestus paired with cirrus). The results could help inform development of cloud vertical overlap assumptions within operational numerical weather prediction models and potentially improve prediction of radiative fluxes for weather, climate, and renewable energy forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Constructing a Merged Cloud-Precipitation Radar Dataset for Tropical Convective Clouds during the DYNAMO/AMIE Experiment at Addu Atoll

To improve understanding of the convective processes key to the Madden-Julian-Oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and Atmospheric Radiation Measurement MJO Investigation Experiment (AMIE) collected four months of observations from three radars, the S-band Polarization Radar (S-Pol), the C-band Shared Mobile Atmospheric Research & Teaching Radar (SMART-R), and Ka-band Zenith Radar (KAZR) on Addu Atoll in the tropical Indian Ocean. This study compares the measurements from the S-Pol and SMART-R to those from the more sensitive KAZR in order to characterize the hydrometeor detection capabilities of the two scanning precipitation radars. Frequency comparisons for precipitating convective clouds and non-precipitating high clouds agree much better than non-precipitating low clouds for both scanning radars due to issues in ground clutter. On average, SMART-R underestimates convective and high cloud tops by 0.3 to 1.1 km, while S-Pol underestimates cloud tops by less than 0.4 km for these cloud types. S-Pol shows excellent dynamic range in detecting various types of clouds and therefore its data are well suited for characterizing the evolution of the 3D cloud structures, complementing the profiling KAZR measurements. For detecting non-precipitating low clouds and thin cirrus clouds, KAZR remains the most reliable instrument. However, KAZR is attenuated in heavy precipitation and underestimates cloud top height due to rainfall attenuation 4.3% of the time during DYNAMO/AMIE. An empirical method to correct the KAZR cloud top heights is described, and a merged radar dataset is produced to provide improved cloud boundary estimates, microphysics and radiative heating retrievals.

Addu Atoll↗

DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗

DOE OSTI2015

Clouds, Aerosols, and Precipitation in the Marine Boundary Layer: An Arm Mobile Facility Deployment

The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21 month (April 2009-December 2010) comprehensive dataset documenting clouds, aerosols and precipitation using the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols and precipitation in the marine boundary layer. Graciosa Island straddles the boundary between the subtropics and midlatitudes in the Northeast Atlantic Ocean, and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1- 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging. The data from at Graciosa are being compared with short-range forecasts made a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well, but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.

clouds↗

DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Vertical motions of the tropical convective cloud spectrum over Darwin, Australia

Three months of profiler observations from Darwin, Australia are separated into seven cloud types (shallow, mid‐level, and deep convective; weak and robust stratiform rain; and transitional and ice‐only anvil) to provide vertical velocity statistics on the full spectrum of tropical convective clouds over the course of a monsoon season. Consistent with past studies, convective updraughts increase in height and magnitude as the convective cloud height increases. Shallow/mid‐level/deep convection has a mean maximum value of 0.3 m s −1 at 3 km/0.6 m s −1 at 5 km/2.5 m s −1 at 8 km. Deep convective extremes approach 18 m s −1 above 8 km while downdraughts in all convective cloud types are maximum below 4 km. Stratiform vertical velocities are weaker and less varied than in active convection, with maximum mean values <0.25 m s −1 . However, stratiform rain regions associated with larger near‐surface reflectivities (and thus rain rates) have stronger mesoscale up‐ and downdraughts than weaker stratiform rain regions. Anvil cloud with little or no rain near the surface also exhibits mesoscale up‐ and downdraughts, but the vertical velocity profile is shifted up in height by at least 2 km. In addition, anvil updraughts were only 20–50% of the magnitudes in the stratiform rain region. Overall, the vertical motion statistics were similar across the pre‐, active and suppressed monsoon periods. The vertical velocity distributions for each cloud type were also generally consistent with the reflectivity distributions (i.e. the strongest updraughts were often linked to the largest reflectivities), allowing for some linkages between the dynamical and microphysical cloud properties. For example, strong updraughts above the 0 °C level in deep convection are associated with strongly sloping reflectivity profiles supporting the importance of cold rain growth processes, and stronger vertical motions are associated with stratiform rain that has a robust bright band.

Schumacher, Courtney↗

DOE OSTI2016

Final Technical Report for "Reducing tropical precipitation biases in CESM"

In state-of-the-art climate models, each cloud type is treated using its own separate cloud parameterization and its own separate microphysics parameterization. This use of separate schemes for separate cloud regimes is undesirable because it is theoretically unfounded, it hampers interpretation of results, and it leads to the temptation to overtune parameters. In this grant, we have created a climate model that contains a unified cloud parameterization (“CLUBB”) and a unified microphysics parameterization (“MG2”). In this model, all cloud types --- including marine stratocumulus, shallow cumulus, and deep cumulus --- are represented with a single equation set. This model improves the representation of convection in the Tropics. The model has been compared with ARM observations. The chief benefit of the project is to provide a climate model that is based on a more theoretically rigorous formulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Detailing cloud property feedbacks with a regime-based decomposition

Here, we report diagnosing the root causes of cloud feedback in climate models and reasons for inter-model disagreement is a necessary first step in understanding their wide variation in climate sensitivities. Here we bring together two analysis techniques that illuminate complementary aspects of cloud feedback. The first quantifies feedbacks from changes in cloud amount, altitude, and optical depth, while the second separates feedbacks due to cloud property changes within specific cloud regimes from those due to regime occurrence frequency changes. We find that in the global mean, shortwave cloud feedback averaged across ten models comes solely from a positive within-regime cloud amount feedback countered slightly by a negative within-regime optical depth feedback. These within-regime feedbacks are highly uniform: In nearly all regimes, locations, and models, cloud amount decreases and cloud albedo increases with warming. In contrast, global-mean across-regime components vary widely across models but are very small on average. This component, however, is dominant in setting the geographic structure of the shortwave cloud feedback: Thicker, more extensive cloud types increase at the expense of thinner, less extensive cloud types in the extratropics, and vice versa at low latitudes. The prominent negative extratropical optical depth feedback has contributions from both within- and across-regime components, suggesting that thermodynamic processes affecting cloud properties as well as dynamical processes that favor thicker cloud regimes are important. The feedback breakdown presented herein may provide additional targets for observational constraints by isolating cloud property feedbacks within specific regimes without the obfuscating effects of changing dynamics that may differ across timescales.

58 GEOSCIENCES↗

DOE OSTI2025

A Comparative Study of Cloud Properties Between Northern and Southern High Latitudes Based on ARM Observations and EAMv2 Simulations

Earth's high-latitude regions show a considerable amount of cloud coverage, which significantly affects global radiation budgets. Here, in this study, we examine ground- and ship-based remote sensing observations from two Atmospheric Radiation Measurement (ARM) field campaigns conducted in the high-latitude regions of Northern and Southern Hemispheres (NH and SH). A comparison of macrophysical properties for low, mid-level, high, and deep clouds shows that the cloud top heights and thickness values are higher in the Arctic for each cloud type compared with the Southern Ocean and Antarctic regions, possibly due to higher sea surface temperatures in the north Atlantic. The Arctic also shows lower ice phase occurrence frequencies compared with the SH high latitudes for low clouds. For high, mid-level and deep clouds, the cloud phase distributions as a function of temperature show small geographical variabilities. The observations are then compared with the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) to evaluate the representations of these cloud properties. Here, the results show that the model captures cloud base and top heights well and reproduces the differences in cloud macrophysical properties between the two hemispheres. But the model underestimates ice phase frequencies for all cloud types. Such model cloud phase bias is strongly correlated with positive relative humidity biases and negative aerosol number concentration biases, indicating an important role of relative humidity and aerosols in modulating cloud phase partitioning in the model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Antarctic Cloud Macrophysical, Thermodynamic Phase, and Atmospheric Inversion Coupling Properties at McMurdo Station—Part II: Radiative Impact During Different Synoptic Regimes

Different cloud types are generated over Antarctica as a result of various synoptic conditions. The cloud characteristics affect their impact on the surface energy budget. In this study, the dominating synoptic regimes over Antarctica (centered on the Ross Ice Shelf) are classified using self-organizing map (SOM) analysis, applied over long-term ERA-Interim 700 hPa geopotential height data. The corresponding cloud properties over McMurdo Station (measured as part of the AWARE campaign) are described and discussed with respect to the synoptic settings and sea-ice extent conditions. Cloud radiative forcing calculations are performed as well, and a particular focus is given to the net longwave “radiatively cloudy/opaque” (RO) regime. These results are compared with measurements performed at the West Antarctic Ice Sheet (WAIS) Divide to examine their variability and applicability to other Antarctic locations. It is found that the McMurdo cloud properties are strongly affected by the regional mesoscale cyclonic activity, which counterweights the larger-scale synoptic regime influence, while the WAIS clouds are more susceptible to the varying synoptic settings. In addition, it is suggested that the positive trend in the (frequent) cyclonic activity near the Antarctic coastal regions makes ice-clouds an increasing prominent contributor for the RO cases, especially during freeze-up and maximum sea-ice conditions.

antarctic clouds↗

DOE OSTI2017

Multiscale Methods for Accurate, Efficient, and Scale-Aware Models

The goal of UWM’s portion of the Multiscale project was to develop a unified cloud parameterization that could simulate all cloud types --- including stratocumulus, shallow cumulus, and deep cumulus --- using the single equation set implemented in CLUBB. An advantage of a unified parameterization methodology is that it avoids the difficult task of interfacing different cloud parameterizations for different cloud types. To interface CLUBB’s clouds to the microphysics, a Monte Carlo interface, SILHS, was further developed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Infrared H{sub 3}{sup +} and CO studies of the galactic core: GCIRS 3 and GCIRS 1W

We have obtained improved spectra of key fundamental band lines of H{sub 3}{sup +}, R(1, 1) {sup l}, R(3, 3) {sup l}, and R(2, 2) {sup l}, and ro-vibrational transitions of CO on sightlines toward the luminous infrared sources GCIRS 3 and GCIRS 1W, each located in the Central Cluster of the Galactic center within several arcseconds of Sgr A*. The spectra reveal absorption occurring in three kinds of gaseous environments: (1) cold dense and diffuse gas associated with foreground spiral/lateral arms; (2) warm and diffuse gas absorbing over a wide and mostly negative velocity range, which appears to fill a significant fraction of the Galaxy's Central Molecular Zone (CMZ); and (3) warm, dense and compact clouds with velocities near +50 km s{sup –1} probably within 1-2 pc of the center. The absorptions by the first two cloud types are nearly identical for all the sources in the Central Cluster, and are similar to those previously observed on sightlines from Sgr A* to 30 pc east of it. Cloud type (3), which has only been observed toward the Central Cluster, shows distinct differences between the sightlines to GCIRS 3 and GCIRS 1W, which are separated on the sky by only 0.33 pc in projection. We identify this material as part of an inward extension of the circumnuclear disk previously known from HCN mapping. Lower limits on the products of the hydrogen ionization rate ζ and the path length L are 2.3 × 10{sup 5} cm s{sup –1} and 1.5 × 10{sup 3} cm s{sup –1} for the warm and diffuse CMZ gas and for the warm and dense clouds in the core, respectively. The limits indicate that the ionization rates in these regions are well above 10{sup –15} s{sup –1}.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

One-Year Electric Field Study at the North Slope of Alaska Field Campaign Report

The Global Electric Circuit (GEC) of the atmosphere provides a unique prospective of the changing climate around the Earth. Monitoring this global electrical signature provides details of the global nature of electrified clouds and thunderstorms that are occurring. Using this very inexpensive measurement system, we can provide much-needed information about the vast electrical system that surrounds us, as well as gaining an understanding of how the electrical properties of global precipitation systems are changing over time. Prior to the OYESNSA field campaign (http://atmos.tamucc.edu/oyesnsa/; https://www.arm.gov/research/campaigns/nsa2017oyesnsa/), much of the electric field data used to compare to the physical properties of electrified clouds were collected in the Antarctica. With the inclusion of this high-quality data set in the Arctic, it allows for the simultaneous observation of the electric field at both poles. Preliminary results from the One-Year Electric Field Study-North Slope of Alaska (OYESNSA) field campaign already show that the GEC appears to be indeed a truly global phenomenon, with very similar fair-weather observations being taken at both poles. This first six months of data has already been uploaded to the ARM Data Center, and preliminary results have been presented at the American Geophysical Union (AGU) 2017 meeting with very positive feedback (http://atmos.tamucc.edu/oyesnsa/AGU_2017_Poster.pdf). The North Slope of Alaska provides a unique location for collecting these electric field measurements. Besides being at the opposite pole from many previous measurements, this site provides a rare opportunity to use the other instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Barrow, Alaska observatory, such as the Ka-band radar, upwardfacing lidar, vertical profile of meteorological measures, and other aerosol measurements. This allows for the unique chance to not only provide information about the global signature of the GEC, but also the physical inputs to the local electric field, by analyzing the physical properties of the simultaneous cloud, wind, and aerosol properties occurring with the electric field. Barrow, Alaska is home to some unique cloud formations that undoubtedly influence the electric field. This field campaign allows for the quantification of the influence to the electric field of these cloud types, as well as other unique phenomenon such as blowing snow. Already, the first six months of data have shown correlations of the electric field to radar reflectivity properties, cloud types, and increased wind (blowing snow and aerosol). Providing an understanding of these inputs to the local electric field, will improve modeling techniques of this system both locally and globally.

North Slope of Alaska, micropulse lidar, Ka-band r↗

DOE OSTI2016

Parsivel Disdrometer Support for MAGIC Field Campaign Report

In the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s second Mobile Facility (AMF2) was deployed on the Horizon Lines cargo ship Spirit traversing a route between Los Angeles, California and Honolulu, Hawaii for one full year. The transect for this deployment was chosen specifically because it crosses the stratocumulus-to-cumulus transition of the North-East Pacific, a region of great climatic interest and a close approximation to the transect used for several focused model intercomparison efforts. The cloud type and cover along this transect vary from low marine stratocumulus with high areal coverage near the California coast to isolated shallow cumulus with much lower areal coverage in the trade wind regime near Hawaii. The low marine stratocumulus decks, with their high albedo, exert a major influence on the shortwave radiation budget in the ocean environment, and thus provide an extremely important forcing of Earth’s climate. The trade cumulus clouds play a large role in the global surface evaporation and also in Earth’s albedo. One of the important science drivers of the MAGIC campaign was to measure the properties of clouds and precipitation, specifically cloud type, fractional coverage, base height, physical thickness, liquid water path (LWP), optical depth, and drizzle and precipitation frequency, amount, and extent. Retrievals of cloud and precipitation properties during the MAGIC campaign relied critically on the calibration of the AMF2 radar systems. For MAGIC this included the KAZR and M-WACR, both fixed zenith-pointing systems, and the 1290 MHz beam steerable wind profiler.

54 ENVIRONMENTAL SCIENCES↗