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DOE OSTI2021

Droplet Number Concentration Value-Added Product

The ndrop_mfrsr value-added product (VAP) provides an estimate of the cloud droplet number concentration of overcast water clouds retrieved from cloud optical depth from the multi-filter rotating shadowband radiometer (MFRSR) instrument and liquid water path (LWP) retrieved from the microwave radiometer (MWR). When cloud layer information is available from vertically pointing lidar and radars in the Active Remote Sensing of Clouds (ARSCL) product, the VAP also provides estimates of the adiabatic LWP and an adiabatic parameter (beta) that indicates how divergent the LWP is from the adiabatic case. quality control (QC) flags (qc_drop_number_conc), an uncertainty estimate (drop_number_conc_toterr), and a cloud layer type flag (cloud_base_type) are useful indicators of the quality and accuracy of any given value of the retrieval. Examples of these major input and output variables are given in sample plots in section 6.0.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Chemical Imaging of Atmospheric Organic Particles in the Eastern North Atlantic Field Campaign Report

The ability to reliably predict future climate is hindered, in major part, by an insufficient understanding of atmospheric aerosol-radiation interactions and aerosol-cloud interactions. (IPCC 2013; Bony and Dufresne 2005) Factors affecting marine stratocumulus clouds are particularly important given that they are the dominant cloud type globally. Global climate models frequently misrepresent ubiquitous marine stratocumulus clouds for a variety of reasons, including a lack of understanding of how these cloud properties change with aerosol cloud condensation nuclei (CCN) concentration and composition.(Nam et al. 2012) Limited information is available regarding the sources of CCN in remote regions where marine stratocumulus clouds dominate. Measurements in these marine locations, which are periodically influenced by anthropogenic emissions, provide additional opportunity to study the impact of humans on cloud characteristics. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Eastern North Atlantic (ENA) observatory on Graciosa Island in the Azores is well suited to study these problems. The ENA site is exposed to a multitude of air masses as well as a variety of cloud regimes, as documented by passive and active satellite cloud retrieval. (Tselioudis et al. 2013) Thus, placing detailed aerosol and cloud measurements at the ARM ENA site may help gain a better understanding of the CCN budget and cloud interactions in remote environments. To provide detailed information on aerosol morphology, composition, and microphysical properties, size- segregated sampling was carried out at the ENA observatory on Graciosa Island during two intensive operating periods (IOPs). IOP1 occurred from June 17 to July 18, 2017, and IOP2 occurred from January 9 to February 21, 2018. Daytime and nighttime sampling were carried out separately to investigate any diurnal differences of aerosol composition due to differing meteorology. For both IOPs, a micro-orifice uniform deposit impactor (MOUDI) was used to collect particles for microscopic and microphysical analysis. A variety of microscopic substrates (formvar coated copper grids, silicon chips, silicon nitride windows, or molybdenum substrates) were used. Microscopic chemical characterization was carried out using computer-controlled scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (CCSEM-EDX) and the synchrotron-based scanning transmission X-ray microscopy coupled with near-edge X-ray absorption spectroscopy (STXM-NEXAFS). (Laskin et al. 2006; Kilcoyne et al. 2003; Moffet et al. 2010) Two light sources were used for this analysis: 1) the Advanced Light Source at Lawrence Berkeley National Laboratory, and 2) The Canadian Light Source.

Eastern North Atlantic, ACE-ENA field campaign, Gr↗

DOE OSTI2022

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

The major characteristics of ice microphysics in Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) bulk-type cloud microphysics originate from the diagnosed ice number concentration, which is a function of the cloud-ice mixing ratio. In this study, we correct numerical errors in ice microphysics processes of the WDM6, in which the cloud-ice shape is assumed as single bullets and examine the impact on regional climate simulations. Here, by rederiving the relationships between cloud microphysics characteristics, including the one linking the cloud-ice mixing ratio and number concentration, we remove numerical errors intrinsic to the description of cloud-ice characteristics in the original WDM6 microphysics scheme. The revised WDM6 is tested using a WRF framework for regional climate simulations over the East Asian region. We find that our correction to the WDM6 improves the model’s performance in capturing the observed distribution of the monsoon rain band. A reduction in cloud ice is significant in the revised WDM6, which strengthens the Western North Pacific High. By conducting the additional sensitivity experiment in which the characteristics of cloud-ice shape are revised as the one for the column type, our study also finds out that the impacts of the existing numerical errors on the simulated monsoon is as large as the ones of the changes in cloud-ice shape.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska

Abstract A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Comparing the Cloud Vertical Structure Derived from Several Methods Based on Radiosonde Profiles and Ground-based Remote Sensing Measurements

The cloud vertical distribution and especially the cloud base height, which is linked to cloud type, is an important characteristic in order to describe the impact of clouds in a changing climate. In this work several methods to estimate the cloud vertical structure (CVS) based on atmospheric sounding profiles are compared, considering number and position of cloud layers, with a ground based system which is taken as a reference: the Active Remote Sensing of Clouds (ARSCL). All methods establish some conditions on the relative humidity, and differ on the use of other variables, the thresholds applied, or the vertical resolution of the profile. In this study these methods are applied to 125 radiosonde profiles acquired at the ARM Southern Great Plains site during all seasons of year 2009 and endorsed by GOES images, to confirm that the cloudiness conditions are homogeneous enough across their trajectory. The overall agreement for the methods ranges between 44-88%; four methods produce total agreements around 85%. Further tests and improvements are applied on one of these methods. In addition, we attempt to make this method suitable for low resolution vertical profiles, which could be useful in atmospheric modeling. The total agreement, even when using low resolution profiles, can be improved up to 91% if the thresholds for a moist layer to become a cloud layer are modified to minimize false negatives with the current data set, thus improving overall agreement.

atmospheric↗

DOE OSTI2022

Framework for idealized climate simulations with spatiotemporal stochastic clouds and planetary-scale circulations

In climate predictions, clouds are the leading source of uncertainty. This is partly because, to simulate the fluid dynamics of climate over the entire globe, a large grid spacing must be used, so clouds are a subgrid-scale parametrization rather than a resolved feature. Here, a framework is investigated with finer grid spacing of O(1) or O(10)km so that some clouds are not subgrid-scale; instead, clouds evolve on the numerical grid. This cloud evolution is achieved using stochastic modeling. Hence, the framework is idealized in the sense that the full fluid dynamics of cloud circulations is still not resolved, and simplified vertical structures are used. Nevertheless, the fluid dynamics model includes evolving clouds that interactively adjust in size, shape, lifetime, and regional coverage. In addition, different cloud types are included with different roles in the climate system, including deep convective clouds and also boundary-layer clouds such as shallow cumulus and stratocumulus clouds. Other basic aspects of the idealized climate system are planetary-scale circulations (e.g., Walker circulation) and radiation. With these ingredients (evolving clouds, planetary-scale circulations, and radiation), the framework has the potential for idealized investigations of climate change with interactive cloud–radiative feedback of individual clouds. Here, the formulation of the model equations is presented, and numerical simulations are shown to illustrate the model dynamics and climate change.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Investigation of the Residual in Column-Integrated Atmospheric Energy Balance Using Cloud Objects

Observationally based atmospheric energy balance is analyzed using Clouds and the Earth’s Radiant Energy System (CERES)-derived TOA and surface irradiance, Global Precipitation Climatology Project (GPCP)-derived precipitation, dry static and kinetic energy tendency and divergence estimated from ERA-Interim, and surface sensible heat flux from SeaFlux. The residual tends to be negative over the tropics and positive over midlatitudes. A negative residual implies that the precipitation rate is too small, divergence is too large, or radiative cooling is too large. The residual of atmospheric energy is spatially and temporally correlated with cloud objects to identify cloud types associated with the residual. Spatially, shallow cumulus, cirrostratus, and deep convective cloud-object occurrence are positively correlated with the absolute value of the residual. The temporal correlation coefficient between the number of deep convective cloud objects and individual energy components, net atmospheric irradiance, precipitation rate, and the sum of dry static and kinetic energy divergence and their tendency over the western Pacific are 0.84, 0.95, and 0.93, respectively. However, when all energy components are added, the atmospheric energy residual over the tropical Pacific is temporally correlated well with the number of shallow cumulus cloud objects over tropical Pacific. Because shallow cumulus alters not enough atmospheric energy compared to the residual, this suggests the following: 1) if retrieval errors associated with deep convective clouds are causing the column-integrated atmospheric energy residual, the errors vary among individual deep convective clouds, and 2) it is possible that the residual is associated with processes in which shallow cumulus clouds affect deep convective clouds and hence atmospheric energy budget over the tropical western Pacific.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

The IRX–β Relation: Insights from Simulations

We study the relationship between the UV continuum slope and infrared excess (IRX≡L{sub IR}/L{sub FUV}) predicted by performing dust radiative transfer on a suite of hydrodynamical simulations of galaxies. Our suite includes both isolated disk galaxies and mergers intended to be representative of galaxies at both z∼0 and z∼2--3. Our low-redshift systems often populate a region around the locally calibrated Meurer et al. relation but move above the relation during merger-induced starbursts. Our high-redshift systems are blue and IR luminous and therefore lie above the Meurer et al. relation. The value of β strongly depends on the dust type used in the RT simulation: Milky-Way-type dust leads to significantly more negative (bluer) slopes compared with Small-Magellanic-Cloud-type dust. The effect on β due to variations in the dust composition with galaxy properties or redshift is the dominant model uncertainty. The dispersion in β is anticorrelated with specific star formation rate (sSFR) and tends to be higher for the z∼2--3 simulations. In the actively star-forming z∼2--3 simulated galaxies, dust attenuation dominates the dispersion in β, whereas in the z∼0 simulations, the contributions of star formation history (SFH) variations and dust are similar. For low-sSFR systems at both redshifts, SFH variations dominate the dispersion. Finally, the simulated z∼2--3 isolated disks and mergers both occupy a region in the IRX--β plane consistent with observed z∼2--3 dusty star-forming galaxies (DSFGs). Thus, contrary to some claims in the literature, the blue colors of high-z DSFGs do not imply that they are short-lived starbursts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Clouds, Precipitation, and Marine Boundary Layer Structure during the MAGIC Field Campaign

The recent ship-based MAGIC (Marine ARM GCSS Pacific Cross-Section Intercomparison (GPCI) Investigation of Clouds) field campaign with the marine-capable Second ARM Mobile Facility (AMF2) deployed on the Horizon Lines cargo container M/V Spirit provided nearly 200 days of intraseasonal high-resolution observations of clouds, precipitation, and marine boundary layer (MBL) structure on multiple legs between Los Angeles, California, and Honolulu, Hawaii. During the deployment, MBL clouds exhibited a much higher frequency of occurrence than other cloud types and occurred more often in the warm season than in the cold season. MBL clouds demonstrated a propensity to produce precipitation, which often evaporated before reaching the ocean surface. The formation of stratocumulus is strongly correlated to a shallow MBL with a strong inversion and a weak transition, while cumulus formation is associated with a much weaker inversion and stronger transition. The estimated inversion strength is shown to depend seasonally on the potential temperature at 700 hPa. The location of the commencement of systematic MBL decoupling always occurred eastward of the locations of cloud breakup, and the systematic decoupling showed a strong moisture stratification. The entrainment of the dry warm air above the inversion appears to be the dominant factor triggering the systematic decoupling, while surface latent heat flux, precipitation, and diurnal circulation did not play major roles. MBL clouds broke up over a short spatial region due to the changes in the synoptic conditions, implying that in real atmospheric conditions the MBL clouds do not have enough time to evolve as in the idealized models. (auth)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Micropulse Lidar Cloud Mask Machine-Learning Value-Added Product Report

Cloud detection algorithms of various techniques have been developed and applied to atmospheric ground-based lidar data to identify cloud boundaries and produce clouds masks. While these algorithms are able to identify a wide variety of cloud types and conditions, it is often observed that the algorithms can still fail to accurately detect clouds that are readily discernible when inspecting the lidar imagery. Based on this observation, an alternative approach for cloud detection is to take advantage of machine-learning capabilities and the trained human eye as an interpreter of lidar images, and in turn, to train a neural network to recognize the desired features in the lidar data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Weak influence of anthropogenic emissions on aerosol, cloud, and rain in the wet season of the Amazon rainforest

Anthropogenic emissions have been shown to affect new particle formation, aerosol concentrations, and clouds. Such effects vary with region, environmental conditions, and cloud types. In the wet season of Amazonia, anthropogenic emissions emitted from Manaus, Brazil, can significantly increase the cloud condensation nuclei (CCN) concentrations compared to the background of mainly natural aerosols. However, the regional response of cloud and rain to anthropogenic emissions in Amazonia remains very uncertain. Here, we aim to quantify how aerosol concentration, cloud, and rain respond to changes in anthropogenic emissions through parameterized new particle formation and primary aerosol emission in the Manaus region and to understand the underlying mechanisms. We ran the atmosphere-only configuration of the HadGEM3 climate model with a nested regional domain that covers most of the rainforest region (720 km by 1200 km with 3 km resolution) under scaled regional emissions. The 7 d simulations show that, in the areas that are affected by anthropogenic emissions, when aerosol and precursor gas emissions are doubled from the baseline emission inventories, aerosol number concentrations increase by 13 %. The nucleation rate that involves sulfuric acid and biogenic compounds generally increases with pollution levels. However, nucleation is suppressed very close to the pollution source, resulting in lower nucleation and soluble Aitken mode aerosol number concentrations. We also found that doubling the anthropogenic emission can increase the cloud droplet number concentrations ( N d ) by 9 %, but cloud water and rain mass mixing ratios do not change significantly. Even very strong reductions in aerosol number concentrations by a factor of 4, which is an unrealistic condition, cause only a 4 % increase in rain over the domain. If we assume our simulation has a fine enough grid resolution and an accurate representation of the relevant atmospheric processes, the simulated weak and non-linear response of cloud and rain properties to linearly scaled anthropogenic emissions suggests that the interactions among aerosol, cloud, and precipitation in the Amazonian convective environment are buffered by microphysical processes. It also implies that the convective environment is resilient to the changes in Nd that occur in response to localized anthropogenic aerosol perturbations.

Wang, Xuemei [University of Leeds (United Kingdom)↗

DOE OSTI2015

Droplet Number Concentration Value Added Product

Cloud droplet number concentration is an important factor in understanding aerosol-cloud interactions. As aerosol concentration increases, it is expected that droplet number concentration (Nd) will increase and droplet size will decrease, for a given liquid water path. This will greatly affect cloud albedo as smaller droplets reflect more shortwave radiation; however, the magnitude and variability of these processes under different environmental conditions is still uncertain.McComiskey et al. (2009) have implemented a method, based onBoers and Mitchell (1994), for calculating Nd from ground-based remote sensing measurements of optical depth and liquid water path. They show that the magnitude of the aerosol-cloud interactions (ACI) varies with a range of factors, including the relative value of the cloud liquid water path (LWP), the aerosol size distribution, and the cloud updraft velocity. Estimates of Nd under a range of cloud types and conditions and at a variety of sites are needed to further quantify the impacts of aerosol cloud interactions. In order to provide data sets for studying aerosol-cloud interactions, the McComiskey et al. (2009) method was implemented as the Droplet Number Concentration (NDROP) value-added product (VAP).

Sivaraman, Chitra↗

DOE OSTI2015

Droplet Number Concentration Value Added Product

Chitra Sivaraman, PNNL↗

DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗

DOE OSTI2020

Cloud regimes over the Amazon Basin: perspectives from the GoAmazon2014/5 campaign

Abstract. Radiosonde observations collected during the GoAmazon2014/5 campaign are analyzed to identify the primary thermodynamic regimes accompanying different modes of convection over the Amazon. This analysis identifies five thermodynamic regimes that are consistent with traditional Amazon calendar definitions of seasonal shifts, which include one wet, one transitional, and three dry season regimes based on a k-means cluster analysis. A multisensor ground-based approach is used to project associated bulk cloud and precipitation properties onto these regimes. This is done to assess the propensity for each regime to be associated with different characteristic cloud frequency, cloud types, and precipitation properties. Additional emphasis is given to those regimes that promote deep convective precipitation and organized convective systems. Overall, we find reduced cloud cover and precipitation rates to be associated with the three dry regimes and those with the highest convective inhibition. While approximately 15 % of the dataset is designated as organized convection, these events are predominantly contained within the transitional regime.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

STORMVEX. Ice Nuclei and Cloud Condensation Nuclei Characterization Field Campaign Report

The relationship between aerosol particles and the formation of clouds is among the most uncertain aspects in our current understanding of climate change. Warm clouds have been the most extensively studied, in large part because they are normally close to the Earth’s surface and only contain large concentrations of liquid droplets. Ice and mixed-phase clouds have been less studied even though they have extensive global coverage and dominate precipitation formation. Because they require low temperatures to form, both cloud types are infrequently found at ground level, resulting in more difficult field studies. Complex mixtures of liquid and ice elements, normally at much lower concentrations than found in warm clouds, require precise separation techniques and accurate identification of phase. Because they have proved so difficult to study, the climatic impact of ice-containing clouds remains unresolved. In this study, cloud condensation nuclei (CCN) concentrations and associated single particles’ composition and size were measured at a high-elevation research site—Storm Peak Lab, east of Steamboat Springs, Colorado, operated by the Desert Research Institute. Detailed composition analyses were presented to compare CCN activation with single-particle composition. In collaboration with the scientists of the Storm Peak Lab Cloud Property Validation Experiment (STORMVEX), our goal was to relate these findings to the cloud characteristics and the effect of anthropogenic activities.

54 ENVIRONMENTAL SCIENCES↗