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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Warm boundary-layer cloud properties and cloud susceptibilities product at the ARM ENA observatory

Cloud liquid water path, albedo, and cloud fraction susceptibilities for marine warm boundary-layer clouds are quantified using the Meteosat retrieved cloud microphysical properties at the ARM Eastern North Atlantic (ENA) observatory in the Azores. The Meteosat cloud retrievals are based on the methods applied by the Clouds and the Earth’s Radiant Energy System (CERES) project, which are specifically tailored to support the ARM user facility over the ARM ground-based observation sites (enavisstpx2dm11minnisX1.c1). Cloud susceptibility is quantified as the slope between cloud properties and Nd within a 1-degree-by-1-degree grid box at each satellite time step using a least-square regression. To reduce uncertainties associated with the adiabatic assumption in the Nd retrieval, for each cloud object, we exclude cloudy pixels at the cloud edge, defined as those adjacent to cloud-free pixels. To minimize influence of cloud heterogeneity and co-variability on the retrieved cloud susceptibility, cloud properties are averaged to 0.25-degree resolution using the 3-km pixel-level retrieval, and the 1-degree cloud susceptibilities are estimated using the smoothed data. Cloud type classification is applied to distinct cloud object, labeling all contiguous cloudy pixels. Marine warm boundary-layer clouds are defined as clouds with 90% of cloud tops below 3km, and Meteosat retrieved cloud phase as liquid.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The observed influence of local anthropogenic pollution on northern Alaskan cloud properties

Due to their importance for the radiation budget, liquid-containing clouds are a key component of the Arctic climate system. Depending on season, they can cool or warm the near-surface air. The radiative properties of these clouds depend strongly on cloud drop sizes, which are governed in part by the availability of cloud condensation nuclei. Here, we investigate how cloud drop sizes are modified in the presence of local emissions from industrial facilities at the North Slope of Alaska. For this, we use aircraft in situ observations of clouds and aerosols from the 5th Department of Energy Atmospheric Radiation Measurement (DOE ARM) Program's Airborne Carbon Measurements (ACME-V) campaign obtained in summer 2015. Comparison of observations from an area with petroleum extraction facilities (Oliktok Point) with data from a reference area relatively free of anthropogenic sources (Utqiagvik/Barrow) represents an opportunity to quantify the impact of local industrial emissions on cloud properties. In the presence of local industrial emissions, the mean effective radii of cloud droplets are reduced from 12.2 to 9.4 µm, which leads to suppressed drizzle production and precipitation. At the same time, concentrations of refractory black carbon and condensation nuclei are enhanced below the clouds. These results demonstrate that the effects of anthropogenic pollution on local climate need to be considered when planning Arctic industrial infrastructure in a warming environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Mean State Cloud Properties in the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)

Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub-grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio-temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean-state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean-state cloud properties across various cloud optical thickness and cloud-top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean-state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.

Geosciences↗
DOE OSTI2017

Retrieval of Boundary Layer 3D Cloud Properties Using Scanning Cloud Radar and 3D Radiative Transfer

Retrievals of cloud optical and microphysical properties for boundary layer clouds, including those widely used by ASR investigators, frequently assume that clouds are sufficiently horizontally homogeneous that scattering and absorption (at all wavelengths) can be treated using one dimensional (1D) radiative transfer, and that differences in the field-of-view of different sensors are unimportant. Unfortunately, most boundary layer clouds are far from horizontally homogeneous, and numerous theoretical and observational studies show that the assumption of horizontal homogeneity leads to significant errors. The introduction of scanning cloud and precipitation radars at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program sites presents opportunities to move beyond the horizontally homogeneous assumption. The primary objective of this project was to develop a 3D retrieval for warm-phase (liquid only) boundary layer cloud microphysical properties, and to assess errors in current 1D (non-scanning) approaches. Specific research activities also involved examination of the diurnal cycle of hydrometeors as viewed by ARM cloud radar, and continued assessment of precipitation impacts on retrievals of cloud liquid water path using passive microwaves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigation of Arctic Cloud Properties and Surface Radiation Based on MOSAiC Shipborne Observations

The Arctic is rapidly changing due to changes of the Earth system. This study investigates cloud fraction, phase partition, cloud type, and their relationships with surface radiation based on yearlong shipborne observations in the Arctic regions. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign provided lidar and radar observations of cloud microphysical properties and surface shortwave (SW) and longwave (LW) radiation. Cloud and radiative properties were examined at daily and monthly resolutions in four seasons. Low clouds were found to be most prevalent throughout the year, followed by deep clouds. The ice phase is the dominant phase except for summer (June–August). Liquid and mixed phases show more significant monthly and annual mean radiative effects in SW and LW than the ice phase. The clouds show net warming effects due to LW heating in most months, while the SW cooling effects of clouds become more dominant for July and August. The cloud and radiation observations from MOSAiC were used to evaluate simulations of the atmospheric component of the Energy Exascale Earth System Model version 2. The simulations show large overestimations of the liquid and mixed phases in the Arctic regions from February to September. The simulations also underestimate the percentages of low clouds and overestimate the percentages of deep clouds throughout the year. Altogether, this work provides a unique analysis of cloud and radiation properties based on high-resolution shipborne observations, which can be used to assist future model evaluation and development.

58 GEOSCIENCES↗
DOE OSTI2015

Improving Representation of Convective Transport for Scale-Aware Parameterization – Part I: Convection and Cloud Properties Simulated with Spectral Bin and Bulk Microphysics

The ultimate goal of this study is to improve representation of convective transport by cumulus parameterization for meso-scale and climate models. As Part I of the study, we perform extensive evaluations of cloud-resolving simulations of a squall line and mesoscale convective complexes in mid-latitude continent and tropical regions using the Weather Research and Forecasting (WRF) model with spectral-bin microphysics (SBM) and with two double-moment bulk microphysics schemes: a modified Morrison (MOR) and Milbrandt and Yau (MY2). Compared to observations, in general, SBM gives better simulations of precipitation, vertical velocity of convective cores, and the vertically decreasing trend of radar reflectivity than MOR and MY2, and therefore will be used for analysis of scale-dependence of eddy transport in Part II. The common features of the simulations for all convective systems are (1) the model tends to overestimate convection intensity in the middle and upper troposphere, but SBM can alleviate much of the overestimation and reproduce the observed convection intensity well; (2) the model greatly overestimates radar reflectivity in convective cores (SBM predicts smaller radar reflectivity but does not remove the large overestimation); and (3) the model performs better for mid-latitude convective systems than tropical system. The modeled mass fluxes of the mid latitude systems are not sensitive to microphysics schemes, but are very sensitive for the tropical case indicating strong microphysics modification to convection. Cloud microphysical measurements of rain, snow and graupel in convective cores will be critically important to further elucidate issues within cloud microphysics schemes.

Fan, Jiwen↗
DOE OSTI2016

Observed correlations between aerosol and cloud properties in an Indian Ocean trade cumulus regime

There are many contributing factors which determine the micro- and macrophysical properties of clouds, including atmospheric vertical structure, dominant meteorological conditions, and aerosol concentration, all of which may be coupled to one another. In the quest to determine aerosol effects on clouds, these potential relationships must be understood. Here we describe several observed correlations between aerosol conditions and cloud and atmospheric properties in the Indian Ocean winter monsoon season. In the CARDEX (Cloud, Aerosol, Radiative forcing, Dynamics EXperiment) field campaign conducted in February and March 2012 in the northern Indian Ocean, continuous measurements were made of atmospheric precipitable water vapor (PWV) and the liquid water path (LWP) of trade cumulus clouds, concurrent with measurements of water vapor flux, cloud and aerosol vertical profiles, meteorological data, and surface and total-column aerosol from instrumentation at a ground observatory and on small unmanned aircraft. We present observations which indicate a positive correlation between aerosol and cloud LWP only when considering cases with low atmospheric water vapor (PWV < 40 kg m –2 ), a criterion which acts to filter the data to control for the natural meteorological variability in the region. We then use the aircraft and ground-based measurements to explore possible mechanisms behind this observed aerosol–LWP correlation. The increase in cloud liquid water is found to coincide with a lowering of the cloud base, which is itself attributable to increased boundary layer humidity in polluted conditions. High pollution is found to correlate with both higher temperatures and higher humidity measured throughout the boundary layer. A large-scale analysis, using satellite observations and meteorological reanalysis, corroborates these covariations: high-pollution cases are shown to originate as a highly polluted boundary layer air mass approaching the observatory from a northwesterly direction. The source air mass exhibits both higher temperatures and higher humidity in the polluted cases. While the warmer temperatures may be attributable to aerosol absorption of solar radiation over the subcontinent, the factors responsible for the coincident high humidity are less evident: the high-aerosol conditions are observed to disperse with air mass evolution, along with a weakening of the high-temperature anomaly, while the high-humidity condition is observed to strengthen in magnitude as the polluted air mass moves over the ocean toward the site of the CARDEX observations. In conclusion, potential causal mechanisms of the observed correlations, including meteorological or aerosol-induced factors, are explored, though future research will be needed for a more complete and quantitative understanding of the aerosol–humidity relationship.

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 OSTI2023

Field Validation of Cloud Properties Sensor (SAIL Field Campaign Report)

The purpose of the Field Validation of Cloud Properties Sensor – SAIL campaign was to deploy Aerodyne’s extended-wavelength TWST cloud optical properties sensor (TWST-CPS) in an operationally relevant environment with co-located, externally validated sensors. Colocation with The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s deployment of its second Mobile Facility (AMF2) on the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign at Crested Butte, Colorado was ideal because of the variety of operational sensors that can measure some of the same cloud properties using different modalities. While cloud property sensors have long existed, they tend to be costly to produce and maintain. Our sensor measures absolute spectral radiance over 440-1700 nm and retrieves cloud optical depth (COD), droplet effective radius (Reff), and thermodynamic phase (Phase). Our prototype is built predominantly from off-the-shelf components and uses uncooled spectrometers. A low-cost, easy-to-use sensor such as this could allow deployment at many more sites for greater spatial coverage. Instrument deployment to test robust operations and a new stray-light baffle design, and the continued development of analysis and retrieval algorithms using the data from this deployment, are central technical objectives of Aerodyne Research’s U.S. Department of Energy Small Business Innovative Research Phase 2 contract (DE-SC0020473, Low-cost shortwave spectroradiometer for retrieval of cloud properties).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Field Validation of Cloud Properties Sensor – SAIL Field Campaign Report

The purpose of the Field Validation of Cloud Properties Sensor – SAIL campaign was to deploy Aerodyne’s extended-wavelength TWST cloud optical properties sensor (TWST-CPS) in an operationally relevant environment with co-located, externally validated sensors. Colocation with The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s deployment of its second Mobile Facility (AMF2) on the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign at Crested Butte, Colorado was ideal because of the variety of operational sensors that can measure some of the same cloud properties using different modalities. While cloud property sensors have long existed, they tend to be costly to produce and maintain. Our sensor measures absolute spectral radiance over 440-1700 nm and retrieves cloud optical depth (COD), droplet effective radius (Reff), and thermodynamic phase (Phase). Our prototype is built predominantly from off-the-shelf components and uses uncooled spectrometers. A low-cost, easy-to-use sensor such as this could allow deployment at many more sites for greater spatial coverage. Instrument deployment to test robust operations and a new stray-light baffle design, and the continued development of analysis and retrieval algorithms using the data from this deployment, are central technical objectives of Aerodyne Research’s U.S. Department of Energy Small Business Innovative Research Phase 2 contract (DE-SC0020473, Low-cost shortwave spectroradiometer for retrieval of cloud properties).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗
DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data: Preprint

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data input to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Linking large-scale circulation patterns to low-cloud properties

Abstract. The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850 hPa synoptic-scale circulation patterns. This approach reveals two distinguishable regimes – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of large-scale patterns. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration, liquid water path, and shortwave cloud radiative effect – CRESW) on 850 hPa circulation patterns over the northeast Pacific Ocean. We find that CRESW spans from −146.8 to −115.5 W m−2, indicating that the range of observed MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite retrievals to help clarify the relationship between synoptic-scale dynamics and cloud physics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Skill of Mesoscale Models in Forecasting Springtime Macrophysical Cloud Properties at the Savannah River Site in the Southeastern US

Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases—WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Southern Ocean Cloud Properties Derived From CAPRICORN and MARCUS Data

The properties of Southern Ocean (SO) liquid phase non precipitating clouds (hereafter clouds) are examined using shipborne data collected during the Measurements of Aerosols, Radiation and Clouds over the Southern Ocean and the Clouds Aerosols Precipitation Radiation and atmospheric Composition Over the SoutheRN ocean I and II campaigns that took place south of Australia during Autumn 2016 and Summer 2017–2018. Cloud properties are derived using data from W-band radars, lidars, and microwave radiometers using an optimal estimation algorithm. The SO clouds tended to have larger liquid water paths (LWP, 115 ± 117 g m -2 ), smaller effective radii (re, 8.7 ± 3 μm), and higher number concentrations (N d , 90 ± 107 cm-3) than typical values of eastern ocean basin stratocumulus. The clouds demonstrated a tendency for the LWP to increase with Nd presumably due to precipitation suppression up to Nd of approximately 100 cm -3 when mean LWP decreased with increasing Nd. Due to higher optical depth, cloud albedos were less susceptible to changes in Nd compared to subtropical stratocumulus. The highest latitude clouds of the datasets, observed along and near the Antarctic coast, presented a distinctly bimodal character. One mode had the properties of marine clouds further north. Additionally, the other mode occurred in an aerosol environment characterized by high cloud condensation nuclei concentrations and elevated sulfate aerosol without obvious continental aerosol markers. These regions of higher cloud condensation nuclei tended to have higher Nd, smaller re and higher LWP suggesting sensitivity of cloud properties to seasonal biogenic aerosol production in the high latitude SO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E

Here, the controls on precipitation onset and the transition from shallow cumulus to congestus are explored using a suite of 16 large–eddy simulations based on the 25 May 2011 event from the Midlatitude Continental Convective Clouds Experiment (MC3E). The thermodynamic variables in the model are relaxed at various timescales to observationally constrained temperature and moisture profiles in order to better reproduce the observed behavior of precipitation onset and total precipitation. Three of the simulations stand out as best matching the precipitation observations and also perform well for independent comparisons of cloud fraction, precipitation area fraction, and evolution of cloud top occurrence. All three simulations exhibit a destabilization over time, which leads to a transition to deeper clouds, but the evolution of traditional stability metrics by themselves is not able to explain differences in the simulations. Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences among the simulations. The inability of environmental profiles alone to discern subtle differences among the simulations and the usefulness of conditionally sampled model quantities argue for hybrid observational/modeling approaches. These combined approaches enable a more complete physical understanding of cloud systems by combining observational sampling of time–varying three–dimensional meteorological quantities and cloud properties, along with detailed representation of cloud microphysical and dynamical processes from numerical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Comparison of Daytime Low-Level Cloud Properties Derived From GOES and ARM SGP Measurements

Large-scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single-layered low-level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground-based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible-Infrared Solar-infrared Split-window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one-to-one GOES and ARM collocated pairs. GOES T eff is highly correlated with ARM T top cloud temperature, having an R 2 value of 0.75, though GOES exhibits a cold bias. GOES-retrieved τ and liquid water path have very good agreement with ARM retrievals with R 2 s of 0.45 and 0.47, while r e (GOES), on average, is about 2 μm greater than ARM r e . An examination of solar and viewing geometry has shown that GOES-retrieved mean r e and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

54 ENVIRONMENTAL SCIENCES↗