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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identifying synoptic controls on boundary layer thermodynamic and cloud properties in a regional forecast model

Although most of our understanding of boundary layer cloudiness is based on idealized, subtropical, barotropic marine environments, boundary layer clouds exist across a range of conditions. In this study, we use the Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) and an automated cold-front-relative analysis framework to explore the boundary layer structure associated with low clouds across a transect through the cold front of a midlatitude synoptic cyclone. The model credibly captures boundary layer structure in line with conceptual models of midlatitude cyclones and ground-based observations at Graciosa Island in the Azores. The warm sector is conditionally unstable, with clouds that are too shallow and with too little liquid water, compared to cloud property retrievals from satellite and surface-based instruments. The cold-frontal region exhibits convection associated with weak stability and ascent. Northwest of the cold front, the boundary layer is well mixed, deeper, and capped by a strong inversion maintained by large-scale subsidence. Simulated clouds in frontal and post-frontal regions are mostly too thick, with too much liquid water and too little cloud base drizzle. The post-frontal clouds are associated with grid-scale updrafts, which appear to be the model's attempt to represent mesoscale organization of cellular convection typically observed in the cold sector of midlatitude cyclones. The deep, well-mixed post-frontal boundary layers and cloudiness are maintained by strong surface fluxes, as in cold air outbreaks. Our analysis framework serves as a unique approach to model verification, and our results offer insights into differences in boundary layer cloud behavior between subtropical and synoptic cold-sector regimes.

Eissner, Jordan M. [Univ. of Kansas, Lawrence, KS ↗
NASA NTRSDate not supplied

166 GHz Ice Scattering Signal in Cold-Season Precipitating Cloud Systems

Surface snowfall can be produced by a variety of forcing mechanisms that are accompanied by different in-cloud physical processes and microphysical composition. For instance, shallow cumuliform snowfall forced by marine cold air outbreaks shows distinct geographic and seasonal signatures and plays in important role in annual snowfall production in many regions worldwide. Coincident CloudSat-CALIPSO observations confirm that oceanic regions exhibiting frequent shallow cumuliform snow are typically composed of mixed-phase cloud and precipitation particles and display passive microwave brightness temperature behavior that is dominated by cloud liquid water emission, while combined Global Precipitation Measurement (GPM) radiometer and surface radar studies of intense shallow cumuliform events indicate significant ice particle scattering signals at the highest GPM radiometer frequencies. Passive microwave imager channels in the 37 to 89 GHz range have long been used to detect precipitation-sized ice particle scattering signatures in clouds. Dual polarization plays an important role in distinguishing between scattering signatures and other background variability. The GPM Microwave Imager (GMI) is the first satellite radiometer to possess dual-polarized channels at 166 GHz. These high-frequency channels are more susceptible to emission and attenuation by cloud liquid water and water vapor than lower-frequency channels, but are also more sensitive to ice scattering. This research studies the ice scattering information contained in the 166 GHz channels compared to the historically used 85/89 GHz channels on previous sensors. A 166 GHz scattering index (SI) will be developed to understand how its correlation with non-precipitating ice scattering at higher altitudes also affects the 166 GHz scattering signature in cold-season precipitating cloud systems. The 166 GHz SI will be used to possibly isolate precipitation scattering signature for different snowfall modes (shallow convective vs. deep stratiform). The supercooled liquid water affecting shallow convective systems and drastically reducing the scattering signal could be detected and quantified, providing additional information for convective snow detection and quantification in solid precipitation retrieval algorithms.

Lisa Milani↗
DOE OSTI2025

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Bogenschutz, Peter A. [Lawrence Livermore National↗
DOE OSTI2024

Using MARCUS, MICRE, and COMBLE data to improve understanding and modeling of cloud, aerosol, and boundary layer processes at high-latitudes

Because it is believed general circulation (GCM) and numerical weather prediction (NWP) models underestimate shortwave radiation over the Southern Ocean (SO) due to inadequate representations of boundary layer (BL) and cloud processes, it is critical to improve our understanding of key aerosol, cloud, precipitation, and BL processes. Over the north Atlantic Ocean (NA), cold-air outbreaks are common, yet few studies of the aerosol and environmental controls of the associated convective BL clouds exist as needed to develop and evaluate GCM representations. Although processes cannot be observed, cloud and aerosol properties can be measured in-situ or remotely retrieved, which when combined with numerical simulations enable process level understanding required to improve model representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mesoscale Organization in Cumulus-Coupled Stratocumulus

Marine cloud systems cover a substantial portion of the world’s oceans. Most of these clouds form relatively close to the ocean surface, typically within one to two kilometers, a region referred to by meteorologists as the marine boundary layer. They are composed predominantly of liquid water, although ice particles can occur in mid- and high-latitude marine clouds during winter. In satellite imagery, these clouds appear bright against the darker ocean surface below, reflecting a large fraction of incoming sunlight back into space that would otherwise warm the ocean. Because marine boundary layer clouds cover such an extensive area of the ocean, they exert a significant influence on Earth’s overall transfer of solar energy absorbed by the surface and thermal energy emitted to space, a balance known as the planetary radiation budget. Marine boundary layer clouds are typically thin, and their formation and dissipation depend on a delicate balance between processes acting at the ocean surface below and the warm, dry air above. They are notoriously difficult to simulate accurately in weather forecast models, which often produce too few marine low clouds in midlatitudes and clouds in tropical regions that are excessively bright, meaning they reflect too much solar radiation. The marine boundary layer is frequently characterized by widespread overcast cloud cover that often transitions from a continuous, single-layer deck to more broken cloud fields toward the tropics. These transitions typically proceed through an intermediate stage in which shallow, broken clouds form beneath the overlying stratiform cloud deck. Once broken clouds develop below the overcast, they frequently self-organize into cloud clusters known as marine boundary layer convective complexes (MBLCCs), although the mechanisms governing the formation and organization of MBLCCs remain poorly understood. Accurately representing these transitions in long-range weather forecast models is essential because they influence the properties of air masses advected over the continental United States and Europe, and they become increasingly important for forecasts on seasonal and longer timescales. We employed two complementary approaches to investigate the processes controlling MBLCCs and their impact on marine cloud cover. Long-term observations from the U.S. Department of Energy’s Eastern North Atlantic (ENA) Observatory provided a unique dataset that allowed us to characterize fundamental properties of MBLCCs, including their typical size and frequency of occurrence. These observations were combined with high-resolution numerical simulations performed on supercomputers to examine the evolution of MBLCCs during cold-air outbreaks over the ENA region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Response of marine post-frontal clouds to Gulf Stream variability

Understanding how Gulf Stream variation influences cloud morphology is critical for evaluating cloud feedback in the western North Atlantic Ocean and beyond, where mesoscale air-sea interactions dominate. This study investigates the impact of altered mean sea surface temperature (SST) and SST gradients on post-frontal cloud characteristics during cold-air outbreaks, using the Weather Research and Forecasting (WRF) model. Three sensitivity experiments are conducted: a control simulation (default SST), Plus4 (uniform SST increase of 4 K), and Gradplus (SST gradient enhanced by 25 %, centered around mean SST). Results reveal distinctly different responses in boundary layer dynamics and cloud macro-physics. In Plus4, a warmer and moister boundary layer reduces total cloud cover but promotes larger cloud sizes and elongated cloud streets, with diminished liquid water and enhanced ice-phase hydrometeors. Conversely, Gradplus amplifies impacts in the upwind colder SST regions, yielding a drier, colder boundary layer, weaker energy transport, and higher liquid water path but reduced ice water content and cloud lines. Tracer analysis highlights that SST modifications alter airmass sources near cloud tops due to the entrainment of ambient air, with Plus4 amplifying boundary layer contributions to cloud-top regions. These findings underscore the spatially varying effects of SST gradients and mean SST on cloud organization and microphysics, emphasizing the need to resolve ocean-atmosphere coupling in global models to improve the prediction of marine cloud feedback under warming scenarios.

AIR-SEA INTERACTION↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Weather and climate needs for Lidar observations from space and concepts for their realization

The spectrum of weather and climate needs for Lidar observations from space is discussed with emphasis on the requirements for wind, temperature, moisture, and pressure data. It is shown that winds are required to realistically depict all atmospheric scales in the tropics and the smaller scales at higher latitudes, where both temperature and wind profiles are necessary. The need for means to estimate air-sea exchanges of sensible and latent heat also is noted. A concept for achieving this through a combination of Lidar cloud top heights and IR cloud top temperatures of cloud streets formed during cold air outbreaks over the warmer ocean is outlined. Recent theoretical feasibility studies concerning the profiling of temperatures, pressure, and humidity by differential absorption Lidar (DIAL) from space and expected accuracies are reviewed. An alternative approach to Doppler Lidar wind measurements also is presented. The concept involves the measurement of the displacement of the aerosol backscatter pattern, at constant heights, between two successive scans of the same area, one ahead of the spacecraft and the other behind it a few minutes later. Finally, an integrated space Lidar system capable of measuring temperature, pressure, humidity, and winds which combines the DIAL methods with the aerosol pattern displacement concept is described.

Atlas, D.↗
NASA NTRS1980

Kinetic energy budget during strong jet stream activity over the eastern United States

Kinetic energy budgets are computed during a cold air outbreak in association with strong jet stream activity over the eastern United States. The period is characterized by large generation of kinetic energy due to cross-contour flow. Horizontal export and dissipation of energy to subgrid scales of motion constitute the important energy sinks. Rawinsonde data at 3 and 6 h intervals during a 36 h period are used in the analysis and reveal that energy fluctuations on a time scale of less than 12 h are generally small even though the overall energy balance does change considerably during the period in conjunction with an upper level trough which moves through the region. An error analysis of the energy budget terms suggests that this major change in the budget is not due to random errors in the input data but is caused by the changing synoptic situation. The study illustrates the need to consider the time and space scales of associated weather phenomena in interpreting energy budgets obtained through use of higher frequency data.

Fuelberg, H. E.↗
NASA NTRS1982

Coast-ocean-atmosphere-ocean mesoscale interaction

In the case of cold air outbreaks, the combination of the coastal shape and the sea surface temperature (SST) pattern have a profound effect in establishing a low level mesoscale atmospheric circulation as a result of differential heating due to both variations in overwater path length and the SST. A convergence (or divergence) line then forms along a line exactly downwind of the major bend in the coastline. All this is consistent with the structure of the cloud patterns seen in a high resolution Landsat picture of the cloud streets and the major features are simulated well with a boundary layer model. The dominant convergence line is marked by notably larger clouds. To its east the convective roll clouds grow downstream in accord with the deepening of the boundary layer. To its west (i.e., coastal side) where the induced pressure field forces a strong westerly component in the boundary layer, the wind shear across the inversion gives rise to Kelvin-Helmholtz waves and billow clouds whose orientation is perpendicular to the shear vector and to the major convergence line. The induced mesoscale circulation will feedback on the ocean by intensifying the wind generated ocean wave growth and altering their orientation. Coastal cyclogenesis is due in large part not only to the fluxes of heat and moisture from the ocean, but particularly to the differential heating and moistening of the boundary layer air when the air trajectories pass over a well defined pattern of SST.

Atlas, D.↗
NASA NTRS1982

A numerical simulation of an atmospheric vortex street

A simplified mesoscale mixed-layer model is utilized to study the flow past an ocean island during a case of cold air outbreak over the Kuroshio Current. Using data taken during AMTEX '75, the governing equations are integrated in time to simulate the development of a Karman vortex street downstream of the island of Cheju-do. The surface layer, inversion layer, and overlying stable layer are all parametrized in the model. Very weak sinusoidal waviness but no vortex street was found in the case of the island. For a mixed layer barely deep enough to allow no part of the mountain on the island to protrude, there was more pronounced waviness but still no vortices. This result may not hold for boundary layers with poor vertical mixing. A numerical simulation of the vortex street yielded good results in comparison. The speed ratio, spacing ratio, and Strouhal number agreed rather closely with observations.

Ruscher, P. H.↗
NASA NTRS1983

The influence of coastal shape on winter mesoscale air-sea interaction

In cold air outbreaks, the combination of coastal shape and sea surface isotherms has a profound effect in the establishment of mesoscale atmospheric circulation, due to differential heating resulting from both overwater path length and underlying sea surface temperature (SST) variations. Where coastal effects are dominant, a mesoscale front forms downstream of the point which marks the major bend in the coastline's orientation. The strength of the induced mesoscale circulation depends on the original contrast between the land air temperature and the SST. It is noted that where the coastline and the isotherm pattern are approximately normal to the mean boundary layer flow, and thermal contrast is sufficiently large, the cloud streets formed downstream will be convective in nature, and oriented with the axis of roll vortices along the wind direction.

Atlas, D.↗
NASA NTRS1983

Large-scale energy transformations in the high latitudes of the Northern Hemisphere

The kinetic energy balance and kinetic energy sources are studied for high latitudes north of 55 deg N with twice daily upper air observations during a seven-year period from 1973 to 1979. Energy variables are presented for 5 deg latitudinal zones from 55 to 75 deg N and for the polar cap north of 75 deg N. Spatial distributions of important energy variables are also presented. The upper level maximum of the cross-isobaric generation in high latitudes is observed in th lower stratosphere above the tropopause level in the winter and becomes insignificant during the summer. The flux convergence of potential energy from the source in lower latitudes is identified as the single major source for kinetic energy in higher latitudes. The contribution of the baroclinic conversion is minor. Examination during the First GARP Global Experiment winter indicates that the cold air outbreaks of the Asian winter monsoon are associated with noticeable changes in the hemispherical distributions of the fields of vertical motion and energetics in the high latitudes.

Kung, E. C.↗
NASA NTRS1985

Lidar observations of vertically organized convection in the planetary boundary layer over the ocean

Observations of a convective planetary boundary layer (PBL) were made with an airborne, downward-looking lidar system over the Atlantic Ocean during a cold air outbreak. The lidar data revealed well-organized, regularly spaced cellular convection with dominant spacial scales between two and four times the height of the boundary layer. It is demonstrated that the lidar can accurately measure the structure of the PBL with high vertical and horizontal resolution. Parameters important for PBL modeling such as entrainment zone thickness, entrainment rate, PBL height and relative heat flux can be inferred from the lidar data. It is suggested that wind shear at the PBL top may influence both entrainment and convective cell size.

Melfi, S. H.↗
NASA NTRS1984

A model for the estimation of the surface fluxes of momentum, heat and moisture of the cloud topped marine atmospheric boundary layer from satellite measurable parameters

A model is developed for the estimation of the surface fluxes of momentum, heat, and moisture of the cloud topped marine atmospheric boundary layer by use of satellite remotely sensed parameters. The parameters chosen for the problem are the integrated liquid water content, q sub li, the integrated water vapor content, q sub vi, the cloud top temperature, and either a measure of the 10 meter neutral wind speed or the friction velocity at the surface. Under the assumption of a horizontally homogeneous, well-mixed boundary layer, the model calculates the equivalent potential temperature and total water profiles of the boundary layer along with the boundary layer height from inputs of q sub li, q sub vi, and cloud top temperature. These values, along with the 10m neutral wind speed or friction velocity and the sea surface temperature are then used to estimate the surface fluxes. The development of a scheme to parameterize the integrated water vapor outside of the boundary layer for the cases of cold air outbreak and California coastal stratus is presented.

Allison, D. E.↗
NASA NTRS1986

Turbulence in a convective marine atmospheric boundary layer

The structure and kinetic energy budget of turbulence in the convective marine atmospheric boundary layer as observed by aircraft during a cold air outbreak have been studied using mixed layer scaling. The results are significantly different from those of previous studies under conditions closer to free convection. The normalized turbulent kinetic energy and turbulent transport are about twice those found during the Air Mass Transformation Experiment (AMTEX). This implies that for a given surface heating the present case is dynamically more active. The difference is mainly due to the greater importance of wind shear in the present case. This case is closer to the roll vortex regime, whereas AMTEX observed mesoscale cellular convection which is closer to free convection. Shear generation is found to provide a significant energy source, in addition to buoyancy production, to maintain a larger normalized turbulent kinetic energy and to balance a larger normalized dissipation. The interaction between turbulent pressure and divergence (i.e., pressure scrambling) is also found to transfer energy from the vertical to the horizontal components, and is expected to be stronger in roll vortices than in m esoscale cells. The sensible heat flux is found to fit well with a linear vertical profile in a clear or subcloud planetary boundary layer (PBL), in good agreement with the results of Lenschow et al., (1980). The heat flux ratio between the PBL top and the surface, derived from the linear fitted curve, is approximately -0.14, in good agreement with that derived from the lidar data for the same case. Near the PBL top, the heat flux profiles are consistent with those of Deardoff (1979) and Deardorff et al. (1980).

Chou, S.-H.↗
NASA NTRS1987

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Weinman, James A.↗