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DOE OSTI2025

Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. Here, this study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the TRACER and ESCAPE field campaigns, machine learning techniques, and parcel model simulations with a super-droplet microphysics scheme. A random forest algorithm identifies in-situ vertical velocity (w), temperature (T), and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μm (N c ,<50), 50 μm ≤ D max ≤ 3000 μm (N c,50–3000 ), and droplet effective diameter (D e ). Results show that LWC, N c,<50 , and N c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c,50–3000 increase, while N c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c,50–3000 , and smaller N c,<50 , while polluted environments produce greater N c,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N c,50–3000 . Polluted conditions result in a greater N c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Abstract. As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Assimilation of satellite microwave observations over the rainbands of tropical cyclones

A novel Bayesian Monte Carlo integration (BMCI) technique was developed to retrieve geophysical variables from satellite microwave radiometer data in the presence of tropical cyclones. The BMCI technique includes three steps: generating a stochastic database, simulating satellite brightness temperatures using a radiative transfer model, and retrieving geophysical variables such as profiles of temperature, relative humidity, and cloud liquid and ice water content from real observations. The technique also provides uncertainty estimates for each retrieval and can output the error covariance matrix of selected parameters. The measurements from the Advanced Technology Microwave Sounder (ATMS) on board Suomi National Polar-Orbiting Partnership (Suomi NPP) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) were used as input. A new technique was developed to correct the ATMS and GMI observations for the beam-filling effect, which is due to small-scale variability of precipitation and clouds when compared with the instrument footprint and also the nonlinear relation between the brightness temperature and precipitation. In addition, the assimilation of the BMCI retrievals into the NASA GEOS model is discussed for Hurricane Maria. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns. Two possible factors that may limit the impact of the BMCI retrievals include 1) the resolution of the model (about 25 km), which was too coarse to show the potential of the BMCI data in improving the representation of tropical storms in the model forecast, and 2) the data assimilation system not being able to consider vertically correlated observation errors.

Isaac Moradi↗

DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Natural versus anthropogenic factors affecting low-level cloud albedo over the North Atlantic

Cloud albedo plays a key role in regulating earth's climate. Cloud albedo depends on column-integrated liquid water content and the density of cloud condensation nuclei, which consists primarily of submicrometer-sized aerosol sulfate particles. A comparison of two independent satellite data sets suggests that, although anthropogenic sulfate emissions may enhance cloud albedo immediately adjacent to the east coast of the United States, over the central North Atlantic Ocean the variability in albedo can be largely accounted for by natural marine and atmospheric processes that probably have remained relatively constant since the beginning of the industrial revolution.

Falkowski, Paul G.↗

NASA NTRS2001

Microwave Polarized Signatures Generated within Cloud Systems: SSM/I Observations Interpreted with Radiative Transfer Simulations

Special Sensor Microwave /Imager (SSM/I) observations in cloud systems are studied over the tropics. Over optically thick cloud systems, presence of polarized signatures at 37 and 85 GHz is evidenced and analyzed with the help of cloud top temperature and optical thickness extracted from visible and IR satellite observations. Scattering signatures at 85 GHz (TbV(85) less than or = 250 K) are associated with polarization differences greater than or = 6 K, approx. 50%, of the time over ocean and approx. 40% over land. In addition. over thick clouds the polarization difference at 37 GHz is rarely negligible. The polarization differences at 37 and 85 GHz do not stem from the surface but are generated in regions of relatively homogeneous clouds having high liquid water content. To interpret the observations, a radiative transfer model that includes the scattering by non-spherical particles is developed. based on the T-matrix approach and using the doubling and adding method. In addition to handling randomly and perfectly oriented particles, this model can also simulate the effect of partial orientation of the hydrometeors. Microwave brightness temperatures are simulated at SSM/I frequencies and are compared with the observations. Polarization differences of approx. 2 K can be simulated at 37 GHz over a rain layer, even using spherical drops. The polarization difference is larger for oriented non-spherical particles. The 85 GHz simulations are very sensitive to the ice phase of the cloud. Simulations with spherical particles or with randomly oriented non-spherical ice particles cannot replicate the observed polarization differences. However, with partially oriented non-spherical particles, the observed polarized signatures at 85 GHz are explained, and the sensitivity of the scattering characteristics to the particle size, asphericity, and orientation is analyzed. Implications on rain and ice retrievals are discussed.

Prigent, Catherine↗

NASA NTRSDate not supplied

Additional Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Section from January 2022

In-flight icing is an important consideration that affects aircraft design, performance, certification and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of additional ice accretion testing conducted in the NASA Icing Research Tunnel in January 2022 with a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small median volumetric diameter (MVD) = 25 μm cloud and a large MVD = 110 μm cloud. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. Another series of tests explored cloud MVD variations from 50 μm to 230 μm while holding constant certain scaling parameters. The variation in ice mass and scanned ice volume across repeat conditions was approximately 50% lower than the uncertainty in the cloud MVD and liquid water content. The measured ice mass and volume calculated from the 3D scans were used to compute the mass to volume ratio that is sometimes referred to as ice density or void fraction. When the ice volume based on the ice shape maximum combined cross section was used to determine this ratio, the resulting values were in the range of 240 to 455 kg/m3. This is consistent with analogous values previously reported in the literature. The ice shape mass and volume increased with MVD from 50 to 230 μm at fixed values of the scaling parameters. The ice mass to volume ratio was approximately constant for all of the cases which showed that the accreted mass and volume increased at approximately the same rate. These results demonstrate the significance of cloud MVD on ice shape mass and volume.

Ice accretion↗

NASA NTRSDate not supplied

Additional Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Section from January 2022

In-flight icing is an important consideration that affects aircraft design, performance, certification and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of additional ice accretion testing conducted in the NASA Icing Research Tunnel in January 2022 with a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small median volumetric diameter (MVD) = 25 μm cloud and a large MVD = 110 μm cloud. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. Another series of tests explored cloud MVD variations from 50 μm to 230 μm while holding constant certain scaling parameters. The variation in ice mass and scanned ice volume across repeat conditions was approximately 50% lower than the uncertainty in the cloud MVD and liquid water content. The measured ice mass and volume calculated from the 3D scans were used to compute the mass to volume ratio that is sometimes referred to as ice density or void fraction. When the ice volume based on the ice shape maximum combined cross section was used to determine this ratio, the resulting values were in the range of 240 to 455 kg/m 3 . This is consistent with analogous values previously reported in the literature. The ice shape mass and volume increased with MVD from 50 to 230 μm at fixed values of the scaling parameters. The ice mass to volume ratio was approximately constant for all of the cases which showed that the accreted mass and volume increased at approximately the same rate. These results demonstrate the significance of cloud MVD on ice shape mass and volume.

Ice accretion, Large drops, Wind tunnel↗

DOE OSTI2021

Observed and Simulated Variability of Droplet Spectral Dispersion in Convective Clouds Over the Amazon

Abstract In this study, the variability of the spectral dispersion of droplet size distributions (DSDs) in convective clouds is investigated. Analyses are based on aircraft measurements of growing cumuli near the Amazon basin, and on numerical simulations of an idealized ice‐free cumulus. In cleaner clouds, the relative dispersion , defined as the ratio of the standard deviation to the mean value of the droplet diameter, is negatively correlated with the ratio of the cloud water content ( ) to the adiabatic liquid water content ( ), while no strong correlation between and is seen in polluted clouds. Bin microphysics numerical simulations suggest that these contrasting behaviors are associated with the effect of collision‐coalescence in cleaner clouds, and secondary droplet activation in polluted clouds, in addition to the turbulent mixing of parcels that experienced different paths within the cloud. Collision‐coalescence simultaneously broadens the DSDs and decreases , explaining the inverse relationship between and in cleaner clouds. Secondary droplet activation broadens the DSDs but has little direct impact on . The combination of a rather modest DSD broadening due to weak collision‐coalescence with enhanced droplet activation in both diluted and highly undiluted cloud regions may contribute to maintain a relatively uniform within polluted clouds. These findings can be useful for parameterizing the shape parameter ( ) of gamma DSDs in bulk microphysics cloud‐resolving models. It is shown that emulating the observed relationship improves the estimation of the collision‐coalescence rate in bulk microphysics simulations compared to the bin simulations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Diagnosing Aircraft Icing Potential from Satellite Cloud Retrievals

The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. This paper describes new techniques for interpreting cloud products derived from satellite data to infer the flight icing threat to aircraft. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water cloud systems, ice and liquid water content (IWC and LWC) profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. Retrievals of the SLW content embedded within overlapping clouds are mapped to the icing threat using guidance from an airfoil modeling study. Compared to PIREPS and ground-based icing remote sensing datasets, the satellite icing detection and intensity accuracies are approximately 90% and 70%, respectively, and found to be similar for both low level and deep ice over water cloud systems. The satellite-derived icing boundaries capture the reported altitudes over 90% of the time. Satellite analyses corresponding to the time and location of several recent aviation accidents and with icing PIREPS are also presented that reveal skill in identifying severe icing conditions. These results demonstrate the utility of satellite cloud retrievals for quantitatively diagnosing the potential for icing conditions on temporal and spatial scales that should be useful to the aviation community. Plans are being developed to deliver these new satellite products to the GOES-R Proving Ground in the near future so that they can be evaluated in operational applications.

Smith, William L., Jr.↗

NASA NTRS2005

The NASA Icing Remote Sensing System

NASA and the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) have an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. A multiple instrument approach is the current emphasis of this activity. Utilizing radar, radiometry, and lidar, a region of supercooled liquid is identified. If the liquid water content (LWC) is sufficiently high, then the region of supercooled liquid cloud is flagged as being an aviation hazard. The instruments utilized for the current effort are an X-band vertical staring radar, a radiometer that measures twelve frequencies between 22 and 59 GHz, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data are post-processed with a LabVIEW program with a resultant supercooled LWC profile and aircraft hazard identification. Remotely sensed measurements gathered during the 2003-2004 Alliance Icing Research Study (AIRS II) were compared to aircraft in-situ measurements. Although the comparison data set is quite small, the cases examined indicate that the remote sensing technique appears to be an acceptable approach.

Reehorst, Andrew L.↗

NASA NTRS2005

NASA Icing Remote Sensing System Comparisons From AIRS II

NASA has an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. A multiple instrument approach is the current emphasis of this activity. Utilizing radar, radiometry, and lidar, a region of supercooled liquid is identified. If the liquid water content (LWC) is sufficiently high, then the region of supercooled liquid cloud is flagged as being an aviation hazard. The instruments utilized for the current effort are an X-band vertical staring radar, a radiometer that measures twelve frequencies between 22 and 59 GHz, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data is post-processed with a LabVIEW program with a resultant supercooled LWC profile and aircraft hazard identification. Individual remotely sensed measurements gathered during the 2003-2004 Alliance Icing Research Study (AIRS II) were compared to aircraft in-situ measurements. Comparisons between the remote sensing system s fused icing product and in-situ measurements from the research aircraft are reviewed here. While there are areas where improvement can be made, the cases examined indicate that the fused sensor remote sensing technique appears to be a valid approach.

Reehorst, Andrew L.↗

DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗

NASA NTRS1984

Ice shapes and the resulting drag increase for a NACA 0012 airfoil

Experimental measurements of the ice shapes and resulting drag increases were measured in the NASA-Lewis Icing Research Tunnel. The measurements were made over a large range of conditions (e.g., airspeed and temperature, drop size and liquid water content of the cloud, and the angle of attack of the airfoil). The measured drag increase did not agree with the existing correlation. Additional results were given which are helpful in understanding the ice structure and the way it forms, and in improving the ice accretion modeling theories. There are data on the ice surface roughness, on the effect of the ice shape on the local droplet catch, and on the relative importance of various parts of the ice shape on the drag increase. Experimental repeatability is also discussed.

Olsen, W.↗

NASA NTRS1984

Ice shapes and the resulting drag increase for a NACA 0012 airfoil

Olsen, W.↗

NASA NTRS1991

Prediction of ice shapes and their effect on airfoil performance

Calculations of ice shapes and the resulting drag increases are presented for experimental data on a NACA 0012 airfoil. They were made with a combination of LEWICE and interactive boundary-layer codes for a wide range of conditions which include air speed and temperature, the droplet size and liquid water content of the cloud, and the angle of attack of the airfoil. In all cases, the calculated results account for the drag increase due to ice accretion and, in general, show good agreement.

Shin, Jaiwon↗

NASA NTRS1991

Prediction of ice shapes and their effect on airfoil performance

Calculations of ice shapes and the resulting drag increases are presented for experimental data on an NACA 0012 airfoil. They were made with a combination of LEWICE and interactive boundary-layer codes for a wide range of conditions which include airspeed and temperature, the droplet size and liquid water content of the cloud, and the angle of attack of the airfoil. In all cases the calculated results account for the drag increase due to ice accretion and, in general, show good agreement with data.

Shin, Jaiwon↗

NASA NTRS1996

Study of the Radiative Properties of Inhomogeneous Stratocumulus Clouds

Clouds play an important role in the radiation budget of the atmosphere. A good understanding of how clouds interact with solar radiation is necessary when considering their effects in both general circulation models and climate models. This study examined the radiative properties of clouds in both an inhomogeneous cloud system, and a simplified cloud system through the use of a Monte Carlo model. The purpose was to become more familiar with the radiative properties of clouds, especially absorption, and to investigate the excess absorption of solar radiation from observations over that calculated from theory. The first cloud system indicated that the absorptance actually decreased as the cloud's inhomogeneity increased, and that cloud forcing does not indicate any changes. The simplified cloud system looked at two different cases of absorption of solar radiation in the cloud. The absorptances calculated from the Monte Carlo is compared to a correction method for calculating absorptances and found that the method can over or underestimate absorptances at cloud edges. Also the cloud edge effects due to solar radiation points to a possibility of overestimating the retrieved optical depth at the edge, and indicates a possible way to correct for it. The effective cloud fraction (Ne) for a long time has been calculated from a cloud's reflectance. From the reflectance it has been observed that the N, for most cloud geometries is greater than the actual cloud fraction (Nc) making a cloud appear wider than it is optically. Recent studies we have performed used a Monte Carlo model to calculate the N, of a cloud using not only the reflectance but also the absorptance. The derived Ne's from the absorptance in some of the Monte Carlo runs did not give the same results as derived from the reflectance. This study also examined the inhomogeneity of clouds to find a relationship between larger and smaller scales, or wavelengths, of the cloud. Both Fourier transforms and wavelet transforms were used to analyze the liquid water content of marine stratocumulus clouds taken during the ASTEX project. From the analysis it was found that the energy in the cloud is not uniformly distributed but is greater at the larger scales than at the smaller scales. This was determined by examining the slope of the power spectrum, and by comparing the variability at two scales from a wavelet analysis.

Batey, Michael↗