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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Vertical Gradient of Size-Resolved Aerosol Compositions over the Arctic Reveals Cloud Processed Aerosol in-Cloud and above Cloud

Arctic aerosols play a significant role in the aerosol-radiation and aerosol-cloud interactions, but ground-based measurements are insufficient to explain the interaction of aerosols and clouds in a vertically stratified Arctic atmosphere. This study shows the vertical variability of aerosol particle composition via tethered balloon system at Oliktok Point, Alaska, in August 2019, at different cloud layers for two case studies (background aerosol condition and polluted condition). Multi-modal micro-spectroscopy analysis of samples during the background case reveals a broadening of chemically specific size distribution above cloud top with a high abundance of sulfate and core-shell morphology, suggesting possible cloud processing of aerosols. The pollution case also indicates a broadening of size distribution at the upper cloud layer with the dominance of carbonaceous aerosol, which suggests the potential of carbonaceous aerosols for Arctic cloud formation. Here this study provides insights into the vertical profile of Arctic aerosols on cloud formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Investigate the Security of Electric Vehicle (EV) Ecosystem Applications

Apps that run on mobile devices are one of critical components of the electric vehicle (EV) ecosystem and pose possible threat actor points of entry that may impact the trust and security of EV charging systems in the future. Mobile apps often rely on communication between cloud servers and users, thereby creating potential points of entry for cyberattacks. Although app stores such as Apple App Store or Google Play Store generally test the security of apps, the cyber aspects may not be sufficient for many entities including DOD, federal fleets, and commercial entities. A more thorough inspection and the ability to influence developers is imminently needed. This research studies security attributes and vulnerabilities of a sample of mobile applications that support key user functions in the EV ecosystem. The study shows that all analyzed apps have security risks, categorized as either high or medium or both and a comprehensive cybersecurity guideline for developing mobile apps is necessary.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Surface-based observations of cold-air outbreak clouds during the COMBLE field campaign

Abstract. Cold-air outbreaks (CAOs) are characterized by extreme air–sea energy exchanges and low-level convective clouds over large areas in the high-latitude oceans. As such, CAOs are an important component of the Earth's climate system. The CAOs in the Marine Boundary Layer Experiment (COMBLE) deployment of the US Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) provided the first comprehensive view of CAOs using a suite of ground-based observations at the northern coast of Norway. Here, cloud and precipitation observations from 13 CAO cases during COMBLE are analyzed. A vertical air motion retrieval technique is applied to the Ka-band ARM Zenith-pointing Radar (KAZR) observations. The CAO cumulus clouds are characterized by strong updrafts with magnitudes between 2–8 m s−1, vertical extents of 1–3 km, and horizontal scales of 0.25–3 km. A strong relationship between our vertical air velocity retrievals and liquid water path (LWP) measurements is found. The LWP measurements exceed 1 kg m−2 in strong updraft areas, and the vertical extent of the updraft correlates well with the LWP values. The CAO cumulus clouds exhibit eddy dissipation rate values between 10−3 and 10−2 m2 s−3 in the lowest 10 km of the atmosphere, and using a radar Doppler spectra technique, evidence of secondary ice production is found during one of the cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cloud Droplet Measurement System for the ARM Tethered Balloon System (TBS) Field Campaign Report

The Mesa Photonics' cloud droplet measurement system (CDMS) performs in situ measurement of droplet size distribution and droplet number density in clouds. These characteristics are important cloud microphysical properties that are critical input parameters for atmospheric models and are also useful for proper calibration and validation of performance of other atmospheric measurement instrumentation. This small campaign was the first project (out of two) that involved integration of the CDMS into the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s tethered balloon system (TBS) and its initial testing at the ARM Southern Great Plains (SGP) atmospheric observatory. The follow-up campaign (AFC07016) involved testing of the CDMS under relevant conditions at the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska and making in situ measurements in clouds. The goal of this small field campaign was to deploy the CDMS on the ARM TBS, demonstrate its integrity and compatibility with the TBS, and test the wireless telecommunication system in preparation for subsequent in-cloud measurements at ARM’s North Slope of Alaska (NSA) observatory at Utqiagvik (formerly Barrow). The specific technical objectives included: (1) Integration of the Mesa Photonics' CDMS into the ARM TBS, (2) testing of the wireless telecommunication system of the CDMS, (3) evaluating the data acquisition software and image processing algorithms especially under high-background-illumination conditions, and (4) testing the ruggedness of the instrument's optical alignment and other performance characteristics. All objectives have been successfully met. The mounting hardware was designed and built mostly at Sandia National Laboratories (SNL), which allowed reliable mounting of the CDMS on the TBS and co-locating the CDMS with other instruments. The field tests were performed by the principal investigator (PI) in collaboration with the TBS operational crew led by D. Dexheimer. The field testing demonstrated good compatibility of the CDMS with the TBS. During the TBS flights, the crew did not experience any operational issues with the CDMS. The instrument demonstrated reliable operational characteristics, including sufficient battery life and good thermal management. The wireless telecommunication system has been successfully tested at TBS flight altitudes of up to 1000 m. During all flights, the CDMS raw data were wirelessly transmitted to the ground station and processed in real time. The ruggedness of the instrument's optical alignment was evaluated by performing calibration of the CDMS (using Mesa Photonics' fixed-size monodisperse droplet generator) before and after the campaign. The calibration was stable within the instrument's measurement precision. In summary, the campaign demonstrated good compatibility of the Mesa Photonics' CDMS with the ARM TBS. The flight tests confirmed the mechanical integrity of the CDMS and stability of its optical layout, as well as reliability of the wireless telecommunication system. Successful completion of this campaign provided a solid basis for the next campaign (AFC07016, November 2020), that involved testing of the CDMS during in-cloud TBS flights at AMF3 at Oliktok Point.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ~92 Tg C yr -1 – equivalent to ~37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

Most Arctic clouds occur below 2 km altitude as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low- level cloud fraction, which is the amount of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from downlooking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC has been operated during four aircraft campaigns taking place in the vicinity of Svalbard during different times of the year and covering more than 25,000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-axis Accelerometry and Rotation Sensing using a Point Source Atom Interferometer

A point source atom interferometer (PSI) is a device where atoms are split and recombined by applying a temporal sequence of Raman pulses during the expansion of a cloud of cold atoms behaving approximately as a point source. Unlike a conventional light pulse atom interferometer, the PSI can produce a signal that corresponds to multi-axis rotation only, independent of acceleration. In addition, it can be used to measure acceleration along one direction, independent of rotation. Here, we describe a modified PSI that can be used to measure multi-axis rotation and multi-axis acceleration. Specifically, this type of PSI can be used to measure two-axes rotation around the directions perpendicular to the light pulses, as well as the acceleration in all three directions, with only one pair of Raman beams. Using two pairs of Raman beams in orthogonal directions sequentially, such a scheme would enable the realization of a complete atom interferometric inertial measurement unit.

Li, Jinyang↗
DOE OSTI2020

A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

Samuel, David↗
DOE OSTI2023

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗
DOE OSTI2023

Southern Ocean Low Cloud and Precipitation Phase Observed During the Macquarie Island Cloud and Radiation Experiment (MICRE)

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops <3 km) and precipitation using ground-based ceilometer, depolarization lidar and vertically-pointing W-band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016 to March 2017. During MICRE, low clouds occurred ~65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold-topped (temperatures ≤0°C). These were thicker and had higher bases on average than warm-topped clouds. 83%–88% of cold-topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150–250 m below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small-particle (low-radar-reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below-cloud, while large-particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold-topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below-cloud precipitation with radar reflectivity factors below about -10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cloud microphysical response to entrainment and mixing is locally inhomogeneous and globally homogeneous: Evidence from the lab

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment–mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (T e ) and flow rate (Q e ) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment–mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (D m ) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As T e and Q e of entrained air increase, however, Cn, L, and D m of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing the Understanding of Cloud Microphysical Processes and Aerosol Indirect Effects in High-Latitude Mixed-Phase Clouds by Linking ARM Measurements with Climate Model Simulations (Final Report)

The key objectives of this project were to advance our understanding of cloud microphysical characteristics and aerosol indirect effects on mixed-phase clouds in high latitudes. To improve the representation of ice and mixed-phase clouds in Earth System Models (ESMs), we propose an integrated observation and modeling study of cloud macro- and microphysical properties, including spatial heterogeneities, mass partitioning between ice crystals and supercooled liquid water, effects of ice nucleating particles (INPs), and efficiency of secondary ice production (SIP), etc. Specifically, we took four main approaches in this project: (1) examining macro- and microphysical properties of ice and mixed-phase clouds based on in-situ and ground-based observations from multiple field campaigns funded by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program, including the Mixed-Phase Arctic Cloud Experiment (M-PACE), Indirect and Semi-Direct Aerosol Campaign (ISDAC), Ice Nucleating Particle Sources at Oliktok Point (INPOP), ARM West Antarctic Radiation Experiment (AWARE), Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS), and Macquarie Island Cloud and Radiation Experiment (MICRE); (2) evaluating the DOE Energy Exascale Earth System Model (E3SM) simulations based on observations, particularly for ice and mixed-phase cloud microphysical properties; (3) examining the impacts of INPs on ice and mixed-phase clouds. Specifically, a series of comparisons were conducted using observations over the Arctic, Southern Ocean, and Antarctica, including comparisons between the lower and higher southern latitudes as well as comparisons between the northern and southern hemispheres. In addition, aerosol indirect effects from distinct sources of dust particles were examined; and (4) investigating the impacts of SIP. Ultimately, these results helped to improve cloud microphysics and aerosol-cloud interaction parameterizations in the E3SM model. Overall, the project provided improved understanding regarding various factors, including thermodynamic, dynamic, and aerosol conditions, on the micro- and macrophysical properties of ice and mixed-phase clouds in the high latitudes. Resulting analysis helped to provide an improved physical basis for refining the current cloud microphysics parameterizations related to ice and mixed-phase clouds in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Dependence of Iron-rich Metal-poor Star Occurrence on Galactic Environment Supports an Origin in Thermonuclear Supernova Nucleosynthesis

It has been suggested that a class of chemically peculiar metal-poor stars called iron-rich metal-poor (IRMP) stars formed from molecular cores with metal contents dominated by thermonuclear supernova nucleosynthesis. If this interpretation is accurate, then IRMP stars should be more common in environments where thermonuclear supernovae were important contributors to chemical evolution. Conversely, IRMP stars should be less common in environments where thermonuclear supernovae were not important contributors to chemical evolution. At constant [Fe/H] ≲ -1, the Milky Way's satellite classical dwarf spheroidal (dSph) galaxies and the Magellanic Clouds have lower [α/Fe] than the Milky Way field and globular cluster populations. This difference is thought to demonstrate the importance of thermonuclear supernova nucleosynthesis for the chemical evolution of the Milky Way's satellite classical dSph galaxies and the Magellanic Clouds. We use data from the Sloan Digital Sky Survey Apache Point Observatory Galactic Evolution Experiment and Gaia to infer the occurrence of IRMP stars in the Milky Way's satellite classical dSph galaxies η dSph and the Magellanic Clouds η Mag , as well as in the Milky Way field η MWF and globular cluster populations η MWGC . In order of decreasing occurrence, we find η dSph = $0.07$$^{+0.02}_{-0.02}$, η Mag = $0.037$$^{+0.007}_{-0.006}$, η MWF = $0.0013$$^{+0.0006}_{-0.0005}$, and a 1σ upper limit η MWGC < 0.00057. These occurrences support the inference that IRMP stars formed in environments dominated by thermonuclear supernova nucleosynthesis and that the time lag between the formation of the first and second stellar generations in globular clusters was longer than the thermonuclear supernova delay time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Parallax-corrected VISST-derived pixel-level products from satellite GOES-16

The NASA Langley group led by William Smith produced GOES-16 satellite cloud retrievals over an approximate 10 by 10 degree region over the CACTI field campaign location. These retrievals are described here: https://www.arm.gov/capabilities/vaps/visst and are available for download here . They use algorithms historically called VISST that are now referred to as SatCORPS. More information can be found in Trepte et al. (2019), Minnis et al. (2021), and Yost et al. (2021). If using this dataset, please cite these references, the CACTI VISST dataset DOI found at the download link above, and this dataset’s DOI. The CACTI VISST pixel-level retrievals are on a 2 km spatial grid and available every 15 minutes (every 10 minutes late in the campaign), producing 21,765 files for the entire field campaign between October 2018 and April 2019. They are not corrected for parallax error, which is an offset in the actual geographical location of a cloud above the surface due to the satellite viewing the cloud partly from the side off nadir. This dataset applies a correction for parallax using the location relative to the satellite and the retrieved cloud top height above the surface, which allows the dataset to be geo-located with surface-based observations. The parallax correction for each location depends on the longitude, latitude and cloud top height above ground level (AGL) for that longitude and latitude in the original VISST files. The cloud top height AGL requires first computing the surface elevation at each VISST grid point. Data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global Digital Elevation Map Version 3 at 30-m resolution is projected onto the VISST grid using conservative coarsening (conserving surface elevation) in the xESMF Python package. The surface elevation is then subtracted from the VISST-retrieved cloud top height above mean sea level. These cloud top heights AGL are then combined with longitude and latitude to estimate the latitude and longitude corrections. Due to variability in cloud top height, the parallax shifts produce an irregular grid of values since higher cloud tops are shifted further than lower cloud tops. A ball tree-based neighbor search with Haversine distance is performed using the Python-based scikit-learn library to find the nearest VISST grid point to each parallax correction-shifted point. The data value of the shifted point is then assigned to that VISST grid point. In this manner, the irregular geographical shifts to correct for parallax are projected back to the rectilinear VISST grid. Because relatively higher clouds should obscure lower clouds, the variable values for the highest cloud top are preferentially chosen if two or more values are assigned to a grid point. The parallax correction should be viewed as an improved but still imperfect estimation of the cloud top locations, largely because the cloud top height is an imperfect retrieval. Please see the attached README document for further information. Users are encouraged to contact the authors with any additional questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2025

Search for a Cloud Phase Feedback in the Arctic Climate System

This project was motivated by a hypothesis involving the transition in lower troposphere temperatures across the freezing point of water. Specifically, at temperatures just below to about ten degrees below freezing, Arctic clouds should be in a mixed-phase, with strong influences from secondary ice production (e.g., the Hallett-Mossop process). At temperatures just above freezing, clouds should become entirely deglaciated. We hypothesized that, with multiple years of ARM data, a statistically significant change could be detected in cloud radiative properties and surface radiative fluxes that could be directly attributed to this phase change. Furthermore, in a gradually warming climate, these phase change-related responses in surface radiation would represent a cloud phase feedback as part of Arctic amplification. We designed this research project to coincide with a new ARM Arctic cloud radar product developed by Ed Luke and collaborators at BNL (Luke et al., 2021: PNAS, doi:10.1073/pnas.2021387118) that explicitly contains SIP cloud properties retrieved from radar data. This research project has successfully concluded after analysis of a much larger North Slope of Alaska (NSA) data sample than originally anticipated, and the results are quite different than originally hypothesized.

58 GEOSCIENCES↗
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↗