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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

A Principal Component-Based Radiative Transfer Forward Model (PCRTM) for Vertically in Homogeneous Cloud

A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.

Li, Hui↗
NASA NTRS2016

Validation of Cloud Properties From Multiple Satellites Using CALIOP Data

The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

Yost, Christopher R.↗
NASA NTRSDate not supplied

Ice-Over-Water Cloud Identification in an Artificial Neural Network Approach

An artificial neural network (ANN) algorithm, employing several Aqua MODIS channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained MLANN was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87% (89%) and 86% (89%) for snow-free (snow-covered) regions during the day and night, respectively. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MLANN is robust seasonally and interannually, and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.

MODIS↗
NASA NTRS2005

Factors Controlling the Properties of Multi-Phase Arctic Stratocumulus Clouds

The 2004 Multi-Phase Arctic Cloud Experiment (M-PACE) IOP at the ARM NSA site focused on measuring the properties of autumn transition-season arctic stratus and the environmental conditions controlling them, including concentrations of heterogeneous ice nuclei. Our work aims to use a large-eddy simulation (LES) code with embedded size-resolved aerosol and cloud microphysics to identify factors controlling multi-phase arctic stratus. Our preliminary simulations of autumn transition-season clouds observed during the 1994 Beaufort and Arctic Seas Experiment (BASE) indicated that low concentrations of ice nuclei, which were not measured, may have significantly lowered liquid water content and thereby stabilized cloud evolution. However, cloud drop concentrations appeared to be virtually immune to changes in liquid water content, indicating an active Bergeron process with little effect of collection on drop number concentration. We will compare these results with preliminary simulations from October 8-13 during MPACE. The sensitivity of cloud properties to uncertainty in other factors, such as large-scale forcings and aerosol profiles, will also be investigated. Based on the LES simulations with M-PACE data, preliminary results from the NASA GlSS single-column model (SCM) will be used to examine the sensitivity of predicted cloud properties to changing cloud drop number concentrations for multi-phase arctic clouds. Present parametrizations assumed fixed cloud droplet number concentrations and these will be modified using M-PACE data.

Fridlind, Ann↗
NASA NTRS1991

The polarization lidar technique for cloud research - A review and current assessment

The development of the polarization lidar technique is reviewed, and the current capabilities and limitations of the technique for the cloud research are discussed. At present, polarization lidar is a key component of climate-research programs designed to characterize the properties of cirrus clouds and is an integral part of multiple remote-sensor studies of mixed-phase cloud systems such as winter mountain storms, making it possible to discriminate between cloud phases and to identify some particle types and orientations. Recent theoretical approaches involving ice crystal ray-tracing and cloud microphysical-model simulations are expected to increase the utility of the polarization lidar technique.

Sassen, Kenneth↗
DOE OSTI2020

Simulating Observations of Southern Ocean Clouds and Implications for Climate

Southern Ocean (S. Ocean) clouds are important for climate prediction. Yet previous global climate models failed to accurately represent cloud phase distributions in this observation-sparse region. In this study, data from the Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES) experiment is compared to constrained simulations from a global climate model (the Community Atmosphere Model, CAM). Nudged versions of CAM are found to reproduce many of the features of detailed in situ observations, such as cloud location, cloud phase, and boundary layer structure. The simulation in CAM6 has improved its representation of S. Ocean clouds with adjustments to the ice nucleation and cloud microphysics schemes that permit more supercooled liquid. Comparisons between modeled and observed hydrometeor size distributions suggest that the modeled hydrometeor size distributions represent the dual peaked shape and form of observed distributions, which is remarkable given the scale difference between model and observations. Comparison to satellite observations of cloud physics is difficult due to model assumptions that do not match retrieval assumptions. Some biases in the model's representation of S. Ocean clouds and aerosols remain, but the detailed cloud physical parameterization provides a basis for process level improvement and direct comparisons to observations. This is crucial because cloud feedbacks and climate sensitivity are sensitive to the representation of S. Ocean clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Wind estimates from cloud motions: Phase 1 of an in situ aircraft verification experiment

An initial experiment was conducted to verify geostationary satellite derived cloud motion wind estimates with in situ aircraft wind velocity measurements. Case histories of one-half hour to two hours were obtained for 3-10km diameter cumulus cloud systems on 6 days. Also, one cirrus cloud case was obtained. In most cases the clouds were discrete enough that both the cloud motion and the ambient wind could be measured with the same aircraft Inertial Navigation System (INS). Since the INS drift error is the same for both the cloud motion and wind measurements, the drift error subtracts out of the relative motion determinations. The magnitude of the vector difference between the cloud motion and the ambient wind at the cloud base averaged 1.2 m/sec. The wind vector at higher levels in the cloud layer differed by about 3 m/sec to 5 m/sec from the cloud motion vector.

Hasler, A. F.↗
DOE OSTI2025

Marine Boundary Layer Cloud Boundaries and Phase Estimation Using Airborne Radar and In Situ Measurements During the SOCRATES Campaign over Southern Ocean

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation.

MBL clouds over Southern Ocean↗
DOE OSTI2019

Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report)

The objectives of this investigation are to provide a deeper understanding of the brightening of middle and high latitude clouds that occurs in many climate models as the climate warms, and to better understand why models give varying estimates of the strength of this negative feedback. The investigation may also provide new information on why models are deficient in their simulation of the present day clouds in these regions. The analysis will reveal which models simulate current clouds most realistically in the regions of interest, and relate this to the strength of the cloud feedbacks in the models. This will improve the uncertainty quantification of climate simulations and will provide theoretical understanding potentially leading to increased reliability of climate change projections. This will enhance the predictive understanding of the Earth system by analyzing the natural and anthropogenic components of global climate change in models. The primary methods involve the study of the clouds in the suite of CMIP5 model climate simulations and comparison of clouds in climate simulations with data from satellite measurement systems, with a particular emphasis on mixed-phase cloud physics, for which ice and water are present simultaneously. State-of-the-art climate model feedback analysis will be combined with new and more comprehensive observations of clouds available from modern measurement systems. Novel use of model simulations in combination with observations enables a deeper understanding of the role of clouds in climate change. Metrics that facilitate model-to-model and model-to-observation comparisons related to mixed-phase cloud physics will be developed and used to highlight challenges and opportunities for improving the state-of-the-science models. This investigation is a collaboration between the University of Washington, where the data work is concentrated and LLNL where the model data and the tools and expertise to do cloud feedback analysis are located. The grant supported the work of 3 different graduate students at different times (McCoy, Wall and Berry) Post-doctoral researcher Blaž Gasparini was also partially supported on this grant and led our collaboration with PNNL scientists. PNNL scientists also collaborated in the work of graduate student Wall (Po-Lun Ma). The main results of our research efforts are reported in 6 papers and 1 book chapter that were supported by this grant.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Wind estimates from cloud motions - Phase 1 of an in situ aircraft verification experiment

An initial experiment has been conducted to verify geostationary-satellite-derived cloud motion wind estimates with in situ aircraft wind velocity measurements. Case histories of 1/2 to 2 h were obtained for 3-10 km diameter cumulus cloud systems on 6 days. Also, one cirrus cloud case was obtained. In most cases the clouds were discrete enough that both the cloud motion and the ambient wind could be measured with the same aircraft Inertial Navigation System (INS). Since the INS drift error is the same for both the cloud motion and wind measurements, the drift error drops out of the relative motion determinations. The magnitude of the vector difference between the cloud motion and the ambient wind at the cloud base averaged 1.2 m/sec. The wind vector at higher levels in the cloud layer differed by about 3 to 5 m/sec from the cloud motion vector.

Hasler, A. F.↗
DOE OSTI2019

Antarctic Cloud Macrophysical, Thermodynamic Phase, and Atmospheric Inversion Coupling Properties at McMurdo Station—Part II: Radiative Impact During Different Synoptic Regimes

Different cloud types are generated over Antarctica as a result of various synoptic conditions. The cloud characteristics affect their impact on the surface energy budget. In this study, the dominating synoptic regimes over Antarctica (centered on the Ross Ice Shelf) are classified using self-organizing map (SOM) analysis, applied over long-term ERA-Interim 700 hPa geopotential height data. The corresponding cloud properties over McMurdo Station (measured as part of the AWARE campaign) are described and discussed with respect to the synoptic settings and sea-ice extent conditions. Cloud radiative forcing calculations are performed as well, and a particular focus is given to the net longwave “radiatively cloudy/opaque” (RO) regime. These results are compared with measurements performed at the West Antarctic Ice Sheet (WAIS) Divide to examine their variability and applicability to other Antarctic locations. It is found that the McMurdo cloud properties are strongly affected by the regional mesoscale cyclonic activity, which counterweights the larger-scale synoptic regime influence, while the WAIS clouds are more susceptible to the varying synoptic settings. In addition, it is suggested that the positive trend in the (frequent) cyclonic activity near the Antarctic coastal regions makes ice-clouds an increasing prominent contributor for the RO cases, especially during freeze-up and maximum sea-ice conditions.

antarctic clouds↗
DOE OSTI2025

The Two Arctic Wintertime Boundary Layer States: Disentangling the Role of Cloud and Wind Regimes in Reanalysis and Observations During MOSAiC

The wintertime central Arctic atmosphere comprises a radiatively clear and a radiatively opaque state, which are linked to synoptic forcing and mixed-phase clouds. Weather and climate models often lack process representations surrounding these states, but prior work mostly treated the problem as an aggregate of synoptic conditions, resulting in partially overlapping biases. Here, we disaggregate the Arctic states and confront ERA5 reanalysis with observations from the MOSAiC campaign over the central Arctic sea ice during winter 2019/2020. Low-level winds and liquid water path (LWP) are combined to derive different synoptic classes. Results show that the clear state is primarily formed by weak/moderate winds and the absence of liquid-bearing clouds, while strong winds and enhanced LWP primarily form the radiatively opaque state. ERA5 struggles to reproduce these basic statistics, shows too weak sensitivity of thermal radiation to synoptic forcing, and overestimates thermal radiation for similar LWP amounts. The latter is caused by a warm bias, which has a pronounced inversion structure and is largest in clear and calm conditions. Under strong synoptic forcing, the warm bias is constant with height and discrepancies in mixed-phase cloud altitude appear. Separating synoptic conditions is regarded as useful for process-oriented evaluation of the Arctic troposphere in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparing Satellite- and Ground-Based Observations of Cloud Occurrence Over High Southern Latitudes

The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at low altitudes over high southern latitudes. These data sets are cross referenced to ground-based measurements from the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station, Antarctica. Compared with AWARE observations, both 2BCL5 and DARDAR underestimate cloud occurrence below 1.5 km, with 2BCL5 and DARDAR distinguishing roughly one third of cloud occurrences observed by AWARE at 0.5 km. While DARDAR identifies greater cloud occurrences than 2BCL5 below 1.5 km, cloud occurrence values for the two data sets have similar differences relative to ground-based measurements. Therefore, the DARDAR retrievals of greater cloud occurrence at low altitudes are likely due to a larger quantity of false positives associated with radar ground clutter or attenuated lidar retrievals. DARDAR cloud occurrences match better with AWARE than 2BCL5 above 5 km. However, the likely underestimation of ground-based measurements at higher altitudes suggests DARDAR may underestimate high level cloud occurrence. Finally, both data sets indicate the presence of liquid containing clouds at temperatures within the homogeneous freezing regime, despite the fact that the ECMWF-AUX data set implemented in their processing clearly indicates temperatures below -38°C. Finally, using AWARE radiosonde (ECMWF-AUX) temperature data, we find that 2BCL5 detects 13.3% (13.8%) of mixed phase clouds below -38°C, while DARDAR detects 5.7% (6.6%) of mixed phase and 1.1% (1.3%) of liquid phase clouds below -38°C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Supercooled Liquid Water Detection Capabilities from Ka-Band Doppler Profiling Radars: Moment-Based Algorithm Formulation and Assessment

The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study develops and assesses a radar-only, moment-based phase partition technique for the demarcation of supercooled liquid water volumes in arctic, MPC conditions. The study utilizes observations from the Ka band profiling radar, the collocated high spectral resolution lidar, and ambient temperature profiles from radio sounding deployments following a statistical analysis of 5.5 years of data (January 2014–May 2019) from the Atmospheric Radiation Measurement observatory at the North Slope of Alaska. The ice/liquid phase partition occurs via a per-pixel, neighborhood-dependent algorithm based on the premise that the partitioning can be deduced by examining the mean values of locally sampled probability distributions of radar-based observables and then compare those against the means of climatologically derived, per-phase probability distributions. Analyzed radar observables include linear depolarization ratio (LDR), spectral width, and vertical gradients of reflectivity factor and radial velocity corrected for vertical air motion. Results highlight that the optimal supercooled liquid water detection skill levels are realized for the radar variable combination of spectral width and reflectivity vertical gradient, suggesting that radar-based polarimetry, in the absence of full LDR spectra, is not as critical as Doppler capabilities. The cloud phase masking technique is proven particularly reliable when applied to cloud tops with an Equitable Threat Score (ETS) of 65%; the detection of embedded supercooled layers remains much more uncertain (ETS = 27%).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Glacially sourced dust as a potentially significant source of ice nucleating particles

Aeolian dusts serve as ice nucleating particles in mixed-phase clouds, and thereby alter the cloud properties and lifetime. Glacial outwash plains are thought to be a major dust source in cold, high latitudes. Due to the recent rapid and widespread retreat of glaciers, high-latitude dust emissions are projected to increase, especially in the Arctic region, which is highly sensitive to climate change. However, the potential contribution of high-latitude dusts to ice nucleation in Arctic low-level clouds is not well acknowledged. Here we show that glacial outwash sediments in Svalbard (a proxy for glacially sourced dusts) have a remarkably high ice nucleating ability under conditions relevant for mixed-phase cloud formation, as compared with typical mineral dusts. The high ice nucleating ability of the sediments is probably governed by the presence of small amounts of organic matter (<1 wt% organic carbon) rather than mineral components. In addition, our results from intensive field measurements and model simulations indicate that the concentrations of atmospheric ice nucleating particles over the Svalbard region are expected to be enhanced in the summertime under the influence of dust emissions from Svalbard and its surroundings. In conclusion, we suggest that high-latitude dust sources have the potential to significantly influence glaciation of Arctic low-level clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Comparisons of Ice Water Path in Deep Convective Systems Among Ground-Based, GOES, and CERES-MODIS Retrievals

Retrievals of convective cloud microphysical properties based on passive satellite imagery are difficult. To help quantify their uncertainties, ice water paths (IWPs) retrieved from the NASA Clouds and the Earth's Radiant Energy System project using Geostationary Operational Environmental Satellite (GOES) and Terra/Aqua MODerate-resolution Imaging Spectroradiometer observations are compared with IWPs retrieved from Next-Generation Radar (NEXRAD) observations over a large domain (32°N to 40°N and 105°W to 91°W) during the 2011 Midlatitude Continental Convective Clouds Experiment field campaign. Based on comparisons of pixel-level (4 km × 4 km) daytime IWP retrievals from NEXRAD and GOES, it is found that NEXRAD- and GOES-retrieved mean IWPs are 2.03 and 1.83 kg m -2 , respectively, for ice-phase cloud in thick anvil area. Their mean difference of 0.20 kg m -2 (with 95% confidence interval: 0.14-0.26 kg m -2 ) is within the uncertainty of NEXRAD retrievals. However, the low correlation between pixel-to-pixel comparisons indicates a large variation in GOES-retrieved IWP. For mixed-phase clouds in thick anvil areas, in addition to IWPs, total water paths (TWPs, sum of ice and liquid water path) are estimated with aid of aircraft measurements for NEXRAD retrievals and corrected using a TWP parameterization for GOES retrievals. The mean values of estimated TWPs from NEXRAD (corrected using aircraft in situ measurements) and GOES are similar. GOES and Clouds and the Earth's Radiant Energy System-MODerate-resolution Imaging Spectroradiometer-retrieved IWPs/TWPs generally do not exceed 5 kg m - 2. Large differences and low correlations exist between satellite and NEXRAD retrievals in stratiform rain areas. Possible reasons for the differences between retrievals are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications

This proposal is emerging from GISS ModelE3 ESM development in the area of cloud physics, so we begin with an example of research needs/gaps from that work. Here, some of our greatest development concerns arise where we lack fundamental process-level understanding, as in ice formation. Namely, it is currently unclear what is the main process that is forming the majority of ice crystals in commonly occurring convection, apparently via secondary ice production at warm temperatures. We are keenly awaiting laboratory data for candidate mechanisms, which is not yet in hand to crucially establish their efficiency. Our progress is also hampered by a lack of uncertainty characterization in currently available measurements of ice crystal number size distributions. Furthermore, the same multiplication process may be responsible for a majority of ice crystals in many extratropical mixed-phase clouds, whose variable representation in CMIP6 ESMs may be a leading cause of differences in cloud phase feedback and ECS. Yet we have been required to deliver an ESM with the cloud physics knowledge at hand. The proposed grand challenge project is AI-enabled via application of machine learning (ML) to climate model and observational data streams (focal area 3), and applications include AI-guided observing system design and model/component/parameterization selection (areas 1 and 2). The project is structurally agnostic as to whether model or observing system components use AI approaches or not, but uncertainties must be estimated and propagatable in both.

58 GEOSCIENCES↗