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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Comparison of Echo 7 field line length measurements to magnetospheric model predictions

The Echo 7 sounding rocket experiment injected electron beams on central tail field lines near L = 6.5. Numerous injections returned to the payload as 'conjugate echoes' after mirroring in the southern hemisphere. Field line lengths calculated from measured conjugate echo bounce times and energies are compared to predictions made by integrating electron trajectories through various magnetospheric models. Although Kp at launch was 3-, quiet time magnetic models best fit the echo measurements. Geosynchronous satellite magnetometer measurements near the Echo 7 field lines during the flight were best modeled by the Olson-Pfitzer Dynamic Model and the Tsyganenko-Usmanov model for Kp = 3. The discrepancy between the models that best fit the Echo 7 data and those that fit the satellite data was most likely due to uncertainties in the small-scale configuration of the magnetospheric models. The field line length measured by the conjugate echoes showed some temporal variation in the magnetic field, also indicated by the satellite magnetometers. This demonstrates the utility an Echo-style experiment could have in substorm studies.

Nemzek, R. J.↗
NASA NTRS2016

The Darkening of the Greenland Ice Sheet: Trends, Drivers and Projections (1981-2100)

The surface energy balance and meltwater production of the Greenland ice sheet (GrIS) are modulated by snow and ice albedo through the amount of absorbed solar radiation. Here we show, using space-borne multispectral data collected during the 3 decades from 1981 to 2012, that summertime surface albedo over the GrIS decreased at a statistically significant (99 %) rate of 0.02 decade(sup -1) between 1996 and 2012. Over the same period, albedo modelled by the Modele Atmospherique Regionale (MAR) also shows a decrease, though at a lower rate (approximately -0.01 decade(sup -1)) than that obtained from space-borne data. We suggest that the discrepancy between modelled and measured albedo trends can be explained by the absence in the model of processes associated with the presence of light-absorbing impurities. The negative trend in observed albedo is confined to the regions of the GrIS that undergo melting in summer, with the dry snow zone showing no trend. The period 1981-1996 also showed no statistically significant trend over the whole GrIS. Analysis of MAR outputs indicates that the observed albedo decrease is attributable to the combined effects of increased near-surface air temperatures, which enhanced melt and promoted growth in snow grain size and the expansion of bare ice areas, and to trends in light-absorbing impurities (LAI) on the snow and ice surfaces. Neither aerosol models nor in situ and remote sensing observations indicate increasing trends in LAI in the atmosphere over Greenland. Similarly, an analysis of the number of fires and BC emissions from fires points to the absence of trends for such quantities. This suggests that the apparent increase of LAI in snow and ice might be related to the exposure of a "dark band" of dirty ice and to increased consolidation of LAI at the surface with melt, not to increased aerosol deposition. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening, with albedo anomalies averaged over the whole ice sheet lower by 0.08 in 2100 than in 2000, driven solely by a warming climate. Future darkening is likely underestimated because of known underestimates in modelled melting (as seen in hindcasts) and because the model albedo scheme does not currently include the effects of LAI, which have a positive feedback on albedo decline through increased melting, grain growth, and darkening.

surface energy↗
NASA NTRS2022

Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty

Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.

Wanshu Nie↗
NASA NTRS1994

Model/data comparisons of ozone in the upper stratosphere and mesosphere

We compare ground-based microwave observations of ozone in the upper stratosphere and mesosphere with daytime observations made from the SME (Solar Mesosphere Explorer) satellite, with nighttime data from the LIMS instrument, and with a diurnal photochemical model. The results suggest that the data are all in reasonable agreement and that the model-data discrepancy is much less than previously thought, particularly in the mesosphere. This appears to be due to the fact that the latest data are lower than earlier reports and the updated model predicts more ozone than older versions. The model and the data agree to within a factor of 1.5 at all altitudes and typically are within 20 percent.

Siskind, David E.↗
NASA NTRS1992

Mutual shadowing and directional reflectance of a rough surface - A geometric-optical model

A Monte Carlo simulation of mutual shadowing effects is presented. Results of these simulations and the corresponding modeling efforts are also presented. Simulation results show that the assumption that all illumination shadows will preferentially occupy the lower surface of crowns is not realistic enough and may cause a considerable discrepancy between modeled results and simulation. Therefore, the model was modified by making the probability that a point on the surface of the crown is illuminated to be a function of height from the base of the crown, while still accounting for the fact that at the hotspot the probability that all illuminated points are viewed is one. With this change, modeled results show better agreement with the simulations.

Li, Xiaowen↗
DOE OSTI2025

A Model Intercomparison Study of Aerosol‐Cloud‐Turbulence Interactions in a Cloud Chamber: 1. Model Results

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

A Comparison between the TOPEX/POSEIDON Data and a Global Ocean General Circulation Model during 1992-1993

The TOPEX/POSEIDON altimetric sea level observation during 1992-1993 was used to validate the simulation made by a global ocean general circulation model (OGCM) forced by the daily wind stress and heat flux derived from the National Meteorological Center operational analysis. The OGCM is a version of the modular ocean model with a horizontal resolution of 2 deg longitude and 1 deg latitude and 22 levels in the vertical. The model simulation is compared to the observation at spatial scales of the order of 500 km and larger. Only the temporal variations are examined. The variability is composed primarily of the annual cycle and intraseasonal fluctuations (periods shorter than 100 days). The basic features of the annual cycle are simulated well by the model. Major discrepancies are found in the eastern tropical Pacific, as well as the eastern North Pacific and most of the interior of the North Atlantic. The culprit is suspected to be the inadequate heat forcing and mixing parameterizations of the model. Significant intraseasonal variability is found in the central North Pacific and the Southern Ocean. The simulation is highly correlated with the observation at periods from 20 to 100 days. The spatial scales are larger than 1000 km in many places. These variabilities are apparently the barotropic response of the ocean to wind forcing. The results of the study provide a basis for future assimilation of the data into the OGCM for improved description of the large-scale ocean variabilities.

Chao, Yi↗
NASA NTRS2015

Downscaling GISS ModelE Boreal Summer Climate over Africa

The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.

Regional climate models↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

A Mission to Observe Ice in Clouds from Space

To date there have been multiple satellite missions to observe and retrieve cloud top properties and the liquid in, and precipitation from, clouds. There are currently a few missions that attempt to measure cloud ice properties as a byproduct of other observations. However, we do not yet quantitatively understand the processes that control the water budget of the upper troposphere where ice is the predominant phase, and how these processes are linked to precipitation processes and the radiative energy budget. The ice in clouds either melts into rain or is detrained, and persists, as cirrus clouds affecting the hydrological and energy cycle, respectively. Fully modeling the Earth's climate and improving weather and climate forecasts requires accurate satellite measurements of various cloud properties at the temporal and spatial scales of cloud processes. The uncertainty in knowledge of these ice characteristics is reflected in the large discrepancies in model simulations of the upper tropospheric water budget. Model simulations are sensitive to the partition of ice between precipitation and outflow processes, i.e., to the parameterization of ice clouds and ice processes. This presentation will describe the Submillimeter-wave InfraRed Ice Cloud Experiment (SIRICE) concept, a satellite mission designed to acquire global Earth radiance measurements in the infrared and submillimeter-wave region (183-874 GHz). If successful, this mission will bridge the measurement gap between microwave sounders and shorter-wavelength infrared and visible sensors. The brightness temperatures at submillimeter-wave frequencies are especially sensitive to cirrus ice particle sizes (because they are comparable to the wavelength). This allows for more accurate ice water path estimates when multiple channels are used to probe into the cloud layers. Further, submillimeter wavelengths offer simplicity in the retrieval algorithms because they do not probe into the liquid and near surface portions of clouds, thus requiring only one term of the radiative transfer equation (ice scattering) to relate brightness temperatures to ice. Scientific justification and the SIRICE approach to measuring ice water path and particle size that span a range encompassing both the hydrologically active and radiatively active components of cloud systems will be presented.

Ackerman, S.↗
NASA NTRS2021

Evaluation and Intercomparison of Wildfire Smoke Forecasts from Multiple Modeling Systems for the 2019 Williams Flats Fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

AOD↗
DOE OSTI2021

22 Ne Phase Separation as a Solution to the Ultramassive White Dwarf Cooling Anomaly

The precise astrometric measurements of the Gaia Data Release 2 have opened the door to detailed tests of the predictions of white dwarf cooling models. Significant discrepancies between theory and observations have been identified, the most striking affecting ultramassive white dwarfs. Cheng et al. found that a small fraction of white dwarfs on the so-called Q branch must experience an extra cooling delay of ~8 Gyr not predicted by current models. 22 Ne phase separation in a crystallizing C/O white dwarf can lead to a distillation process that efficiently transports 22 Ne toward its center, thereby releasing a considerable amount of gravitational energy. Here, using state-of-the-art Monte Carlo simulations, we show that this mechanism can largely resolve the ultramassive cooling anomaly if the delayed population consists of white dwarfs with moderately above-average 22 Ne abundances. We also argue that 22 Ne phase separation can account for the smaller cooling delay currently missing for models of white dwarfs with more standard compositions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Role of Internal Variability in Springtime Arctic Amplification from 1980 to 2022

Arctic amplification (AA) refers to the enhanced warming of the Arctic relative to the global average due to rising greenhouse gases, measured as the ratio of Arctic-mean to global-mean surface air temperature (SAT) trends. From 1980 to 2022, annual-mean AA reached 4.2 (Arctic defined as north of 70°N). Climate models simulate AA but fail to reproduce its magnitude. Sweeney et al. attributed much of this model–observation discrepancy to internal variability. AA shows seasonality and so does the discrepancy. Spring (March–May) shows the largest gap: Observed AA is 4.2, while the multimodel mean is 2.7. This raises several questions: 1) What role does internal variability play in observed spring AA? 2) How does simulated spring AA compare to observations when internal variability is removed? 3) If internal variability is significant, what mechanisms drive it? To address these, we adapted the machine learning algorithm from Sweeney et al., training on simulated multidecadal spring SAT and sea level pressure (SLP) trend maps. Our results show that internal variability enhanced spring Arctic warming by 37% and reduced global warming by 10%. Removing internal variability reconciles the spring AA discrepancy. The estimated internal contribution to Arctic spring warming is supported by an independent dynamical adjustment approach. We identify an atmospheric circulation pattern in observations associated with this internal warming. Observed internal Siberian SAT and SLP trends follow the simulated SAT–SLP relationship but lie at the distribution’s extreme, suggesting models generally underestimate internal variability unless the observed configuration reflects a rare real-world realization.

Arctic↗
NASA NTRS1997

Understanding the Latitude Structure of Nitric Oxide in the Mesosphere and Lower Thermosphere

The goal of the proposed work was to understand the latitude structure of nitric oxide in the mesosphere and lower thermosphere. The problem was portrayed by a clear difference between predictions of the nitric oxide distribution from chemical/dynamical models and data from observations made by the Solar Mesosphere Explorer (SMEE) in the early to mid eighties. The data exhibits a flat latitude structure of NO, the models tend to produce at equatorial maximum. The first task was to use the UARS-HALOE data to confirm the SME observations. The purpose of this first phase was to verify the UARS-NO structure is consistent with the SME data. The next task was to determine the cause of the discrepancy between modeled and observed nitric oxide latitude structure. The result from the final phase indicated that the latitude structure in the Photo-Electron (PE) production rate was the most important.

Fuller-Rowell, T.J.↗
DOE OSTI2019

Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output

In experiments conducted on the Z-machine at Sandia National Laboratories, dynamic material properties cannot be analyzed using traditional analytic methods, necessitating solving an inverse problem. Bayesian model calibration is a statistical framework for solving an inverse problem to estimate parameters input into a computational model in the presence of multiple uncertainties. Disentangling input parameter uncertainty and model misspecification is often poorly identified problem. When using computational models for physical parameter estimation, the issue of parameter identifiability must be carefully considered to obtain accurate and precise estimates of physical parameters. Additionally, in dynamic material properties applications, the experimental output is a function, velocity over time. While we can sample an arbitrarily large number of points from the measured velocity, these curves only contain a finite amount of information about the calibration parameters. In this report, we propose modifications to the Bayesian model calibration framework to simplify and improve the estimation of physical parameters with functional outputs. Specifically, we propose scaling the likelihood function by an effective sample size rather than modeling the discrepancy function; and modularizing input nuisance parameters with weakly identified parameters. We evaluate the performance of these proposed methods using a statistical simulation study and then apply these methods to estimate parameters of the tantalum equation of state. We conclude that these proposed methods can provide simple, fast, and statistically valid alternatives to the full Bayesian model calibration procedure; and that these methods can be used to estimate parameters of the equation of state for tantalum.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Observing Ice in Clouds from Space

There are many satellite observations of cloud top properties and the liquid and rain content of clouds, however, we do not yet quantitatively understand the processes that control the water budget of the upper troposphere where ice is the predominant phase, and how these processes are linked to precipitation processes and the radiative energy budget. The ice in clouds in the upper troposphere either melts into rain or is detrained, and persists, as cirrus clouds affecting the hydrological and energy cycle, respectively. Fully modeling the Earth's climate and improving weather and climate forecasts requires accurate satellite measurements of various cloud properties at the temporal and spatial scales of cloud processes. These properties include cloud horizontal and vertical structure, cloud water content and some measure of particle sizes and shapes. The uncertainty in knowledge of these ice characteristics is reflected in the large discrepancies in model simulations of the upper tropospheric water budget. Model simulations are sensitive to the partition of ice between precipitation and outflow processes, i.e., to the parameterization of ice clouds and ice processes. One barrier to achieving accurate global ice cloud properties is the lack of adequate observations at millimeter and submillimeter wavelengths (183-874 GHz). Recent advances in instrumentation have allowed for the development and implementation of an airborne submillimeter-wave radiometer. The brightness temperatures at these frequencies are especially sensitive to cirrus ice particle sizes (because they are comparable to the wavelength). This allows for more accurate ice water path estimates when multiple channels are used to probe into the cloud layers. Further, submillimeter wavelengths offer simplicity in the retrieval algorithms because they do not probe into the liquid and near surface portions of clouds, thus requiring only one term of the radiative transfer equation (ice scattering) to relate brightness temperatures to ice. The next step is a satellite mission designed to acquire global Earth radiance measurements in the submillimeter-wave region, thus bridging the measurement gap between microwave sounders and shorter-wavelength infrared and visible sensors. This presentation provides scientific justification and an approach to measuring ice water path and particle size from a satellite platform that spans a range encompassing both the hydrologically active and radiatively active components of cloud systems.

Ackerman, S.↗
NASA NTRS2016

Black Carbon Absorption at the Global Scale Is Affected by Particle-Scale Diversity in Composition

Atmospheric black carbon (BC) exerts a strong, but uncertain, warming effect on the climate. BC that is coated with non-absorbing material absorbs more strongly than the same amount of BC in an uncoated particle, but the magnitude of this absorption enhancement (E(sub abs)) is not well constrained. Modelling studies and laboratory measurements have found stronger absorption enhancement than has been observed in the atmosphere. Here, using a particle-resolved aerosol model to simulate diverse BC populations, we show that absorption is overestimated by as much as a factor of two if diversity is neglected and population-averaged composition is assumed across all BC-containing particles. If, instead, composition diversity is resolved, we find E(sub abs) = 1 - 1.5 at low relative humidity, consistent with ambient observations. This study offers not only an explanation for the discrepancy between modelled and observed absorption enhancement, but also demonstrates how particle-scale simulations can be used to develop relationships for global-scale models.

Climate models↗