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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Spatial and temporal variability of turbulence dissipation rate in complex terrain

To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ~4 km 2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ~300 km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model

Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the cloud-resolving model (CRM) called the System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness–radiation as well as the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (–3.2°S, –60.6°W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux as well as (b) to determine the relationship between the integrated cloud fraction, radiative fluxes and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAM simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have a meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from the previous day can be used to estimate the cloud fraction as well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud–radiation as well as dynamic and thermodynamic variables of the atmosphere during the periods of this study, being able to obtain atmospheric variables that are impossible to obtain in an observational way.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluation of ARM tethered-balloon system instrumentation for supercooled liquid water and distributed temperature sensing in mixed-phase Arctic clouds

A tethered-balloon system (TBS) has been developed and is being operated by Sandia National Laboratories (SNL) on behalf of the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) User Facility in order to collect in situ atmospheric measurements within mixed-phase Arctic clouds. Periodic tethered-balloon flights have been conducted since 2015 within restricted airspace at ARM's Advanced Mobile Facility 3 (AMF3) in Oliktok Point, Alaska, as part of the AALCO (Aerial Assessment of Liquid in Clouds at Oliktok), ERASMUS (Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems), and POPEYE (Profiling at Oliktok Point to Enhance YOPP Experiments) field campaigns. The tethered-balloon system uses helium-filled 34 m 3 helikites and 79 and 104 m 3 aerostats to suspend instrumentation that is used to measure aerosol particle size distributions, temperature, horizontal wind, pressure, relative humidity, turbulence, and cloud particle properties and to calibrate ground-based remote sensing instruments. Supercooled liquid water content (SLWC) sondes using the vibrating-wire principle, developed by Anasphere Inc., were operated at Oliktok Point at multiple altitudes on the TBS within mixed-hase clouds for over 200 h. Sonde-collected SLWC data were compared with liquid water content derived from a microwave radiometer, Ka-band ARM zenith radar, and ceilometer at the AMF3, as well as liquid water content derived from AMF3 radiosonde flights. The in situ data collected by the Anasphere sensors were also compared with data collected simultaneously by an alternative SLWC sensor developed at the University of Reading, UK; both vibrating-wire instruments were typically observed to shed their ice quickly upon exiting the cloud or reaching maximum ice loading. Temperature sensing measurements distributed with fiber optic tethered balloons were also compared with AMF3 radiosonde temperature measurements. Combined, the results indicate that TBS-distributed temperature sensing and supercooled liquid water measurements are in reasonably good agreement with remote sensing and radiosonde-based measurements of both properties. From these measurements and sensor evaluations, tethered-balloon flights are shown to offer an effective method of collecting data to inform and constrain numerical models, calibrate and validate remote sensing instruments, and characterize the flight environment of unmanned aircraft, circumventing the difficulties of in-cloud unmanned aircraft flights such as limited flight time and in-flight icing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms

Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version (version 4; V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper. The IIR Level 2 algorithm has been modified in the V4 data release to improve the accuracy of the retrievals in clouds of very small (close to 0) and very large (close to 1) effective emissivities. To reduce biases at very small emissivities that were made evident in V3, the radiative transfer model used to compute clear-sky brightness temperatures over oceans has been updated and tuned for the simulations using Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data to match IIR observations in clear-sky conditions. Furthermore, the clear-sky mask has been refined compared to V3 by taking advantage of additional information now available in the V4 CALIOP 5 km layer products used as an input to the IIR algorithm. After sea surface emissivity adjustments, observed and computed brightness temperatures differ by less than ±0.2 K at night for the three IIR channels centered at 08.65, 10.6, and 12.05 µm, and inter-channel biases are reduced from several tens of Kelvin in V3 to less than 0.1 K in V4. We have also improved retrievals in ice clouds having large emissivity by refining the determination of the radiative temperature needed for emissivity computation. The initial V3 estimate, namely the cloud centroid temperature derived from CALIOP, is corrected using a parameterized function of temperature difference between cloud base and top altitudes, cloud absorption optical depth, and CALIOP multiple scattering correction factor. As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3 and increases the number of retrievals. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12.05 and 10.6 µm and at 12.05 and 08.65 µm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice habit models from the recent “TAMUice2016” database, namely the single-hexagonal-column model and the eight-element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.05 and 10.6 µm and effective diameter as derived from in situ measurements at tropical and midlatitudes during the Tropical Composition, Cloud, and Climate Coupling (TC4) and Small Particles in Cirrus Science and Operations Plan (SPARTICUS) field experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multifrequency radar observations of clouds and precipitation including the G-band

Abstract. Observations collected during the 25 February 2020 deployment of the Vapor In-Cloud Profiling Radar at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research, something that until now was only discussed in theory. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the Ka–G pairing can generate differential reflectivity signal several decibels larger than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The observations also showed that G-band signals experience non-Rayleigh scattering in regions where Ka- and W-band signal do not, thus demonstrating the potential of G-band radars for sizing sub-millimeter ice crystals and droplets. Observed peculiar radar reflectivity patterns also suggest that G-band radars could be used to gain insight into the melting behavior of small ice crystals. G-band signal interpretation is challenging, because attenuation and non-Rayleigh effects are typically intertwined. An ideal liquid-free period allowed us to use triple-frequency Ka–W–G observations to test existing ice scattering libraries, and the results raise questions on their comprehensiveness. Overall, this work reinforces the importance of deploying radars (1) with sensitivity sufficient enough to detect small Rayleigh scatters at cloud top in order to derive estimates of path-integrated hydrometeor attenuation, a key constraint for microphysical retrievals; (2) with sensitivity sufficient enough to overcome liquid attenuation, to reveal the larger differential signals generated from using the G-band as part of a multifrequency deployment; and (3) capable of monitoring atmospheric gases to reduce related uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A differential emissivity imaging technique for measuring hydrometeor mass and type

Abstract. The Differential Emissivity Imaging Disdrometer (DEID) is a new evaporation-based optical and thermal instrument designed to measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. Hydrometeor spatial dimensions are measured on a heated metal plate using an infrared camera by exploiting the much higher thermal emissivity of water compared with metal. As a melted hydrometeor evaporates, its mass can be directly related to the loss of heat from the hotplate assuming energy conservation across the hydrometeor. The heat loss required to evaporate a hydrometeor is found to be independent of environmental conditions including ambient wind velocity, moisture level and temperature. The difference in heat loss for snow vs. rain for a given mass offers a method for discriminating precipitation phase. The DEID measures hydrometeors at sampling frequencies of up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm, respectively, determined by the size of the hotplate and the thermal camera specifications. Measurable snow water equivalent (SWE) precipitation rates range from 0.001 to 200 mm h−1, as validated against a standard weighing bucket. Preliminary field experiment measurements of snow and rain from the winters of 2019 and 2020 provided continuous automated measurements of precipitation rate, snow density and visibility. Measured hydrometeor size distributions agree well with canonical results described in the literature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Biogeochemical and plant trait mechanisms drive enhanced methane emissions in response to whole-ecosystem warming

Abstract. Climate warming perturbs ecosystem carbon (C) cycling, causing both positive and negative feedbacks on greenhouse gas emissions. In 2016, we began a tidal marsh field experiment in two vegetation communities to investigate the mechanisms by which whole-ecosystem warming alters C gain, via plant-driven sequestration in soils, and C loss, primarily via methane (CH4) emissions. Here, we report the results from the first 4 years. As expected, warming of 5.1 ∘C more than doubled CH4 emissions in both plant communities. We propose this was caused by a combination of four mechanisms: (i) a decrease in the proportion of CH4 consumed by CH4 oxidation, (ii) more C substrates available for methanogenesis, (iii) reduced competition between methanogens and sulfate-reducing bacteria, and (iv) indirect effects of plant traits. Plots dominated by Spartina patens consistently emitted more CH4 than plots dominated by Schoenoplectus americanus, indicating key differences in the roles these common wetland plants play in affecting anaerobic soil biogeochemistry and suggesting that plant composition can modulate coastal wetland responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing root–soil interactions in wetland plants: root exudation and radial oxygen loss

Abstract. Plant rhizosphere processes, such as root exudation and root oxygen loss (ROL), could have significant impacts on the dynamics and magnitude of wetland methane fluxes and other biogeochemical processes but are rarely measured directly. Here, we measure root exudation and ROL from Schoenoplectus americanus and Spartina patens, two plants that have had opposite relationships between biomass and methane flux in field experiments (positive in S. patens, negative in S. americanus). We found contrasting rates of ROL in the two species, with S. americanus releasing orders of magnitude more oxygen (O2) to the soil than S. patens. At the same time, S. patens exuded high amounts of carbon to the soil, and much of the rhizosphere carbon pool was reduced compared to exudates from other wetland species. This work suggests that the relative inputs of O2 and carbon to the rhizosphere vary significantly between wetland plant species, potentially with major consequences on biogeochemical cycling, and highlights the importance of understanding how plant rhizosphere processes mediate soil biogeochemistry at a community level. As global change drivers continue to affect wetlands, future research should consider how feedbacks from plant rhizosphere processes may exacerbate or mitigate coastal wetland methane emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations

The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.

58 GEOSCIENCES↗
DOE OSTI2022

The PFLOTRAN Reaction Sandbox

Abstract. As modern reactive transport simulators evolve to accommodate the demands of a user community, researchers need a platform for prototyping new biogeochemical processes, many of which are niche and specific to laboratory or field experiments. The PFLOTRAN Reaction Sandbox leverages modern, object-oriented Fortran in an attempt to provide such an environment within an existing reactive transport simulator. This work describes the PFLOTRAN Reaction Sandbox concept and implementation through several illustrative examples. Reaction Sandbox Biodegradation Hill customizes the existing microbially mediated biodegradation reaction formulation within PFLOTRAN to better match empirical data. Reaction Sandbox Simple provides an isolated environment for testing numerous preconfigured kinetic rate expressions and developing user intuition. Reaction Sandbox Example serves as a template for creating new sandboxes within PFLOTRAN.

58 GEOSCIENCES↗
DOE OSTI2022

Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to -0.007 m 3 m -3 for SWC and from -0.68 to -0.2 mm d -1 for soil evaporation). Simulations related to nitrogen balance and soil CO 2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N 2 O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N 2 O efflux simulation bias was between -0.13 and -0.1 mgNm -2 d -1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO 2 efflux simulations bias was 0.04–0.17 gCm -2 d -1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

58 GEOSCIENCES↗
DOE OSTI2020

DataHawk2 files from LAPSE-RATE

This dataset includes measurements obtained using the University of Colorado DataHawk2 UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

TTWISTOR files from LAPSE-RATE

This dataset includes measurements obtained using the University of Colorado TTwistor UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Talon files from LAPSE-RATE

This dataset includes measurements obtained using the University of Colorado Talon UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Radar-based Snow Property Profile Retrievals for NSA

The product contains estimated snow microphysical properties retrieved primarily from vertically-profiling radar observations using a priori constraints that are dependent on atmospheric state. The product provides data at 1-minute time resolution, including snowfall rate at the surface and vertical profiles at 300-meter resolution of snow particle size distribution parameters and snow water contents. Estimated uncertainties for the snowfall rates and snow water contents are also provided. The product is obtained using an optimal estimation retrieval in which radar forward model and a priori state characteristics are based on expected snow particle microphysical and radar scattering properties evaluated from field experiment data.

54 ENVIRONMENTAL SCIENCES↗