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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Design and Performance of a Miniature Lidar Wind Profiler (MLWP)

The directional velocity of the wind is one of the most critical components for understanding meteorological and other dynamic atmospheric processes. Altitude-resolved wind velocity measurements, also known as wind profiles or soundings, are especially necessary for providing data for meteorological forecasting and overall global circulation models (GCM's). Wind profiler data are also critical in identifying possible dangerous weather conditions for aviation. Furthermore, a system has yet to be developed for wind profiling from the surface of Mars which could also meet the stringent requirements on size, weight, and power of such a mission. Obviously, a novel wind profiling approach based on small and efficient technology is required to meet these needs. A lidar system based on small and highly efficient semiconductor lasers is now feasible due to recent developments in the laser and detector technologies. The recent development of high detection efficiency (50%), silicon-based photon-counting detectors when combined with high laser pulse repetition rates and long receiver integration times has allowed these transmitter energies to be reduced to the order of microjoules per pulse. Aerosol lidar systems using this technique have been demonstrated for both Q-switched, diode-pumped solid-state laser transmitters (lambda = 523 nm) and semiconductor diode lasers (lambda = 830 nm); however, a wind profiling lidar based on this technique has yet to be developed. We will present an investigation of a semiconductor-laser-based lidar system which uses the "edge-filter" direct detection technique to infer Doppler frequency shifts of signals backscattered from aerosols in the planetary boundary layer (PBL). Our investigation will incorporate a novel semiconductor laser design which mitigates the deleterious effects of frequency chirp in pulsed diode lasers, a problem which has limited their use in such systems in the past. Our miniature lidar could be used on a future Mars lander and perhaps find its own niche in terrestrial applications due to its potential low cost an small size.

Cornwell, Donald M., Jr.↗
DOE OSTI2026

Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

AI↗
NASA NTRS2003

Radiation Risk Projections for Space Travel

Space travelers are exposed to solar and galactic cosmic rays comprised of protons and heavy ions moving with velocities close to the speed of light. Cosmic ray heavy ions are known to produce more severe types of biomolecular damage in comparison to terrestrial forms of radiation, however the relationship between such damage and disease has not been fully elucidated. On Earth, we are protected from cosmic rays by atmospheric and magnetic shielding, and only the remnants of cosmic rays in the form of ground level muons and other secondary radiations are present. Because human epidemiology data is lacking for cosmic rays, risk projection must rely on theoretical understanding and data from experimental models exposed to space radiation using charged particle accelerators to simulate space radiation. Although the risks of cancer and other late effects from cosmic rays are currently believed to present a severe challenge to space travel, this challenge is centered on our lack of confidence in risk projections methodologies. We review biophysics and radiobiology data on the effects of the cosmic ray heavy ions, and the current methods used to project radiation risks . Cancer risk projections are described as a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Risk projections for space travel are described using Monte-Carlo sampling from subjective error di stributions that represent the lack of knowledge in each factor that contributes to the projection model in order to quantify the overall uncertainty in risk projections. This analysis is applied to space mi ssion scenarios including lunar colony, deep space outpost, and a Mars mission. Results suggest that the number of days in space where cancer mortality risks can be assured at a 95% confidence level to be below the maximum acceptable risk for radi ation workers on Earth or the International Space Station is only on the order of 100-200 days. Approaches to reduce these unceI1ainties and mitigate risks are described.

Cucinotta, Francis↗
DOE OSTI2021

Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity

Abstract Earth is currently undergoing a global increase in atmospheric vapor pressure deficit (VPD), a trend which is expected to continue as climate warms. This phenomenon has been associated with productivity decreases in ecosystems and yield penalties in crops, with these losses attributed to photosynthetic limitations arising from decreased stomatal conductance. Such VPD increases, however, have occurred over decades, which raises the possibility that stomatal acclimation to VPD plays an important role in determining plant productivity under high VPD. Furthermore, evidence points to more far‐ranging and complex effects of elevated VPD on plant physiology, extending to the anatomical, biochemical, and developmental levels, which could vary substantially across species. Because these complex effects are typically not considered in modeling frameworks, we conducted a quantitative literature review documenting temperature‐independent VPD effects on 112 species and 59 traits and physiological variables, in order to develop an integrated and mechanistic physiological framework. We found that VPD increase reduced yield and primary productivity, an effect that was partially mediated by stomatal acclimation, and also linked with changes in leaf anatomy, nutrient, and hormonal status. The productivity decrease was also associated with negative effects on reproductive development, and changes in architecture and growth rates that could decrease the evaporative surface or minimize embolism risk. Cross‐species quantitative relationships were found between levels of VPD increase and trait responses, and we found differences across plant groups, indicating that future VPD impacts will depend on community assembly and crop functional diversity. Our analysis confirms predictions arising from the hydraulic corollary to Darcy's law, outlines a systemic physiological framework of plant responses to rising VPD, and provides recommendations for future research to better understand and mitigate VPD‐mediated climate change effects on ecosystems and agro‐systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗
NASA NTRS2013

Mars Sample Return Using Commercial Capabilities: ERV Trajectory and Capture Requirements

Mars Sample Return was presented as the highest priority planetary science mission of the next decade [1]. Lemke et al. [2] present a Mars Sample Return mission concept in which the sample is returned directly from the surface of Mars to an Earth orbit. The sample is recovered in Earth Orbit instead of being transferred between spacecraft in Mars Orbit. This paper provides the details of this sample recovery in Earth orbit and presents as such a sub-element of the overall Mars sample return concept given in [2]. We start from the assumption that a Mars Ascent Vehicle (MAV), initially landed on Mars using a modified SpaceX Dragon capsule, has successfully delivered the sample, already contained within an Earth Return Vehicle (ERV), to a parking orbit around Mars. From the parking orbit, the ERV imparts sufficient Delta-V to inject itself into an earthbound trajectory and to be captured into an Earth orbit eventually. We take into account launch window and Delta-V considerations as well as the additional constraint of increased safety margins imposed by planetary protection regulations. We focus on how to overcome two distinct challenges of the sample return that are driven by the issues of planetary protection: (1) the design of an ERV trajectory meeting all the requirements including the need to avoid contamination of Earth's atmosphere; (2) the concept of operations for retrieving the Martian samples in Earth orbit in a safe way. We present an approach to retrieve the samples through a rendezvous between the ERV and a second SpaceX Dragon capsule. The ERV executes a trajectory that brings it from low Mars orbit (LMO) to a Moon-trailing Earth orbit at high inclination with respect to the Earth-Moon plane. After a first burn at Trans-Earth Injection (TEI), the trajectory uses a second burn at perigee during an Earth flyby maneuver to capture the ERV in Earth orbit. The ERV then uses a non-propulsive Moon flyby to come to a near-circular Moon-trailing orbit. To perform the Earth Orbit Rendezvous (EOR), a second Dragon capsule is then launched from Earth and a similar lunar flyby is performed to rendezvous with the ERV. The requirements for rendezvous, close proximity operations and capture of the sample canister are described. A concept of operations for sample retrieval is presented along with design specifications of the ERV, the required modifications to the Dragon capsule, as well as the hardware, software, sensors, actuators, and capture mechanisms used. In our concept, a container is mounted to the front hatch of Dragon, capable of accommodating the sample canister and sealing it from the rest of the capsule. The sample canister is captured using a robotic arm with a magnetic grappling mechanism. Dragon then performs a propulsive maneuver to return to Earth for a controlled re-entry while the ERV (sans sample container) is left in the Moon trailing orbit. Contingency cases and related mitigation strategies are also discussed, including the advantages and disadvantages of performing the ERV rendezvous with a crew.

Faber, Nicolas F.↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2023

Microbial sensitivity to temperature and sulfate deposition modulates greenhouse gas emissions from peat soils

Abstract Peatlands are among the largest natural sources of atmospheric methane (CH 4 ) worldwide. Microbial processes play a key role in regulating CH 4 emissions from peatland ecosystems, yet the complex interplay between soil substrates and microbial communities in controlling CH 4 emissions as a function of global change remains unclear. Herein, we performed an integrated analysis of multi‐omics data sets to provide a comprehensive understanding of the molecular processes driving changes in greenhouse gas (GHG) emissions in peatland ecosystems with increasing temperature and sulfate deposition in a laboratory incubation study. We sought to first investigate how increasing temperatures (4, 21, and 35°C) impact soil microbiome–metabolome interactions; then explore the competition between methanogens and sulfate‐reducing bacteria (SRBs) with increasing sulfate concentrations at the optimum temperature for methanogenesis. Our results revealed that peat soil organic matter degradation, mediated by biotic and potentially abiotic processes, is the main driver of the increase in CO 2 production with temperature. In contrast, the decrease in CH 4 production at 35°C was linked to the absence of syntrophic communities and the potential inhibitory effect of phenols on methanogens. Elevated temperatures further induced the microbial communities to develop high growth yield and stress tolerator trait‐based strategies leading to a shift in their composition and function. On the other hand, SRBs were able to outcompete methanogens in the presence of non‐limiting sulfate concentrations at 21°C, thereby reducing CH 4 emissions. At higher sulfate concentrations, however, the prevalence of communities capable of producing sufficient low‐molecular‐weight carbon substrates for the coexistence of SRBs and methanogens was translated into elevated CH 4 emissions. The use of omics in this study enhanced our understanding of the structure and interactions among microbes with the abiotic components of the system that can be useful for mitigating GHG emissions from peatland ecosystems in the face of global change.

AminiTabrizi, Roya↗
NASA NTRS2017

Multi-Wavelength Laser Transmitter for the Two-Step Laser Time-of-Flight Mass Spectrometer

Missions to diverse Outer Solar System bodies will require investigations that can detect a wide range of organics in complex mixtures, determine the structure of selected molecules, and provide powerful insights into their origin and evolution. Previous studies from remote spectroscopy of the Outer Solar System showed a diverse population of macromolecular species that are likely to include aromatic and conjugated hydrocarbons with varying degrees of methylation and nitrile incorporation. In situ exploration of Titan's upper atmosphere via mass and plasma spectrometry has revealed a complex mixture of organics. Similar material is expected on the Ice Giants, their moons, and other Outer Solar System bodies, where it may subsequently be deposited onto surface ices. It is evident that the detection of organics on other planetary surfaces provides insight into the chemical and geological evolution of a Solar System body of interest and can inform our understanding of its potential habitability. We have developed a prototype two-step laser desorption/ionization time-of-flight mass spectrometer (L2MS) instrument by exploiting the resonance-enhanced desorption of analyte. We have successfully demonstrated the ability of the L2MS to detect hydrocarbons in organically-doped analog minerals, including cryogenic Ocean World-relevant ices and mixtures. The L2MS instrument operates by generating a neutral plume of desorbed analyte with an IR desorption laser pulse, followed at a delay by a ultraviolet (UV) laser pulse, ionizing the plume. Desorption of the analyte, including trace organic species, may be enhanced by selecting the wavelength of the IR desorption laser to coincide with IR absorption features associated with vibration transitions of minerals or organic functional groups. In this effort, a preliminary laser developed for the instrument uses a breadboard mid-infrared (MIR) desorption laser operating at a discrete 3.475 μm wavelength, and a breadboard UV ionization laser operating at a wavelength of 266 nm. The MIR wavelength was selected to overlap the C-H stretch vibrational transition of certain aromatic hydrocarbons, and the UV wavelength provides additional selectivity to aromatic species via UV resonance-enhanced multiphoton ionization effects. The use of distinct laser wavelengths allows efficient coupling to the vibrational and electronic spectra of the analyte in independent desorption and ionization steps, mitigating excess energy that can lead to fragmentation during the ionization process and leading to selectivity that can aid in data interpretation.

Yu, Anthony W.↗
DOE OSTI2024

Surface-to-Interior Transport Timescales and Ventilation Patterns in a Time-Dependent Circulation Driven by Sustained Climate Warming

Abstract The effect of climate warming in response to rising atmospheric CO 2 on the ventilation of the ocean remains uncertain. Here we make theoretical advances in elucidating the relationship between ideal age and transit time distribution (TTD) in a time-dependent flow. Subsequently, we develop an offline tracer-transport model to characterize the ventilation patterns and time scales in the time-evolving circulation for the 1850–2300 period as simulated with the Community Earth System Model version 1 (CESMv1) under a business-as-usual warming scenario. We found that by 2300 2.1% less water originates from the high-latitude deep water formation regions (both hemispheres) compared to 1850. In compensation, there is an increase in the water originating from the subantarctic. We also found that slowing meridional overturning circulation causes a gradual increase in mean age during the 1850–2300 period, with a globally averaged mean-age increase of ∼110 years in 2300. Where and when the water will be re-exposed to the atmosphere depends on the post-2300 circulation. For example, if we assume that the circulation persists in its year-2300 state (scenario 1), the mean interior-to-surface transit time in year 1850 is ∼1140 years. In contrast, if we assume that the circulation abruptly recovers to its year-1850 state (scenario 2), the mean interior-to-surface transit time in 1850 is only ∼740 years. By 2300, these differences become even larger; in scenario 1, the mean interior-to-surface transit time increases by ∼200 years, whereas scenario 2 decreases by ∼80 years. The dependence of interior-to-surface transit time on the future ocean circulation produces an additional unavoidable uncertainty in the long-term durability of marine carbon dioxide removal strategies. Significance Statement The ocean’s circulation, when altered by climate warming, can affect its capacity to absorb heat and CO 2 , which are crucial for the global climate. In our study, we investigated how global warming, caused by rising CO 2 levels, might impact the ocean circulation—the way water moves from deep ocean to the surface and vice versa. We discovered that by 2300, if we continue on our current warming trajectory, the origins of water within the ocean will shift, with less coming from deep, cold zones near the poles and more from subantarctic regions. As a result, deep water will take longer time before it resurfaces than shallow water. How quickly this water travels from deep regions to the surface could change, depending on the state of future ocean circulation. If the circulation remains as predicted in 2300, this journey will take longer. Conversely, if it reverts to the pattern in 1850, the process will be quicker. This variability introduces added uncertainty to strategies aimed at mitigating climate change by storing CO 2 in the ocean. Our work highlights the intricate ways in which climate change can influence our oceans, potentially affecting our plans to mitigate global warming.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

Air quality control↗
DOE OSTI2021

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O 2 –O 2 , MODIS, and Suomi-NPP VIIRS

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Understanding Effective Diameter and Its Application to Terrestrial Radiation in Ice Clouds

The cloud property known as "effective diameter" or "effective radius", which in essence is the cloud particle size distribution (PSD) volume at bulk density divided by its projected area, is used extensively in atmospheric radiation transfer, climate modeling and remote sensing. This derives from the assumption that PSD optical properties can be uniquely described in terms of their effective diameter, D(sub e), and their cloud water content (CWC), henceforth referred to as the D(sub e)-CWC assumption. This study challenges this assumption, showing that while the D(sub e)-CWC assumption appears generally valid for liquid water clouds, it appears less valid for ice clouds in regions where (1) absorption is not primarily a function of either the PSD ice water content (IWC) or the PSD projected area, and (2) where wave resonance (i.e. photon tunneling) contributes significantly to absorption. These two regions often strongly coincide at terrestrial wavelengths when De less than ~60 m, which is where this D(sub e)-CWC assumption appears poorest. Treating optical properties solely in terms of D(sub e) and IWC may lead to errors up to 24%, 26% and 20% for terrestrial radiation in the window region regarding the absorption and extinction coefficients and the single scattering albedo, respectively. Outside the window region, errors may reach 33% and 42% regarding absorption and extinction. The magnitude and sign of these errors can change rapidly with wavelength, which may produce significant errors in climate modeling, remote sensing and other applications concerned with the wavelength dependence of radiation. Where the D(sub e)-CWC assumption breaks down, ice cloud optical properties appear to depend on D(sub e), IWC and the PSD shape. Optical property parameterizations in climate models and remote sensing algorithms based on historical PSD measurements may exhibit errors due to previously unknown PSD errors (i.e. the presence of ice artifacts due to the shattering of larger ice particles on the probe inlet tube during sampling). More recently developed cloud probes are designed to mitigate this shattering problem. Using realistic PSD shapes for a given temperature (and/or IWC) and cloud type may minimize errors associated with PSD shape in ice optics parameterizations and remote sensing algorithms. While this topic was investigated using two ice optics schemes (the Yang et al., 2005 database and the modified anomalous diffraction approximation, or MADA), a physical understanding of the limitations of the D(sub e)-IWC assumption was made possible by using MADA. MADA allows one to approximate the contribution of photon tunneling to absorption relative to other optical processes, which reveals that part of the error regarding the D(sub e)-IWC assumption can be associated with tunneling. By relating the remaining error to the radiation penetration depth in bulk ice (DELTA L) due to absorption, the domain where the D(sub e)-IWC assumption is weakest was described in terms of D(sub e) and DELTA L.

Mitchell, D. L.↗
NASA NTRS2024

Aera-Mip: Emission Pathways, Remaining Budgets, and Carbon Cycle Dynamics Compatible With 1.5 and 2 °C Global Warming Stabilization

While international climate policies now focus on limiting global warming to well below 2 °C or pursuing a 1.5 °C level of global warming, the climate modelling community has not provided an experimental design in which all Earth system models (ESMs) converge and stabilize at the same prescribed global warming levels. This gap hampers accurate estimations based on comprehensive ESMs of the carbon emission pathways and budgets needed to meet such agreed warming levels and of the associated climate impacts under temperature stabilization. Here, we apply the Adaptive Emission Reduction Approach (AERA) with ESMs to provide such simulations in which all models converge at 1.5 and 2.0 °C warming levels by adjusting their emissions over time. These emission-driven simulations provide a wide range of emission pathways and resulting atmospheric CO 2 projections for a given warming level, uncovering uncertainty ranges that were previously missing in the traditional Coupled Model Intercomparison Project (CMIP) scenarios with prescribed greenhouse gas concentration pathways. Meeting the 1.5 °C warming level requires a 40 % (full model range: 7 % to 76 %) reduction in multi-model mean CO 2 -forcing-equivalent (CO 2 -fe) emissions from 2025 to 2030, a 98 % (57 % to 127 %) reduction from 2025 to 2050, and a stabilization at 1.0 (−1.7 to 2.9) PgC yr−1 from 2100 onward after the 1.5 °C global warming level is reached. Meeting the 2.0 °C warming level requires a 47 % (8 % to 92 %) reduction in multi-model mean CO 2 -fe emissions until 2050 and a stabilization at 1.7 (−1.5 to 2.7) PgC yr−1 from 2100 onward. The on-average positive emissions under stabilized global temperatures are the result of a decreasing transient climate response to cumulative CO 2 -fe emissions over time under stabilized global warming. This evolution is consistent with a slightly negative zero emissions commitment – initially assumed to be zero – and leads to an increase in the post-2025 CO 2 -fe emission budget by a factor of 2.2 (−0.8 to 6.9) by 2150 for the 1.5 °C warming level and a factor of 1.4 (0.9 to 2.4) for the 2.0 °C warming level compared to its first estimate in 2025. The median CO 2 -only carbon budget by 2150, relative to 2020, is 800 GtCO 2 for the 1.5 °C warming level and 2250 GtCO 2 for the 2.0 °C warming level. These median values exceed the median IPCC AR6 estimates by 60 % for the 1.5 °C warming level and 67 % for 2.0 °C. Some of the differences may be explained by the choice of the mitigation scenario for non-CO 2 radiative agents. Our simulations highlight shifts in carbon uptake dynamics under stabilized temperature, such as a cessation of the carbon sinks in the North Atlantic and in tropical forests. On the other hand, the Southern Ocean remains a carbon sink centuries after temperatures stabilize. Overall, this new type of warming-level-based emission-driven simulation offers a more coherent assessment across climate models and opens up a wide range of possibilities for studying both the carbon cycle and climate impacts, such as extreme events, under climate stabilization.

Adaptive Emission Reduction Approach↗
NASA NTRS1993

Adaptive digital beamforming for a CDMA mobile communications payload

In recent years, Spread-Spectrum Code Division Multiple Access (CDMA) has become a very popular access scheme for mobile communications due to a variety of reasons: excellent performance in multipath environments, high scope for frequency reuse, graceful degradation near saturation, etc. In this way, a CDMA system can support simultaneous digital communication among a large community of relatively uncoordinated users sharing a given frequency band. Nevertheless, there are also important problems associated with the use of CDMA. First, in a conventional CDMA scheme, the signature sequences of asynchronous users are not orthogonal and, as the number of active users increases, the self-noise generated by the mutual interference between users considerably degrades the performance, particularly in the return link. Furthermore, when there is a large disparity in received powers - due to differences in slant range or atmospheric attenuation - the non-zero cross-correlation between the signals gives rise to the so-called near-far problem. This leads to an inefficient utilization of the satellite resources and, consequently, to a drastic reduction in capacity. Several techniques were proposed to overcome this problem, such as Synchronized CDMA - in which the signature sequences of the different users are quasi-orthogonal - and power control. At the expense of increased network complexity and user coordination, these techniques enable the system capacity to be restored by equitably sharing the satellite resources among the users. An alternative solution is presented based upon the use of time-reference adaptive digital beamforming on board the satellite. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference source. In order to use a time-reference adaptive antenna in a communications system, the main challenge is to obtain a reference signal highly correlated with the desired user signal and uncorrelated with the interferences. CDMA lends itself very easily to the generation of such a reference signal, thanks to the a priori knowledge of the user's signature sequence. First, the integration of an adaptive antenna in an asynchronous CDMA system is analyzed. The adaptive antenna system can provide increased interference rejection - much higher than that afforded by the code alone - and, since CDMA is mainly interference limited, any reduction in interference converts directly and linearly into an increase in capacity. Analyses and computer simulations are presented that show how an asynchronous CDMA system incorporating adaptive beamforming can provide at least as much capacity as a synchronous system. More importantly, the proposed concept allows the near-far effect to be mitigated without requiring a tight coordination of the users in terms of transmitted power control or network synchronization. The system is extremely robust to the near-far effect because the signals reaching the satellite from directions other than that of the desired user - which are likely to have different power levels - are adaptively canceled by the antenna. Finally, a payload architecture is presented that illustrates the practical implementation of this concept. This digital payload architecture demonstrates that with the advent of high performance CMOS digital processing, the on-board implementation of complex DSP techniques - in particular Digital Beamforming - has become possible, being most attractive for Mobile Satellite Communications.

Munoz-Garcia, Samuel G.↗
DOE OSTI2024

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Meteorology & Atmospheric Sciences↗
NASA NTRS2016

Liquid Oxygen/Liquid Methane Integrated Power and Propulsion

The proposed paper will cover ongoing work at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) on integrated power and propulsion for advanced human exploration. Specifically, it will present findings of the integrated design, testing, and operational challenges of a liquid oxygen / liquid methane (LOx/LCH4) propulsion brassboard and Solid Oxide Fuel Cell (SOFC) system. Human-Mars architectures point to an oxygen-methane economy utilizing common commodities, scavenged from the planetary atmosphere and soil via In-Situ Resource Utilization (ISRU), and common commodities across sub-systems. Due to the enormous mass gear-ratio required for human exploration beyond low-earth orbit, (for every 1 kg of payload landed on Mars, 226 kg will be required on Earth) increasing commonality between spacecraft subsystems such as power and propulsion can result in tremendous launch mass and volume savings. Historically, propulsion and fuel cell power subsystems have had little interaction outside of the generation (fuel cell) and consumption (propulsion) of electrical power. This was largely due to a mismatch in preferred commodities (hypergolics for propulsion; oxygen & hydrogen for fuel cells). Although this stove-piped approach benefits from simplicity in the design process, it means each subsystem has its own tanks, pressurization system, fluid feed system, etc. increasing overall spacecraft mass and volume. A liquid oxygen / liquid methane commodities architecture across propulsion and power subsystems would enable the use of common tankage and associated pressurization and commodity delivery hardware for both. Furthermore, a spacecraft utilizing integrated power and propulsion could use propellant residuals - propellant which could not be expelled from the tank near depletion due to hydrodynamic considerations caused by large flow demands of a rocket engine - to generate power after all propulsive maneuvers are complete thus utilizing previously wasted mass. Such is the case for human and robotic planetary landers. Although many potential benefits through integrated power & propulsion exist, integrated operations have yet to be successfully demonstrated and many challenges have already been identified the most obvious of which is the large temperature gradient. SOFC chemistry is exothermic with operating temperatures in excess of 1,000 K; however, any shared commodities will be undoubtedly stored at cryogenic temperatures (90-112 K) for mass efficiency reasons. Spacecraft packaging will drive these two subsystems in close proximity thus heat leak into the commodity tankage must be minimized and/or mitigated. Furthermore, commodities must be gasified prior to consumption by the SOFC. Excess heat generated by the SOFC could be used to perform this phase change; however, this has yet to be demonstrated. A further identified challenge is the ability of the SOFC to handle the sudden power spikes created by the propulsion system. A power accumulator (battery) will likely be necessary to handle these sudden demands while the SOFC thermally adjusts. JSC's current SOFC test system consists of a 1 kW fuel cell designed by Delphi. The fuel cell is currently undergoing characterization testing at the NASA JSC Energy Systems Test Area (ESTA) after which a Steam Methane Reformer (SMR) will be integrated and the combined system tested in closed-loop. The propulsion brassboard is approximately the size of what could be flown on a sounding rocket. It consists of one 100 lbf thrust "main" engine developed for NASA by Aerojet and two 10 lbf thrusters to simulate a reaction control system developed at NASA JSC. This system is also under development and initial testing at ESTA. After initial testing, combined testing will occur which will provide data on the fuel cell's ability to sufficiently handle the power spikes created by the propulsion system. These two systems will also be modeled using General-Use Nodal Network Solver (GUNNS) software. Once anchored with test data, this model will be used to extrapolate onto other firing profiles and used to size the power accumulator.

Banker, Brian↗