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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗
NASA NTRS2011

Northwest Regional Climate Assessment

Objectives are to establish a continuing, inclusive National process that: 1) synthesizes relevant science and information 2) increases understanding of what is known & not known 3) identifies information needs related to preparing for climate variability and change, and reducing climate impacts and vulnerability 4) evaluates progress of adaptation & mitigation activities 5) informs science priorities 6) builds assessment capacity in regions and sectors 7) builds understanding & skilled use of findings

Lipschultz, Fred↗
DOE OSTI2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Validation of TRMM Multi-Satellite Precipitation Analysis (TMPA) Products in the Peruvian Andes

The relevance of accurate and timely rainfall estimates cannot be overstated. The rainfall gauge network is still insufficient across significant areas worldwide. Rainfall estimates fromspaceborne sensors present an opportunity to supplement the existing network and enable the development of critical, near real time applications. However, the societal benefits of such systems can only be realized if the estimates are properly validated and the performance of existing products accurately described. In this study, two products generated by the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are validated for the Peruvian Andes. This is a region of complex topography that poses significant challenges to the retrieval of rainfall values fromspace. The TMPA products, both research grade (3B42V7) and near real time (3B42RT), are compared against in situ data. Different observation lengths are studied and the results are analyzed in light of geographic, topographic and climatic constraints. The Time Series of the science grade product were also studied under Dynamic TimeWarping and Hierarchical Clustering to streamline intertile comparisons. The TMPA products showa good agreementwith the gauge values, especially formore prolonged observation periods (over 8 days). The validation results display a strong regional dependence as a consequence of differences in the climate and topography. This region-specific performance calls for additional, detailed case studies and localized validation efforts. Overall the TMPAwas found to performadequately and provide quality information for a number of applications requiring timely estimates in convenient formats.

satellites↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
NASA NTRS2005

National Aeronautics and Space Administration (NASA) Earth Science Research for Energy Management: Overview of Energy Issues and an Assessment of the Potential for Application of NASA Earth Science Research - Part 1

Effective management of energy resources is critical for the U.S. economy, the environment, and, more broadly, for sustainable development and alleviating poverty worldwide. The scope of energy management is broad, ranging from energy production and end use to emissions monitoring and mitigation and long-term planning. Given the extensive NASA Earth science research on energy and related weather and climate-related parameters, and rapidly advancing energy technologies and applications, there is great potential for increased application of NASA Earth science research to selected energy management issues and decision support tools. The NASA Energy Management Program Element is already involved in a number of projects applying NASA Earth science research to energy management issues, with a focus on solar and wind renewable energy and developing interests in energy modeling, short-term load forecasting, energy efficient building design, and biomass production.

Atmospheric models↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗
NASA NTRS2004

Biology and The Future of Mars

It is possible that at some time in the future we might recreate a habitable climate on Mars returning it to the life-bearing state it may have enjoyed early in its history. Our studies of Mars are still in a preliminary state but everything we have learned suggests that it may he possible to restore Mars to a habitable climate. Long part of the intersection of science and fiction (eg. Clarke, 1995), serious studies of planetary ecosynthesis on Mars began after the results of the Viking mission indicated that all the compounds needed for life were present on the surface of Mars is some accessible form (Averner and MacElroy, 1976; McKay et al., 1991; Fogg, 1995). Recent work has focused on the use of climate models to compute the timescales to warm Mars (McKay et al., 1991 ; McKay and Marinova, 2001). Planetary ecosynthesis on Mars has implications for the objectives and conduct of robotic and human exploration. In particular the question of forward contamination must be considered in a new way if we wish to control the introduction of life to Mars in advance of planetary ecosynthesis.

McKay, Christopher P.↗
DOE OSTI2021

Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data from: "Lab and Field Warming Similarly Advance Germination Date and Limit Germination Rate for High and Low Elevation Provenances of Two Widespread Subalpine Conifers"

This archive contains data used to draw conclusions in “Lab and Field Warming Similarly Advance Germination Date and Limit Germination Rate for High and Low Elevation Provenances of Two Widespread Subalpine Conifers”, by Kueppers et al. 2017. Cone collection and field experiments were conducted on Niwot Ridge, Colorado, with the latter completed within the Alpine Treeline Warming Experiment (ATWE). Laboratory germination experiments were conducted at the University of California Merced. Geospatial files are also provided for additional geographical context.There are four data file formats in this package: comma-separated values (.csv), Microsoft Excel (.xlsx), keyhole markup language (.kml), and ESRI shapefile (.shp). The latter two are geospatial file formats. .csv files can be opened using any simple text editor program such as TextEdit (Mac) and Notepad (Windows), and .xlsx files can be opened using Microsoft Excel or Google Sheets. The .kml file contains coordinate data, and is compatible with Google Earth and Google Maps. ESRI shapefiles (.shp) are compatible with any geospatial software able to read the file type, such as QGIS and ESRI’s ArcGIS Suite. This data user’s guide is available in as a .pdf, and can be opened using software such as Adobe Acrobat Reader.-----------------------------------------------------------------------------------------------------------------------------------Accurately predicting upslope shifts in subalpine tree ranges with warming requires understanding how future forest populations will be affected by climate change, as these are the seed sources for new tree line and alpine populations. Early life history stages are particularly sensitive to climate and are also influenced by genetic variation among populations. We tested the climate sensitivity of germination and initial development for two widely distributed subalpine conifers, using controlled-environment growth chambers with one temperature regime from subalpine forest in the Colorado Rocky Mountains and one 5 °C warmer, and two soil moisture levels. We tracked germination rate and timing, rate of seedling development, and seedling morphology for two seed provenances separated by ~300 m elevation. Warming advanced germination timing and initial seedling development by a total of ~2 weeks, advances comparable to mean differences between provenances. Advances were similar for both provenances and species; however, warming reduced the overall germination rate, as did low soil moisture, only for Picea engelmannii. A three-year field warming and watering experiment planted with the same species and provenances yielded responses qualitatively consistent with the lab trials. Together these experiments indicate that in a warmer, drier climate, P. engelmannii germination, and thus regeneration, could decline, which could lead to declining subalpine forest populations, while Pinus flexilis forest populations could remain robust as a seed source for upslope range shifts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Game-Changing Science with SmallSats and Hosted Payloads

This talk outlines the opportunities using space-based remote sensing to collect the basic climate research data that will serve as the foundation of our science, our models, and our predictive capabilities. With SmallSats and hosted payloads to supplement data from our traditional large spacecraft, and with constellations of SmallSats and with hosted payloads, the Science Mission Directorate is able to make new types of observations, such as synchronic temporal and spatial measurements of the same events. Our data enables science-based decision making. Our models and predictions inform and educate our decision makers at every level.

remote sensing↗
NASA NTRS2016

The Climate of Early Mars

The nature of the early Martian climate is one of the major unanswered questions of planetary science. Key challenges remain, but a new wave of orbital and in situ observations and improvements in climate modeling have led to significant advances over the past decade. Multiple lines of geologic evidence now point to an episodically warm surface during the late Noachian and early Hesperian periods 3–4 Ga. The low solar flux received by Mars in its first billion years and inefficiency of plausible greenhouse gases such as CO2 mean that the steady-state early Martian climate was likely cold. A denser CO2 atmosphere would have caused adiabatic cooling of the surface and hence migration of water ice to the higher-altitude equatorial and southern regions of the planet. Transient warming caused melting of snow and ice deposits and a temporarily active hydrological cycle, leading to erosion of the valley networks and other fluvial features. Precise details of the warming mechanisms remain unclear, but impacts, volcanism, and orbital forcing all likely played an important role. The lack of evidence for glaciation across much of Mars’s ancient terrain suggests the late Noachian surface water inventory was not sufficient to sustain a northern ocean. Though mainly inhospitable on the surface, early Mars may nonetheless have presented significant opportunities for the development of microbial life.

Mars↗
NASA NTRS2002

The Pascal Mars Scout Mission

Except for Earth, Mars is the planet most amenable to surface-based climate studies. Its surface is accessible, and the kind of observations that are needed, such as meteorological measurements from a long-lived global network, are readily achievable. Weather controls the movement of dust, the exchange of water between the surface and atmosphere, and the cycling of CO2 between the poles. We know there is a weather signal, we know how to measure it, and we know how to interpret it. Pascal seeks to understand the long-term global behavior of near-surface weather systems on Mars, how they interact with its surface, and, therefore, how they control its climate system. To achieve this, Pascal delivers 18 Science Stations to the surface of the planet that operate for three Mars years (5.6 Earth years). The network has stations operating in the tropics, midlatitudes, and polar regions of both hemispheres. During entry, descent, and landing, each Pascal probe acquires deceleration measurements to determine thermal structure, and descent images to characterize local terrain. On the surface, each Science Station takes daily measurements of pressure, opacity, temperature, wind speed, and water vapor concentration and monthly panoramic images of the landing environment. These data will characterize the planet's climate system and how atmosphere-surface interactions control it. The Pascal mission is named after 17th century French Scientist, Blaise Pascal, who pioneered measurements of atmospheric pressure. Pressure is the most critical measurement because it records the "heartbeat" of the planet's general circulation and climate system.

Haberle, R. M.↗
NASA NTRS2016

Progress Towards AIRS Science Team Version-7 at SRT

The AIRS Science Team Version-6 retrieval algorithm is currently producing level-3 Climate Data Records (CDRs) from AIRS that have been proven useful to scientists in understanding climate processes. CDRs are gridded level-3 products which include all cases passing AIRS Climate QC. SRT has made significant further improvements to AIRS Version-6. At the last Science Team Meeting, we described results using SRT AIRS Version-6.22. SRT Version-6.22 is now an official build at JPL called 6.2.4. Version-6.22 results are significantly improved compared to Version-6, especially with regard to water vapor and ozone profiles. We have adapted AIRS Version-6.22 to run with CrIS/ATMS, at the Sounder SIPS which processed CrIS/ATMS data for August 2014. JPL AIRS Version-6.22 uses the Version-6 AIRS tuning coefficients. AIRS Version-6.22 has at least two limitations which must be improved before finalization of Version-7: Version-6.22 total O3 has spurious high values in the presence of Saharan dust over the ocean; and Version-6.22 retrieved upper stratospheric temperatures are very poor in polar winter. SRT Version-6.28 addresses the first concern. John Blaisdell ran the analog of AIRS Version-6.28 in his own sandbox at JPL for the 14th and 15th of every month in 2014 and all of July and October for 2014. AIRS Version-6.28a is hot off the presses and addresses the second concern.

CrIS↗
NASA NTRS1992

Something is wrong with climate theory

We frequently forget that there is more to science than the making of a more precise measurement or a more elaborate calculation. It is even more than applying to new problems the methods that worked on old problems. These activities, which keep most of us busy most of the time, are important, but the new and unexpected discoveries are more important. And many radically new discoveries arise from scientific puzzles, the "anomalies." We believe that studies of past climates have exposed an anomaly that deserves attention and that may result in a fundamentally new understanding of the climate system. The poles have been much warmer throughout much of Earth's history than they are now. Ice-age episodes with durations of millions of years have been separated by periods of hundreds of millions of years that have left little or no evidence of polar ice sheets. The data are best for the most recent of these ice-free episodes so we will concentrate on the Eocene.

Non-NASA Center↗
NASA NTRS2014

Temperature and Humidity Profiles in the TqJoint Data Group of AIRS Version 6 Product for the Climate Model Evaluation

The Atmospheric Infrared Sounder (AIRS) mission is entering its 13th year of global observations of the atmospheric state, including temperature and humidity profiles, outgoing long-wave radiation, cloud properties, and trace gases. Thus AIRS data have been widely used, among other things, for short-term climate research and observational component for model evaluation. One instance is the fifth phase of the Coupled Model Intercomparison Project (CMIP5) which uses AIRS version 5 data in the climate model evaluation. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the home of processing, archiving, and distribution services for data from the AIRS mission. The GES DISC, in collaboration with the AIRS Project, released data from the version 6 algorithm in early 2013. The new algorithm represents a significant improvement over previous versions in terms of greater stability, yield, and quality of products. The ongoing Earth System Grid for next generation climate model research project, a collaborative effort of GES DISC and NASA JPL, will bring temperature and humidity profiles from AIRS version 6. The AIRS version 6 product adds a new "TqJoint" data group, which contains data for a common set of observations across water vapor and temperature at all atmospheric levels and is suitable for climate process studies. How different may the monthly temperature and humidity profiles in "TqJoint" group be from the "Standard" group where temperature and water vapor are not always valid at the same time? This study aims to answer the question by comprehensively comparing the temperature and humidity profiles from the "TqJoint" group and the "Standard" group. The comparison includes mean differences at different levels globally and over land and ocean. We are also working on examining the sampling differences between the "TqJoint" and "Standard" group using MERRA data.

climate model evaluation↗
DOE OSTI2023

PiCAM: A Raspberry Pi-based open-source, low-power camera system for monitoring plant phenology in Arctic environments

Time-lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high-frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite-derived phenology products. However, in remote regions including the high-latitude Arctic, deploying time-lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges. Here, we present a novel, low-power, compact, lightweight time-lapse camera system, called power-interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low-Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology. Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (< –30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground-based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products.

54 ENVIRONMENTAL SCIENCES↗