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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Remote sensing of smoke, clouds, and fire using AVIRIS data

Clouds remain the greatest element of uncertainty in predicting global climate change. During deforestation and biomass burning processes, a variety of atmospheric gases, including CO2 and SO2, and smoke particles are released into the atmosphere. The smoke particles can have important effects on the formation of clouds because of the increased concentration of cloud condensation nuclei. They can also affect cloud albedo through changes in cloud microphysical properties. Recently, great interest has arisen in understanding the interaction between smoke particles and clouds. We describe our studies of smoke, clouds, and fire using the high spatial and spectral resolution data acquired with the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).

Gao, Bo-Cai↗
NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗
NASA NTRS2000

Autonomous, Full-Time Cloud Profiling at Arm Sites with Micro Pulse Lidar

Since the early 1990's technology advances permit ground based lidar to operate full time and profile all significant aerosol and cloud structure of the atmosphere up to the limit of signal attenuation. These systems are known as Micro Pulse Lidars (MPL), as referenced by Spinhirne (1993), and were first in operation at DOE Atmospheric Radiation Measurement (ARM) sites. The objective of the ARM program is to improve the predictability of climate change, particularly as it relates to cloud-climate feedback. The fundamental application of the MPL systems is towards the detection of all significant hydrometeor layers, to the limit of signal attenuation. The heating and cooling of the atmosphere are effected by the distribution and characteristics of clouds and aerosol concentration. Aerosol and cloud retrievals in several important areas can only be adequately obtained with active remote sensing by lidar. For cloud cover, the height and related emissivity of thin clouds and the distribution of base height for all clouds are basic parameters for the surface radiation budget, and lidar is essetial for accurate measurements. The ARM MPL observing network represents the first long-term, global lidar study known within the community. MPL systems are now operational at four ARM sites. A six year data set has been obtained at the original Oklahoma site, and there are several years of observations at tropical and artic sites. Observational results include cloud base height distributions and aerosol profiles. These expanding data sets offer a significant new resource for cloud, aerosol and atmospheric radiation analysis. The nature of the data sets, data processing algorithms, derived parameters and application results are presented.

Spinhirne, James D.↗
NASA NTRS2000

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Shepherd, J. Marshall↗
NASA NTRS2000

Satellite and Ground-based Radiometers Reveal Much Lower Dust Absorption of Sunlight than Used in Climate Models

The ability of dust to absorb solar radiation and heat the atmosphere is one of the main uncertainties in climate modeling and the prediction of climate change. Dust absorption is not well known due to limitations of in situ measurements. New techniques to measure dust absorption are needed in order to assess the impact of dust on climate. Here we report two new independent remote sensing techniques that provide sensitive measurements of dust absorption. Both are based on remote sensing. One uses satellite spectral measurements, the second uses ground based sky measurements from the AERONET network. Both techniques demonstrate that Saharan dust absorption of solar radiation is several times smaller than the current international standards. Dust cooling of the earth system in the solar spectrum is therefore significantly stronger than recent calculations indicate. We shall also address the issue of the effects of dust non-sphericity on the aerosol optical properties.

Kaufman, Y. J.↗
NASA NTRS2002

Smooth Sailing for Weather Forecasting

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

Source record↗
NASA NTRS1998

Climate Ocean Modeling on Parallel Computers

Ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change. However, modeling the ocean circulation at various spatial and temporal scales is a very challenging computational task.

partition↗
NASA NTRS2011

Airborne Remote Observations of L-Band Radio Frequency Interference and Implications for Satellite Missions

Passive remote sensing of the Earth s surface and atmosphere from space has significant importance in operational and research environmental studies, in particular for the scientific understanding, monitoring and prediction of climate change and its impacts. Passive remote sensing requires the measurement of naturally occurring radiations, usually of very low power levels, which contain essential information on the physical process under investigation. As such, these sensed radio frequency bands are a unique natural resource enabling space borne passive sensing of the atmosphere and the Earth s surface that deserves adequate allocation to the Earth Exploration Satellite Service and absolute protection from interference. Unfortunately, radio frequency interference (RFI) is an increasing problem for Earth remote sensing, particularly for passive observations of natural emissions. Because these natural signals tend to be very weak, even low levels of interference received by a passive sensor may degrade the fidelity of scientific data. The characteristics of RFI (low-level interference and radar-pulse noise) are not well known because there has been no systematic surveillance, spectrum inventory or mapping of RFI. While conducting a flight experiment over central Tennessee in May 2010, RFI, a concern for any instrument operating in the passive L band frequency, was observed across 16 subbands between 1402-1427 MHz. Such a survey provides rare characterization data from which to further develop mitigation technologies as well as to identify bandwidths to avoid in future sensor formulation.

Laymon, Charles↗
NASA NTRS2012

Airborne Measurements of Atmospheric Pressure made Using an IPDA Lidar Operating in the Oxygen A-Band

We report airborne measurements of atmospheric pressure made using an integrated path differential absorption (IPDA) lidar that operates in the oxygen A-band near 765 nm. Remote measurements of atmospheric temperature and pressure are needed for NASA s Active Sensing of CO2 Emissions Over Nights, Days, and Seasons (ASCENDS) mission to measure atmospheric CO2. Accurate measurements of tropospheric CO2 on a global scale are very important in order to better understand its sources and sinks and to improve our predictions of climate change. The goal of ASCENDS is to determine the CO2 dry mixing ratio with lidar measurements from space at a level of ~1 ppm. Analysis to date shows that with current weather models, measurements of both the CO2 column density and the column density of dry air are needed. Since O2 is a stable molecule that uniformly mixed in the atmosphere, measuring O2 absorption in the atmosphere can be used to infer the dry air density. We have developed an airborne (IPDA) lidar for Oxygen, with support from the NASA ESTO IIP program. Our lidar uses DFB-based seed laser diodes, a pulsed modulator, a fiber laser amplifier, and a non-linear crystal to generate wavelength tunable 765 nm laser pulses with a few uJ/pulse energy. The laser pulse rate is 10 KHz, and average transmitted laser power is ~20 mW. Our lidar steps laser pulses across a selected line O2 doublet near 764.7 nm in the Oxygen A-band. The direct detection lidar receiver uses a 20 cm diameter telescope, a Si APD detector in Geiger mode, and a multi-channel scalar to detect and record the time resolved laser backscatter in 40 separate wavelength channels. Subsequent analysis is used to estimate the transmission line shape of the doublet for the laser pulses reflected from the ground. Ground based data analysis allows averaging from 1 to 60 seconds to increase SNR in the transmission line shape of the doublet. Our retrieval algorithm fits the expected O2 lineshapes against the measurements and determines the atmospheric pressure by minimizing the error between the observations and model. We first demonstrated our airborne lidar during flights during summer 2010. We made several improvements and made measurements during the Ascends flights during July 2011. More information about the technique, lidar instrument, airborne measurements, and pressure estimates will be described in the presentation.

Riris, Haris↗
NASA NTRSDate not supplied

Improved Treatment of Snow Over Ice Sheets in the NASA GISS Climate Model: Towards Ice Sheet–Climate Coupling

Representing the interactions between ice sheets and climate is essential for more accurate prediction of climate change and sea level rise. Ice sheets interact with the overlying atmosphere via the accumulation of snow and its compaction into firn, then ice, as well as the melting of surface snow and ice and the creation of runoff water. Getting an adequate representation of heat transfer, compaction, and melting processes is essential for an accurate representation of snow on land ice in global climate models. We are implementing an improved snow model on top of land ice as part of an effort to couple the NASA GISS climate model with the PISM ice sheet model. The new snow model includes additional layers and processes that are not currently incorporated (e.g., liquid water retention, percolation and refreezing, and snow densification), and mass and energy transfer methods that are consistent with both static ice sheets (with implicit iceberg fluxes) and interactive ice sheets (with explicit dynamics). We are tuning the densification scheme of this snow model with temperature and density data from common FirnCover and SumUp observations at locations in the accumulation zone of Greenland, and we compare the resulting density profiles to other SumUp density profiles in Greenland and Antarctica. We will assess the impact of this new snow model in climate model simulations with a static ice sheet compared with the previous (simpler) 2-layer snow model. Finally, we aim to use the non-coupled simulations as a baseline to assess the impact of dynamic coupling with an interactive ice sheet model.

snow↗
DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗
DOE OSTI2021

A Modular System for Increasing Predictiveness for Extreme Climate Predictions

We know that climate change is poised to reshape our world, but we lack clear enough predictions about precisely how. The preponderance of these changes is associated with human activity, specifically the emission of CO 2 and other greenhouse gases. Problematically, projections of climate change continue to be marred by unacceptably large uncertainties which hamper informed decision-making and cost society a chance to adapt proactively and effectively. These uncertainties stem from deficiencies in predictions of future greenhouse gas emissions, but also from inaccuracies in the representation of the physical models used to predict the climate response to such emissions. The uncertainties in projections associated with the inaccurate representation of climate physics, chemistry and biology are similar to those that plagued the first global climate models developed fifty years ago, despite more than a factor 10 8 increase in computer performance. Our transformational question is then, how can the accuracy of climate projections be dramatically improved by applying recent advances in the computational and data sciences to train the models with the wealth of data being constantly collected about the ongoing changes in the climate system?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Arctic Critical Infrastructure: Assessing and Predicting the Risk to Critical Permafrost Infrastructure from Climate Change: A New Thermomechanical Approach

This study presents the development of a computational framework designed to predict the interaction between permafrost and infrastructure, addressing potential failure modes and mitigation strategies in the context of climate change. The framework, rooted in advanced modeling and simulation (mod/sim) techniques, integrates thermomechanical coupling to account for the complex interplay between heat flow, ice content, and mechanical behavior in permafrost. Existing models fail to fully capture these dynamics, particularly as they relate to the effects of ice saturation on structural integrity. Our innovative Arctic Coastal Erosion (ACE) framework fills this gap by coupling thermal and mechanical models to accurately simulate subsidence and deformation in permafrost environments. We applied the ACE framework to a representative runway, demonstrating its capability to predict settlement due to rising temperatures and subsequent permafrost thaw. This proof-of-concept showcases the potential of the framework to evaluate risks to Arctic infrastructure, which supports over four million people and 70% of existing permafrost-based structures. By simulating various infrastructure types and environmental conditions, our research offers insights into failure mechanisms and evaluates structural solutions to mitigate risk. The anticipated deliverables, including a prototype runway exemplar, position this project as a critical advancement in permafrost infrastructure modeling, with applications in national security and resilience planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography

Abstract Wetlands store 20–30% of the world’s soil carbon, and identifying the microbial controls on these carbon reserves is essential to predicting feedbacks to climate change. Although viral infections likely play important roles in wetland ecosystem dynamics, we lack a basic understanding of wetland viral ecology. Here 63 viral size-fraction metagenomes (viromes) and paired total metagenomes were generated from three time points in 2021 at seven fresh- and saltwater wetlands in the California Bodega Marine Reserve. We recovered 12,826 viral population genomic sequences (vOTUs), only 4.4% of which were detected at the same field site two years prior, indicating a small degree of population stability or recurrence. Viral communities differed most significantly among the seven wetland sites and were also structured by habitat (plant community composition and salinity). Read mapping to a new version of our reference database, PIGEONv2.0 (515,763 vOTUs), revealed 196 vOTUs present over large geographic distances, often reflecting shared habitat characteristics. Wetland vOTU microdiversity was significantly lower locally than globally and lower within than between time points, indicating greater divergence with increasing spatiotemporal distance. Viruses tended to have broad predicted host ranges via CRISPR spacer linkages to metagenome-assembled genomes, and increased SNP frequencies in CRISPR-targeted major tail protein genes suggest potential viral eco-evolutionary dynamics in response to both immune targeting and changes in host cell receptors involved in viral attachment. Together, these results highlight the importance of dispersal, environmental selection, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography.

Environmental Sciences & Ecology↗
NASA NTRS1994

Regional climates in the GISS general circulation model: Surface air temperature

One of the more viable research techniques into global climate change for the purpose of understanding the consequent environmental impacts is based on the use of general circulation models (GCMs). However, GCMs are currently unable to reliably predict the regional climate change resulting from global warming, and it is at the regional scale that predictions are required for understanding human and environmental responses. Regional climates in the extratropics are in large part governed by the synoptic-scale circulation and the feasibility of using this interscale relationship is explored to provide a way of moving to grid cell and sub-grid cell scales in the model. The relationships between the daily circulation systems and surface air temperature for points across the continental United States are first developed in a quantitative form using a multivariate index based on principal components analysis (PCA) of the surface circulation. These relationships are then validated by predicting daily temperature using observed circulation and comparing the predicted values with the observed temperatures. The relationships predict surface temperature accurately over the major portion of the country in winter, and for half the country in summer. These relationships are then applied to the surface synoptic circulation of the Goddard Institute for Space Studies (GISS) GCM control run, and a set of surface grid cell temperatures are generated. These temperatures, based on the larger-scale validated circulation, may now be used with greater confidence at the regional scale. The generated temperatures are compared to those of the model and show that the model has regional errors of up to 10 C in individual grid cells.

Hewitson, Bruce↗
DOE OSTI2022

Giant kelp genetic monitoring before and after disturbance reveals stable genetic diversity in Southern California

Given the impacts of climate change and other anthropogenic stressors on marine systems, there is a need to accurately predict how species respond to changing environments and disturbance regimes. The use of genetic tools to monitor temporal trends in populations gives ecologists the ability to estimate changes in genetic diversity and effective population size that may be undetectable by traditional census methods. Although multiple studies have used temporal genetic analysis, they usually involve commercially important species, and rarely sample before and after disturbance. In this study, we run a temporal analysis of giant kelp, Macrocystis pyrifera , genetic diversity over the scope of 10 years (2008-2018) using the same microsatellite marker panel to assess the genetic consequences of disturbance in several populations of giant kelp ( Macrocystis pyrifera ) in the Southern California Bight. The study is a rare pre- and post-disturbance microsatellite analysis that included declines to giant kelp caused by the 2015/16 El Nino Southern Oscillation event. We used canopy biomass estimated by remote sensing (Landsat) to quantify the extent of disturbance to kelp beds, and sea surface temperature data to understand how kelp was pushed towards its temperature limits during this period. Despite prolonged periods with decreased canopy at several sites, no changes in genetic structure and allelic richness were observed. We argue that giant kelp in the region is best described as a “patchy population” system where true extinctions are rare. We discuss how deep refugia of subsurface sporophytes and cryptic microscopic life stages could have kept genetic diversity through disturbance. Given the increasing effects of climate change and uncertainty in modeling impacts of species with cryptic life history stages, we suggest further investigation to reveal the role such stages play in species resilience. Genetic monitoring studies of sites selected by remote census demographic and climate surveys should be continued in the future given the predicted impacts of climate change.

Klingbeil, III, William H.↗