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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Climate Anomalies Correlation with Population Trends of the Eastern Towhee in the Mid-Eastern U.S.

The species of sparrow identified by foraging under dense thickets, the Eastern Towhee (Pipilo erythrophthalmus) resides in the Eastern United States, some populations either permanently living in the southeastern US or migratory populations living in the North. The mostly migratory Eastern Towhee has been experiencing population changes since the 1980s in the Eastern Seaboard. This species is increasing during the winter and decreasing during the summer, which is their breeding season. The Christmas Bird Count shows that in the winter, the Eastern Towhee populations have been increasing in their northern wintering range by 0.2625% in Maryland, 0.056% in West Virginia, 0.133% in New Jersey, 0.049% in Virginia, and 0.224% in Delaware from 1984-2019. The Breeding Bird Survey Data shows that in the summer, the Eastern Towhee populations have been declining in the Eastern United States by -4.0635% in Maryland, -3.073% in West Virginia, -15.0722% in New Jersey, -3.9375% in Virginia, and -6.3735% in Delaware from 1984-2015. The average climate anomalies in June range from -0.108 to 0.3705 degrees Celsius while the average climate anomalies in January range from 0.1395 to 0.5014 degrees Celsius. The Eastern Towhee are moving northward towards their northern wintering range during the winter and we explore the hypothesis of northward range shift in association with climate change. Our research displays how the behavior patterns of species are varying due to this stressor and how to better conserve species like the Eastern Towhee.

Mahlet Mesfin↗
NASA NTRSDate not supplied

20-Years of Atmospheric Temperature, Water Vapor, Cloud, and Surface Temperature Anomalies and Trends Derived From Operational Hyperspectral Ir Sounders

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The SiFSAP retrieval algorithm also uses the same climatology a priori and associated covariances, which makes it ideal for generating high quality products for both weather and climate applications. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRSDate not supplied

Two Decades of Atmospheric and Surface Temperature, Water Vapor, and Cloud Trends Derived from Satellite Remote Sensors

Satellite remote sensor such as Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

remote sensing↗
NASA NTRS2024

Northern Brazil Agriculture: Measuring Soybean Yields in Northern Brazil During El Niño-Southern Oscillation Conditions to Evaluate Trends in Agricultural Production and Support Crop Forecasting, 1984 – 2023

As one of the largest agricultural exporters in the world, Brazil’s crop productivity is highly influential to the world’s food supply. Crop productivity across Brazil is heavily influenced by climatic factors, such as the El Niño-Southern Oscillation (ENSO), which drives spatially heterogenous effects on growing conditions. To understand the relationship between ENSO conditions and staple crop productivity, the team used 1984 – 2023 climatic data and vegetation indices for four Brazilian states: Bahia, Mato Grosso, Pará, and Tocantins. The team focused on soybean growing areas in each state derived from annual land use/land cover classifications. Monthly mean Normalized Difference Vegetation Index (NDVI) per state were calculated using multispectral imagery from the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imagery (OLI) sensors. In addition, the team used crop production indices, ENSO anomalies, temperature, and precipitation data in this study. The team found that although monthly ENSO anomaly did have a positive relationship between temperature and a slight negative relationship with precipitation, ENSO and monthly soy NDVI were not significantly correlated. Results find no association between ENSO conditions and NDVI in the study area at the state level spatial resolution during the study period. Furthermore, the team found that there was no correlation between cumulative growing season soy NDVI and detrended soy production yield for this study region at the spatial and temporal scale of the analysis. Findings from this project will be used by the United States Department of Agriculture’s Foreign Agriculture Service to inform crop productivity forecasting.

remote sensing↗
NASA NTRSDate not supplied

Uneven Inflation Load Share Trends in Clusters

The use of parachute clusters for payload recovery is still seeing widespread use ever since the early days of WWII. By involving the (near) simultaneous deployment of several smaller and identical canopies connected to the payload, cluster systems offer flexibility in tailoring to needed descent rates and load management, as well as providing robustness against individual canopy deployment or opening failure. Their downside, of course, resides in the possibility of differing inflation rates by each cluster member as caused by deployment variability, canopy-to-canopy interference, etc. Such variability leads to the lead-lag phenomenon, which causes uneven loading among the parachutes, often times leaving a single canopy to take up a significant portion of the system’s inflation loads. Herein we investigate how serious such an effect can be in terms of the number N of cluster members, underinflation drag of the lagging canopies, inflation swiftness of the leader in comparison to the laggards’, disreefing cutter activation staggering and pre-disreefing drag area. Two new metrics are used to highlight load share unevenness, namely, the peak and average leader canopy riser load in comparison to the leader’s drag during no-lead-lag; and leader peak load, as compared to total peak load. Results are calculated from data collected in NASA’s Orion/CPAS test program, as well as from simple algebraic expressions informing leader drag as sustained in different cluster systems (i.e., of different N) and varying leader inflation time relative to the laggards. Generally, using large-N cluster systems confers better load sharing among canopies. However, and in deployments where significant lead-lag occur, large-N systems may feature greater leader overload excursions, oftentimes in excess of 50% the no-lead-lag levels. These excursions are particularly made worse when cutter activation among the members are far from simultaneous and the laggards’ pre-disreefing drag area is small in comparison to the leader’s.

parachutes↗
NASA NTRSDate not supplied

Trends in Human Spaceflight: Analysis of Observed Propulsion Failure Modes Following NASA’s Artemis I Mission

In 2022, NASA’s Artemis program returned the agency to lunar space, propelled by the Orion European Service Module (ESM). Through rigorous ground test campaigns for Artemis I and II and the collection and transmission of operational data during the Artemis I flight, the Safety and Mission Assurance (S&MA) team created and maintained a robust database of component nonconformances. This study analyzes in-flight and ground processing findings on the Artemis ESM propulsion subsystem to track reliability and safety metrics, as well as comparing the data to that of previous human spaceflight programs, Apollo and Space Shuttle. In drawing a comparison to the Artemis I flight, recommendations can be made for the Artemis II crewed mission to ensure best practices for flight safety, including redundancy and failure tolerance in the system. The results reinforce a need for robust safety standards and caution against the complacency of a single successful test flight.

human spaceflight safety↗
NASA NTRS2024

Narragansett Bay Water Resources: Using Earth Observations to Identify Trends in Harmful Algal Blooms in Narragansett Bay

Narragansett Bay in Rhode Island is known for its quahog, or hard-shell clam, shellfisheries. However, increased levels in harmful algal blooms (HABs) and high phytoplankton biomass events pose threats to quahog populations, creating conditions that limit quahog growth and reproduction. Quahog shortages, along with public health concerns associated with contamination of shellfish from HAB-produced neurotoxins, have contributed to shellfishery closures. NASA DEVELOP partnered with the Environmental Protection Agency’s National Health and Environmental Effects Research Laboratory and Rhode Island’s Department of Environmental Management to use Earth observations and the partners’ in-situ data to visualize phytoplankton bloom events within the bay. We used Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 and 9 Operational Land Imager (OLI)data to track several proxies of phytoplankton biomass from June 2016 to October 2023. Multiple sensors and in situ datasets were used to assess the feasibility of monitoring phytoplankton biomass accurately in the relatively small sized Narragansett Bay. We determined that Phytoplankton fluorescence line height was the best parameter to monitor phytoplankton biomass in the bay. Although the relatively small size of the bay and the optical complexity of these nearshore waters can pose a challenge, this assessment showed that Earth observations can be a useful complement to the in-situ monitoring of phytoplankton biomass in Narragansett Bay.

Sentinel-3↗