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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comment on “Five Decades of Observed Daily Precipitation Reveal Longer and More Variable Drought Events Across Much of the Western United States”

Abstract Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Global Precipitation Trends across Spatial Scales Using Satellite Observations

Little dispute surrounds the observed global temperature changes over the past decades. As a result, there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in precipitation across spatial scales. We show that warm climate regions exhibit decreasing precipitation trends, while arid and polar climate regions show increasing trends. At the country scale, precipitation seems to have increased in 96 countries, and decreased in 104. We also explore precipitation changes over 237 global major basins. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset, PERSIANN-CDR, used in this study.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

MUlti-SpEctral, MUlti-SpEcies, MUlti-SatEllite (MUSES) Retrieval Algorithm: Towards Extending Multi-Decadal NASA EOS Atmospheric Composition Data Records

Multi-Spectra, Multi-Species, Multi-Sensors (MUSES): Builds off of heritage from the Tropospheric Emission Spectrometer (TES) optimal estimation (OE) algorithm to combine a priori and satellite data, including rigorous error analysis diagnostics and observation operators needed for trend analysis, climate model evaluation, and data assimilation; has generic design to incorporate forward model radiances from hyperspectral measurements from multiple sensors into the joint retrieval algorithm.

Fu, Dejian↗
NASA NTRS2012

Changes in Extratropical Storm Track Cloudiness 1983-2008: Observational Support for a Poleward Shift

Climate model simulations suggest that the extratropical storm tracks will shift poleward as a consequence of global warming. In this study the northern and southern hemisphere storm tracks over the Pacific and Atlantic ocean basins are studied using observational data, primarily from the International Satellite Cloud Climatology Project, ISCCP. Potential shifts in the storm tracks are examined using the observed cloud structures as proxies for cyclone activity. Different data analysis methods are employed, with the objective to address difficulties and uncertainties in using ISCCP data for regional trend analysis. In particular, three data filtering techniques are explored; excluding specific problematic regions from the analysis, regressing out a spurious viewing geometry effect, and excluding specific cloud types from the analysis. These adjustments all, to varying degree, moderate the cloud trends in the original data but leave the qualitative aspects of those trends largely unaffected. Therefore, our analysis suggests that ISCCP data can be used to interpret regional trends in cloudiness, provided that data and instrumental artefacts are recognized and accounted for. The variation in magnitude between trends emerging from application of different data correction methods, allows us to estimate possible ranges for the observational changes. It is found that the storm tracks, here represented by the extent of the midlatitude-centered band of maximum cloud cover over the studied ocean basins, experience a poleward shift as well as a narrowing over the 25 year period covered by ISCCP. The observed magnitudes of these effects are larger than in current generation climate models (CMIP3). The magnitude of the shift is particularly large in the northern hemisphere Atlantic. This is also the one of the four regions in which imperfect data primarily prevents us from drawing firm conclusions. The shifted path and reduced extent of the storm track cloudiness is accompanied by a regional reduction in total cloud cover. This decrease in cloudiness can primarily be ascribed to low level clouds, whereas the upper level cloud fraction actually increases, according to ISCCP. Independent satellite observations of radiative fluxes at the top of the atmosphere are consistent with the changes in total cloud cover. The shift in cloudiness is also supported by a shift in central position of the mid-troposphere meridional temperature gradient. We do not find support for aerosols playing a significant role in the satellite observed changes in cloudiness. The observed changes in storm track cloudiness can be related to local cloud-induced changes in radiative forcing, using ERBE and CERES radiative fluxes. The shortwave and the longwave components are found to act together, leading to a positive (warming) net radiative effect in response to the cloud changes in the storm track regions, indicative of positive cloud feedback. Among the CMIP3 models that simulate poleward shifts in all four storm track areas, all but one show decreasing cloud amount on a global mean scale in response to increased CO2 forcing, further consistent with positive cloud feedback. Models with low equilibrium climate sensitivity to a lesser extent than higher-sensitivity models simulate a poleward shift of the storm tracks.

Bender, Frida A-M.↗
NASA NTRS1993

Nimbus-7 Earth radiation budget calibration history. Part 1: The solar channels

The Earth Radiation Budget (ERB) experiment on the Nimbus-7 satellite measured the total solar irradiance plus broadband spectral components on a nearly daily basis from 16 Nov. 1978, until 16 June 1992. Months of additional observations were taken in late 1992 and in 1993. The emphasis is on the electrically self calibrating cavity radiometer, channel 10c, which recorded accurate total solar irradiance measurements over the whole period. The spectral channels did not have inflight calibration adjustment capabilities. These channels can, with some additional corrections, be used for short-term studies (one or two solar rotations - 27 to 60 days), but not for long-term trend analysis. For channel 10c, changing radiometer pointing, the zero offsets, the stability of the gain, the temperature sensitivity, and the influences of other platform instruments are all examined and their effects on the measurements considered. Only the question of relative accuracy (not absolute) is examined. The final channel 10c product is also compared with solar measurements made by independent experiments on other satellites. The Nimbus experiment showed that the mean solar energy was about 0.1 percent (1.4 W/sqm) higher in the excited Sun years of 1979 and 1991 than in the quiet Sun years of 1985 and 1986. The error analysis indicated that the measured long-term trends may be as accurate as +/- 0.005 percent. The worse-case error estimate is +/- 0.03 percent.

Kyle, H. Lee↗
DOE OSTI2017

Parallel peak pruning for scalable SMP contour tree computation

As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Here in this paper, we report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Contour Tree Computation by Data Parallel Peak Pruning

As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Furthermore, we report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg V lg t) parallel steps and O(V lg V) work for data with V samples and t contour tree supernodes, and implementations with more than 30× parallel speed up on both CPU using TBB and GPU using Thrust and up 70× speed up compared to the serial sweep and merge algorithm.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Analysis of launch site processing effectiveness for the Space Shuttle 26R payload

A trend analysis study has been performed on problem reports recorded during the Space Shuttle 26R payload's processing cycle at NASA-Kennedy, using the defect-flow analysis (DFA) methodology; DFA gives attention to the characteristics of the problem-report 'population' as a whole. It is established that the problem reports contain data which distract from pressing problems, and that fully 60 percent of such reports were caused during processing at NASA-Kennedy. The second major cause of problem reports was design defects.

Flores, Carlos A.↗
NASA NTRS1981

Review of AIDS development

The operation and implementation of the aircraft integrated data system AIDS are described. The system is described as an engineering tool with strong emphasis on analysis of recorded information. The AIDS is primarily directed to the monitoring of parameters related to: the safety of the flight; the performance of the aircraft; the performance of the flight guidance system; and the performance and condition of the engines. The system provide short term trend analysis on a trend chart that is updated by the flight engineer on every flight that lasts more than 4 flight hours. Engine data prints are automatically presented during take-off and in the case of limit excedance, e.g., the print shows an automatically reported impending hotstarts on engine nr. 1. Other significant features are reported.

Vermeulen, H. C.↗
NASA NTRS2016

Natural and Anthropogenic Aerosol Trends from Satellite and Surface Observations and Model Simulations over the North Atlantic Ocean from 2002 to 2012

Aerosols contribute to Earths radiative budget both directly and indirectly, and large uncertainties remain in quantifying aerosol effects on climate. Variability in aerosol distribution and properties, as might result from changing emissions and transport processes, must be characterized. In this study, variations in aerosol loading across the eastern seaboard of theUnited States and theNorthAtlanticOcean during 2002 to 2012 are analyzed to examine the impacts of anthropogenic emission control measures using monthly mean data from MODIS, AERONET, and IMPROVE observations and Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulation.MODIS observes a statistically significant negative trend in aerosol optical depth (AOD) over the midlatitudes (-0.030 decade(sup-1)). Correlation analyses with surface AOD from AERONET sites in the upwind region combined with trend analysis from GOCART component AOD confirm that the observed decrease in the midlatitudes is chiefly associated with anthropogenic aerosols that exhibit significant negative trends from the eastern U.S. coast extending over the western North Atlantic. Additional analysis of IMPROVE surface PM(sub 2.5) observations demonstrates statistically significant negative trends in the anthropogenic components with decreasing mass concentrations over the eastern United States. Finally, a seasonal analysis of observational datasets is performed. The negative trend seen by MODIS is strongest during spring (MAM) and summer (JJA) months. This is supported by AERONET seasonal trends and is identified from IMPROVE seasonal trends as resulting from ammonium sulfate decreases during these seasons.

Jongeward, Andrew R.↗
NASA NTRS2012

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was ~0.2 during 1986-2010. A higher area-averaged LAI change (~0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.

McManus, K. M.↗
NASA NTRS2025

Jemez Pueblo Agriculture: Monitoring Rangeland Conditions to Inform Drought and Land Management in New Mexico

The Jemez Pueblo, a community located in the semi-arid high desert region of northcentral New Mexico, faces challenges with drought mitigation and livestock management. NASA DEVELOP partnered with the Pueblo of Jemez Natural Resources Department and The Nature Conservancy to provide recent and historical rangeland conditions to aid present decision-making, which relies on cultural practices, site familiarity, available resources, and physical data. This partnership offers the opportunity to support management practices by leveraging remote sensing and virtual fencing technologies. Our project explored Rangeland Analysis Platform (RAP) products incorporating NASA Earth observation Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 remote-sensed imagery as an agricultural land management tool. We performed linear regression analysis to evaluate RAP data to field data and explored RAP annual herbaceous biomass, ground cover trends, and climatic factors at the pasture level. We then computed study area summary statistics, performed pixel-by-pixel trend analysis across the study period, and evaluated ground cover trends near water sources. Based on available data, RAP was poorly correlated to bare ground and herbaceous biomass, generally underestimating the former and overestimating the latter. To improve the evaluation of RAP products, we suggest increasing the spatiotemporal distribution of the field dataset. According to RAP, from 1986 to 2023, the site became hotter and drier, bare ground increased, and annual forbs and grasses increased while perennials decreased. RAP products did not agree with field-collected data; however, the trends over time may be useful for informing rangeland management actions.

cattle grazing management↗
DOE OSTI2011

Reliability of EUCLIDIAN: An autonomous robotic system for image-guided prostate brachytherapy

Purpose: Recently, several robotic systems have been developed to perform accurate and consistent image-guided brachytherapy. Before introducing a new device into clinical operations, it is important to assess the reliability and mean time before failure (MTBF) of the system. In this article, the authors present the preclinical evaluation and analysis of the reliability and MTBF of an autonomous robotic system, which is developed for prostate seed implantation. Methods: The authors have considered three steps that are important in reliability growth analysis. These steps are: Identification and isolation of failures, classification of failures, and trend analysis. For any one-of-a-kind product, the reliability enhancement is accomplished through test-fix-test. The authors have used failure mode and effect analysis for collection and analysis of reliability data by identifying and categorizing the failure modes. Failures were classified according to severity. Failures that occurred during the operation of this robotic system were considered as nonhomogenous Poisson process. The failure occurrence trend was analyzed using Laplace test. For analyzing and predicting reliability growth, commonly used and widely accepted models, Duane's model and the Army Material Systems Analysis Activity, i.e., Crow's model, were applied. The MTBF was used as an important measure for assessing the system's reliability. Results: During preclinical testing, 3196 seeds (in 53 test cases) were deposited autonomously by the robot and 14 critical failures were encountered. The majority of the failures occurred during the first few cases. The distribution of failures followed Duane's postulation as well as Crow's postulation of reliability growth. The Laplace test index was -3.82 (<0), indicating a significant trend in failure data, and the failure intervals lengthened gradually. The continuous increase in the failure occurrence interval suggested a trend toward improved reliability. The MTBF was 592 seeds, which implied that several prostate seed implantation cases would be possible without encountering any critical failure. The shape parameter for the MTBF was 0.3859 (<1), suggesting a positive reliability growth of this robotic system. At 95% confidence, the reliability for deposition of 65 seeds was more than 90%. Conclusions: Analyses of failure mode strongly indicated a gradual improvement of reliability of this autonomous robotic system. High MTBF implied that several prostate seed implant cases would be possible without encountering any critical failure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Network Intrusion Detection and Visualization using Aggregations in a Cyber Security Data Warehouse

The challenge of achieving situational understanding is a limiting factor in effective, timely, and adaptive cyber-security analysis. Anomaly detection fills a critical role in network assessment and trend analysis, both of which underlie the establishment of comprehensive situational understanding. To that end, we propose a cyber security data warehouse implemented as a hierarchical graph of aggregations that captures anomalies at multiple scales. Each node of our pro-posed graph is a summarization table of cyber event aggregations, and the edges are aggregation operators. The cyber security data warehouse enables domain experts to quickly traverse a multi-scale aggregation space systematically. We describe the architecture of a test bed system and a summary of results on the IEEE VAST 2012 Cyber Forensics data.

anomaly detection↗
NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Snow depth↗
NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° 3 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the upper Great Plains and Northeast to -1 to -9 mm/yr in the West and South, net radiation flux trends range from +0.05 to +0.20 W/sq. m yr in the East to -0.05 to -0.20 W/sq. m yr in the West, and U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W/sq. m yr, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Michael F. Jasinski↗
NASA NTRS2010

Characterizing DSN System Frequency Stability with Spacecraft Tracking Data

This paper describes a recent effort in characterizing frequency stability performance of the ground system in the NASA Deep Space Network (DSN). Unlike the traditional approach where performance is obtained from special calibration sessions that are both time consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This method significantly increases the amount of data available for analysis, roughly by two orders of magnitude; making it possible to conduct trend analysis with reasonable confidence. Since the system is monitored daily, any significant variation in performance can be detected timely. This helps the DSN maintain its performance commitment to customers.

performance analysis↗
NASA NTRS1988

An expert system for satellite and instrument data anomaly and fault isolation

A prototype Generic Payload Operations Control System (GPOCC) is being developed at the NASA Jet Propulsion Laboratory to provide a low-cost command and control processing center for science instruments and small payloads. The GPOCC supports the difficult transition from integration and test to flight operations. The prototype will incorporate four expert systems to perform telemetry, command, and mission planning functions as well as telecommunications scheduling. The first of these expert systems to be developed will perform telemetry data analysis and fault isolation, as well as propose corrective action. This Data Analysis Module (DAM) will monitor telemetry data and perform continual data monitoring and trend analysis based on a knowledge base and historic data archived on an optical disk storage device. The system maintains a continuous knowledge database of past system performance characteristics. The goal of the Data Analysis Module is to achieve consistent, dependable and validatable performance, to demonstrate thorough, reliable and fast reasoning, and to reduce the concentration demanded of flight analysis personnel.

Busse, Carl↗