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A general overview of the USGS land remote sensing program is presented. The contents include: 1) Brief overview of USGS land remote sensing program; 2) Highlights of JACIE work at USGS; 3) Update on NASA/USGS Landsat Data Continuity Mission; and 4) Notes on alternative data sources.
An assessment was made of the needs of a group of potential users of satellite remotely sensed data (state, regional, and local agencies) involved in natural resources management in five states, and alternative data management systems to satisfy these needs are outlined. Tasks described include: (1) a comprehensive data needs analysis of state and local users; (2) the design of remote sensing-derivable information products that serve priority state and local data needs; (3) a cost and performance analysis of alternative processing centers for producing these products; (4) an assessment of the impacts of policy, regulation and government structure on implementing large-scale use of remote sensing technology in this community of users; and (5) the elaboration of alternative institutional arrangements for operational Earth Observation Data Management Systems (EODMS). It is concluded that an operational EODMS will be of most use to state, regional, and local agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products.
State, local, and regional agencies involved in natural resources management were investigated as potential users of satellite remotely sensed data. This group's needs are assessed and alternative data management systems serving some of those needs are outlined. It is concluded that an operational earth observation data management system will be of most use to these user agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products.
This data package contains raw gas chromatography–mass spectrometry (GC-MS) data for isoprene/monoterpene emission rates used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. The attached data files require use of the Agilent MassHunter software. Alternatively, data has been exported into Excel files and can be accessed at [DOI]. For further information on how to Raw isoprene and monoterpene emission rates data
The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO 2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO 2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO 2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.
ROOT is planning to move from TTree to RNTuple as the data storage format for HL-LHC in order to, for example, speed up the IO, make the files smaller, and have a modern C++ API. Initially, RNTuple was not planned to support the same set of C++ data structures as TTree supports. CMS has explored the necessary transformations in its standard persistent data types to switch to RNTuple. Many challenges were encountered as alternative data structures were explored. This contribution will discuss the challenges uncovered and how collaboration with the ROOT team allowed them to be overcome. The solution to the challenges allows progressive changes to the CMS data types rather than requiring a sudden change to all data types to be stored in RNTuple. Once the solution was achieved, storage performance comparisons using the CMS data types were possible between RNTuple and TTree. This contribution will also present the results of those comparisons.
The goal of this exploratory study was to investigate alternative data sources and develop methodologies to estimate trends and adjustment factors that could be applied to calibrate statistics generated from the outdated 2002 VIUS. The main focus of this study was on utilizing the FHWA’s traffic data (i.e., VTRIS and TMAS), along with other supplemental freight data (i.e., FAF and CFS), to examine changes in truck volumes and truck weight distributions over time. Ultimately, this effort seeks to develop certain adjustments/factors that could be used to realign perspective VIUS data. Unfortunately, as pointed out previously in this technical memorandum, there are no readily available data sources that could be used to explicitly validate the estimated results from this study. This study compared the estimated distributions from the 2002 VTRIS to the 2002 VIUS data to establish linkages between the two datasets. With distributions and patterns generated based on VTRIS and TMAS data series for additional years, trend factors were then estimated and applied to “update” VIUS-based information. This study resulted in three key highlights: (1) Updated estimates on truck volume and truck VMT can be utilized to better estimate on energy uses by truck. (2) Vehicle class distribution, percent/share of empty trucks, and average truck payload could be updated by utilizing more current annual data series such as VTRIS and TMAS. (3) Additional information could be estimated (e.g., distance distribution by operational type) by integrating other data sources, such as CFS and FAF, instead of relying on the 2002 VIUS data.
The accuracy of the current Wallops Flight Facility (WFF) data smoothing techniques for a variety of radars and payloads is examined. Alternative data reduction techniques are given and recommendations are made for improving radar data processing at WFF. A data adaptive algorithm, based on Kalman filtering and smoothing techniques, is also developed for estimating payload trajectories above the atmosphere from noisy time varying radar data. This algorithm is tested and verified using radar tracking data from WFF.
A statistical procedure to assess level-II continental resources using Landsat MSS digital data is presented. The statistical procedure involves a two-stage cluster sample within a stratified random sample. The utility of this procedure is assessed by using it to estimate the areal extent of the conifer and hardwood resources of the continental U.S. National estimates of conifer and hardwood derived using this sampling procedure were within 3 percent of U.S. Forest Service (USFS) figures. According to the Landsat-based study, 11 percent of the country is conifer forest and 12 percent is hardwood. The corresponding USFS figures are 13 and 15 percent, respectively. Comparison of the MSS classification products and airphotos showed that the conifer cover class was correctly identified 74 percent of the time and hardwood 80 percent of the time. The average classification accuracy countrywide for the four cover types considered (conifer, hardwood, water, and 'other') is 74 percent, and the overall accuracy is 85 percent. The statistical procedure provides a method of incorporating Landsat MSS digital data as a second state for level-II continental resource assessment. Alternate data sources, e.g., satellite and aircraft photographic imagery, may also be used in conjunction with this statistical model.
One of the major obstacles in identifying and interpreting a trend is the small number of data points. Future trending reports will begin with 1983 data. As the problem/failure data are aggregated by year, there are just seven observations (1983 to 1989) for the 1990 reports. Any statistical inferences with a small amount of data will have a large degree of uncertainty. Consequently, a regression technique approach to identify a trend is limited. Though trend determination by failure mode may be unrealistic, the data may be explored for consistency or stability and the failure rate investigated. Various alternative data analysis procedures are briefly discussed. Techniques that could be used to explore problem/failure data by failure mode are addressed. The data used are taken from Section One, Space Shuttle Main Engine, of the Calspan Quarterly Report dated April 2, 1990.
The NASA Langley Surface Radiation Budget (SRB) project, historically associated with GEWEX, has provided estimates of surface and TOA shortwave and longwave radiative fluxes using International Satellite Cloud Climatology Project (ISCCP) cloud properties and radiances as primary input. The first release was in 1995 (Whitlock et al., 1995) at daily 2.5° resolution. Subsequent releases have improved physics, temporal and spatial resolution, as well as increasing the total length of the time series. Release 4 in 2019 (Kummerow et al., 2019) is on a 1° nested grid, with temporal resolutions from 3-hourly to daily and monthly. It originally spanned from July 1983 to December 2017 and has since been extended through 2019. Release 4 uses the ISCCP H series of data, a complete reprocessing which retains more pixels than the previous D series, used in prior SRB versions. SRB data has been used extensively by a wide range of customers, including but not limited to the solar energy community, climate modelers, architects, oceanographers, and the agricultural community. It is an especial feature of SRB that global radiation budget data are available from the 1983 through 2000 period, which is prior to CERES providing alternate data, and then 2000 through near present to provide overlap coverage. Here we use the 36.5 year record of SRB Release 4 to examine the changes in shortwave and longwave fluxes over the period, including the changes in different regions of the globe. We examine energy budget features such as hemispheric symmetry and implied meridional transport of energy. We look at the variability of these features prior to and during the CERES period and compare to later products. We also look at Release 5, currently under development. This version will provide 0.5° resolution. In addition to surface and TOA radiative fluxes it will include fluxes at several atmospheric layers, and some spectral features beyond the broadband shortwave and longwave fluxes in Release 4 and prior. The shortwave algorithm is undergoing a complete change from the Pinker-Laszlo algorithm. Shortwave fluxes will now be calculated from a forward call to the Fu-Liou radiative transfer model as modified by the CERES team (Rose et al., 2006). Input datasets are being improved, including surface properties, atmospheric profiles of temperature and humidity, aerosols, and total solar irradiance. Preliminary results from Release 5 and comparisons to Release 4 will be shown.
The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.
An expert system is described for monitoring and controlling navigation processes in real-time. The ART-based system features data-driven computation, accommodation of synchronous and asynchronous data, temporal modeling for individual time intervals and chains of time intervals, and hypothetical reasoning capabilities that consider alternative interpretations of the state of navigation processes. The concept is illustrated in terms of the NAVEX system for monitoring and controlling the high speed ground navigation console for Mission Control at Johnson Space Center. The reasoning processes are outlined, including techniques used to consider alternative data interpretations. Installation of the system has permitted using a single operator, instead of three, to monitor the ascent and entry phases of a Shuttle mission.
Premise Hybrid capture with high‐throughput sequencing (Hyb‐Seq) is a powerful tool for evolutionary studies. The applicability of an Asteraceae family‐specific Hyb‐Seq probe set and the outcomes of different phylogenetic analyses are investigated here. Methods Hyb‐Seq data from 112 Asteraceae samples were organized into groups at different taxonomic levels (tribe, genus, and species). For each group, data sets of non‐paralogous loci were built and proportions of parsimony informative characters estimated. The impacts of analyzing alternative data sets, removing long branches, and type of analysis on tree resolution and inferred topologies were investigated in tribe Cichorieae. Results Alignments of the Asteraceae family‐wide Hyb‐Seq locus set were parsimony informative at all taxonomic levels. Levels of resolution and topologies inferred at shallower nodes differed depending on the locus data set and the type of analysis, and were affected by the presence of long branches. Discussion The approach used to build a Hyb‐Seq locus data set influenced resolution and topologies inferred in phylogenetic analyses. Removal of long branches improved the reliability of topological inferences in maximum likelihood analyses. The Astereaceae Hyb‐Seq probe set is applicable at multiple taxonomic depths, which demonstrates that probe sets do not necessarily need to be lineage‐specific.
Modifications to the edge crack torsion (ECT) test are studied to improve the reliability of this test for measuring the mode-III fracture toughness, G (sub IIIc), of laminated tape fiber-reinforced polymeric (FRP) composites. First, the data reduction methods currently used in the ECT test are evaluated and deficiencies in their accuracy are discussed. An alternative data reduction technique, which uses a polynomial form to represent ECT specimen compliance solution, is evaluated and compared to FEA (finite element analysis) results. Second, seven batches of ECT specimens are tested, each batch containing specimens with a preimplanted midplane edge delamination and midplane plies with orientations of plus theta divided by minus theta, with theta ranging from 0 degrees to 90 degrees in 15-degree increments. Tests on these specimens show that intralaminar cracking occurs in specimens from all batches except for which theta = 15 degrees and 30 degrees. Tests on specimens of these two batches are shown to result in mode-III delamination growth at the intended ply interface. The findings from this study are encouraging steps towards the use of the ECT test as a standardized method for measuring G (sub IIIc), although further modification to the data reduction method is required to make it suitable for use as part of a standardized test method.
Data to support results obtained in technology assessment studies are presented. Objectives, starting points, and future study tasks are outlined. Key design issues discussed in appendices include: data allocation, transposition network design, fault tolerance and trustworthiness, logic design, processing element of existing components, number of processors, the host system, alternate data base memory designs, number representation, fast div 521 instruction, architectures, and lockstep array versus synchronizable array machine comparison.
The purpose of this paper is to investigate a group of potential users of satellite remotely sensed data - state, local, and regional agencies involved in natural resources management. We assess this group's needs in five states and outline alternative data management systems to serve some of those needs. We conclude that an operational Earth Observation Data Management System (EODMS) will be of most use to these user agencies if it provides a full range of information services - from raw data acquisition to interpretation and dissemination of final information products.
The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.