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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

da Silva, Arlindo↗
NASA NTRS1975

Switch selector processing

The proper program execution of the switch selector commands that provide sequencing signals to the launch vehicle hardware was verified. Correct execution of each step necessary to activate a switch selector command and maintenance of the minimum timing requirements between steps are verified through analysis of a series of 6D/LVDC intermediate timing cases. Nominal and perturbed switch selector feedback conditions are simulated to verify correct processing of all realistic and timing combinations. A command which is in progress can be properly terminated by issuance of a forced reset. This is verified by forcing the conditions which require a forced reset and observing that the proper termination is completed.

Source record↗
NASA NTRS2004

Improvement of the TOPEX and Jason Orbit Time Series: Precision Orbit Determination, Calibration, Validation and Improvement Through the Combined Reduction and Analysis of GPS, SLR, DORIS and Altimeter Data

Orbit error is a major component in the overall error budget of all altimeter satellite missions. Jason-I is no exception and a 1 cm radial orbit accuracy goal has been set, which represents a factor of two improvement over what is currently being achieved for TOPEX/Poseidon (TP). Our current analysis suggests this goal has been met and even improved upon, but the challenge is to be able to continually achieve this high accuracy, verify the performance and characterize and quantify the remaining errors over the lifetime of the mission. The computation, verification and error characterization of such high accuracy orbits requires the reduction and analysis of all available tracking data (GPS, SLR, DORIS and altimeter). Current analysis also indicates the history of TP orbits can be further improved employing new solution strategies developed and tested on Jason-I. Our research focuses on the calibration, validation and improvement of orbit accuracies using all available tracking data including altimetry. We will compute and distribute well centered Jason orbits with an accuracy of better than 1-cm in the radial component. In addition to the orbits themselves, a characterization of the orbit error will be distributed and accumulated as a time series of orbit performance metrics to track anomalies and trends. The long time series of orbit error characterization will enable a better understanding of the remaining orbit errors and its impact on the altimeter data analysis. As part of this research effort we are also significantly improving the current level of TP orbit accuracy, re-computing new high-accuracy TP orbits from the beginning of the TP mission and continuing into the future (as long as TP is healthy). Our funded research effort will result in a complete and consistent time series of improved orbits for both TP and Jason, significantly benefiting the long time series of altimeter data analysis and the TP/Jason dual mission. The resultant high accuracy orbits and the characterization of their error will allow further improvements to the accuracy and overall quality of the altimeter measurement time series making possible further strides in radar altimeter remote sensing.

Luthcke, Scott B.↗
NASA NTRS2019

Trend Quality Ozone from NPP OMPS: The Version 2 Processing

A version 2 processing of data from two ozone monitoring instruments on Suomi NPP, the OMPS nadir ozone mapper and the OMPS nadir ozone profiler, has now been completed. The previously released data were useful for many purposes but were not suitable for use in ozone trend analysis. In this processing, instrument artifacts have been identified and corrected, an improved scattered light correction and wavelength registration have been applied, and soft calibration techniques were implemented to produce a calibration consistent with data from the series of SBUV/2 instruments. The result is a high-quality ozone time series suitable for trend analysis. Total column ozone data from the OMPS nadir mapper now agree with data from the SBUV/2 instrument on NOAA 19 with a zonal average bias of −0.2 % over the 60° S to 60° N latitude zone. Differences are somewhat larger between OMPS nadir profiler and N19 total column ozone, with an average difference of −1.1 % over the 60° S to 60° N latitude zone and a residual seasonal variation of about 2 % at latitudes higher than about 50°. For the profile retrieval, zonal average ozone in the upper stratosphere (between 2.5 and 4 hPa) agrees with that from NOAA 19 within ±3 % and an average bias of −1.1 %. In the lower stratosphere (between 25 and 40 hPa) agreement is within ±3 % with an average bias of +1.1 %. Tropospheric ozone produced by subtracting stratospheric ozone measured by the OMPS limb profiler from total column ozone measured by the nadir mapper is consistent with tropospheric ozone produced by subtracting stratospheric ozone from MLS from total ozone from the OMI instrument on Aura. The agreement of tropospheric ozone is within 10 % in most locations.

OMPS nadir ozone mapper↗
NASA NTRSDate not supplied

Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline

The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.

Paul Horton↗
NASA NTRS1994

Oceanographic results from analysis of ERS-1 altimetry

Large scale dynamic ocean topography and its variations were observed using ERS-1 radar altimeter measurements. The altimeter measurements analyzed are primarily from the ESA ocean product (OPR02) and from the Interim Geophysical Data Records (IGDR) generated by NOAA from the fast delivery (FD) data during the ERS-1 35 day repeat orbit phase. The precise orbits used for the dynamic topography solution are computed using dual satellite crossover measurements from ERS-1 and TOPEX (Topology Ocean Experiment)/Poseidon (T/P) as additional tracking data, and using improved models and constants which are consistent with T/P. Analysis of the ERS-1 dynamic topography solution indicates agreement with the T/P solution at the 5 cm root mean square level, with regional differences as large as 15 cm tide gauges at the 8 to 9 cm level. There are differences between the ERS-1 OPR02 and IGDR determined dynamic topography solutions on the order of 5 cm root mean square. Mesoscale oceanic variability time series obtained using collinear analysis of the ERS-1 altimeter data show good qualitative agreement when compared with the T/P results.

Tapley, B. D.↗
NASA NTRS2016

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results: Cloud Coverage - Part II

This is the second part of a study on how temporal sampling frequency affects satellite retrievals in support of the Deep Space Climate Observatory (DSCOVR) mission. Continuing from Part 1, which looked at Earth's radiation budget, this paper presents the effect of sampling frequency on DSCOVR-derived cloud fraction. The output from NASA's Goddard Earth Observing System version 5 (GEOS-5) Nature Run is used as the "truth". The effect of temporal resolution on potential DSCOVR observations is assessed by subsampling the full Nature Run data. A set of metrics, including uncertainty and absolute error in the subsampled time series, correlation between the original and the subsamples, and Fourier analysis have been used for this study. Results show that, for a given sampling frequency, the uncertainties in the annual mean cloud fraction of the sunlit half of the Earth are larger over land than over ocean. Analysis of correlation coefficients between the subsamples and the original time series demonstrates that even though sampling at certain longer time intervals may not increase the uncertainty in the mean, the subsampled time series is further and further away from the "truth" as the sampling interval becomes larger and larger. Fourier analysis shows that the simulated DSCOVR cloud fraction has underlying periodical features at certain time intervals, such as 8, 12, and 24 h. If the data is subsampled at these frequencies, the uncertainties in the mean cloud fraction are higher. These results provide helpful insights for the DSCOVR temporal sampling strategy.

GEOS-5↗
NASA NTRSDate not supplied

Using Time Series Data Products to Support ASHRAE’s Historic and Future Climate Data Needs through NASA’s POWER Web Services

To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".

time series↗
NASA NTRS1994

Comparison of the seasonal and interannual variability of phytoplankton pigment concentrations in the Peru and California Current systems

Monthly composite images from the global coastal zone color scanner (CZCS) data set are used to provide an initial illustration and comparison of seasonal and interannual variability of phytoplankton pigment concentration along the western coasts of South and North America in the Peru Current system (PCS) and California Current system (CCS). The analysis utilizes the entire time series of available data (November 1978 to June 1986) to form a mean annual cycle and an index of interannual variability for a series of both latitudinal and cross-shelf regions within each current system. Within 100 km of the coast, the strongest seasonal cycles in the CCS are in two regions, one between 34 deg and 45 deg N and the second between 24 deg and 29 deg N, each with maximum concentrations (greater than 3.0 mg m(exp-3)) in May-June. Weaker seasonal variability is present north of 45 deg N and in the Southern California Bight region (32 deg N). Within the PCS, in the same 100-km-wide coastal region, highest (greater than 45 deg S) and lowest (less than 20 deg S) latitude regions have a similar seasonal cycle with maximum concentrations (greater than 1.5 mg m(exp -3)) during the austral spring, summer, and fall, matching that evident throughout the CCS. Between these regions, off northern and central Chile, the seasonal maximum occurs during July-August (austral winter), contrary to the influence of upwelling favorable winds. Within the CCS, the dominant feature of interannual variability in the 8-year time series is a strong negative concentration anomaly in 1983, an El Nino year. The relative value of this negative anomaly is strongest off central California and is followed by an even stronger negative anomaly is strongest off central California and is followed by an even stronger negative anomaly in 1984 off Baja, California. In the PCS, strong negative anomalies during the 1982-1983 El Nino period are evident only off the Peruvian coast and are evident there only in the regions 100 km or more from the coast. Although negative anomalies associated with the El Nino were not present at higher latitudes (more than approximately 20 deg S) in the PCS, the extremely sparse sampling weakens our confidence in the results of the interannual analysis in this region. An upper estimate of the systematic winter bias remaining in the global CZCS data after reprocessing with the multiple scattering algorithm is given in the appendix.

Thomas, A. C.↗
NASA NTRS1998

Decadal Air-Sea Interaction in the North Atlantic Based on Observations and Modeling Results

The decadal, 12-14 year, cycle observed in the North Atlantic SST and tide gauge data was examined using the NCEP/NCAR reanalyses, COADS data and an ocean model simulation. Besides this decadal mode, a shorter, subdecadal period of about 8 years exists in tide gauge data north of 40N, in the subpolar SST and in the winter North Atlantic Oscillation (NAO) index and in subpolar winter heat flux values. The decadal cycle is a well separated mode in a singular spectrum analysis (SSA) for a time series of SST EOF mode 1 with a center over the Gulf Stream extension. Tide gauge and SST data are consistent in that both show a significant subdecadal periodicity exclusively in the subpolar gyre, but in subtropics the 12-14 year period is the prominent, but nonstationary, decadal signal. The main finding of this study is that this 12-14 year cycle can be constructed based on the leading mode of the surface heat flux. This connection to the surface heat flux implicates the participation of the thermohaline circulation in the decadal cycle. During the cycle starting from the positive index phase of NAO, SST and oceanic heat content anomalies are created in subtropics due to local heat flux and intensification of the thermohaline circulation. The anomalies advect to the subpolar gyre where they are amplified by local heat flux and are part of the negative feedback of thermohaline circulation on itself. Consequently the oceanic thermohaline circulation slows down and the opposite cycle starts. The oscillatory nature would not be possible without the active atmospheric participation in the cycle, because it provides the unstable interaction through heat flux, without it, the oceanic mode would be damped. This analysis suggests that the two principal modes of heat flux variability, corresponding to patterns similar to North Atlantic Oscillation (NAO) and Western Atlantic (WA), are part of the same decadal cycle and an indirect measure of the north-south movement of the storm tracks.

Hakkinen, Sirpa↗
NASA NTRS2008

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗
NASA NTRS1982

Ultraviolet observations of interacting binary Be stars

Initial results from the analysis of a series of timed, high resolution IUE observations of HR 2142, phi Per, CX Dra, KX An, AU Mon, and TT Hya are presented. The data base for HR 2142 also includes Copernicus U1 and U2 observations. Variable absorption lines, indicative of mass flow in the system, are observed in all objects except phi Per. Evidence was also found of mass outflow in the form of winds and/or discrete components. Variable N V absorption was observed in CX Dra and AU Mon and emission features in KX And and phi Per (C IV only). U1 data reveals the presence of complex structure in the gas stream in HR 2142. These observations are compared with those of Be stars which are not thought to be interacting binaries.

Peters, G. J.↗
NASA NTRS1989

Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and synergistically-related infectious diseases.

Rochon, Gilbert L.↗
NASA NTRS1992

Catalog-to-catalog reductions - Results for the FK, N30, and GC catalogs

It is shown that previous computations of catalog-to-catalog differences, whether by the bin-and-average method or by least-squares adjustment combined with an expansion in an incomplete set of basis functions, have all been performed at inappropriate epoch(s) of place. As a consequence, such results are frequently much more informative regarding the systematic differences between the proper motion systems than they are with respect to the systematic differences between the positional systems. Moreover, and for the same reason, some sets of proper motion differences really do not portray the true state of the evolution of the proper motion systems. Finally, this has practical effects in other astrometrical work, for instance, in the analysis of earth rotation time series. This error is corrected by arguing for a more meaningful epoch of place for catalog-to-catalog comparisons. This method of infinitely overlapping circles is applied at these other epochs to the FK sequence of catalogs, the N30, and the GC, and as a corollary the effectiveness of this new technique is shown.

Taff, L. G.↗
NASA NTRS1998

The Winds of B Supergiants

This report summarizes the progress of the work conducted under the program "The Winds of B Supergiants," conducted by Raytheon STX Corporation. The report consists of a journal article "Wind variability in B supergiants III. Corotating spiral structures in the stellar wind of HD 64760." The first step in the project was the analysis of the 1996 time series of 2 B supergiants and an O star. These data were analyzed and reported on at the ESO workshop, "Cyclical Variability in Stellar Winds."

Fullerton, A. W.↗
NASA NTRS2005

DORIS weekly solutions: status report and open problems

Since the official start of the International DORIS Service (IDS) in 2003, several Analysis Centers have submitted time series of station coordinates in SINEX format. We discuss here the different DORIS weekly solutions available for ITRF 2004 and for the IERS Combinationa Campaign Project.

DORIS↗
NASA NTRS2013

Spatial and Temporal Dust Source Variability in Northern China Identified Using Advanced Remote Sensing Analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas.

spectral mixing analysis↗