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At least 1,099 records · Page 61

Methane from the Tropospheric Emission Spectrometer (TES)

TES V5 CH4 captures latitudinal gradients, regional variability and interannual variation in the free troposphere. V5 joint retrievals offer improved sensitivity to lower troposphere. Time series extends from 2004 to present. V5 reprocessing in progress. Upper tropospheric bias. Mitigated by N2O correction. Appears largely spatially uniform, so can be corrected. How to relate free-tropospheric values to surface emissions.

methane gas (CH4)↗

Classical Accreting Pulsars with NICER

Soft excesses are very common center dot Lx > 1038 erg/s - reprocessing by optically thick material at the inner edge of the accretion disk center dot Lx < 1036 erg/s - photoionized or collisionally heated diffuse gas or thermal emission from the NS surface center dot Lx ~ 1037 erg/s - either or both types of emission center dot NICER observations of soft excesses in bright X‐ray pulsars combined with reflection modeling will constrain the ionization state, metalicity and dynamics of the inner edge of the magnetically truncated accretion disk Reflection models of an accretion disk for a hard power law - Strong soft excess below 3 keV from hot X‐ray heated disk - For weakly ionized case: strong recombination lines - Are we seeing changes in the disk ionization in 4U1626‐26? 13 years of weekly monitoring with RXTE PCA center dot Revealed an unexpectedly large population of Be/X‐ray binaries compared to the Milky Way center dot Plotted luminosities are typical of "normal" outbursts (once per orbit) center dot The SMC provides an excellent opportunity to study a homogenous population of HMXBs with low interstellar absorption for accretion disk studies. Monitoring with NICER will enable studies of accretion disk physics in X‐ray pulsars center dot The SMC provides a potential homogeneous low‐absorption population for this study center dot NICER monitoring and TOO observations will also provide measurements of spinfrequencies, QPOs, pulsed fluxes, and energy spectra.

Wilson-Hodge, Colleen A.↗

Suomi NPP VIIRS Ocean Color Data Product Early Mission Assessment

Following the launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polarorbiting Partnership (NPP) spacecraft, the NASA NPP VIIRS Ocean Science Team (VOST) began an evaluation of ocean color data products to determine whether they could continue the existing NASA ocean color climate data record (CDR). The VOST developed an independent evaluation product based on NASA algorithms with a reprocessing capability. Here we present a preliminary assessment of both the operational ocean color data products and the NASA evaluation data products regarding their applicability to NASA science objectives.

NPP↗

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr−1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

NDVI↗

The Hopkins Ultraviolet Telescope: The Final Archive

The Hopkins Ultraviolet Telescope (HUT) was a 0.9 m telescope and moderate-resolution (Delta)lambda equals 3 A) far-ultraviolet (820-1850 Å) spectrograph that flew twice on the space shuttle, in 1990 December (Astro-1, STS-35) and 1995 March (Astro-2, STS-67). The resulting spectra were originally archived in a nonstandard format that lacked important descriptive metadata. To increase their utility, we have modified the original datareduction software to produce a new and more user-friendly data product, a time-tagged photon list similar in format to the Intermediate Data Files (IDFs) produced by the Far Ultraviolet Spectroscopic Explorer calibration pipeline. We have transferred all relevant pointing and instrument-status information from locally-archived science and engineering databases into new FITS header keywords for each data set. Using this new pipeline, we have reprocessed the entire HUT archive from both missions, producing a new set of calibrated spectral products in a modern FITS format that is fully compliant with Virtual Observatory requirements. For each exposure, we have generated quicklook plots of the fully-calibrated spectrum and associated pointing history information. Finally, we have retrieved from our archives HUT TV guider images, which provide information on aperture positioning relative to guide stars, and converted them into FITS-format image files. All of these new data products are available in the new HUT section of the Mikulski Archive for Space Telescopes (MAST), along with historical and reference documents from both missions. In this article, we document the improved data-processing steps applied to the data and show examples of the new data products.

Hopkins↗

Propagation of Radiosonde Pressure Sensor Errors to Ozonesonde Measurements

Several previous studies highlight pressure (or equivalently, pressure altitude) discrepancies between the radiosonde pressure sensor and that derived from a GPS flown with the radiosonde. The offsets vary during the ascent both in absolute and percent pressure differences. To investigate this problem further, a total of 731 radiosonde-ozonesonde launches from the Southern Hemisphere subtropics to Northern mid-latitudes are considered, with launches between 2005 - 2013 from both longer-term and campaign-based intensive stations. Five series of radiosondes from two manufacturers (International Met Systems: iMet, iMet-P, iMet-S, and Vaisala: RS80-15N and RS92-SGP) are analyzed to determine the magnitude of the pressure offset. Additionally, electrochemical concentration cell (ECC) ozonesondes from three manufacturers (Science Pump Corporation; SPC and ENSCI-Droplet Measurement Technologies; DMT) are analyzed to quantify the effects these offsets have on the calculation of ECC ozone (O3) mixing ratio profiles (O3MR) from the ozonesonde-measured partial pressure. Approximately half of all offsets are 0.6 hPa in the free troposphere, with nearly a third 1.0 hPa at 26 km, where the 1.0 hPa error represents 5 persent of the total atmospheric pressure. Pressure offsets have negligible effects on O3MR below 20 km (96 percent of launches lie within 5 percent O3MR error at 20 km). Ozone mixing ratio errors above 10 hPa (30 km), can approach greater than 10 percent ( 25 percent of launches that reach 30 km exceed this threshold). These errors cause disagreement between the integrated ozonesonde-only column O3 from the GPS and radiosonde pressure profile by an average of +6.5 DU. Comparisons of total column O3 between the GPS and radiosonde pressure profiles yield average differences of +1.1 DU when the O3 is integrated to burst with addition of the McPeters and Labow (2012) above-burst O3 column climatology. Total column differences are reduced to an average of -0.5 DU when the O3 profile is integrated to 10 hPa with subsequent addition of the O3 climatology above 10 hPa. The RS92 radiosondes are superior in performance compared to other radiosondes, with average 26 km errors of -0.12 hPa or +0.61 percent O3MR error. iMet-P radiosondes had average 26 km errors of -1.95 hPa or +8.75 percent O3MR error. Based on our analysis, we suggest that ozonesondes always be coupled with a GPS-enabled radiosonde and that pressure-dependent variables, such as O3MR, be recalculated-reprocessed using the GPS-measured altitude, especially when 26 km pressure offsets exceed 1.0 hPa 5 percent.

pressure measurements↗

On the Lack of Correlation Between Mg II 2796, 2803 Angstrom and Lyman alpha Emission in Lensed Star-Forming Galaxies

We examine the Mg II 2796, 2803 Angstrom, Lyman alpha, and nebular line emission in five bright star-forming galaxies at 1.66 less than z less than 1.91 that have been gravitationally lensed by foreground galaxy clusters. All five galaxies show prominent Mg II emission and absorption in a P Cygni profile. We find no correlation between the equivalent widths of Mg II and Lyman alpha emission. The Mg II emission has a broader range of velocities than do the nebular emission line profiles; the Mg II emission is redshifted with respect to systemic by 100 to 200 km s(exp−1). When present, Lyman alpha is even more redshifted. The reddest components of Mg II and Lyman alpha emission have tails to 500-600 km s(exp−1), implying a strong outflow. The lack of correlation in the Mg II and Lyman alpha equivalent widths, the differing velocity profiles, and the high ratios of Mg II to nebular line fluxes together suggest that the bulk of Mg II emission does not ultimately arise as nebular line emission, but may instead be reprocessed stellar continuum emission.

Emission↗

A Spurious Jump in the Satellite Record: Has Antarctic Sea Ice Expansion Been Overestimated?

Recent estimates indicate that the Antarctic sea ice cover is expanding at a statistically significant rate with a magnitude one-third as large as the rapid rate of sea ice retreat in the Arctic. However, during the mid-2000s, with several fewer years in the observational record, the trend in Antarctic sea ice extent was reported to be considerably smaller and statistically indistinguishable from zero. Here, we show that much of the increase in the reported trend occurred due to the previously undocumented effect of a change in the way the satellite sea ice observations are processed for the widely used Bootstrap algorithm data set, rather than a physical increase in the rate of ice advance. Specifically, we find that a change in the intercalibration across a 1991 sensor transition when the data set was reprocessed in 2007 caused a substantial change in the long-term trend. Although our analysis does not definitively identify whether this change introduced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid- 2000s, and numerous studies that have relied on these observations should be reexamined to determine the sensitivity of their results to this change in the data set. Furthermore, a number of recent studies have investigated physical mechanisms for the observed expansion of the Antarctic sea ice cover. The results of this analysis raise the possibility that much of this expansion may be a spurious artifact of an error in the processing of the satellite observations.

Cryosphere↗

Release 2 data products from the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler

The OMPS Limb Profiler (LP) was launched on board the NASA Suomi National Polar-orbiting Partnership (SNPP) satellite in October 2011. OMPS-LP is a limb-scattering hyperspectral sensor that provides ozone profiling capability at 1.5 km vertical resolution from cloud top to 60 km altitude. The use of three parallel slits allows global coverage in approximately four days. We have recently completed a full reprocessing of all LP data products, designated as Release 2, that improves the accuracy and quality of these products. Level 1 gridded radiance (L1G) changes include intra-orbit and seasonal correction of variations in wavelength registration, revised static and intra-orbit tangent height adjustments, and simplified pixel selection from multiple images. Ozone profile retrieval changes include removal of the explicit aerosol correction, exclusion of channels contaminated by stratospheric OH emission, a revised instrument noise characterization, improved synthetic solar spectrum, improved pressure and temperature ancillary data, and a revised ozone climatology. Release 2 data products also include aerosol extinction coefficient profiles derived with the prelaunch retrieval algorithm. Our evaluation of OMPS LP Release 2 data quality is good. Zonal average ozone profile comparisons with Aura MLS data typically show good agreement, within 5-10% over the altitude range 20-50 km between 60 deg S and 60 deg N. The aerosol profiles agree well with concurrent satellite measurements such as CALIPSO and OSIRIS, and clearly detect exceptional events such as volcanic eruptions and the Chelyabinsk bolide in February 2013.

Limb scatter↗

The InSAR Scientific Computing Environment

We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms

geodetic imaging↗

Use of Reference Frames for Interplanetary Navigation at JPL

Navigation of interplanetary spacecraft is typically based on range, Doppler, and differential interferometric measurements made by ground-based telescopes. Acquisition and interpretation of these observations requires accurate knowledge of the terrestrial reference frame and its orientation with respect to the celestial frame. Work is underway at JPL to reprocess historical VLBI and GPS data to improve realizations of the terrestrial and celestial frames. Improvements include minimal constraint alignment, improved tropospheric modeling, better orbit determination, and corrections for antenna phase center patterns.

International Celestial Reference Frame (ICRF)↗

Height Error Correction for the New SRTM Elevation Product

The Shuttle Radar Topography Mission (SRTM), carrying a single-pass interferometric synthetic aperture radar(SAR) instrument, collected a global elevation data set, which has been widely used in scientific, military and commercial communities. In the new proposed NASA SRTM reprocessing task, the SRTM elevation data is going to be processed at higher spatial resolution and with improved height accuracy. Upon completion, the improved SRTM product will be freely available. This paper describes the calibration approaches for reduction of elevation ripple effects and height accuracy improvements.

Synthetic Aperture Radar Interferometry (InSAR)↗

Detection of Extremes with AIRS and CrIS

Climate change is expected to be detected first as changes in extreme values rather than in mean values. The availability of data of from two instruments in the same orbit, AIRS data for the past eleven years and AIRS and CrIS data from the past year, provides an opportunity to evaluate this using examples of climate relevance: Desertification, seen as changes in hot extremes, severe storm, seen as a change in extremely cold clouds and the warming of the polar zone. We use AIRS to establish trends for the 1%tile, the mean and 99%tile brightness temperatures measured with the 900 cm(exp -1) channel from AIRS for the past 11 years. This channel is in the clearest part of the 11 micron atmospheric window. Substantial trends are seen for land and ocean, which in the case of the 1%tile (cold) extremes are related to the current shift of deep convection from ocean to land. Changes are also seen in the 99%tile for day tropical land, but their interpretation is at present unclear. We also see dramatic changes for the mean and 99%tile of the North Polar area. The trends are an order of magnitude larger than the instrument trend of about 3 mK/year. We use the statistical distribution from the past year derived from AIRS to evaluate the accuracy of continuing the trends established with AIRS with CrIS data. We minimize the concern about differences in the spectral response functions by limiting the analysis to the channel at 900 cm(exp -1).While the two instruments agree within 100 mK for the global day/night land/ocean mean values, there are significant differences when evaluating the1% and 99%tiles. We see a consistent warm bias in the CrIS data relative to AIRS for the 1%tile (extremely cold, cloudy) data in the tropical zone, particularly for tropical land, but the bias is not day/night land/ocean consistent. At this point the difference appears to be due to differences in the radiometric response of AIRS and CrIS to differences in the day/night land/ocean cloud types. Unless the effect can be mitigated by a future reprocessing the CrIS data, it will significantly complicate the concatenation of the AIRS and CrIS data records for the continuation of trends in extreme values.

sampling bias↗

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

SRT Status and Plans for Version-7

The AIRS Science Team Version-6 retrieval algorithm is currently producing level-3 Climate Data Records (CDRs) from AIRS that have been proven useful to scientists in understanding climate processes. CDRs are gridded level-3 products which include all cases passing AIRS Climate QC. SRT has made significant further improvements to AIRS Version-6. Research is continuing at SRT toward the development of AIRS Version-7. At the last Science Team Meeting, we described results using SRT AIRS Version-6.19. SRT Version-6.19 is now an official build at JPL called 6.2. SRTs latest version is AIRS Version-6.22. We have also adapted AIRS Version-6.22 to run with CrISATMS. AIRS Version-6.22 and CrIS Version- 6.22 both run now on JPL computers, but are not yet official builds. The main reason for finalization of Version-7, and using it in the relatively near future for the future processing and reprocessing of old AIRS data, is to produce even better CDRs for use by climate scientists. For this reason all results shown in this talk use only AIRS Climate QC.

CDRs↗

Results from CrIS/ATMS Obtained Using an "AIRS Version-6 Like" Retrieval Algorithm

AIRS and CrIS Version-6.22 O3(p) and q(p) products are both superior to those of AIRS Version-6.Monthly mean August 2014 Version-6.22 AIRS and CrIS products agree reasonably well with OMPS, CERES, and witheach other. JPL plans to process AIRS and CrIS for many months and compare interannual differences. Updates to thecalibration of both CrIS and ATMS are still being finalized. We are also working with JPL to develop a joint AIRS/CrISlevel-1 to level-3 processing system using a still to be finalized Version-7 retrieval algorithm. The NASA Goddard DISCwill eventually use this system to reprocess all AIRS and recalibrated CrIS/ATMS. .

CrIS ATMS↗

The Process of Bringing Dark Data to Light: The Rescue of the Early Nimbus Satellite Data

Myriad environmental satellite missions are currently orbiting the earth. The comprehensive monitoring by these sensors provide scientists, policymakers, and the public critical information on the earths weather and climate system. The state of the art technology of our satellite monitoring system is the legacy of the first environment satellites, the Nimbus systems launched by NASA in the mid-1960s. Such early data can extend our climate record and provide important context in longer-term climate changes. However, the data was stowed away and, over the years, largely forgotten. It was nearly lost before its value was recognized and attempts to recover the data were undertaken. This paper covers what it took the authors to recover, navigate and reprocess the data into modern formats so that it could be used as a part of the satellite climate record. The procedures to recover the Nimbus data, from both film and tape, could be used by other data rescue projects, however the algorithms presented will tend to be Nimbus specific. Data rescue projects are often both difficult and time consuming but the data they bring back to the science community makes these efforts worthwhile.

dark↗

Systems Engineering and Management Applications of ISO 9001:2015 for Government

The manufacturing segment of the business world is busy assessing the impact of ISO 9001:2015, and updating their management systems to meet the required compliance date. What does the new revision mean for government agencies that deliver large engineering projects rather than mass production? In fact, the standard, especially the new revision, can be used quite readily for government agencies, or applied to specific projects, once it is understood in terms of the similarities with systems engineering and project management. From there it can be extrapolated to "mission realization" systems, and a Quality Management System (QMS) is a logical result that can bring order to processes and systems that likely already exist in some fashion. ISO 9001:2015 is less product-oriented than previous versions. It can be more broadly applied to public organizations as well as private; and to services (missions) as well as products. The emphasis on risk management in the revised standard provides the needed balance for weighing decisions with respect to cost, schedule, technical, safety, and regulatory compliance; so if this is not part of agency governance already, this is a good place to start, especially for large engineering projects. The Systems Engineering standard used for this analysis is from NASA's NPR 7123.1 NASA Systems Engineering Processes and Requirements; however, those who are more familiar with ISO/IEC 26702 Systems Engineering-application and management of the systems engineering process, or SAE/EIA 632 Processes for Engineering a System will also recognize the similarities. In reality, the QMS outlined by ISO 9001 reinforces the systems engineering processes, and serves to ensure that they are adequately implemented, although most of the ISO 9001 literature emphasizes the production and process aspects of the standard. Rather than beginning with ISO 9001and getting lost in the vocabulary, it is useful to begin with the systems engineering lifecycle. Identification of stakeholder expectations, identifying solutions, creating specific product or service designs, production of the product or service, delivery to the public, and the associated management, planning, and control processes, are a familiar place to begin thinking of the overall system of identifying, designing, and competing a project or mission. Lining up this lifecycle with the ISO requirements (see Figure 1) illustrates how a quality management system is concerned with the same processes, and provides a governance and assurance function. If implemented properly, there are cost savings resulting from less rework, repair, reprocessing, failures, misplaced documents, and similar types of deficiencies1. Starting with an organization's systems engineering processes allows the organization to use their own terminology for a QMS plan, and tailor the plan to their own project or organization, so that it is more easily developed, understood, and implemented.

Shepherd, Christena C.↗