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At least 613 records · Page 34

ROSAT implementation of a proposed multi-mission x ray data format

Until recently little effort has been made to ensure that data from X-ray telescopes are delivered in a format that reflects the common characteristics that most X-ray datasets share. Instrument-specific data-product design hampers the comparison of X-ray measurements made by different detectors and should be avoided whenever possible. The ROSAT project and the High Energy Astrophysics Science Archive Research Center (HEASARC) have defined a set of X-ray data products ('rationalized files') for ROSAT data that can be used for distribution and archiving of data from other X-ray missions. This set of 'rationalized files' has been defined to isolate instrument-independent and instrument-specific quantities using standards FITS constructs to ensure portability. We discuss the usage of the 'rationalized files' by ROSAT for data distribution and archiving, with particular emphasis on discrimination between instrument-independent and instrument-specific quantities, and discuss application of this format to data from other X-ray missions.

Corcoran, M.↗

CALIPSO Level 3 Stratospheric Aerosol Product: Version 1.00 Algorithm Description and Initial Assessment

In August 2018,the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project released a new level 3 stratospheric aerosol profile data product derived from nearly 12 years of measurements acquired by the space-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP).This monthly averaged, gridded level 3 product is based on version 4.2 of the CALIOP level 1 and level 2 data products, which feature significantly improved calibration that now makes it possible to reliably retrieve profiles of stratospheric aerosol extinction and backscatter coefficients. This paper describes the science algorithm and data handling techniques that were developed to generate the CALIPSO version 1.00 level 3 stratospheric aerosol profile product. Further, we show that the retrieved extinction profiles capture the major stratospheric perturbations over the last decade resulting from volcanic eruptions, extreme smoke events, and signatures of stratospheric dynamics. Initial assessment of the product by inter-comparison with the stratospheric aerosol retrievals from the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) indicates good agreement in the tropical stratospheric aerosol layer (30oN-30oS),where the average difference between zonal mean extinction profiles is typically less than 25% between 20km and 30km. However, differences can exceed 100% in the very low aerosol loading regimes found above 25 km at higher latitudes.

Jayanta Kar↗

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus pollen, a significant aeroallergen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Yin 2007) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust (Yin 2007). The use of satellite data products for studying phenology is well documented (White and Nemani 2006). We are modifying the DREAM model to incorporate pollen transport. The linkages already exist with DREAM through PHAiRS (Public Health Applications in remote Sensing) to the public health community. This linkage has the potential to fill this data gap so that health effects of pollen can better be tracked for linkage with health outcome data including asthma, respiratory effects, myocardial infarction, and lost work days. DREAM is based on the SKIRON/Eta modeling system and the Eta/NCEP regional atmospheric model. The dust modules of the entire system incorporate the state of the art parameterizations of all the major phases of the atmospheric dust life such as production, diffusion, advection, and removal. These modules also include effects of the particle size distribution on aerosol dispersion. The dust production mechanism is based on the viscous/turbulent mixing, shear-free convection diffusion, and soil moisture. In addition to these sophisticated mechanisms, very high resolution databases, including elevation, soil properties, and vegetation cover are utilized. The DREAM model was modified to use pollen sources instead of dust (PREAM). Pollen release will be estimated based on satellite-derived phenology of Juniperus spp. communities. The MODIS surface reflectance product (MOD09) will provide information on the start of the plant growing season, growth stage, peak greenness, dry-down and pollen release. Ground based observational records of pollen release timing and quantities will be used as verification. Techniques developed using MOD09 surface reflectance products will be directly applicable to the next generation sensors such as VIIRS. The resulting deterministic model for predicting and simulating pollen emission and downwind concentration to study details of phenology and meteorology and their dependencies. This information will be used to support the Centers for Disease Control and Prevention (CDC)'s National Environmental Public Health Tracking Program (EPHT) and the State of New Mexico environmental public health decision support for asthma and allergies alerts

Luvall, Jeffrey C.↗

Targeting and Localization for Mars Rover Operations

A design and a partially developed application framework were presented for improving localization and targeting for surface spacecraft. The program has value for the Mars Science Laboratory mission, and has been delivered to support the Mars Exploration Rovers as part of the latest version of the Maestro science planning tool. It also has applications for future missions involving either surface-based or low-altitude atmospheric robotic vehicles. The targeting and localization solutions solve the problem of how to integrate localization estimate updates into operational planning tools, operational data product generalizations, and flight software by adding expanded flexibility to flight software, the operations data product pipeline, and operations planning tools based on coordinate frame updates during a planning cycle.

Powell, Mark W.↗

Fine-scale structure in the far-infrared Milky-Way

This final report summarizes the work performed and which falls into five broad categories: (1) generation of a new data product (mosaics of the far-infrared emission in the Milky Way); (2) acquisition of associated data products at other wavelengths; (3) spatial filtering of the far-infrared mosaics and resulting images of the FIR fine-scale structure; (4) evaluation of the spatially filtered data; (5) characterization of the FIR fine-scale structure in terms of its spatial statistics; and (6) identification of interstellar counterparts to the FIR fine-scale structure.

Waller, William H.↗

EOS MLS Science Data Processing System: A Description of Architecture and Capabilities

This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.

Microwave Limb Sounder (MLS)↗

A Ground Validation Network for the Global Precipitation Measurement Mission

A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.

Schwaller, Mathew R.↗

A Ground Validation Network for the Global Precipitation Measurement Mission

A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was -1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching -3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.

Schwaller, Mathew R.↗

Processing Spacecraft Data Without Confusion

Producing multiple versions of the same data product for the same time frame with the same remotely sensed inputs can be a recipe for disaster. Yet, amidst the commotion of satellite launch and early operations (LEO), such data processing is needed. After LEO, the situation gets worse. Processing newly arriving data ("forward processing") is augmented with reprocessing and algorithm development, comparison, evaluation, and testing -- often happening all at the same time. The problem can be analyzed in three main parts -- maintaining multiple versions of algorithms and data so that end-product users are not overwhelmed,allocating computer resources efficiently, and simplifying production operations so that va st amounts of data can be processed with minimal staff and fewer errors. OMIDAPS provides a framework for execution of algorithms that transform lower level data acquired by OMI on NASA's Aura satellite into higher level science data products. In contrast to traditional science data processing systems, we address all parts of the problem with an innovative approach allowing multiple data processing to run within a single physical system. The data products, imports, exports, and execution planning are all segregated into distinct "ArchiveSets." This paper describes reasons for multiple concurrent productions on a typical satellite data processing project using OMI as an example. It describes the virtual data processing system concept and its advantages over separate physical processing strings. It explores the specific implementation of the virtual systems within OMIDAPS and discusses some of the implications of our approach and describes how virtual processing is used to accomplish the overall mission of OMI data processing.

Tilmes, Curt↗

Description of the TCERT Vetting Reports for Data Release 25

This document, the Kepler Instrument Handbook (KIH), is for Kepler and K2 observers, which includes the Kepler Science Team, Guest Observers (GOs), and astronomers doing archival research on Kepler and K2 data in NASAs Astrophysics Data Analysis Program (ADAP). The KIH provides information about the design, performance, and operational constraints of the Kepler flight hardware and software, and an overview of the pixel data sets available. The KIH is meant to be read with these companion documents:1. Kepler Data Processing Handbook (KSCI-19081) or KDPH (Jenkins et al., 2016). The KDPH describes how pixels downlinked from the spacecraft are converted by the Kepler Data Processing Pipeline (henceforth just the pipeline) into the data products delivered to the MAST archive. 2. Kepler Archive Manual (KDMC-10008) or KAM (Thompson et al., 2016). The KAM describes the format and content of the data products, and how to search for them.3. Kepler Data Characteristics Handbook (KSCI-19040) or KDCH (Christiansen et al., 2016). The KDCH describes recurring non-astrophysical features of the Kepler data due to instrument signatures, spacecraft events, or solar activity, and explains how these characteristics are handled by the pipeline.4. Kepler Data Release Notes 25 (KSCI-19065) or DRN 25 (Thompson et al., 2015). DRN 25 describes signatures and events peculiar to individual quarters, and the pipeline software changes between a data release and the one preceding it.Together, these documents supply the information necessary for obtaining and understanding Kepler results, given the real properties of the hardware and the data analysis methods used, and for an independent evaluation of the methods used if so desired.

Instrument↗

NPOESS Preparatory Project (NPP) Science Overview

NPP Instruments are: (1) well understood thanks to instrument comprehensive test, characterization and calibration programs. (2) Government team ready for October 25 launch followed by instrument activation and Intensive Calibration/Validation (ICV). NPP Data Products preliminary work includes: (1) JPSS Center for Satellite Applications and Research (STAR) team ready to support NPP ICV and operational data products. (2) NASA NPP science team ready to support NPP ICV and EOS data continuity.

Butler, James J.↗

Antarctic Iceberg Tracking Based on Time Series of Aqua AMSRE Microwave Brightness Temperature Measurements

Observations of icebergs are identified as one of the requirements for the GEOSS (Global Earth Observation System of Systems) in the area of reducing loss of life and property from natural and human-induced disasters. However, iceberg observations are not included among targets in the GEOSS 10-Year Implementation Plan, and thus there is an unfulfilled need for iceberg detection and tracking in the near future. Large Antarctic icebergs have been tracked by the National Ice Center and by the academic community using a variety of satellite sensors including both passive and active microwave imagers, such as SSM/I (Special Sensor Microwave/Imager) deployed on the DMSP (Defense Meteorological Satellite Program) spacecraft. Improvements provided in recent years by NASA and non-NASA satellite radars, scatterometers, and radiometers resulted in an increased number of observed icebergs and even prompted a question: Is The Number of Antarctic Icebergs Really Increasing? [D.G. Long, J. Ballantyne, and C. Bertoia, Eos, Transactions of the American Geophysical Union 83 (42): 469 & 474, 15 October 2002]. AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) represents an improvement over SSM/I, its predecessor. AMSR-E has more measurement channels and higher spatial resolution than SSM/I. For example, the instantaneous field of view of the AMSR-E s 89-GHz channels is 6 km by 4 km versus 16 km by 14 km for SSM/I s comparable 85-GHz channels. AMSR-E, deployed on the Aqua satellite, scans across a 1450-km swath and provides brightness temperature measurements with nearglobal coverage every one or two days. In polar regions, overlapping swaths generate coverage up to multiple times per day and allow for creation of image time series with high temporal resolution. Despite these advantages, only incidental usage of AMSR-E data for iceberg tracking has been reported so far, none in an operational environment. Therefore, an experiment was undertaken in the RPC (Rapid Prototyping Capability) of NASA s (National Aeronautics and Space Administration) Applied Science Program to demonstrate that passive microwave brightness temperature measurements acquired by AMSR-E can be effectively used to track iceberg movement around Antarctica. The RPC s robust computation environment enabled processing of terabytes of data products available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. Iceberg tracking based on the AMSR-E Level 2A data product was compared with records from the National Ice Center for currently existing icebergs. Some icebergs as small as roughly 10 km in size were easily observed, but tracking of many others, even larger ones, was obscured by presence of sea ice surrounding the icebergs. The best results, such as for the large iceberg A22A, were achieved when an iceberg was in open ocean.

Blonski, Slawomir↗

Rover Sequencing and Visualization Program

The Rover Sequencing and Visualization Program (RSVP) is the software tool for use in the Mars Exploration Rover (MER) mission for planning rover operations and generating command sequences for accomplishing those operations. RSVP combines three-dimensional (3D) visualization for immersive exploration of the operations area, stereoscopic image display for high-resolution examination of the downlinked imagery, and a sophisticated command-sequence editing tool for analysis and completion of the sequences. RSVP is linked with actual flight-code modules for operations rehearsal to provide feedback on the expected behavior of the rover prior to committing to a particular sequence. Playback tools allow for review of both rehearsed rover behavior and downlinked results of actual rover operations. These can be displayed simultaneously for comparison of rehearsed and actual activities for verification. The primary inputs to RSVP are downlink data products from the Operations Storage Server (OSS) and activity plans generated by the science team. The activity plans are high-level goals for the next day s activities. The downlink data products include imagery, terrain models, and telemetered engineering data on rover activities and state. The Rover Sequence Editor (RoSE) component of RSVP performs activity expansion to command sequences, command creation and editing with setting of command parameters, and viewing and management of rover resources. The HyperDrive component of RSVP performs 2D and 3D visualization of the rover s environment, graphical and animated review of rover-predicted and telemetered state, and creation and editing of command sequences related to mobility and Instrument Deployment Device (IDD) operations. Additionally, RoSE and HyperDrive together evaluate command sequences for potential violations of flight and safety rules. The products of RSVP include command sequences for uplink that are stored in the Distributed Object Manager (DOM) and predicted rover state histories stored in the OSS for comparison and validation of downlinked telemetry. The majority of components comprising RSVP utilize the MER command and activity dictionaries to automatically customize the system for MER activities. Thus, RSVP, being highly data driven, may be tailored to other missions with minimal effort. In addition, RSVP uses a distributed, message-passing architecture to allow multitasking, and collaborative visualization and sequence development by scattered team members.

Cooper, Brian↗

SHADOZ (Southern Hemisphere ADditional OZonesondes): An Ozonesonde Network for Satellite Validation, Climatology and Modeling

In the past 5 years, new tropical ozone data products have been developed from TOMS and other satellites, During this period, global chemical-transport models have been used for ozone assessment studies. However, there has been a lack of independent ozone profiles in the tropics for evaluation of the data sets and models. In 1998, NASA's Goddard Space Flight Center, Wallops Flight Facility and NOAA's CMDL (Climate Monitoring and Diagnostics Lab), began a 2-year project to collect a consistent data set by augmenting ozonesonde launches at southern hemisphere tropical sites The measurements are available to the scientific community at a single electronic location - the SHADOZ website at NASA/Goddard: http://code9l6.gsfc.nasa.gov/Data services/Shadoz/shadoz hmpg2.html. Stations in SHADOZ include four islands in the Pacific: Fiji, Tahiti, San Cristobal (Galapagos) and American Samoa. Two sites are at and in the Atlantic: Natal (Brazil) and Ascension Island. Three other sites span Africa (Nairobi and Irene, South Africa) and the Indian Ocean (Reunion Island and Watukosek in Java, Indonesia). All SHADOZ sites are using ECC-type sondes, with the conversion from JMD sondes at Java in 1999, but there are variations in sonde preparation technique and data processing. During the 1998-1999 period, more than 550 sondes were incorporated into the SHADOZ data base. Examples from these measurements illustrate the tropical wave-one pattern in total ozone which is easily detectable by satellite. They also show that the wave-one pattern appears to be in the troposphere, as assumed in creating the modified-residual tropospheric ozone data product from TOMS. SHADOZ will add data from intensive field campaigns from time to time. Recent contributions to the SHADOZ archive are from the INDOEX (Indian Ocean Experiment January-March 1999)sondes at the Maldives (5N, 73E) and 27 sondes on the US NOAA oceanographic vessel, the FIN Ronald H Brown between Virginia (US) and Mauritius via CapeTown, during a cruise in January and February 1999. In 2000, as part of the SAFARI-2000 experiment and a validation project called Southern African Validation for EOS (SAVE), enhancement of ozonesonde launches at Irene (South Africa) will extend the data set from this site beyond the 1998-1999 period.

Thompson, Anne M.↗

How Can NASA Science Benefit Solar Energy Development and Assessment?

This is a keynote presentation for a workshop highlighting the use of remote sensing and modeling products derived with NASA missions and Earth system modeling for solar energy system development. We broadly introduce NASA's fleet of satellite producing publicly available data products that would be useful for the information needed solar energy development. We also introduce the work in Earth system science modeling that provides additional global gridded data products that are also relevant. We identify that one issue is obtaining and using this data is the level of expertise needed to find it. NASA has developed a new Earth Action program to address this gap and bring the data closer to the user. Now under the Energy Resources element, the NASA LaRC's POWER (Prediction of Worldwide Renewable Energy) project has developed a web services platform that provides key information needed for solar energy development as analysis-ready. This means that the data for particular locations are immediately useful for help provide the information needed by engineers and architects to plan for solar systems. Thus, the presentation shows why NASA products would be relevant and then shows examples of finding the needed data through the POWER web portal.

remote sensing↗

Earth Radiation Budget Experiment (ERBE) archival and April 1985 results

This paper describes the Earth Radiation Budget Experiment (ERBE) data products being made available to the community. The Science Team used ten validation criteria to judge the acceptability of the data for archival. These criteria are listed, and uncertainty estimates based on them for four typical data products are presented. A brief description of the radiation budget for April 1985 from the combined data of ERBE and NOAA-9 concludes this paper.

Barkstrom, B.↗

Global Carbon Budget 2016

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere the global carbon budget is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as +/- 1(sigma), reflecting the current capacity to characterize the annual estimates of each component of the global carbon budget. For the last decade available (2006-2015), EFF was 9.3+/-0.5 GtC/yr, ELUC 1.0+/-0.5 GtC/yr,GATM 4.5+/-0.1 GtC/yr, SOCEAN 2.6+/-0.5 GtC/yr, and SLAND 3.1+/-0.9 GtC/yr. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9+/-0.5 GtC/yr, showing a slowdown in growth of these emissions compared to the average growth of 1.8/yr that took place during 2006-2015.Also, for 2015, ELUC was 1.3+/-0.5 GtC/yr, GATM was 6.3+/-0.2 GtC/yr, SOCEAN was 3.0+/-0.5 GtC/yr, and SLAND was 1.9+/-0.9 GtC/yr. GATM was higher in 2015 compared to the past decade (2006-2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4+/-0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2% (range of -1.0 to +1.8% ) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Nino conditions of 2015-2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565+/-55 GtC (2075+/-205 GtCO2) for 1870-2016, about 75% from EFF and 25% from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set.

Quéré, Corinne Le↗

Update on Rover Sequencing and Visualization Program

The Rover Sequencing and Visualization Program (RSVP) has been updated. RSVP was reported in Rover Sequencing and Visualization Program (NPO-30845), NASA Tech Briefs, Vol. 29, No. 4 (April 2005), page 38. To recapitulate: The Rover Sequencing and Visualization Program (RSVP) is the software tool to be used in the Mars Exploration Rover (MER) mission for planning rover operations and generating command sequences for accomplishing those operations. RSVP combines three-dimensional (3D) visualization for immersive exploration of the operations area, stereoscopic image display for high-resolution examination of the downlinked imagery, and a sophisticated command-sequence editing tool for analysis and completion of the sequences. RSVP is linked with actual flight code modules for operations rehearsal to provide feedback on the expected behavior of the rover prior to committing to a particular sequence. Playback tools allow for review of both rehearsed rover behavior and downlinked results of actual rover operations. These can be displayed simultaneously for comparison of rehearsed and actual activities for verification. The primary inputs to RSVP are downlink data products from the Operations Storage Server (OSS) and activity plans generated by the science team. The activity plans are high-level goals for the next day s activities. The downlink data products include imagery, terrain models, and telemetered engineering data on rover activities and state. The Rover Sequence Editor (RoSE) component of RSVP performs activity expansion to command sequences, command creation and editing with setting of command parameters, and viewing and management of rover resources. The HyperDrive component of RSVP performs 2D and 3D visualization of the rover s environment, graphical and animated review of rover predicted and telemetered state, and creation and editing of command sequences related to mobility and Instrument Deployment Device (robotic arm) operations. Additionally, RoSE and HyperDrive together evaluate command sequences for potential violations of flight and safety rules. The products of RSVP include command sequences for uplink that are stored in the Distributed Object Manager (DOM) and predicted rover state histories stored in the OSS for comparison and validation of downlinked telemetry. The majority of components comprising RSVP utilize the MER command and activity dictionaries to automatically customize the system for MER activities.

Cooper, Brian↗