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NASA NTRS2019

The Effects of a 1998 Observing System Change on MERRA-2-Based Ozone Profile Simulations

Model simulations of ozone (O3) driven by meteorological reanalyses are useful for filling observational gaps and interpreting observed O3 variability and trends. However, the transport circulation of reanalysis products is impacted by changes to the observing system (the data assimilated into the reanalyses). We examine the impacts of these changes on simulated O3 from two models, Global Modeling Initiative (GMI) Chemistry Transport Model (GMI CTM) and Modern‐Era Retrospective Analysis for Research Applications version 2 (MERRA‐2) GMI Replay (M2 GMI Replay) simulation, using observations from global ozonesondes (>50,000 profiles) and satellites from 1980 to 2016. Both models are constrained by meteorology from the NASA MERRA‐2 reanalysis, and both use versions of NASA's GMI chemical mechanism. We focus on an observing system change affecting simulated O3 after 1998, associated with the assimilation of temperature and humidity data from new microwave profiling satellites. A large post‐1998 O3 increase, mainly confined to 15–20‐km altitude, of ~10 Dobson units (DU) in midlatitudes occurs in the GMI CTM, worsening the bias compared to observations. In contrast, an increase in M2 GMI Replay simulation O3 of ~10 DU is observed only near −60° latitude, reducing the bias compared to observations. The GMI CTM O3 high biases display a Quasi‐Biennial Oscillation (QBO)‐like periodicity that result from excessive transport from the tropical stratosphere to the midlatitude lower stratosphere during the QBO westerly phase. We quantify O3 discontinuities caused by MERRA‐2 observing system changes and demonstrate how the MERRA‐2 Global Modeling Initiative Replay simulation dampens the effects of these changes and QBO‐driven artifacts on simulated lower stratospheric and total O3. We caution against using simulations driven by a reanalysis to derive multidecadal O3 trends, especially prior to 1998.

Replay↗
NASA NTRS2018

Multidecadal Changes in the UTLS Ozone from the MERRA-2 Reanalysis and the GMI Chemistry Model

Long-term changes of ozone in the UTLS (Upper Troposphere / Lower Stratosphere) reflect the response to decreases in the stratospheric concentrations of ozone-depleting substances as well as changes in the stratospheric circulation induced by climate change. To date, studies of UTLS ozone changes and variability have relied mainly on satellite and in-situ observations as well as chemistry-climate model simulations. By comparison, the potential of reanalysis ozone data remains relatively untapped. This is despite evidence from recent studies, including detailed analyses conducted under SPARC (Scalable Processor Architecture) Reanalysis Intercomparison Project (S-RIP), that demonstrate that stratospheric ozone fields from modern atmospheric reanalyses exhibit good agreement with independent data while delineating issues related to inhomogeneities in the assimilated observations. In this presentation, we will explore the possibility of inferring long-term geographically and vertically resolved behavior of the lower stratospheric (LS) ozone from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications -2) reanalysis after accounting for the few known discontinuities and gaps in its assimilated input data. This work builds upon previous studies that have documented excellent agreement between MERRA-2 ozone and ozonesonde observations in the LS. Of particular importance is a relatively good vertical resolution of MERRA-2 allowing precise separation of tropospheric and stratospheric ozone contents. We also compare the MERRA-2 LS ozone results with the recently completed 37-year simulation produced using Goddard Earth Observing System in "replay" mode coupled with the GMI (Global Modeling Initiative) chemistry mechanism. Replay mode dynamically constrains the model with the MERRA-2 reanalysis winds, temperature, and pressure. We will emphasize the areas of agreement of the reanalysis and replay and interpret differences between them in the context of our increasing understanding of model transport driven by assimilated winds.

Ozone↗
NASA NTRS2019

Comparing Flood-Producing Catastrophic Hurricanes in GEOS: A Study of Juan 1985 and Harvey 2017

Hurricanes Juan (1985) and Harvey (2017) bear remarkable similarities because of the exceptional amount of accumulated precipitation over coastal regions in the Gulf, consequent to the peculiar and rare situation in which a hurricane stalls for several days on a coastal area, displaying minimal progress. This is particularly dangerous because: a) terrestrial drag affect only part of the circulation so that weakening is not as fast as when there is rapid motion inland; b) a part of the circulation remains over water and as such low-level moisture flux convergence is maintained; c) precipitation lasts for a very long time over the same area; d) moisture recycling from flooded areas further contributes to maintaining flood conditions. From the forecast perspective, hurricanes with track singularities such as stalling points, loops, or multiple landfalls over the same region are exceptionally difficult to predict because they result from small variations of opposing mechanisms and are therefore intrinsically unstable. Intensity fluctuations and track singularities are strongly coupled. The representation of fluxes undergoing differential drag (ocean vs. land) inside the same circulation is critically dependent upon resolution. Recent modeling development efforts within the NASA Global Modeling and Assimilation Office (GMAO) have brought the horizontal resolution of the global cubed-sphere Goddard Earth Observing System (GEOS) atmospheric model down to 12 km. This has led to innovative findings in the field of tropical cyclone (TC) modeling within a global framework. The GEOS atmospheric model and data assimilation system (DAS), used to produce the MERRA-2 (Modern Era Retrospective analysis for Research and Applications) reanalysis, has a "Replay" capability that adds a forcing term to the model equations to constrain it to follow a specified trajectory. This capability is used to produce global, downscaled versions of the 50 km MERRA-2 reanalysis at a 12.5 km resolution. The full DAS and replay modes both use the Incremental Analysis Update or IAU for applying an analysis increment to the model's background state four times each day at 00Z, 06Z, 12Z and 18Z. In a full DAS mode, a six-hour free run of the model is made (Predictor Step), writing states that will be used as backgrounds or first-guesses for the analysis centered on 00Z. The Analysis system then produces its best estimate of the 00Z state by blending backgrounds with observations. The difference between this best estimate and the model's original first guess at 00Z is treated as an analysis increment and added uniformly as a forcing term to the model during a second, now forced, run of the 12Z to 03Z window, which produces the assimilated results. Replay mode is very similar, except only the 00Z background is needed from the predictor segment, which is thus half as long, and instead of doing an analysis, a previously analyzed state at 00Z is read in. The increment is then computed and applied to the corrector step in the same way as in the original DAS. When downscaling horizontal spatial filters can be applied to the "analysis" increment used to force the Corrector step, so as to reproduce the full meteorological state or selectively choose or discard certain forcings. In this presentation, full global hindcast simulations of Juan and operational forecasts of Harvey are compared.

Flooding↗
NASA NTRS1994

Delta/Wind Launch with Isolated Cameras from Continuous Recording

The Wind spacecraft represents one of NASA's contributions to the International Solar Terrestrial Program (ISTP), an international effort to quantify the effects of solar energy on the Earth's magnetic field. Wind will provide continuous measurement of the solar wind, particularly charged particles and magnetic field data. The specific objectives of Wind are to: (1) provide complete plasma, energetic particle, and magnetic field input for magnetospheric and ionospheric studies; (2) determine the magnetospheric output to interplanetary space in the upstream region; (3) investigate basic plasma processes occurring in the near-Earth solar wind; and (4) provide baseline ecliptic plane observations to be used in heliospheric studies. This videotape shows the pre-dawn launch of the Wind spacecraft aboard a Delta 7925 on November 1, 1994. After the countdown and launch, the tape shows the activity in the Telemetry Room at Kennedy Space Center, where people are following the progress of the spacecraft. Following the activity in the telemetry room, there are four different replays of the launch from different locations. After showing the replays of the launch, the video returns to the Telemetry Room when an important stage in the launch and flight is achieved.

Source record↗
NASA NTRS2002

STS-111 Flight Day 3 Highlights

On Flight Day 3 of STS-111, the crew of Endeavour (Kenneth Cockrell, Commander; Paul Lockhart, Pilot; Franklin Chang-Diaz, Mission Specialist; Philippe Perrin, Mission Specialist) and the Expedition 5 crew (Valery Korzun, Commander; Peggy Whitson, Flight Engineer; Sergei Treschev, Flight Engineer), begin their final approach towards the International Space Station (ISS). From cameras aboard the ISS, live video of Endeavour is shown as it approaches the station. The Orbiter is maneuvered slowly to a position for docking, and cameras from multiple angles show this process. As it is maneuvered, there are clear views of its payload bay, which includes the Leonardo MultiPurpose Logistics Module (MPLM) and the Mobile Base System (MBS), both of which will be installed on the ISS during this mission. In the final stages of the docking procedure there is close-up footage of Endeavour meeting the ISS's Pressurized Mating Adapter 2 on the Destiny Laboratory Module. Inside the ISS, the Expedition 4 crew (Yury Onufrienko, Commander; Daniel Bursch, Flight Engineer; Carl Walz, Flight Engineer), which will be replaced by the Expedition 5 crew, prepares for final docking. Crew members are shown transferring equipment from the Endeavour to the ISS, prior to a replay of the mating of the two crafts. In the replay, the hatch is shown being opened and the two newly arrived crews are greeted with excitement by Expedition 4 crewmembers. The video closes with footage of the Quest airlock used for EVA (extravehicular activity) egress, and the Canadarm 2 robotic arm.

Source record↗
NASA NTRS2002

STS-110/Atlantic/ISS 8A Pre-Launch On Orbit-Landing-Crew Egress

The crew of STS-110, which consists of Commander Michael Bloomfield, Pilot Stephen Frick, and Mission Specialists Rex Walheim, Ellen Ochoa, Lee Morin, Jerry Ross, and Steven Smith is introduced at the customary pre-flight meal. The narrator provides background information on the astronauts during suit-up. Each crew member is shown in the White Room before boarding Space Shuttle Atlantis, and some display signs to loved ones. Launch footage includes the following replays: Beach Tracker, VAB, Pad B, Tower 1, DLTR-3, Grandstand, Cocoa Beach DOAMS, Playalinda DOAMS, UCS-23, SLF Convoy, OTV-154, OTV-163, OTV-170 (mislabeled), and OTV-171 (mislabeled). After the launch, NASA administrator Sean O'Keefe gives a speech to the Launch Control Center, with political dignitaries present. While on-orbit, Atlantis docks with the International Space Station (ISS), and Canadarm 2 on the ISS lifts the S0 Truss out of the orbiter's payload bay. The video includes highlights of three extravehicular activities (EVAs). In the first, the S0 Truss is fastened to the Destiny Laboratory Module on the ISS. During the third EVA, Walheim and Smith assist in the checkout of the handcart on the S0 Truss. The Atlantis crew is shown gathered together with the Expedition 4 crew of the ISS, and again by itself after undocking. Replays of the landing include: VAB, Tower 1, Mid-field, Runway South End, Runway North End, Tower 2, Playalinda DOAMS, Cocoa Beach DOAMS, and Pilot Point of View (PPOV). After landing, Commander Bloomfield lets each of his crew members give a short speech.

Source record↗
NASA NTRS2001

STS-105/Discovery/ISS 7A.1: Pre-Launch Activities, Launch, Orbit Activities and Landing

The crew of Space Shuttle Discovery on STS-105 is introduced at their pre-launch meal and at suit-up. The crew members include Commander Scott Horowitz, Pilot Rick Sturckow, and Mission Specialists Patrick Forrester and Daniel Barry, together with the Expedition 3 crew of the International Space Station (ISS). The Expedition 3 crew includes Commander Frank Culbertson, Soyuz Commander Vladimir Dezhurov, and Flight Engineer Mikhail Tyurin. When the astronauts depart for the launch pad in the Astrovan, their convoy is shown from above. Upon reaching the launch pad, they conduct a walk around of the shuttle, display signs for family members while being inspected in the White Room, and are strapped into their seats onboard Disciovery. The video includes footage of Discovery in the Orbiter Processing Facility, and some of the pre-launch procedures at the Launch Control Center are shown. The angles of launch replays include: TV-1, Beach Tracker, VAB, Pad A, Tower 1, UCS-15, Grandstand, OTV-70, Onboard, IGOR, and UCS-23. The moment of docking between Discovery and the ISS is shown from inside Discovery's cabin. While in orbit, the crew conducted extravehicular activities (EVAs) to attach an experiments container, and install handrails on the Destiny module of the ISS. The video shows the docking and unloading of the Leonardo Multipurpose Logistics Module (MPLM) onto the ISS. The deployment of a satellite from Discovery with the coast of the Gulf of Mexico in the background is shown. Cape Canaveral is also shown from space. Landing replays include VAB, Tower 1, mid-field, South End SLF, North End SLF, Tower 2, Playalinda DOAMS, UCS-23, and Pilot Point of View (PPOV). NASA Administrator Dan Goldin meets the crew upon landing and participates in their walk around of Discovery. The video concludes with a short speech by commander Horowitz.

Source record↗
NASA NTRS2019

Quantifying Contributions of Land Carbon Fluxes Variability and Atmospheric Transport Variability to Atmospheric CO2 Variability

Much of the uncertainty in predicting variations in greenhouse gases originates in the complex dynamics of the land ecosystem and the atmosphere. To reduce the uncertainties, it is useful to decouple the contributions of land carbon fluxes and atmospheric transport to atmospheric carbon variability. Here we isolate these contributions using a version of the NASA GEOS model that couples carbon, energy and water cycles between the land and the atmosphere. Our current study is a follow-on to a preliminary analysis that suggested that an extreme event (e.g., imposed drought) in a free running AGCM simulation affects atmospheric CO2 more through its impact on atmospheric transport than through a modification of land carbon fluxes. In order to more carefully isolate the effects of the land carbon variability and atmospheric transport variability on atmospheric CO2 variability, we conducted two coupled-AGCM simulations in replay mode, a mode that forces the model's evolution of weather to match that of the MERRA-2 reanalysis. In the control simulation, the land carbon fluxes and the atmospheric CO2 concentrations, as well as the meteorology, are simulated over 2001-2015. We compute the climatological seasonal cycles of net land carbon production from this control simulation and then prescribe, in a second simulation, these climatological carbon fluxes to the atmosphere in the same replay mode. By comparing the atmospheric CO2 variability produced in the two simulations, we fully isolate the part of this variability associated with land surface fluxes. Relative contributions of land flux variability and atmospheric transport variability to CO2 variability are quantified both on a regional basis and as a function of height into the atmosphere to support interpretation of both ground-based and satellite observations.

Lee, Eunjee↗
NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan↗
NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan M.↗
NASA NTRS1968

The development of spectro-signature indicators of root disease

The development and testing of airborne sensors that might be effective in discrimination root rot infected trees from healthy ones are outlined. The sensing device is composed of a thermal infrared radiometer and an instant replay video scan system.

Wear, J. F.↗
NASA NTRS1978

GCF HSD error control

A selective repeat automatic repeat request (ARQ) system was implemented under software control in the Ground Communications Facility error detection and correction (EDC) assembly at JPL and the comm monitor and formatter (CMF) assembly at the DSSs. The CMF and EDC significantly improved real time data quality and significantly reduced the post-pass time required for replay of blocks originally received in error. Since the remote mission operation centers (RMOCs) do not provide compatible error correction equipment, error correction will not be used on the RMOC-JPL high speed data (HSD) circuits. The real time error correction capability will correct error burst or outage of two loop-times or less for each DSS-JPL HSD circuit.

Hung, C. K.↗
NASA NTRS1984

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation

A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation.

Haley, D. C.↗
NASA NTRS1986

Evaluation of automated decision making methodologies and development of an integrated robotic system simulation: Study results

The implementation of a generic computer simulation for manipulator systems (ROBSIM) is described. The program is written in FORTRAN, and allows the user to: (1) Interactively define a manipulator system consisting of multiple arms, load objects, targets, and an environment; (2) Request graphic display or replay of manipulator motion; (3) Investigate and simulate various control methods including manual force/torque and active compliance control; and (4) Perform kinematic analysis, requirements analysis, and response simulation of manipulamotion. Previous reports have described the algorithms and procedures for using ROBSIM. These reports are superseded and additional features which were added are described. They are: (1) The ability to define motion profiles and compute loads on a common base to which manipulator arms are attached; (2) Capability to accept data describing manipulator geometry from a Computer Aided Design data base using the Initial Graphics exchange Specification format; (3) A manipulator control algorithm derived from processing the TV image of known reference points on a target; and (4) A vocabulary of simple high level task commands which can be used to define task scenarios.

Haley, D. C.↗
NASA NTRS1984

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation, appendix A

A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed were: (1) Capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) Capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) Postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) Investigation and simulation of various control methods including manual force/torque and active compliance control; (5) Evaluation and implementation of three obstacle avoidance methods; (6) Video simulation and edge detection; and (7) Software simulation validation. This appendix is the user's guide and includes examples of program runs and outputs as well as instructions for program use.

Haley, D. C.↗
NASA NTRS1987

Space shuttle main engine vibration data base

In order to evaluate Space Shuttle Main Engine (SSME) vibration data without having to constantly replay analog tapes, the SSME Vibration Data Base was developed. This data base contains data that have been digitized at a high sample rate for the entire test duration. It provides quick and efficient recall capabilities for numerious computation and display routines. The data base components are described as well as some of the compution and display features.

Lewallen, Pat↗
NASA NTRS1988

Transformational derivation of programs using the Focus system

A program derivation support system called Focus is being constructed. It will formally derive programs using the paradigm of program transformation. The following issues are discussed: (1) the integration of validation and program derivation activities in the Focus system; (2) its tree-based user interface; (3) the control of search spaces in program derivation; and (4) the structure and organization of program derivation records. The inference procedures of the system are based on the integration of functional and logic programming principles. This brings about a synthesis of paradigms that were heretofore considered far apart, such as logical and executable specifications and constructive and transformational approaches to program derivation. A great emphasis has been placed, in the design of Focus, on achieving small search spaces during program derivation. The program manipulation operations such as expansion, simplification and rewriting were designed with this objective. The role of operations that are expensive in search spaces, such as folding, has been reduced. Program derivations are documented in Focus in a way that the high level descriptions of derivations are expressed only using program level information. All the meta-level information, together with dependencies between derivations of program components, is automatically recorded by the system at a lower level of description for its own use in replay.

Reddy, Uday S.↗
NASA NTRS1989

The role of flight planning in aircrew decision performance

The role of flight planning in increasing the safety and decision-making performance of the air transport crews was investigated in a study that involved 48 rated airline crewmembers on a B720 simulator with a model-board-based visual scene and motion cues with three degrees of freedom. The safety performance of the crews was evaluated using videotaped replays of the flight. Based on these evaluations, the crews could be divided into high- and low-safety groups. It was found that, while collecting information before flights, the high-safety crews were more concerned with information about alternative airports, especially the fuel required to get there, and were characterized by making rapid and appropriate decisions during the emergency part of the flight scenario, allowing these crews to make an early diversion to other airports. These results suggest that contingency planning that takes into account alternative courses of action enhances rapid and accurate decision-making under time pressure.

Pepitone, Dave↗