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NASA NTRSDate not supplied

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS

Stratospheric composition and transport patterns are changing because of increasing greenhouse gas concentrations and declining levels of ozone depleting substances. Superimposed on forced trends are: dynamical variability at a range of time scales and unforeseen perturbations such as those resulting from injections of volcanic material and smoke from PyroCb events. Chemical reanalyses can be powerful tools for studying the composition of the stratosphere and disentangling forced responses from internal variability. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO) using the recently developed GMAO Constituent Data Assimilation System. Called the MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM), this reanalysis consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to the present. M2-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. The dynamics and tropospheric water vapor are constrained by assimilated meteorological fields from MERRA-2. The assimilated constituent fields and meteorology are provided to users at a 50-km horizontal resolution and a three-hourly frequency. Monthly analysis uncertainties and flags are also provided to the users. M2-SCREAM is an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of M2-SCREAM and selected results of a process-based evaluation of this product using independent data. As an illustration, we will discuss the perturbation to stratospheric composition from the eruption of Hunga Tonga–Hunga Ha'apai in January 2022 as seen in the reanalysis. We will also propose potential scientific applications of M2-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

MERRA-2↗
NASA NTRS2004

The Nonlinear Coupling of Alfven and Lower Hybrid Waves in Space Plasma

Space plasmas support a wide variety of waves, and wave-particle interactions as well as wave-wave interactions which are of crucial importance to magnetospheric and ionospheric plasma behavior. The excitation of lower hybrid waves (LHWs) in particular is a widely discussed mechanism of interaction between plasma species in space and is one of the unresolved questions of magnetospheric multi-ion plasmas. It is demonstrated that large-amplitude Alfven waves may generate LHWs in the auroral zone and ring current region and in some cases (particularly in the inner magnetosphere) this serves as the Alfven wave saturation mechanism. We present several examples of observational data which illustrate that the proposed mechanism is a plausible candidate to explain certain classes of LHW generation events in the ionosphere and magnetosphere and demonstrate electron and ion energization involving these processes. We discuss the morphology dynamics and level of LHW activity generated by electromagnetic ion cyclotron (EMIC) waves during the May 2-7 1998 storm period on the global scale. The LHWs were calculated based on a newly developed self-consistent model (Khazanov et. al. 2002) that couples the system of two kinetic equations: one equation describes the ring current (RC) ion dynamic and another equation describes the evolution of EMIC waves. It is found that the LHWs are excited by helium ions due to their mass dependent drift in the electric field of EMIC waves. The level of LHW activity is calculated assuming that the induced scattering process is the main saturation mechanism for these waves. The calculated LHWs electric fields are consistent with the observational data.

Khazanov, George V.↗
NASA NTRS2019

OGLE-2017-BLG-1186: First Application of Asteroseismology and Gaussian Processes to microlensing

We present the analysis of the event OGLE-2017-BLG-1186 from the 2017 Spitzer microlensing campaign. This is a remarkable microlensing event because its source is photometrically bright and variable, which makes it possible to perform an asteroseismic analysis using ground-based data. We find that the source star is an oscillating red giant with average timescale of ∼9 d. The asteroseismic analysis also provides us source properties including the source angular size (∼27 μas) and distance (∼11.5 kpc), which are essential for inferring the properties of the lens. When fitting the light curve, we test the feasibility of Gaussian processes (GPs) in handling the correlated noise caused by the variable source. We find that the parameters from the GP model are generally more loosely constrained than those from the traditional χ(exp 2) minimization method. We note that this event is the first microlensing system for which asteroseismology and GPs have been used to account for the variable source. With both finite-source effect and microlens parallax measured, we find that the lens is likely a ∼0.045 Mʘ brown dwarf at distance ∼9.0 kpc, or a ∼0.073 Mʘ ultracool dwarf at distance ∼9.8 kpc. Combining the estimated lens properties with a Bayesian analysis using a Galactic model, we find a ∼ 35 per cent probability for the lens to be a bulge object and ∼ 65 per cent to be a background disc object.

S.-S. Li↗
NASA NTRSDate not supplied

The CLVTOPS Toolchain for NASA Space Launch System Liftoff Analysis and Post Flight Validation

This paper showcases the unique technical capabilities of the CLVTOPS multi-body flight dynamics toolchain developed by Marshall Space Flight Center (MSFC) for analyzing NASA’s Space Launch System (SLS) liftoff events. The CLVTOPS toolchain integrates high-fidelity simulations, geometric algorithms, advanced data analytics, and post-flight telemetry to demonstrate positive clearance between separating bodies and inform design decisions that enhance mission reliability. Proper liftoff separation is crucial to the success of the launch vehicle’s mission; vehicle impacts with the launch tower and supporting components incur a heightened risk of mission failure. For liftoff analysis, the CLVTOPS toolchain enables the integration of vehicle, launch pad, and environmental input models for the investigation of key clearance effectors. Furthermore, recent enhancements to the CLVTOPS toolchain allow for validation via photogrammetric trajectory reconstruction and plume pressure impingement estimation on the tower. The following sections will walk through the tool-chain, SLS liftoff ground rules and assumptions, key models, standard analysis, recent enhancements, and post-flight validation of the Artemis I mission liftoff event.

CLVTOPS↗
NASA NTRS1992

Patterns of seismic activity preceding large earthquakes

A mechanical model of seismic faults is employed to investigate the seismic activities that occur prior to major events. The block-and-spring model dynamically generates a statistical distribution of smaller slipping events that precede large events, and the results satisfy the Gutenberg-Richter law. The scaling behavior during a loading cycle suggests small but systematic variations in space and time with maximum activity acceleration near the future epicenter. Activity patterns inferred from data on seismicity in California demonstrate a regional aspect; increased activity in certain areas are found to precede major earthquake events. One example is given regarding the Loma Prieta earthquake of 1989 which is located near a fault section associated with increased activity levels.

Shaw, Bruce E.↗
NASA NTRS1990

Observations of ionospheric flux ropes above South Pole

Numerical techniques have been used to investigate two different models of the current flow in ionospheric flux ropes, giving attention to the problem of mapping the electric field from the ionosphere down to balloon altitudes and to the isolated event that occurred at about 1625 UT on January 3, 1986. Also examined are Greenland magnetometer chain and Iqaluit magnetometer data, which imply that the isolated event was localized in longitude as well as 'global', in the sense that it occurred at conjugate points. IMP 8 solar wind plasma and IMF data indicate that the even occurred just as a strong southward turning of the IMF passed the earth.

Lin, Z. M.↗
NASA NTRS1993

Windshear Database for Forward-Looking Systems Certification

This document contains a description of a comprehensive database that is to be used for certification testing of airborne forward-look windshear detection systems. The database was developed by NASA Langley Research Center, at the request of the Federal Aviation Administration (FAA), to support the industry initiative to certify and produce forward-look windshear detection equipment. The database contains high resolution, three dimensional fields for meteorological variables that may be sensed by forward-looking systems. The database is made up of seven case studies which have been generated by the Terminal Area Simulation System, a state-of-the-art numerical system for the realistic modeling of windshear phenomena. The selected cases represent a wide spectrum of windshear events. General descriptions and figures from each of the case studies are included, as well as equations for F-factor, radar-reflectivity factor, and rainfall rate. The document also describes scenarios and paths through the data sets, jointly developed by NASA and the FAA, to meet FAA certification testing objectives. Instructions for reading and verifying the data from tape are included.

Switzer, G. F.↗
NASA NTRS2007

Reinforced Carbon-Carbon Subcomponent Flat Plate Impact Testing for Space Shuttle Orbiter Return to Flight

Following the tragedy of the Space Shuttle Columbia on February 1, 2003, a major effort commenced to develop a better understanding of debris impacts and their effect on the Space Shuttle subsystems. An initiative to develop and validate physics-based computer models to predict damage from such impacts was a fundamental component of this effort. To develop the models it was necessary to physically characterize Reinforced Carbon-Carbon (RCC) and various debris materials which could potentially shed on ascent and impact the Orbiter RCC leading edges. The validated models enabled the launch system community to use the impact analysis software LS DYNA to predict damage by potential and actual impact events on the Orbiter leading edge and nose cap thermal protection systems. Validation of the material models was done through a three-level approach: fundamental tests to obtain independent static and dynamic material model properties of materials of interest, sub-component impact tests to provide highly controlled impact test data for the correlation and validation of the models, and full-scale impact tests to establish the final level of confidence for the analysis methodology. This paper discusses the second level subcomponent test program in detail and its application to the LS DYNA model validation process. The level two testing consisted of over one hundred impact tests in the NASA Glenn Research Center Ballistic Impact Lab on 6 by 6 in. and 6 by 12 in. flat plates of RCC and evaluated three types of debris projectiles: BX 265 External Tank foam, ice, and PDL 1034 External Tank foam. These impact tests helped determine the level of damage generated in the RCC flat plates by each projectile. The information obtained from this testing validated the LS DYNA damage prediction models and provided a certain level of confidence to begin performing analysis for full-size RCC test articles for returning NASA to flight with STS 114 and beyond.

Melis, Matthew E.↗
NASA NTRS2014

SN 2010j1: Optical to Hard X-Ray Observations Reveal an Explosion Embedded in a Ten Solar Mass Cocoon

Some supernovae (SNe) may be powered by the interaction of the SN ejecta with a large amount of circumstellar matter (CSM). However, quantitative estimates of the CSM mass around such SNe are missing when the CSM material is optically thick. Specifically, current estimators are sensitive to uncertainties regarding the CSM density profile and the ejecta velocity. Here we outline a method to measure the mass of the optically thick CSM around such SNe. We present new visible-light and X-ray observations of SN 2010jl (PTF 10aaxf), including the first detection of an SN in the hard X-ray band using NuSTAR. The total radiated luminosity of SN 2010jl is extreme atleast 9 1050 erg. By modeling the visible-light data, we robustly show that the mass of the circumstellar material within 1016 cm of the progenitor of SN 2010jl was in excess of 10M_. This mass was likely ejected tens of years prior to the SN explosion. Our modeling suggests that the shock velocity during shock breakout was 6000 km s1, decelerating to 2600 km s1 about 2 yr after maximum light. Furthermore, our late-time NuSTAR and XMM spectra of the SN presumably provide the first direct measurement of SN shock velocity 2 yr after the SN maximum light measured to be in the range of 2000-4500 km s1 if the ions and electrons are in equilibrium, and_2000 km s1 if they are not in equilibrium. This measurement is in agreement with the shock velocity predicted by our modeling of the visible-light data. Our observations also show that the average radial density distribution of the CSM roughly follows an r2 law. A possible explanation for the _10M_ of CSM and the wind-like profile is that they are the result of multiple pulsational pair instability events prior to the SN explosion, separated from each other by years.

circumstellar matter (CSM)↗
NASA NTRS2016

Integrated Medical Model (IMM) Optimization Version 4.0 Functional Improvements

The IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support.

risk assessment↗
NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction.The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier than VCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS1982

A model study of Fuego volcanic aerosol dispersion in the lower stratosphere

A zonally averaged time-dependent primitive equation model is used to simulate the dispersion of both a carbon 14 injection and the volcanic aerosol from the 1974 Fuego eruption. It is noted that both injections occurred at low latitudes to midlatitudes in the Northern Hemisphere. The eddy flux terms, which account for the major portion of the transport in the lower stratosphere of this model, are specified in a manner similar to that of Harwood and Pyle (1975). Comparisons with data underline the ability of the model to simulate the vertical character of the tracer while maintaining reasonable meridional transport times. For the aerosol study, the simulated 1/e decay time at 37 deg N and for the 16- to 21-km altitude region is 9 months, whereas lidar measurements at the same latitude give a decay time of 8 months. The simulated vertical width at half-maximum for the aerosol tracer at 37 deg N and 19 deg N and for 6 months after the event possesses values of 5.0 km and 3.6 km, respectively, whereas the observed lidar values are 4.4 km and 3.0 km, respectively. The tracer transport to the Southern Hemisphere also is in qualitative agreement with the limited data that are available.

Remsberg, E. E.↗
NASA NTRS2012

The Use of Satellite-Measured Aerosol Optical Depth to Constrain Biomass Burning Emissions Source Strength in a Global Model GOCART

Small particles in the atmosphere, called "atmospheric aerosol" have a direct effect on Earth climate through scattering and absorbing sunlight, and also an indirect effect by changing the properties of clouds, as they interact with solar radiation as well. Aerosol typically stays in the atmosphere for several days, and can be transported long distances, affecting air quality, visibility, and human health not only near the source, but also far downwind. Smoke from vegetation fires is one of the main sources of atmospheric aerosol; other sources include anthropogenic pollution, dust, and sea salt. Chemistry transport models (CTMs) are among the major tools for studying the atmospheric and climate effects of aerosol. Due to the considerable variation of aerosol concentrations and properties on many temporal and spatial scales, and the complexity of the processes involved, the uncertainties in aerosol effects on climate are large, as is featured in the latest report of Intergovernmental Panel on Climate Change (IPCC) in 2007. Reducing this uncertainty in the models is very important both for predicting future climate scenarios and for regional air quality forecasting and mitigation. During vegetation fires, also called biomass burning (BB) events, complex mixture of gases and particles is emitted. The amount of BB emissions is usually estimated taking into account the intensity and size of the fire and the properties of burning vegetation. These estimates are input into CTMs to simulate BB aerosol. Unfortunately, due to large variability of fire and vegetation properties, the quantity of BB emissions is very difficult to estimate and BB emission inventories provide numbers that can differ by up to the order of magnitude in some regions. Larger uncertainties in data input make uncertainties in model output larger as well. A powerful way to narrow the range of possible model estimates is to compare model output to observations. We use satellite observations of aerosol properties, specifically aerosol optical depth, which is directly proportional to the amount of aerosol in the atmosphere, and compare it to the model output. Assuming the model represents aerosol transport and particle properties correctly, the amount of BB emissions determines the simulated aerosol optical depth. In this study, we explore the regional performance of 13 commonly used emission estimates. These are each input to global Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. We then evaluate how well each emission estimate reproduces the smoke aerosol optical depth measured by the MODIS instrument. We compared GOCART-simulate aerosol optical depth with that measured from the satellite for 124 fire cases around the world during 2006 and 2007. We summarize the regional performance of each emission inventory and discuss reasons for their differences by considering the assumptions made during their development. We also show that because stronger wind disperses smoke plumes more readily, in cases with stronger wind, a larger increase in emission amount is needed to increase aerosol optical depth. In quiet, low-wind-speed environments, BB emissions produce a more significant increase in aerosol optical depth, other things being equal. Using the region-specific, quantitative relationships derived in our paper, together with the wind speed obtained from another source for a given fire case, we can constrain the amount of emission required in the model to reproduce the observations. The results of this paper are useful to the developers of BB emission inventories, as they show the strengths and weaknesses of individual emission inventories in different regions of the globe, and also for modelers who use these inventories and wish to improve their model results.

Petrenko, Mariya↗
NASA NTRS1993

An evaluation of the National Meteorological Center weekly hindcast of upper-ocean temperature along the eastern Pacific equator in January 1992

The upper-ocean temperature distribution along the Pacific equator from 139 to 103 deg W was observed in January 1992 with temperature profiles recorded from a ship and inferred from an ocean general circulation model calculation involving data assimilation (i.e., hindcast). An El Nino episode was in progress. The l00-m-thick mixed layer depth, the mixed-layer temperature, and the depth-averaged temperature below the thermocline were similar in both data products. Considerable differences occurred in the representation of the 15-25 C thermocline, such as the depth-averaged temperatures above and below the 20 C isotherm, the east-west slope of the 20 C isotherm, and a 1000-km-wide depression. The longitudinal-averaged root-mean-square difference between the hindcast and observed depths of the center of the thermocline was 17 m. Most of the disparities could be attributed to a high wavenumber transient event that the model-based assimilation system was not intended to resolve.

Halpern, David↗
NASA NTRS1998

Central Plasma Sheet Ion Properties as Inferred from Ionospheric Observations

A method of inferring central plasma sheet (CPS) temperature, density, and pressure from ionospheric observations is developed. The advantage of this method over in situ measurements is that the CPS can be studied in its entirely, rather than only in fragments. As a result, for the first time, comprehensive two-dimensional equatorial maps of CPS pressure, density, and temperature within the isotropic plasma sheet are produced. These particle properties are calculated from data taken by the Special Sensor for Precipitating Particles, version 4 (SSJ4) particle instruments onboard DMSP F8, F9, F10, and F11 satellites during the entire year of 1992. Ion spectra occurring in conjunction with electron acceleration events are specifically excluded. Because of the variability of magnetotail stretching, the mapping to the plasma sheet is done using a modified Tsyganenko [1989] magnetic field model (T89) adjusted to agree with the actual magnetotail stretch at observation time. The latter is inferred with a high degree of accuracy (correlation coefficient -0.9) from the latitude of the DMSP b2i boundary (equivalent to the ion isotropy boundary). The results show that temperature, pressure, and density all exhibit dawn-dusk asymmetries unresolved with previous measurements. The ion temperature peaks near the midnight meridian. This peak, which has been associated with bursty bulk flow events, widens in the Y direction with increased activity. The temperature is higher at dusk than at dawn, and this asymmetry increases with decreasing distance from the Earth. In contrast, the density is higher at dawn than at dusk, and there appears to be a density enhancement in the low-latitude boundary layer regions which increases with decreasing magnetic activity. In the near-Earth regions, the pressure is higher at dusk than at dawn, but this asymmetry weakens with increasing distance from the Earth and may even reverse so that at distances X less than approx. 10 to -12 R(sub E), depending on magnetic activity, the dawn sector has slightly higher pressure. The temperature and density asymmetries in the near-Earth region are consistent with the ion westward gradient/curvature drift as the ions ExB convect earthward. When the solar wind dynamic pressure increases, CPS density and pressure appear to increase, but the temperature remains relatively constant. Comparison with previously published work indicates good agreement between the inferred pressure, temperature, and density and those obtained from in situ data. This new method should provide a continuous mechanism to monitor the pressure, temperature, and density in the magnetotail with unprecedented comprehensiveness.

Wing, Simon↗