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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Electrostatic Precipitation of Dust in the Martian Atmosphere: Implications for the Utilization of Resources During Future Manned Exploration Missions

Future human missions to Mars will require the utilization of local resources for oxygen, fuel. and water. The In Situ Resource Utilization (ISRU) project is an active research endeavor at NASA to develop technologies that can enable cost effective ways to live off the land. The extraction of oxygen from the Martian atmosphere. composed primarily of carbon dioxide, is one of the most important goals of the Mars ISRU project. The main obstacle is the relatively large amount of dust present in the Martian atmosphere. This dust must be efficiently removed from atmospheric gas intakes for ISRU processing chambers. A common technique to achieve this removal on earth is by electrostatic precipitation, where large electrostatic fields are established in a localized region to precipitate and collect previously charged dust particles. This technique is difficult to adapt to the Martian environment, with an atmospheric pressure of about one-hundredth of the terrestrial atmosphere. At these low pressures. the corona discharges required to implant an electrostatic charge to the particles to be collected is extremely difficult to sustain and the corona easily becomes biopolar. which is unsuitable for particle charging. In this paper, we report on our successful efforts to establish a stable corona under Martian simulated conditions. We also present results on dust collecting efficiencies with an electrostatic precipitator prototype that could be effectively used on a future mission to the red planet

Calle, Carlos I.↗
NASA NTRS2003

A Model Study of the Impact of Magnetic Field Reversal on Atmospheric Composition during Solar Proton Events

During a polarity transition of the Earth's magnetic field, the structure and strength of the field change significantly from their present values. This will alter the global pattern of charged particle precipitation into the atmosphere. Thus, particle precipitation is possible into regions that are at the moment effectively shielded by the Earth's magnetic field. A two-dimensional global fbHy coupled chemistry, radiation, and transport model of the atmosphere has been used to investigate how the increased particle precipitation affects the chemical composition of the middle and lower atmosphere. Ozone losses resulting from large energetic particle events are found to increase significantly, with resultant losses similar to those observed m the Antarctic ozone hole of the 1990s. This results in significant increases in surface UV-B radiation as well as changes in stratospheric temperature and circulation over a period of several months after large particle events.

Sinnhuber, Miaiam↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRSDate not supplied

ELMO: ELectron Microburst Observatory Mission to Study Microbursts

We describe the ELectron Microburst Observatory mission, ELMO which is proposed as a CubeSat constellation mission to fully characterize microburst event spatial extent systematically for the first time both in latitude and longitude. ELMO comprises 4 CubeSats two per orbit plane in two orbit planes. ELMO will fly in a high inclination LEO at about 500 km in altitude. The CubeSats will systematically separate both in latitude and longitude over the mission lifetime and will carry MERIT, Miniaturized Electron and Proton Telescope as the the payload. MERIT has been built,tested and delivered to fly on NASA's HERMES, Lunar Gateway mission. MERIT is a solid state detector particle telescope with two identical sensor heads pointed zenith- and nadir-wards enabling measurement of both downgpoing and upwelling electrons thereby accurately estimating electron precipitation into the atmosphere. MERIT will measure electron and protons over a wide energy range in multiple differential channels with a very time resolution of less than 4 ELMO will quantify for the first time the contribution of microbursts to radiation belt electron loss using systematic coordinated multipoint measurements. Energetic electron precipitation affects atmospheric chemistry and therefore climate change. ELMO measures microbursts with unprecedented time and energy resolution. ELMO provides critical knowledge of electron loss processes required for quantitative prediction of global electron fluxes.

Shri Kanekal↗
NASA NTRS2014

Millimeter- and Submillimeter-Wave Remote Sensing Using Small Satellites

Cloud ice properties and processes play fundamental roles in atmospheric radiation and precipitation. Limited knowledge and poor representation of clouds in global climate models have led to large uncertainties about cloud feedback processes under climate change. Ice clouds have been used as a tuning parameter in the models to force agreement with observations of the radiation budget at the top of the atmosphere, and precipitation at the bottom. The lack of ice cloud measurements has left the cloud processes at intermediate altitudes unconstrained. Millimeter (mm) and submillimeter (submm)-wave radiometry is widely recognized for its potential to fill the cloud measurement gap in the middle and upper troposphere. Analyses have shown that channels from 183900 GHz offer good sensitivity to ice cloud scattering and can provide ice water path (IWP) products to an accuracy of 25 by simultaneously retrieving ice particle size (Dme) and IWP. Therefore, it is highly desirable to develop a cost-effective, compact mm/submm-wave instrument for cloud observations that can be deployed on future small satellites.This paper presents a conceptual study for a mm/submm-wave instrument for multispectral measurements of ice clouds. It discusses previous work at these frequencies by NASA Goddard Space Flight Center (GSFC) and the current instrument study, as well as receiver architectures and their anticipated performance. And finally, it describes a microsatellite prototype intended for use with this mm/submm-wave instrument.

small satellites↗
NASA NTRS1980

The importance of energetic particle precipitation on the chemical composition of the middle atmosphere

The present review deals with the importance of three distinct classes of precipitation which directly deposit energy into the middle atmosphere, viz. galactic cosmic radiation, energetic solar protons and relativistic electron precipitation from the earth's radiation belts. Chemical considerations during particle precipitation are discussed, with special emphasis on the relative production rate of odd nitrogen and odd hydrogen species during ionizing particle precipitation. The long residence time of NO in the upper stratosphere, where catalytic interaction with O3 is most effective, requires that this mechanism be included in future modeling of global distribution of O3. Other situations causing O3 depletion are also identified.

Thorne, R. M.↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

High-spatial-resolution passive microwave sounding systems

The principal contributions of this combined theoretical and experimental effort were to advance and demonstrate new and more accurate techniques for sounding atmospheric temperature, humidity, and precipitation profiles at millimeter wavelengths, and to improve the scientific basis for such soundings. Some of these techniques are being incorporated in both research and operational systems. Specific results include: (1) development of the MIT Microwave Temperature Sounder (MTS), a 118-GHz eight-channel imaging spectrometer plus a switched-frequency spectrometer near 53 GHz, for use on the NASA ER-2 high-altitude aircraft, (2) conduct of ER-2 MTS missions in multiple seasons and locations in combination with other instruments, mapping with unprecedented approximately 2-km lateral resolution atmospheric temperature and precipitation profiles, atmospheric transmittances (at both zenith and nadir), frontal systems, and hurricanes, (3) ground based 118-GHz 3-D spectral images of wavelike structure within clouds passing overhead, (4) development and analysis of approaches to ground- and space-based 5-mm wavelength sounding of the upper stratosphere and mesosphere, which supported the planning of improvements to operational weather satellites, (5) development of improved multidimensional and adaptive retrieval methods for atmospheric temperature and humidity profiles, (6) development of combined nonlinear and statistical retrieval techniques for 183-GHz humidity profile retrievals, (7) development of nonlinear statistical retrieval techniques for precipitation cell-top altitudes, and (8) numerical analyses of the impact of remote sensing data on the accuracy of numerical weather predictions; a 68-km gridded model was used to study the spectral properties of error growth.

Staelin, D. H.↗
DOE OSTI2020

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth’s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently-developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Investigating Hydrometeorological Conditions Associated with Increasing Dust Events in Southwestern United States

A recent study (Tong et al. 2017) shows a rapid intensification of dust storm activity over the southwestern United States in the past decades. For example, the frequency of windblown dust storms has increased 240 percent from 1990s to 2000s. Increasing dust storms can worsen air quality in the region. The study also finds that the intensification of dust events has a close connection with a fast-rising infectious disease (valley fever) caused by inhaling soil-dwelling fungi (Coccidioides immitis and C. posadasii) in the southwestern United States. Hydrometeorological conditions play an important role in dust storm activity, including winds, precipitation, soil moisture, atmospheric boundary stability, land surface types, etc. In this presentation, we describe our preliminary results of linking the dust trends to variations of hydrometeorological conditions at regional and global scales, using a number of Earth observations, including products from NLDAS (North American Land Data Assimilation System), TRMM (Tropical Rainfall Measuring Mission), and MERRA (Modern Era Retrospective-analysis for Research and Applications) datasets, from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The GES DISC, one of the 12 NASA data centers, is home to multidisciplinary data archives such as precipitation, hydrology, atmospheric chemistry, atmospheric dynamics, etc. We demonstrate that multidisciplinary datasets and services at GES DISC can be used for interdisciplinary investigation to understand the interactions of Earth system components from an observational perspective.

satellite↗
DOE OSTI2026

Record-Breaking Atmospheric River Drives April 2024 Extreme Precipitation in the United Arab Emirates and the Surrounding Gulf Region

In mid-April 2024, the United Arab Emirates (UAE) and the surrounding Gulf region experienced unprecedented rainfall and catastrophic flooding, causing widespread damage, loss of life, and significant economic costs. During the 3-day period from 15 to 17 April, rainfall in the UAE exceeded 100 mm in the hardest-hit areas, with more than 170% of the typical annual precipitation recorded in just 72 h. The event was associated with exceptionally strong integrated water vapor transport (IVT), driven by a persistent low pressure system and a focused corridor of moisture transport. This environment, combined with favorable dynamical and convective conditions, triggered intense thunderstorms and widespread flooding. This article examines the role of an atmospheric river (AR) in the April 2024 extreme precipitation event, emphasizing the contribution of extreme IVT to preconditioning and amplifying the heavy rainfall. While this event has previously been described primarily in terms of a mesoscale convective system (MCS) and potential vorticity (PV) streamers, here we document and illustrate its close relationship to a record-breaking moisture transport corridor. This AR-based perspective highlights how large-scale moisture-transport frameworks can complement synoptic and mesoscale analyses in understanding extreme rainfall in arid and semiarid regions such as the UAE and the Gulf. The event serves as a stark example of the vulnerabilities faced by arid regions, where atmospheric conditions conducive to extreme flooding may become more frequent in the future.

Massoud, Elias [ORNL] (ORCID:0000000217725361)↗
DOE OSTI2021

A Constrained Data Assimilation Algorithm Based on GSI Hybrid 3D-EnVar and Its Application

Abstract Data assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations.

Wang, Jia↗
DOE OSTI2024

Low-Level Liquid-Bearing Clouds Contribute to Seasonal Lower Atmosphere Stability and Surface Energy Forcing over a High-Mountain Watershed Environment

Measurements of atmospheric structure and surface energy budgets distributed along a high-altitude moun tain watershed environment near Crested Butte, Colorado, from two separate, but coordinated, field campaigns, Surface Atmosphere Integrated field Laboratory (SAIL) and Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH), are analyzed. This study identifies similarities and differences in how clouds influence the radiative budget over one snow-free summer season (2022) and two snow-covered seasons (2021/22; 2022/23) for this alpine location. A relationship between lower-tropospheric stability stratification and longwave radiative flux from the presence or absence of clouds is identified. When low clouds persisted, often with signatures of supercooled liquid in win ter, the lower troposphere experienced weaker stability, while radiatively clear skies that are less likely to be influenced by liquid droplets were associated with appreciably stronger lower-tropospheric stratification. Corresponding surface turbu lent heat fluxes partitioned differently based upon the cloud–stability stratification regime derived from early morning radiosounding profiles. Combined with the differences in the radiative budget largely resulting from dramatic seasonal dif ferences in surface albedo, the lower atmosphere stratification, surface energy budget, and near-surface thermodynamics are shown to be modified by the effective longwave radiative forcing of clouds. The diurnal evolution of thermodynamics and surface energy components varied depending on the early morning stratification state. Thus, the importance of quies cent versus synoptically active large-scale meteorology is hypothesized as a critical forcing for cloud properties and associ ated surface energy budget variations. The physical relationships between clouds, radiation, and stratification can provide a useful suite of metrics for process understanding and to evaluate numerical models in such an undersampled, highly com plex terrain environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗
NASA NTRSDate not supplied

Formation and Propagation of Atmospheric River and Its Impact on Extreme Precipitation Events in the North Pacific and the Western North America

Seasonal and interannual evolution patterns of atmospheric rivers (AR) in the North Pacific are examined as a function of the formation region where an AR is first detected using 43-year MERRA-2 reanalysis data. Integrated water vapor transport (IVT) is used to detect AR with latitude dependent thresholds of IVT to better detect AR-like features in the high latitudes. Based on 3-hourly AR statistics, three main AR genesis regions in the North Pacific (i.e., South China Sea (SCS), Western North Pacific (WNP) and Central Pacific Ocean and Hawaiian Islands (CPO) are identified. WNP is the main source of AR with 1475 ARs detected for 43 cold seasons (NDJFM). Over 70% of all AR formed in the WNP has longer than 2-day. On the other hand, AR from CPO tends to have shorter lifetime than those from WNP and SCS. While propagation patterns of AR from SCS and WNP are similar, AR from WNP tends to reach mature phase quicker and shows higher change of impacting west coast of North America. Longevity and strength of AR are also examined based on three large-scale circulation modes (e.g. ENSO, WP, and EAJS) over the East Asia identified from eigen analysis of upper-level zonal winds. During El Nino, the number of AR formed in the western Pacific increased by 20%. First two days, the average size and intensity shows little difference compared with ARs in La Nina years. AR appears to grow in size in El Nino vs. La Nina years. Positive phase of WP correlated with less, but larger and stronger AR formation over WNP and SCS regions. EAJS has little impact in the numbers of AR and its size, but ARs in a stronger EAJS tends to grow larger. Atmospheric circulation associated with the initial formation and propagation of AR from the different regions in the North Pacific as well as its impact on extreme precipitation events in the North Pacific and the west coast of North America will be also discussed.

Integrated water vapor transport↗
NASA NTRS2020

How Magnetically Conjugate Atmospheres and the Magnetosphere Participate in the Formation of Low‐Energy Electron Precipitation in the Region of Diffuse Aurora

The electron precipitation in the region of the diffuse aurora should be considered as a two‐step process (Khazanov et al., 2017, https://doi.org/10.1002/2016GL072063). The first one is the interaction of plasma sheet electrons with electrostatic electron cyclotron and/or whistler waves, moving those electrons into the loss cone to precipitate in both magnetically conjugate atmospheres. The second step is the interaction of these electrons with the ionosphere and atmosphere via their elastic and nonelastic collisions and reflection (backscatter) of degraded electrons back to magnetosphere and conjugate ionospheres. This paper presents the results of a newly developed scenario of non steady‐state electron precipitation dynamics that accounts for magnetosphere‐ionosphere‐atmosphere energy interplay over the entire energy range of the plasma sheet electron population and their affiliated secondary electrons. It also studies how both magnetically conjugate auroral regions work together with the magnetosphere in the formation of electron precipitation in the region of the diffuse aurora with the energy range coverage from 1 eV up to 10 keV.

George V Khazanov↗
NASA NTRS2019

Extreme Wet and Dry Conditions Affected Differently by Greenhouse Gases and Aerosols

Global warming due to greenhouse gases and atmospheric aerosols alter precipitation rates, but the influence on extreme precipitation by aerosols relative to greenhouse gases is still not well known. Here we use the simulations from the Precipitation Driver and Response Model Intercomparison Project that enable us to compare changes in mean and extreme precipitation due to greenhouse gases with those due to black carbon and sulfate aerosols, using indicators for dry extremes as well as for moderate and very extreme precipitation. Generally, we find that the more extreme a precipitation event is, the more pronounced is its response relative to global mean surface temperature change, both for aerosol and greenhouse gas changes. Black carbon (BC) stands out with distinct behavior and large differences between individual models. Dry days become more frequent with BC-induced warming compared to greenhouse gases, but so does the intensity and frequency of extreme precipitation. An increase in sulfate aerosols cools the surface and thereby the atmosphere, and thus induces a reduction in precipitation with a stronger effect on extreme than on mean precipitation. A better understanding and representation of these processes in models will provide knowledge for developing strategies for both climate change and air pollution mitigation.

Sillmann, Jana↗
NASA NTRS1990

Ion precipitation from the inner plasma sheet due to stochastic diffusion

Plasma sheet ions do not conserve their first adiabatic invariant when the magnetic field is appreciably tail-like. They do conserve a different adiabatic invariant but only to linear, rather than exponential, accuracy in the appropriate small parameter. Thus significant stochastic diffusion can occur for particles crossing the separatrix dividing the segments of orbits on which the particles cross and do not cross the tail midplane. Such ions can escape the plasma sheet and precipitate into the atmosphere. Stochastic scattering is strongest from those field lines where the ion's Larmor period in the normal component of the neutral sheet magnetic field approximately equals its bounce period. By comparing the rates of stochastic ion loss and convection in the tail, it is possible to estimate the location and thickness of the inner edge of the ion plasma sheet created by stochastic ion loss. Ions of different masses precipitate into the atmosphere at slightly different locations. Since wave particle interactions are not needed, this precipitation will always occur and should be particularly evident during quiet geomagnetic conditions, when it is less likely to be masked by other precipitation mechanisms.

Zelenyi, L.↗