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NASA NTRS2015

International Space Station Data Collection for Disaster Response

Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous sensor systems in higher altitude polar orbits. NASA remote sensing assets on the station began collecting International Disaster Charter (IDC) response data in May 2012. The initial NASA ISS sensor systems responding to IDC activations included the ISS Agricultural Camera (ISSAC), mounted in the Window Observational Research Facility (WORF); the Crew Earth Observations (CEO) Facility, where the crew collects imagery using off-the-shelf handheld digital cameras; and the Hyperspectral Imager for the Coastal Ocean (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response in 2012 there have been 108 IDC activations; NASA sensor systems have collected data for thirty-two of these events. Of the successful data collections, eight involved two or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program.

Stefanov, William L.↗

NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗

NASA NTRS2024

The Correlation Between Arctic Sea Ice, Cloud Phase and Radiation Using A-Train Satellites

Climate warming has a stronger impact on Arctic climate and sea ice cover (SIC) decline than previously thought. Better understanding and characterization of the relationship between sea ice and clouds and the implications for surface radiation is key to improving our confidence in Arctic climate projections. Here we analyze the relationship between sea ice, cloud phase and surface radiation over the Arctic, defined as north of 60° N, using active- and passive-sensor satellite observations from three different datasets. We find that all datasets agree on the climatology of and seasonal variability in total and liquid-bearing (liquid and mixed-phase) cloud covers. Similarly, our results show a robust relationship between decreased SIC and increased liquid-bearing clouds in the lowest levels (below 3 km) for all seasons (strongest in winter) but summer, while increased SIC and ice clouds are positively correlated in two of the three datasets. A refined map correlation analysis indicates that the relationship between SIC and liquid-bearing clouds can change sign over the Bering, Barents and Laptev seas, likely because of intrusions of warm air from low latitudes during winter and spring. Finally, the increase in liquid clouds resulting from decreasing SIC is associated with enhanced radiative cooling at the surface. Our findings indicate that the newly formed liquid clouds reflect more shortwave (SW) radiation back to space compared to the surface, generating a cooling effect of the surface, while their downward longwave (LW) radiation is similar to the upward LW surface emission, which has a negligible radiative impact on the surface. This overall cooling effect should contribute to dampening future Arctic surface warming as SIC continues to decline.

Arctic climate↗

NASA NTRS1996

AIRSAR Data for Geological and Geomorphological Mapping in the Great Sandy Desert and Pilbara Regions of Western Australia

Enhancements of AIRSAR data have demonstrated the benefits of synthetic aperture radar (SAR) for revealing an additional and mich higher level of information about the composition of the terrain than enhancements f either SPOT-PAN or Landsat TM data. With appropriate image processing techniques, surface and near surface geological structures, hydrological systems (both current and ancient) and landform features, have been evidenced in a diverse range of landscapes. In the Great Sandy Desert region where spectral variability is minimal, radar's sensitivity to the micromorphology of sparse exposures of subcrop and lag gravels has provided a new insight into the region's geological framework, its landforms, and their evolution. In the Pilbara region, advanced processing of AIRSAR data to unmix the backscatter between and within the three frequencies of data has highlighted subsurface extensions of greenstone lithologies below sand cover and morphological evidence of past flow conditions under former climate regimes. On the basis of these observations, it is recommend that radar remote sensing technology involving the use of high resolution, polarimetric data be seriously considered as a viable tool for exploration in erosional and depositional environments located within Australia's mineral and oil-prospective provinces.

Tapley, Ian J.↗

NASA NTRS1998

A Survey of Variable Extragalactic Sources with XTE's All Sky Monitor (ASM)

The original goal of the project was the near real-time detection of AGN utilizing the SSC 3 of the ASM on XTE which does a deep integration on one 100 square degree region of the sky. While the SSC never performed sufficiently well to allow the success of this goal, the work on the project has led to the development of a new analysis method for coded aperture systems which has now been applied to ASM data for mapping regions near clusters of galaxies such as the Perseus Cluster and the Coma Cluster. Publications are in preparation that describe both the new method and the results from mapping clusters of galaxies.

Jernigan, Garrett↗

NASA NTRS1999

The NASA/MSFC Global Reference Atmospheric Model: 1999 Version (GRAM-99)

The latest version of Global Reference Atmospheric Model (GRAM-99) is presented and discussed. GRAM-99 uses either (binary) Global Upper Air Climatic Atlas (GUACA) or (ASCII) Global Gridded Upper Air Statistics (GGUAS) CD-ROM data sets, for 0-27 km altitudes. As with earlier versions, GRAM-99 provides complete geographical and altitude coverage for each month of the year. GRAM-99 uses a specially-developed data set, based on Middle Atmosphere Program (MAP) data, for 20-120 km altitudes, and NASA's 1999 version Marshall Engineering Thermosphere (MET-99) model for heights above 90 km. Fairing techniques assure smooth transition in overlap height ranges (20-27 km and 90-120 km). GRAM-99 includes water vapor and 11 other atmospheric constituents (O3, N2O, CO, CH4, CO2, N2, O2, O, A, He and H). A variable-scale perturbation model provides both large-scale (wave) and small-scale (stochastic) deviations from mean values for thermodynamic variables and horizontal and vertical wind components. The small-scale perturbation model includes improvements in representing intermittency ("patchiness"). A major new feature is an option to substitute Range Reference Atmosphere (RRA) data for conventional GRAM climatology when a trajectory passes sufficiently near any RRA site. A complete user's guide for running the program, plus sample input and output, is provided. An example is provided for how to incorporate GRAM-99 as subroutines in other programs (e.g., trajectory codes).

Justus, C. G.↗

NASA NTRS2003

"Land-Cover Conversion in Amazonia, The Role of ENV" Ironment and Substrate composition in Modifying SOI

LBA research from the first phase of LBA focused on three broad categories: 1) mapping land cover and quantifying rates of change, persistence of pasture, and area of recovering forest; 2) evaluating the role of environmental factors and land-use history on soil biogeochemistry; and 3) quantifying the natural and human controls on stream nutrient concentrations. The focus of the research was regional, concentrating primarily in the state of RondBnia, but also included land-cover mapping in the vicinity of Maraba, Para, and Manaus, Amazonas. Remote sensing analysis utilized Landsat Thematic Mapper (TM) and Multispectral Scanner (MS S) data to map historical patterns of land-cover change. Specific questions addressed by the remote sensing component of the research included: 1) what is the areal extent of dominant land-cover classes? 2) what are the rates of change of dominant land cover through processes of deforestation, disturbance and regeneration? and 3) what are the dynamic properties of each class that characterize temporal variability, duration, and frequency of repeat disturbance? Biogeochemical analysis focused on natural variability and impacts of land-use/land-cover changes on soil and stream biogeochemical properties at the regional scale. An emphasis was given to specific soil properties considered to be primary limiting factors regionally, including phosphorus, nitrogen, base cations and cation-exchange properties. Stream sampling emphasized the relative effects of the rates and timing of land-cover change on stream nutrients, demonstrating that vegetation conversion alone does not impact nutrients as much as subsequent land use and urbanization.

Roberts, Dar A.↗

NASA NTRS2013

WISE J233237.05-505643.5: A Double-Peaked Broad-Lined AGN with Spiral-Shaped Radio Morphology

We present radio continuum mapping, optical imaging and spectroscopy of the newly discovered double-peaked broad-lined AGN WISE J233237.05-505643.5 at redshift z = 0.3447. This source exhibits an FR-I and FR-II hybrid-morphology, characterized by bright core, jet, and Doppler-boosted lobe structures in ATCA continuum maps at 1.5, 5.6, and 9 GHz. Unlike most FR-II objects, W2332-5056 is hosted by a disk-like galaxy. The core has a projected 5" linear radio feature that is perpendicular to the curved primary jet, hinting at unusual and complex activity within the inner 25 kpc. The multi-epoch optical-near-IR photometric measurements indicate significant variability over a 3-20 year baseline from the AGN component. Gemini-South optical data shows an unusual double-peaked emission-line features: the centroids of the broad-lined components of H-alpha and H-beta are blueshifted with respect to the narrow lines and host galaxy by approximately 3800 km/s. We examine possible cases which involve single or double supermassive black holes in the system, and discuss required future investigations to disentangle the mystery nature of this system.

galaxies: individual↗

NASA NTRS2017

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Earth System Data Record↗

NASA NTRS2022

Multisensor Machine Learning to Retrieve High Spatiotemporal Resolution Land Surface Temperature

Climate change is making heat waves more frequent, long-lasting, and severe. While multiple satellite types provide data to monitor surface temperature, geostationary (GEO) sensors provide near-continuous, continental-scale observations which can better capture the diurnal variability of land surface temperature (LST) than intermittent observations from low-earth orbit (LEO) sensors. However, standard products from GEO satellites are available at coarsened spatial and temporal resolutions compared to the native sensor resolution. Using datasets from the NASA Earth Exchange, we leveraged co-located, co-temporal observations from LEO and GEO satellites to learn a data-driven mapping using a convolutional neural network. The resulting NASA Earth eXchange Artificial Intelligence LST (NEXAI-LST) achieved a mean absolute error of 1.73 K relative to the target LEO product and improves on both spatial and temporal resolution [2 km, 10 minute] compared to the GEO full disk standard product [10 km, hourly]. In validation against measurements from a ground-based sensor network, NEXAI-LST achieves similar or better fit than both LEO and GEO standard products, while depending none of the prior knowledge of land surface and atmospheric states required by physical-statistical models. Further, application of the model to unseen LEO and GEO satellites demonstrates robust generalization of the model across spatial region, time of day, and sensor. In support of NASA’s open-source science initiative, we make our NEXAI-LST product, model, and codes available to facilitate data exploration and further studies.

Kate Marie Duffy↗

NASA NTRS2022

First Observations of Mesospheric OH Emission Profiles from OMPS/LP

The hydroxyl radical (OH) is one of the most reactive trace species and plays several important roles in the photochemical equilibrium and energy balance in the mesosphere. Global observations of OH from satellite instruments have a role to play in the study of OH and water vapor variations. This study describes an advanced algorithm to detect mesospheric OH emission profiles from the Suomi NPP satellite Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP). A triplet technique has been adapted to the OMPS/LP radiance measurements for determining OH emission signatures and OH index (OHI) from the OH A2Σ+-X2Π 0-0 band near the 308.8-nm wavelength. The derived mesospheric profiles provide an overall picture of the vertical distribution of OHI between 55 and 84 km and seasonal and latitudinal variability of the strength and height of the OHI. The observed annual cycle is correlated with the water vapor cycle and anticorrelated with the mesospheric temperature cycle. The data show that the relationships persist during the period of April 2012–December 2020. The seasonal behavior of OHI may be associated with variations in solar illumination or mesospheric water vapor abundance. The influence of solar illumination is dominant in the midlatitudes, while the OHI pattern is dominated by water vapor photolysis and other influences in the tropics.

Observations of Mesospheric OH Emission↗

NASA NTRS2020

Quantifying Spatiotemporal Variability of Glacier Algal Blooms and the Impact on Surface Albedo in Southwestern Greenland

Albedo reduction due to light-absorbing impurities can substantially enhance ice sheet surface melt by increasing surface absorption of solar energy. Glacier algae have been suggested to play a critical role in darkening the ablation zone in southwestern Greenland. It was very recently found that the Sentinel-3 Ocean and Land Colour Instrument (OLCI) band ratio R709 nm∕R673 nm can characterize the spatial patterns of glacier algal blooms. However, Sentinel-3 was launched in 2016, and current data are only available over three melting seasons (2016–2019). Here, we demonstrate the capability of the MEdium Resolution Imaging Spectrometer (MERIS) for mapping glacier algae from space and extend the quantification of glacier algal blooms over southwestern Greenland back to the period 2004–2011. Several band ratio indices (MERIS chlorophyll a indices and the impurity index) were computed and compared with each other. The results indicate that the MERIS two-band ratio index (2BDA) R709 nm∕R665 nm is very effective in capturing the spatial distribution and temporal dynamics of glacier algal growth on bare ice in July and August. We analyzed the interannual (2004–2011) and summer (July–August) trends of algal distribution and found significant seasonal and interannual increases in glacier algae close to the Jakobshavn Isbrae Glacier and along the middle dark zone between the altitudes of 1200 and 1400 m. Using broadband albedo data from the Moderate Resolution Imaging Spectroradiometer (MODIS), we quantified the impact of glacier algal growth on bare ice albedo, finding a significant correlation between algal development and albedo reduction over algae-abundant areas. Our analysis indicates the strong potential for the satellite algal index to be used to reduce bare ice albedo biases in regional climate model simulations.

surface albedo↗

NASA NTRS1995

A molecular line study of NGC 1333/IRAS 4

Molecular line surveys and fully sampled spectral line maps at 1.3 and 0.87 mm are used to examine the physical and chemical characteristics of the extreme Class I sources IRAS 4A and 4B in the L1450/NGC 1333 molecular cloud complex. A very well collimated, jetlike molecular flow emanates from IRAS 4A, with a dynamical age of a few thousand years. Symmetric, clumpy structure along the outflow lobes suggests that there is considerable variability in the mass-loss rate or wind velocity even at this young age. Molecular emission lines toward IRAS 4A and 4B are observed to be weak in the velocity range corresponding to quiescent material surrounding the young stellar objects (YSOs). Depletion factors of 10-20 are observed for all molecules, including CO, even for very conservative mass estimates from the measured millimeter and submillimeter dust continuum. However, abundance scaled with respect to CO are similar to other dark molecular cloud cores. Such depletions could be mimicked by high dust and optical depths or increased grain emissivities at the observing frequencies of 230 and 435 GHz, but the millimeter and submillimeter spectral energy distributions suggest that this is unlikely over the single-dish size scales of 5000-10,000 AU. Dense, outflowing gas is found to be kinematically, but not spatially, distinct from the quiescent material on these size scales. If CO is used as a chemical standard for the high-velocity gas, we find substantial enhancements in abundances of several molecules in outflowing material, most notably CS, SIO, and CH3OH. The SiO emission is kinematically well displaced from the bulk cloud velocity and likely arises from directly shocked material. As is the case for CO, however, the outflow features from more volatile species are centered near the cloud velocity and are often characterized by quite low rotational temperatures. We suggest that grain-grain collisions induced by velocity shear zones surrounding the outflow axes transiently desorb the grain mantles, resulting in large abundance enhancements of selected species. Similar results have recently been obtained in several other low-mass YSOs, where outflowing gas is often both kinematically and spatailly distinct, and are illustrative of the ability of accretion and outflow processes to simultaneously modify the composition of the gas and dust surrounding young stars.

Blake, Geoffrey A.↗

NASA NTRS1998

Bi-Level Integrated System Synthesis (BLISS)

BLISS is a method for optimization of engineering systems by decomposition. It separates the system level optimization, having a relatively small number of design variables, from the potentially numerous subsystem optimizations that may each have a large number of local design variables. The subsystem optimizations are autonomous and may be conducted concurrently. Subsystem and system optimizations alternate, linked by sensitivity data, producing a design improvement in each iteration. Starting from a best guess initial design, the method improves that design in iterative cycles, each cycle comprised of two steps. In step one, the system level variables are frozen and the improvement is achieved by separate, concurrent, and autonomous optimizations in the local variable subdomains. In step two, further improvement is sought in the space of the system level variables. Optimum sensitivity data link the second step to the first. The method prototype was implemented using MATLAB and iSIGHT programming software and tested on a simplified, conceptual level supersonic business jet design, and a detailed design of an electronic device. Satisfactory convergence and favorable agreement with the benchmark results were observed. Modularity of the method is intended to fit the human organization and map well on the computing technology of concurrent processing.

Sobieszczanski-Sobieski, Jaroslaw↗

NASA NTRS2002

Long-Term Simulation of Dust Distribution with the GOCART Model: Correlation with the North Atlantic Oscillation

Global distribution of aeolian dust is simulated from 1981 to 1996 with the Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART) model. The results are assessed with in-situ measurements and the Total Ozone Mapping Spectrometer (TOMS) aerosol products. The annual budget over the different continents and oceans are analyzed. It is found that there is a maximum of 25% difference of global annual emission from the minimum in 1996 to the maximum in 1988. There is a downward trend of dust emission over Africa and East Asia, of 6 and 2 Tg/yr, respectively. The inter-annual variability of dust distribution is analyzed over the North Atlantic and Africa. It is found that in winter most of the North Atlantic and Africa dust loading is correlated with the North Atlantic Oscillation. The GOCART model indicates that a controlling factor of such correlation can be attributed to dust emission from the Sahel. The Bodele depression is the major dust source in winter and its inter-annual variability is highly correlated with the NAO. However, it is not possible to conclude without further analysis that the North Atlantic Oscillation is forcing the inter-annual variability of dust emission and in-turn dust concentration over the North Atlantic.

Ginoux, P.↗

NASA NTRS2011

What We've Learned from Approximately 12 Years of MISR Aerosol Observations

The NASA Earth Observing System's Multi-angle Imaging SpectroRadiometer (MISR) has been imaging the planet about once per week for nearly 12 years. These data contain a wealth of information about the current state, as well as the variability of Earth's surface and atmosphere. For airborne particles specifically, horizontal distributions of aerosol optical depth, aerosol type, and near-source aerosol plume height are now routinely derived. The results are being applied to a wide range of problems, from constraining aerosol direct radiative forcing of climate and assessing its seasonal and secular trends, to material transports of smoke and dust, to mapping volcanic ash plumes and near-surface aerosol pollution. The value of these data is multiplied many times by combining it with data from other sources, and using the aggregate to constrain models. This talk will summarize the latest developments, and will also discuss current work aimed at making further advancements in these areas.

Kahn, Ralph↗

NASA NTRS2014

A High Speed, Radiation Hard X-Ray Imaging Spectroscometer for Planetary Investigations

X-ray observations provide a unique window into fundamental processes in planetary physics, and one that is complementary to observations obtained at other wavelengths. We propose to develop an X-ray imaging spectrometer (0.1-10 keV band) that, on orbital planetary missions, would measure the elemental composition, density, and temperature of the hot plasma in gas giant magnetospheres, the interaction of the Solar wind with the upper atmospheres of terrestrial planets, and map the elemental composition of the surfaces of the Galilean moons and rocky or icy airless systems on spatial scales as small as a few meters. The X-ray emission from gas giants, terrestrial planets and moons with atmospheres, displays diverse characteristics that depend on the Solar wind's interaction with their upper atmospheres and/or magnetospheres. Our imaging spectrometer, as part of a dedicated mission to a gas giant, will be a paradigm changing technology. On a mission to the Jovian system, our baseline instrument would map the elemental composition of the rocky and icy surfaces of the Galilean moons via particle-induced X-ray fluorescence. This instrument would also measure the temperature, density and elemental abundance of the thermal plasma in the magnetosphere and in the Io plasma torus (IPT), explore the interaction of the Solar wind with the magnetosphere, and characterize the spectrum, flux, and temporal variability of X-ray emission from the polar auroras. We will constrain both the mode of energy transport and the effective transport coefficients in the IPT and throughout the Jovian magnetosphere by comparing temporal and spatial variations of the X-ray emitting plasma with those seen from the cooler but energetically dominant 5 eV plasma.

Kraft, R. P.↗

NASA NTRS2023

Editorial: Understanding the Causes of Asymmetries in Earth’S Magnetosphere-Ionosphere System

Geomagnetic activity observed in geospace, the upper atmosphere, and on the ground results from solar-terrestrial interactions. Such interactions correspond to the coupling between the solar wind, magnetosphere, and the thermosphere-ionosphere (MIT) system (Khazanov, 2016). However, given the complexity of the whole system and its large spatial scale and long-term solar variability, effects resulting from this coupling can be asymmetric. For example, inter-hemispherical asymmetric responses can arise when a hemisphere receives more energy than the other (e.g., Knipp et al., 2021; Pakhotin et al., 2021), local time effects can take place due to the occurrence of intense dawn-dusk interplanetary electric fields (e.g.,Haaland et al., 2017), and asymmetric geomagnetic field and mapping are caused by the Earth’s dipole offset and tilt (e.g., Laundal et al., 2017). The drivers that generate asymmetric MIT coupling response are generally recognized as long term: solar activity (Zhang et al., 2022) and dipole offset and tilt (Laundal et al., 2017); middle term: seasons (Lu et al., 2010); and short term: the y and z components of the interplanetary magnetic field (IMF) (Cowley, 1981; Li et al., 2011; Knipp et al., 2021). Thermospheric neutral mass density can present local time asymmetries associated with IMF By (Forster et al., 2017), and inter-hemispheric asymmetries can be generated by cross-hemispheric propagation of large-scale gravity waves (Bruinsma and Forbes, 2007). In addition, forcing from the mesosphere and lower thermosphere can generate inter-hemispheric neutral wind asymmetric patterns that can in turn asymmetrically impact neutral density in different hemispheres (Stober et al., 2021).

magnetosphere-ionophere coupling↗