Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post-storm cooling in the middle-thermosphere. We find that both NRLMSIS 2.0 and JB2008-ML do not account for post-storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g. the 2003 Halloween storms). Conversely, HASDM-ML and CHAMP-ML do show evidence of post-storm cooling indicating that this phenomenon is present in the original datasets. Results show that density reductions up to 40% can occur 1--3 days post-storm depending on location and the strength of the storm

Seasonal dependence of northern high‐latitude upper thermospheric winds: A quiet time climatological study based on ground‐based and space‐based measurements

This paper investigates the large‐scale seasonal dependence of geomagnetically quiet time, northern high‐latitude F region thermospheric winds by combining extensive observations from eight ground‐based (optical remote sensing) and three space‐based (optical remote sensing and in situ) instruments. To provide a comprehensive picture of the wind morphology, data are assimilated into a seasonal empirical vector wind model as a function of season, latitude, and local time in magnetic coordinates. The model accurately represents the behavior of the constituent data sets. There is good general agreement among the various data sets, but there are some major offsets between GOCE and the other data sets, especially on the duskside. The assimilated wind patterns exhibit a strong and large duskside anticyclonic circulation cell, sharp latitudinal gradients in the duskside auroral zone, strong antisunward winds in the polar cap, and a weaker tendency toward a dawnside cyclonic circulation cell. The high‐latitude wind system shows a progressive intensification of wind patterns from winter to equinox to summer. The latitudinal extent of the duskside circulation cell does not depend strongly on season. Zonal winds show a mainly diurnal variation (two extrema) around polar and middle latitudes and semidiurnal variation (four extrema) at auroral latitudes; meridional winds are primarily diurnal at all high latitudes. The strength of zonal winds channeling through the auroral zone on the duskside is strongest in the summer season. The vorticity of the wind pattern increases from winter to summer, whereas divergence is maximum in equinox. In all three seasons, divergence is weaker than vorticity.Key PointsFirst ever investigation of the large‐scale seasonal dependence of northern high‐latitude upper thermospheric winds in magnetic coordinatesResults show progressive intensification of wind circulation from winter to equinox to summerThe vorticity increases from winter to summer. In all the seasons, the strongest divergences occur primarily in and above auroral latitudesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136373/1/jgra53329.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136373/2/jgra53329_am.pd

Linkages between the cold summer mesopause and thermospheric zonal mean circulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95392/1/grl28827.pd

HL‐TWiM Empirical Model of High‐Latitude Upper Thermospheric Winds

We present an empirical model of thermospheric winds (High‐latitude Thermospheric Wind Model [HL‐TWiM]) that specifies F region high‐latitude horizontal neutral winds as a function of day of year, latitude, longitude, local time, and geomagnetic activity. HL‐TWiM represents the large‐scale neutral wind circulation, in geomagnetic coordinates, for the given input conditions. The model synthesizes the most extensive collection to date of historical high‐latitude wind measurements; it is based on statistical analyses of several decades of F region thermospheric wind measurements from 21 ground‐based stations (Fabry‐Perot Interferometers and Scanning Doppler Imaging Fabry‐Perot Interferometers) located at various northern and southern high latitudes and two space‐based instruments (UARS WINDII and GOCE). The geomagnetic latitude and local time dependences in HL‐TWiM are represented using vector spherical harmonics, day of year and longitude variations are represented using simple harmonic functions, and the geomagnetic activity dependence is represented using quadratic B splines. In this paper, we describe the HL‐TWiM formulation and fitting procedures, and we verify the model against the neutral wind databases used in its formulation. HL‐TWiM provides a necessary benchmark for validating new wind observations and tuning our physical understanding of complex wind behaviors. Results show stronger Universal Time variation in winds at southern than northern high latitudes. Model‐data intra‐annual comparisons in this study show semiannual oscillation‐like behavior of GOCE winds, rarely observed before in wind data.Key PointsWe developed a comprehensive empirical model of high‐latitude F region thermospheric winds (HL‐TWiM)Universal Time variations in high‐latitude winds are stronger in the Southern than Northern HemisphereHL‐TWiM provides a necessary benchmark for validating new high‐latitude wind observations and tuning first principal modelsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/1/jgra55363_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/2/jgra55363-sup-0001-Figure_SI-S01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/3/jgra55363.pd

Atomic oxygen in the mesosphere and lower thermosphere derived from SABER : Algorithm theoretical basis and measurement uncertainty

Peer reviewedPublisher PD

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

We thank the participants of the three cohorts (UKB, Generation Scotland (STRADL) and LBC1936) for their participation and the research teams for their work in collecting, processing and giving access to these data for analysis. We are also thankful to the brain donors to the Allen Human Brain Atlas, BrainSpan Atlas and Human Brain Transcriptome Project, and to the people who collected and processed the data and made it openly available For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission.Peer reviewe

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Ground-based optical detection of atmospheric waves in the upper mesosphere and lower thermosphere.

Internal waves and atmospheric tides, which propagate into the upper mesosphere and lower thermosphere, are the natural response of the atmosphere to perturbations such as cumulus convection, flow over topography, and solar heating. Seasonal and local time trends have been studied by observing the terrestrial \rm OH(X\sp2\Pi ) and \rm O(\sp1S) airglow layers at the University of Michigan, Peach Mountain Airglow Observatory \rm (43\sp\circ N,\ 83\sp\circ W). Using a Michelson interferometer, OH(3,1) band intensities and neutral temperature at 89 km have been measured. Simultaneously, a Fabry-Perot interferometer was used to measure the zonal and meridional wind velocities at 89 km and 97 km. In addition, an all-sky CCD camera was used to measure the spatial morphology of airglow perturbations. This work is important to the field because it presents the first long term multi-instrument observation of the \rm OH(X\sp2\Pi ) airglow. Additionally, eighty continuous hours of observations were made in mid-January, 1991 at Thule Air Base, Greenland \rm (76.3\sp\circ N,\ 68.5\sp\circ W) using simliar observation techniques. In agreement with recent theory, analysis indicates semi-annual seasonal fluctuations in the OH(3,1) number density and annual fluctuations in temperature. Average local time fluctuations were compared to the Global Scale Wave Model (GSWM) of Hagan et al. (1995) and the Horizontal Wind Model (HWM93) of Hedin et al. (1993) to investigate the seasonal trends in tidal modulation. The data indicates that a strong semi-diurnal tide (12-hr) dominates the region in the winter months, while other months show equal contributions from both the diurnal (24-hr) and semi-diurnal tides. Spectral analysis of the high-latitude observations shows that coherent tidal oscillations were present in the region. A climatological study of wave activity at three scales was also conducted; short period gravity waves ($$ 24 hr). Results indicate that short period gravity wave activity is maximum during the summer, while the longer period wave activity exhibits a semi-annual trend with maxima during winter and summer.Ph.D.Aerospace engineeringApplied SciencesOpticsPhysics, Atmospheric SciencePure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/129976/2/9711954.pd