877 research outputs found

    Evaluation of TIEGCM based on GOCE neutral density

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    The Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM), as one of the most advanced physical models of the Earth’s thermosphere and ionosphere, is not only widely used in scientific research, but also has essential reference value in aerospace operations. In this study, we use Gravity field and steady-state Ocean Circulation Explorer (GOCE) neutral density to evaluate the accuracy of the TIEGCM. The assessment is performed on both time and spatial scales. The time scales are conducted annually, monthly, and daily, while the spatial scales are carried out in terms of altitude, latitude, and local time. On the time scales, the performance of the TIEGCM on the monthly time scale is better than that on the annual time scale. Also, the performance on the daily time scale is better than that on the monthly time scale. The relative deviation shows a significant seasonal variation, that is, larger in winter and summer and smaller in spring and autumn. In addition, the relative deviation shows a negative correlation with F10.7 and Ap. On the spatial scale, with the increase in altitude, the average relative deviation of the model becomes larger in general. The relative deviation is usually larger at middle latitudes in the Northern Hemisphere and high latitudes in the Southern Hemisphere. Finally, on the scale of local time, the relative deviation changes more dramatically in local morning than at dusk

    Climatological predictions of the auroral zone locations driven by moderate and severe space weather events

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    Auroral zones are regions where, in an average sense, aurorae due to solar activity are most likely spotted. Their shape and, similarly, the geographical locations most vulnerable to extreme space weather events (which we term ‘danger zones’) are modulated by Earth’s time-dependent internal magnetic field whose structure changes on yearly to decadal timescales. Strategies for mitigating ground-based space weather impacts over the next few decades can benefit from accurate forecasts of this evolution. Existing auroral zone forecasts use simplified assumptions of geomagnetic field variations. By harnessing the capability of modern geomagnetic field forecasts based on the dynamics of Earth’s core we estimate the evolution of the auroral zones and of the danger zones over the next 50 years. Our results predict that space-weather related risk will not change significantly in Europe, Australia and New Zealand. Mid-to-high latitude cities such as Edinburgh, Copenhagen and Dunedin will remain in high-risk regions. However, northward change of the auroral and danger zones over North America will likely cause urban centres such as Edmonton and Labrador City to be exposed by 2070 to the potential impact of severe solar activity

    Investigating the impact of space weather on the polar atmosphere using rigorous statistical methods

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    I de senere år har det vært en økning i observasjonsbaserte, re-analytiske og modellbaserte studier som viser korrelasjoner mellom dag-til-dag og år-til-år solaktivitet og klima-/vær-mønstre. Det overordnede målet med avhandlingen er å undersøke to solklima-mekanismer, den Kjemisk-Dynamiske koblingen og Mansurov-effekten. Den Kjemisk-Dynamiske koblingen er knyttet til ioniseringen av den øvre atmosfæren (¡50 km) som skjer ved energisk partikkelnedbør (EPP). Dette resulterer i produksjon av nitrogen- og hydrogenoksider (NOx og HOx). Disse molekylene bryter effektivt ned ozon, og kan derfor endre strålingsbalansen i atmosfæren, noe som igjen potensielt kan føre til en kaskadeeffekt av dynamisk induserte atmosfæriske værendringer i polaratmosfæren. Mansurov-effekten er knyttet til det interplanetariske magnetfeltet (IMF) og dets evne til å modulere den globale elektriske kretsen (GEC). Dette antas å videre påvirke den polare troposfæren gjennom å endre de fysiske prosessene bak dannelse og vekst av skyer. Effekten antas å være nesten umiddelbar, noe som gir en fysisk forbindelse mellom verdensrommet og den nedre del av Jordens atmosfære. Begge mekanismene har blitt studert ved hjelp av sofistikerte statistiske analysemetoder. For den Kjemisk-Dynamiske koblingen, bruker vi SOCOL3-MPIOM-modellen for å sammenligne temperaturforskjeller i den nordlige atmosfæren i modellkjøringen med og uten EPP. Analysen bygger på en nylig studie som viser at EPP hovedsakelig påvirker den nordlige atmosfæriske temperaturen rett før og under forstyrrede forhold i den stratosfæriske polare jetstrøm. Vi finner svært signifikante temperaturresponser rett før hendelser karakterisert som små stratosfæriske oppvarminger, forhold assosiert med en svekket polar jetstrøm og økt bølgeaktivitet. De største temperaturforskjellene er synlig i februar, men bare for den siste halvdel (1955–2008) av simuleringsperioden (1900–2008). Funnene antyder at den Kjemisk-Dynamiske koblingen kan spille en avgjørende rolle i stratosfæriske forhold om vinteren og bekrefter eksistensen av den Kjemisk-Dynamiske koblingen i modellen. Ved å bruke data fra OMNIweb og ERA5 re-analyse over tidsperioden 1968–2020, undersøkes forbindelsen mellom IMF By og polart atmosfærisk trykk på havnivå. I motsetning til tidligere publiserte studier om Mansurov-effekten, finner vi ingen signifikant respons etter å ha tatt hensyn til autokorrelasjon og kontrollert for falsk deteksjonsandel (false discovery rate). Tidligere studier har også fremhevet en 27-dagers syklisk trykkrespons i sine resultater som indirekte bevis for en fysisk forbindelse. Vi demonstrerer at denne periodiske trykkresponsen oppstår som et resultat av de statistiske metodene som er brukt, og kan derfor ikke brukes som en indikator på en fysisk sammenheng. Videre oppdages en hittil ukjent robust og statistisk signifikant korrelasjon mellom IMF By og polart atmosfærisk trykk ved havnivå. Korrelasjonen er tydelig i perioden mars-april-mai på begge halvkuler, men med en tilsynelatende ufysisk timing med hensyn til Mansurov-effekten. I alt fremhever resultatene det generelle behovet for grundig statistisk testing, samt behovet for varsomhet når man bruker spesifikke metoder sammen med periodiske og autokorrelerte variabler.Recent years have seen a surge in observational, re-analysis, and model-based studies providing evidence of statistical correlations between day-to-day to interannual solar activity and climate/weather patterns. The overarching objective of this thesis is to delve into the theory of two solar-climate mechanisms, the Chemical-Dynamical coupling and the Mansurov effect. The Chemical-Dynamical coupling is linked to the ionization of the upper atmosphere (¡50 km) by energetic particle precipitation (EPP), resulting in the production of odd nitrogen and hydrogen oxides (NOx and HOx). These compounds are effective ozone depleters, and can alter the radiative balance of the atmosphere, potentially leading to a cascading effect in dynamically induced atmospheric weather changes observable in the polar atmosphere. The Mansurov effect is related to the interplanetary magnetic field (IMF) and its ability to modulate the global electric circuit (GEC), which is further assumed to impact the polar troposphere through cloud generation processes. It is hypothesised to occur nearly instantaneously, providing a physical link between near-Earth-space and the lower atmosphere. These topics will be studied with sophisticated statistical analysis methods. For the Chemical-Dynamical coupling, we use the SOCOL3-MPIOM model to compare the northern polar atmospheric temperature differences in ensemble members with and without EPP. The analyses builds on recent re-analysis evidence showing that EPP mostly impacts the northern polar atmospheric temperature right before and during disturbed Polar Vortex (PV) conditions. We find highly significant temperature responses during conditions set up by minor Sudden Stratospheric Warmings (SSW), associated with disturbed polar vortex and enhanced planetary wave activity. The largest anomalies are seen in February, and only for the latter half (1955–2008) of the simulation period (1900–2008). The findings suggest that during winter, the Chemical-Dynamical coupling could play a crucial role in stratospheric conditions and confirms the existence of the chemical-dynamical link in the model. By using ERA5 atmospheric re-analysis data and OMNIweb IMF data spanning 1968–2020, the connection between the IMF By and polar surface pressure is investigated. Contrary to prior published studies on the Mansurov effect, no significant response is found after accounting for autocorrelation and multiple hypothesis testing. In addition, prior studies highlight a 27-day cyclic pressure response as indirect evidence of a physical link. However, we show that this periodic pressure behaviour occurs as a statistical artefact of the methods, and is not a reliable indicator of a causal connection. Furthermore, a new robust and statistically significant correlation is determined between the IMF By and polar surface pressure. It is found in the time-period March-April-May for both hemispheres, but with an unphysical timing with respect to the Mansurov hypothesis. The analyses highlight the general need for rigorous statistical testing, as well as the need for caution when deploying certain methodologies with periodic and highly autocorrelated variables.Doktorgradsavhandlin

    Multi-instrumental analysis of the day-to-day variability of equatorial plasma bubbles

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    This paper presents a multi-instrument observational analysis of the equatorial plasma bubbles (EPBs) variation over the American sector during a geomagnetically quiet time period of 07–10 December 2019. The day-to-day variability of EPBs and their underlying drivers are investigated through coordinately utilizing the Global-scale Observations of Limb and Disk (GOLD) ultraviolet images, the Ionospheric Connection Explorer (ICON) in-situ and remote sensing data, the global navigation satellite system (GNSS) total electron content (TEC) observations, as well as ionosonde measurements. The main results are as follows: 1) The postsunset EPBs’ intensity exhibited a large day-to-day variation in the same UT intervals, which was fairly noticeable in the evening of December 07, yet considerably suppressed on December 08 and 09, and then dramatically revived and enhanced on December 10. 2) The postsunset linear Rayleigh-Taylor instability growth rate exhibited a different variation pattern. It had a relatively modest peak value on December 07 and 08, yet a larger peak value on December 09 and 10. There was a 2-h time lag of the growth rate peak time in the evening of December 09 from other nights. This analysis did not show an exact one-to-one relationship between the peak growth rate and the observed EPBs intensity. 3) The EPBs’ day-to-day variation has a better agreement with that of traveling ionospheric disturbances and atmospheric gravity waves signatures, which exhibited relatively strong wavelike perturbations preceding/accompanying the observed EPBs on December 07 and 10 yet relatively weak fluctuations on December 08 and 09. These coordinate observations indicate that the initial wavelike seeding perturbations associated with AGWs, together with the catalyzing factor of the instability growth rate, collectively played important roles to modulate the day-to-day variation of EPBs. A strong seeding perturbation could effectively compensate for a moderate strength of Rayleigh-Taylor instability growth rate and therefore their combined effect could facilitate EPB development. Lacking proper seeding perturbations would make it a more inefficient process for the development of EPBs, especially with a delayed peak value of Rayleigh-Taylor instability growth rate

    Horizontal structure of convergent wind shear associated with sporadic E layers over East Asia

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    At present, the main detection instruments for observing sporadic E (Es) layers are ground-based radars, dense networks of ground-based global navigation satellite system (GNSS) receivers, and GNSS radio occultation, but they cannot capture the whole picture of the horizontal structure of Es layers. This study employs the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension model (WACCM-X 2.1) to derive the horizontal structure of the ion convergence region (HSICR) to explore the shapes of the large-scale Es layers over East Asia for the period from June 1 to August 31, 2008. The simulation produced the various shapes of the HSICRs elongated in the northwest−southeast, northeast−southwest, or composed of individual small patches. The close connection between Es layer critical frequency (foEs) and vertical ion convergence indicates that the HSICR is a good candidate for revealing and explaining the horizontal structure of the large-scale Es layers

    Application of SuperDARN Assimilative Mapping Technique To TS18 Plasma Convection Model

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    Specification of ionospheric convection over the entire high-latitude region using only measurements is desirable for space weather nowcasting but is not feasible. Instead, assimilative techniques are used to combine spatially sparse measurements with a complete background model. The Super Dual Auroral Radar Network (SuperDARN) Assimilative Mapping technique (SAM) is one such technique [Cousins et al., 2013a]. The SAM technique combines available line-of-sight velocity measurements from SuperDARN with a climatological high-latitude convection model producing a global solution of the electrostatic potential for a given period of interest (typically 1-2 minutes). The background model and velocity measurements are weighted according to each components\u27 estimated error. While the background model error covariance matrix necessary for the SAM technique has been determined for the CS10 model [Cousins and Shepherd, 2010], a relatively recent climatological model derived using SuperDARN radar measurements, it has not been determined for the latest model, the TS18 model [Thomas and Shepherd, 2018]. The TS18 features several advancements in vector preprocessing and selection, and most importantly, included data from radars located at middle and polar latitudes that had not yet been built when earlier models (including the CS10) were constructed. To obtain the error covariance matrix for the TS18 model, the dominant modes of variability in the model are represented as a set of empirical orthogonal functions (EOFs) by fitting a set of basis functions to residuals between the model and a large set of SuperDARN observations. The procedure for obtaining these EOFs involves minimizing a non-linear cost equation to obtain multiple sets of coefficients representing both time and spatial variability. We compare the structure of the resulting EOFs to those obtained from the CS10 model and investigate differences in application output for specific time periods as well as average differences between this procedure, the previous version, and other assimilative techniques in general, and for different interplanetary magnetic field (IMF) conditions, dipole tilt angles, and universal time bins. The results presented herein indicate that the improvements in the TS18 model carry through into the SAM application, with higher potentials present at lower latitudes and higher cross-polar-cap potentials in certain conditions

    Heliophysics and Amateur Radio:Citizen Science Collaborations for Atmospheric, Ionospheric, and Space Physics Research and Operations

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    The amateur radio community is a global, highly engaged, and technical community with an intense interest in space weather, its underlying physics, and how it impacts radio communications. The large-scale observational capabilities of distributed instrumentation fielded by amateur radio operators and radio science enthusiasts offers a tremendous opportunity to advance the fields of heliophysics, radio science, and space weather. Well-established amateur radio networks like the RBN, WSPRNet, and PSKReporter already provide rich, ever-growing, long-term data of bottomside ionospheric observations. Up-and-coming purpose-built citizen science networks, and their associated novel instruments, offer opportunities for citizen scientists, professional researchers, and industry to field networks for specific science questions and operational needs. Here, we discuss the scientific and technical capabilities of the global amateur radio community, review methods of collaboration between the amateur radio and professional scientific community, and review recent peer-reviewed studies that have made use of amateur radio data and methods. Finally, we present recommendations submitted to the U.S. National Academy of Science Decadal Survey for Solar and Space Physics (Heliophysics) 2024–2033 for using amateur radio to further advance heliophysics and for fostering deeper collaborations between the professional science and amateur radio communities. Technical recommendations include increasing support for distributed instrumentation fielded by amateur radio operators and citizen scientists, developing novel transmissions of RF signals that can be used in citizen science experiments, developing new amateur radio modes that simultaneously allow for communications and ionospheric sounding, and formally incorporating the amateur radio community and its observational assets into the Space Weather R2O2R framework. Collaborative recommendations include allocating resources for amateur radio citizen science research projects and activities, developing amateur radio research and educational activities in collaboration with leading organizations within the amateur radio community, facilitating communication and collegiality between professional researchers and amateurs, ensuring that proposed projects are of a mutual benefit to both the professional research and amateur radio communities, and working towards diverse, equitable, and inclusive communities

    PROBABILISTIC SHORT TERM SOLAR DRIVER FORECASTING WITH NEURAL NETWORK ENSEMBLES

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    Commonly utilized space weather indices and proxies drive predictive models for thermosphere density, directly impacting objects in low-Earth orbit (LEO) by influencing atmospheric drag forces. A set of solar proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7, are created from a mixture of ground based radio observations and satellite instrument data. These solar drivers represent heating in various levels of the thermosphere and are used as inputs by the JB2008 empirical thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM) relies on JB2008, and forecasts of solar drivers made by a linear algorithm, to produce forecasts of density. Density forecasts are useful to the space traffic management community and can be used to determine orbital state and probability of collision for space objects. In this thesis, we aim to provide improved and probabilistic forecasting models for these solar drivers, with a focus on providing first time probabilistic models for S10.7, M10.7, and Y10.7. We introduce auto-regressive methods to forecast solar drivers using neural network ensembles with multi-layer perceptron (MLP) and long-short term memory (LSTM) models in order to improve on the current operational forecasting methods. We investigate input data manipulation methods such as backwards averaging, varied lookback, and PCA rotation for multivariate prediction. We also investigate the differences associated with multi-step and dynamic prediction methods. A novel method for splitting data, referred to as striped sampling, is introduced to produce statistically consistent machine learning data sets. We also investigate the effects of loss function on forecasting performance and uncertainty estimates, as well as investigate novel ensemble weighting methods. We show the best models for univariate forecasting are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all univariate approaches offer an improvement, with best models improving between 48 and 59% on relative mean squared error (MSE) with respect to persistence, which is used as the baseline model in this work. We show also that a stacked neural network ensemble approach significantly outperforms the operational linear method. When using MV-MLE (multivariate multi-lookback ensemble), we see improvements in performance error metrics over the operational method on all drivers. The multivariate approach also yields an improvement of root mean squared error (RMSE) for F10.7, S10.7, M10.7, and Y10.7 of 17.7%, 12.3%, 13.8%, 13.7% respectively, over the current operational method. We additionally provide the first probabilistic forecasting models for S10.7, M10.7, and Y10.7. Ensemble approaches are leveraged to provide a distribution of predicted values, allowing an investigation into robustness and reliability (R&R) of uncertainty estimates, using the calibration error score (CES) metric and calibration curves. Univariate models provided similar uncertainty estimates as other works, while improving on performance metrics. We also produce probabilistic forecasts using MV-MLE, which are well calibrated for all drivers, providing an average CES of 5.63%

    BDS GNSS for Earth Observation

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    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community
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