Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

TAPECLIP system specification Final report

TAPECLIP data system for processing ionograms from analog magnetic tap

The ESPAS e-infrastructure

ESPAS provides an e-Infrastructure to support access to a wide range of archived observations and model derived data for the near-Earth space environment, extending from the Earth's middle atmosphere up to the outer radiation belts. To this end, ESPAS will serve as a central access hub for researchers who wish to exploit multi-instrument multipoint data for scientific discovery, model development and validation, and data assimilation, among others. Observation based and model enhanced scientific understanding of the physical state of the Earth's space environment and its evolution is critical to advancing space weather and space climate studies, two very active branches of current scientific research. ESPAS offers an interoperable data infrastructure that enables users to find, access, and exploit near-Earth space environment observations from ground-based and spaceborne instruments and data from relevant models, obtained from distributed repositories. In order to facilitate efficient user queries ESPAS allows a highly flexible workflow scheme to select and request the desired data sets. ESPAS has the strategic goal of making Europe a leading player in the efficient use and dissemination of near-Earth space environment information offered by institutions, laboratories and research teams in Europe and worldwide, that are active in collecting, processing and distributing scientific data. Therefore, ESPAS is committed to support and foster new data providers who wish to promote the easy use of their data and models by the research community via a central access framework. ESPAS is open to all potential users interested in near-Earth space environment data, including those who are active in basic scientific research, technical or operational development and commercial applications

A three-dimensional regional assimilative model of the ionospheric electron density

The focus of this thesis is on the development, implementation, and validation of a three-dimensional regional assimilative model of the ionospheric electron density. Empirical climatological models, like the International Reference Ionosphere (IRI) model (Bilitza et al. 2017), cannot predict the whole ionospheric variability, specifically under disturbed magnetic conditions. The model presented in this work has the purpose to improve the IRI description by implementing a data assimilation procedure, based on ionospheric measurements collected by several ground-based or satellite-based instruments. The first phase of the development of the model, called IRI UPdate (IRI UP), is devoted to update the IRI model by ingesting effective indices (IG12eff and R12eff) calculated after assimilating F2 layer characteristics values, measured by a network of ionosondes or derived by vertical total electron content values measured by a network of Global Navigational Satellite Systems receivers. The ingestion of effective indices in the IRI model allows to significantly improve the F2 layer peak density and height description. Being the F2 layer peak an anchor point for the whole IRI’s vertical electron density profile, such procedure allows to update the whole profile. The second phase of the development of the model is devoted to improve the modeling of the topside part of the ionospheric vertical electron density profile by making use of the IRI UP method and in-situ measurements collected by Swarm satellites. Finally, a procedure called IonoPy, embedding the two aforementioned steps, assimilates the whole bottomside electron density profile measured by an ionosonde, thus further improving the ionospheric plasma description in the bottomside ionosphere. All the procedures described in this thesis have been tested and validated by comparing them with other similar models or with independent datasets, for both quiet and disturbed conditions

Bayesian approach to ionospheric imaging with Gaussian Markov random field priors

Ionosfääri on noin 60–1000 kilometrin korkeudella sijaitseva ilmakehän kerros, jossa kaasuatomien ja -molekyylien elektroneja on päässyt irtoamaan auringon säteilyn ja auringosta peräisin olevien nopeiden hiukkasten vaikutuksesta. Näin syntyneillä ioneilla ja vapailla elektroneilla on sähkö- ja magneettikenttien kanssa vuorovaikuttava sähkövaraus. Ionosfäärillä on siksi merkittävä rooli radioliikenteessä. Se voi mahdollistaa horisontin yli tapahtuvat pitkät radiolähetykset heijastamalla lähetetyn sähkömagneettisen signaalin takaisin maata kohti. Toisaalta ionosfääri vaikuttaa myös sen läpäiseviin korkeampitaajuuksisiin signaaleihin. Esimerkiksi satelliittipaikannuksessa ionosfäärin vaikutus on parhaassakin tapauksessa otettava huomioon, mutta huonoimmassa se voi estää paikannuksen täysin. Näkyvin ja tunnetuin ionosfääriin liittyvä ilmiö lienee revontulet. Yksi keskeisistä suureista ionosfäärin tutkimuksessa on vapaiden elektronien määrä kuutiometrin tilavuudessa. Käytännössä elektronitiheyden mittaaminen on mahdollista mm. tutkilla, kuten Norjan, Suomen ja Ruotsin alueilla sijaitsevalla EISCAT-tutkajärjestelmällä, sekä raketti- tai satelliittimittauksilla. Mittaukset voivat olla hyvinkin tarkkoja, mutta tietoa saadaan ainoastaan tutkakeilan suunnassa tai mittalaitteen läheisyydestä. Näillä menetelmillä ionosfäärin tutkiminen laajemmalla alueella on siten vaikeaa ja kallista. Olemassa olevat paikannussatelliitit ja vastaanotinverkot mahdollistavat ionosfäärin elektronitiheyden mittaamisen alueellisessa, ja jopa globaalissa mittakaavassa, ensisijaisen käyttötarkoituksensa sivutuotteena. Satelliittimittausten ajallinen ja paikallinen kattavuus on hyvä, ja kaiken aikaa kasvava, mutta esimerkiksi tarkkoihin tutkamittauksiin verrattuna yksittäisten mittausten tuottama informaatio on huomattavasti vähäisempää. Tässä väitöstyössä kehitettiin tietokoneohjelmisto ionosfäärin elektronitiheyden kolmiulotteiseen kuvantamiseen. Menetelmä perustuu matemaattisten käänteisongelmien teoriaan ja muistuttaa lääketieteessä käytettyjä viipalekuvausmenetelmiä. Satelliittimittausten puutteellisesta informaatiosta johtuen työssä on keskitytty etenkin siihen, miten ratkaisun löytymistä voidaan auttaa tilastollisesti esitetyllä fysikaalisella ennakkotiedolla. Erityisesti työssä sovellettiin gaussisiin Markovin satunnaiskenttiin perustuvaa uutta korrelaatiopriori-menetelmää. Menetelmä vähentää merkittävästi tietokonelaskennassa käytettävän muistin tarvetta, mikä lyhentää laskenta-aikaa ja mahdollistaa korkeamman kuvantamisresoluution.Ionosphere is the partly ionised layer of Earth's atmosphere caused by solar radiation and particle precipitation. The ionisation can start from 60 km and extend up to 1000 km altitude. Often the interest in ionosphere is in the quantity and distribution of the free electrons. The electron density is related to the ionospheric refractive index and thus sufficiently high densities affect the electromagnetic waves propagating in the ionised medium. This is the reason for HF radio signals being able to reflect from the ionosphere allowing broadcast over the horizon, but also an error source in satellite positioning systems. The ionospheric electron density can be studied e.g. with specific radars and satellite in situ measurements. These instruments can provide very precise observations, however, typically only in the vicinity of the instrument. To make observations in regional and global scales, due to the volume of the domain and price of the aforementioned instruments, indirect satellite measurements and imaging methods are required. Mathematically ionospheric imaging suffers from two main complications. First, due to very sparse and limited measurement geometry between satellites and receivers, it is an ill-posed inverse problem. The measurements do not have enough information to reconstruct the electron density and thus additional information is required in some form. Second, to obtain sufficient resolution, the resulting numerical model can become computationally infeasible. In this thesis, the Bayesian statistical background for the ionospheric imaging is presented. The Bayesian approach provides a natural way to account for different sources of information with corresponding uncertainties and to update the estimated ionospheric state as new information becomes available. Most importantly, the Gaussian Markov Random Field (GMRF) priors are introduced for the application of ionospheric imaging. The GMRF approach makes the Bayesian approach computationally feasible by sparse prior precision matrices. The Bayesian method is indeed practicable and many of the widely used methods in ionospheric imaging revert back to the Bayesian approach. Unfortunately, the approach cannot escape the inherent lack of information provided by the measurement set-up, and similarly to other approaches, it is highly dependent on the additional subjective information required to solve the problem. It is here shown that the use of GMRF provides a genuine improvement for the task as this subjective information can be understood and described probabilistically in a meaningful and physically interpretative way while keeping the computational costs low

High Frequency Surface Backscatter Coefficients

Over-the-horizon radar is a class of high frequency (HF) radar used for long range (1000-3000 km) wide area surveillance. These radars utilise the refractive properties of the ionosphere to illuminate targets beyond the Earth's horizon, and consequently their performance is highly dependent on the ionosphere. Accurate models of the radar ground backscatter are required to accurately assess the ionospheric propagation conditions and thus the expected performance of over-the-horizon radars for operational purposes. The ground backscatter coefficient characterises the amount of radiation scattered back from a surface towards a receiver per unit area. While the backscatter coefficient of the sea is well understood and may be calculated from theory if the sea state is known, the backscatter coefficient of land at high frequencies is not well understood. To calculate the land backscatter coefficients over Northern Australia, a methodology which compares observed backscatter ionograms to those synthesised using HF radio wave ray tracing techniques through model ionospheres was developed. The results from this ionogram comparison method were compared to sea backscatter coefficients calculated from theory using sea state data. Data from the Jindalee Operational Radar Network (JORN) frequency management systems backscatter sounders from September 2015 and March 2016 were analysed and maps of the backscatter coefficients across Northern Australia were developed. The effects of the ray propagation and surface properties, including radar frequency, topography, soil moisture and vegetation cover on the backscatter coefficients were investigated. It was found that desert-like regions had a much lower backscatter coefficient than mountainous/tropical regions. A weak positive correlation between the backscatter coefficient and the soil moisture and surface roughness was observed; however, it was found that the vegetation structure had the largest effect on the backscatter coefficient.Thesis (MPhil) -- University of Adelaide, School of Physical Sciences, 202

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Towards Understanding Differential Ion Mobility and its Applications for Analytical and Medicinal Chemistry

This PhD thesis, titled “Towards Understanding Differential Ion Mobility Spectrometry and its Applications in Analytical and Medicinal Chemistry,” encompasses a broad effort to understand the principles that underpin differential mobility spectrometry (DMS), and how the DMS technique can be employed within the analytical and medicinal facets of chemistry. Specifically, this work highlights the components of the ion-neutral interaction potential that are pertinent to rationalize an ion’s DMS behaviour and how such information can be modelled using in silico and machine-learning approaches. Understanding the nature of ion-neutral interactions is especially important when DMS experiments are conducted in microsolvating environments (i.e., those in which the carrier gas is seeded with small amounts of a volatile solvent vapour), as components of the interaction potential can be used to predict molecular properties that are routinely screened during drug discovery. In the Chapter 1, we introduce the ion-solvent interactions that are intrinsic to DMS experiments and how microsolvation can impact an ion’s mobility. We specifically emphasize the significance of ion solvent clusters and how the waveform used in DMS separations fosters a dynamic solvation environment. Because field-induced heating is modulated such that an analyte undergoes many cycles of solvent condensation and evaporation at charge-dense regions of the analyte, DMS effectively samples interactions that may resemble the dynamics of solvation within the analyte’s primary solvation shell. In this regard, DMS can be utilized to probe characteristics of a molecule related to its insipient solvation, which, when used in conjunction with quantum-chemical calculations and/or machine learning algorithms, affords accurate predictions of that molecule’s physicochemical properties. In addition to the information regarding an analyte’s physicochemical properties that can be gleaned from DMS measurements in microsolvating environments (Chapter 2), ion microsolvation can help alleviate complications related to field-induced heating. This phenomenon is explored in Chapter 3, where microsolvation was found to stabilize analytes through the formation of localized ion-solvent clusters. In particular, the chapter explores the DMS behaviour of the MP1 peptide, which, when exposed to a microsolvation partner, underwent chemical transformations that reduced the observed charge state of MP1 from [MP1 + 3H]3+ to [MP1 + 2H]2+, and shielded protonated MP1 from fragmentation induced by collisional activation within the DMS cell. This behaviour suggests that microsolvation provides analytes with a solvent “air-bag,” which could play a role in retaining native-like ion configurations during DMS separations that operate well above the low-field limit. Chapter 4, titled Protonation-Induced Chirality Drives Separation by DMS, explores a fascinating phenomenon that can be probed by DMS. In short, chiral species possessing a permanent stereocenter and a prochiral, tertiary amine can form two diastereomers upon protonation during electrospray ionization. The resulting diastereomers exhibit distinct conformations that are resolvable by DMS, constituting the first measurement of this behaviour in the gas phase. Protonation-induced chirality appears to be a general phenomenon, as N-protonation at the tertiary amino moiety of 13 chiral compounds that contained a prochiral, tertiary amine moiety. The analytical utility of DMS is further exemplified in Chapter 5, where DMS and tandem mass spectrometry (MS) were used to distinguish a set of seven cannabinoids. Detection of analytes as argentinated species (i.e., [M + Ag]+ adducts) also led to the discovery that argentination promotes distinct fragmentation patterns for each cannabinoid, enabling their partial distinction by tandem-MS. By adding DMS to the tandem-MS workflow, each cannabinoid was resolved in a pure N2 DMS environment, allowing for accurate assessment of cannabinoid levels within commercial products with excellent accuracy and limits of detection/quantitation. In addition to the analytical utility provided by DMS and the other ion mobility spectrometry (IMS) techniques, IMS-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion’s collision cross section (CCS). Chapter 6 discusses the software package MobCal-MPI, which was developed to calculate CCSs efficiently and accurately at arbitrary field strengths via the trajectory method, including those accessed during DMS experiments. While significant progress has been made towards modelling the phenomenon of differential mobility, there are still several properties that have yet to be captured by in silico models. This thesis concludes with Chapter 7, which outlines unresolved issues in the field and suggests several directions in which future research endeavours can be directed

Radiowave Scattering Structure In The Disturbed Auroral Ionosphere: Some Measured Properties

Thesis (Ph.D.) University of Alaska Fairbanks, 196

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion