Empirical Modeling of the Total Electron Content of the Ionosphere

With the appearance of such satellite systems as GPS, GLONASS, Galileo, and others, the total electron content TEC measured by means of navigational satellites became a key parameter characterizing a state of the ionized space. In turn, functioning of navigational and telecommunication systems needs models of TEC for an estimation of accuracy of positioning, for the short-term and long-term prediction of this parameter. In this Chapter, empirical models of the total electron content are presented. The new result is their comparison. It is shown that the majority of them provide an adequate accuracy and reliability. As the basic application of TEC measurements, the problem of determination of maximum concentration NmF2 of the ionosphere with use of its equivalent slab thickness τ is considered. It is shown that existing models of τ are not global and do not provide sufficient accuracy in determining NmF2. An approach for new global model is offered

An investigation of new ionospheric models using multi-source measurements and neural networks

Ionosphere is one of the atmospheric layers that has a major impact on human beings since it significantly affects the radio propagation on Earth, and between satellites and Earth (e.g., Global Navigation Satellite Systems (GNSS) signal transmission). The variation of the electrons in the ionosphere is strongly influenced by the space weather due to solar and cosmic radiation. Hence, the short/long-term trend of the free electrons in the ionosphere has been regarded as very important information for both space weather and GNSS positioning. On the other hand, precisely quantifying the distribution and variation of free electrons at a high spatio-temporal resolution is often a challenge if the number of the electrons (electron density) is detected only from the traditional ionospheric sensors (e.g., ionosonde and topside sounder and Incoherent Scatter Radar (ISR)) due to their low spatio-temporal coverage. This disadvantage is also inherited from the empirical ionospheric model developed based on these data sources. Nowadays, the availability of advanced observation techniques, such as GNSS Radio Occultation (RO) and satellite altimetry, for the measurement of Electron Density (Ne) and related parameters (e.g., hmF2, NmF2, Vertical Scale Height (VSH), Electron Density Profile (EDP) and Vertical Total Electron Content (VTEC)) in the ionosphere has heralded a new era for space weather research in the upper atmosphere. The new sources of data for ionospheric modelling can improve not only the accuracy but also the reliability of the model (such as[96] for hmF2 and [28] for VTEC). In this study, Helmert Variance Component Estimation (VCE) aided Weight Total Least Squares (WTLS) is selected for modelling global VTEC using International GNSS Service stations, satellite altimetry and GNSS-RO measurements. The results show that the new VTEC model outperforms the traditional global ionospheric VTEC Model by at least 1.5 Total Electron Content Unit (TECU) over the ocean. This improvement is expected to be significant in the refinement of global ionospheric VTEC Model development. As is well known, the most traditional models developed are prone to the effects of inherent assumptions (e.g. for the construction of the base functions in the models) which may lead to large biases in the prediction. In this study, an innovative machine learning technique (i.e. Neural Network (NN)) is investigated as the modelling method to address this issue. Different from the traditional modelling method, neither the observation equations (or the so called `design matrix'), nor apriori knowledge of the relationship (both of them can be considered as the source of the aforementioned assumptions) is required in the modelling process of a NN. This network system can automatically construct an optimal regression function based on a large amount of sample data and the designed network [43]. In this study, Deep Neural Network (DNN), which is an advanced Artificial Neural Network (ANN) (with more than one hidden layer), is investigated for their usability of VSH and topside EDP modelling, as well as the relationship between Ne and electron temperature. The results reveal that the new VSH model agrees better than the traditional model with regards to either out-of-sample measurements or the external reference (i.e. ISR data). In addition, the new model can represent the characteristic of VSH in the equatorial region better than that of traditional approaches during geomagnetic storms. The relationship between Ne and Electron Temperature (Te) investigated from ISR data can be used to improve the performance of the current Te model. The local time-altitude variation of the model outputs agrees well with that from a physical model (i.e., Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM)). The new topside EDP model takes hmF2 and NmF2 into consideration as part of the variable set. Comparing with the reference data (i.e., out- of-sample COSMIC data, GRACE and ISR data), the new model agrees much better than the International Reference Ionosphere (IRI)-2016 model. In addition, an advanced NN technique, Bidirectional Long Short-Term Memory (Bi-LSTM), is utilised to forecast hmF2 by using the hmF2 measured by Australian ionosondes in the five hours prior. The forecast results are better than the results from real-time models in the next five hours. The new model performs also better than the current hmF2 model (i.e., AMTB [2] and shubin [96] models, which is used inside IRI-2016 model) by at least 10km in most ionosonde stations. Overall, the neural network technique has a great potential in being utilised in the ionospheric modelling. In addition to the accuracy improvement, the physical mechanism can be observed from the model outputs as well. In future work, the neural network is expected to be further applied in some other space weather studies (e.g., Dst, solar flare, etc)

A comprehensive study of reported high metallicity giant HII regions. I. Detailed abundance analysis

We present long-slit observations in the optical and near infrared of fourteen HII regions in the spiral galaxies: NGC 628, NGC 925, NGC 1232 and NGC 1637, all of them reported to have solar or oversolar abundances according to empirical calibrations. For seven of the observed regions, ion-weighted temperatures from optical forbidden auroral to nebular line ratios have been obtained and for six of them, the oxygen abundances derived by standard methods turn out to be significantly lower than solar. The other one, named CDT1 in NGC 1232, shows an oxygen abundance of 12+log(O/H) = 8.95+-0.20 and constitutes, to the best of our knowledge, the first high metallicity HII region for which accurate line temperatures, and hence elemental abundances, have been derived. For the rest of the regions no line temperature measurements could be made and the metallicity has been determined by means of both detailed photoionisation modelling and the sulphur abundance parameter S_23. Only one of these regions shows values of O_23 and S_23 implying a solar or oversolar metallicity. According to our analysis, only two of the observed regions can therefore be considered as of high metallicity. The two of them fit the trends previously found in other high metallicity HII regions, i.e. N/O and S/O abundance ratios seem to be higher and lower than solar respectively.Comment: Accepted for publication by MNRA

Optimization of PEDOT: PSS thin film for organic solar cell application

As a clean and renewable energy source, the development of the organics solar cells is very promising due to the inorganic solar cell inconvenient production process and material shortness. In this work, P3HT: PCBM bulk-heterojunction devices were produced by spin coating organic layers onto ITO coated glass in air, and deposited it with an Au layer as top metal electrode. Inverted devices were fabricated with and without PEDOT:PSS. Then, several attempts have been conducted to improve power conversion efficiency by optimizing different thicknesses of the interlayer between active layer and metal. Power conversion efficiency, short circuit current, open circuit voltage and fill factor were measured on all produced devices. In contrast, the devices with 50 nm thickness of PEDOT: PSS layer showed as better solar cell with 0.0394% efficiency compared to the devices without PEDOT:PSS. As a result, introduction of PEDOT:PSS layer on active layer improves hole collection at the metal / active layer interface

Assessment of the Performance of Ionospheric Models with NavIC Observations during Geomagnetic Storms

The paper presents an assessment of the performances of the global empirical models: International Reference Ionosphere (IRI)-2016 and the NeQuick2 model derived ionospheric Total Electron Content (TEC) with respect to the Navigation with Indian Constellation (NavIC)/ Indian Regional Navigation Satellite System(IRNSS) estimated TEC under geomagnetic storm conditions. The present study is carried out over Indore (Geographic: 22.52$^{\circ}$N 75.92$^{\circ}$E and Magnetic Dip: 32.23$^{\circ}$N, located close to the northern crest of the Equatorial Ionization Anomaly (EIA) region of the Indian sector). Analysis has been performed for an intense storm (September 6-10, 2017), a moderate storm (September 26-30, 2017) and a mild storm (January 17-21, 2018) that fall in the declining phase of the present solar cycle. It is observed that both IRI-2016 and NeQuick2 derived TEC are underestimates when compared with the observed TEC from NavIC and therefore fail to predict storm time changes in TEC over this region and requires real data inclusion from NavIC for better prediction over the variable Indian longitude sector.Comment: 4 pages, 4 figures, accepted for publication in the proceedings of the 2020 URSI Regional Conference on Radio Science(URSI-RCRS 2020

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

A process-based model of conifer forest structure and function with special emphasis on leaf lifespan

We describe the University of Sheffield Conifer Model (USCM), a process-based approach for simulating conifer forest carbon, nitrogen, and water fluxes by up-scaling widely applicable relationships between leaf lifespan and function. The USCM is designed to predict and analyze the biogeochemistry and biophysics of conifer forests that dominated the ice-free high-latitude regions under the high pCO2 “greenhouse” world 290–50 Myr ago. It will be of use in future research investigating controls on the contrasting distribution of ancient evergreen and deciduous forests between hemispheres, and their differential feedbacks on polar climate through the exchange of energy and materials with the atmosphere. Emphasis is placed on leaf lifespan because this trait can be determined from the anatomical characteristics of fossil conifer woods and influences a range of ecosystem processes. Extensive testing of simulated net primary production and partitioning, leaf area index, evapotranspiration, nitrogen uptake, and land surface energy partitioning showed close agreement with observations from sites across a wide climatic gradient. This indicates the generic utility of our model, and adequate representation of the key processes involved in forest function using only information on leaf lifespan, climate, and soils

Nuclear Transparency to Intermediate-Energy Protons

Nuclear transparency in the (e,e'p) reaction for 135 < Tp < 800 MeV is investigated using the distorted wave approximation. Calculations using density-dependent effective interactions are compared with phenomenological optical potentials. Nuclear transparency is well correlated with proton absorption and neutron total cross sections. For Tp < 300 MeV there is considerable sensitivity to the choice of optical model, with the empirical effective interaction providing the best agreement with transparency data. For Tp > 300 MeV there is much less difference between optical models, but the calculations substantially underpredict transparency data and the discrepancy increases with A. The differences between Glauber and optical model calculations are related to their respective definitions of the semi-inclusive cross section. By using a more inclusive summation over final states the Glauber model emphasizes nucleon-nucleon inelasticity, whereas with a more restrictive summation the optical model emphasizes nucleon-nucleus inelasticity; experimental definitions of the semi-inclusive cross section lie between these extremes.Comment: uuencoded gz-compressed tar file containing revtex and bbl files and 5 postscript figures, totalling 31 pages. Uses psfi