A5063360182- Ionosphere and magnetosphere dynamics
- Geomagnetism and Paleomagnetism Studies
- Solar and Space Plasma Dynamics
- Earthquake Detection and Analysis
- Solar Radiation and Photovoltaics
- Seismic Waves and Analysis
- Astro and Planetary Science
- Geophysical and Geoelectrical Methods
- Stellar, planetary, and galactic studies
- Space exploration and regulation
- GNSS positioning and interference
- Geophysics and Gravity Measurements
- Big Data Technologies and Applications
- Economic and Technological Developments in Russia
- CCD and CMOS Imaging Sensors
- Global Energy and Sustainability Research
- Photocathodes and Microchannel Plates
- Safety and Risk Management
- Scientific Research and Discoveries
- Optical Systems and Laser Technology
- Energy Load and Power Forecasting
- Advanced Research in Science and Engineering
- Radiation Effects in Electronics
- Optical Wireless Communication Technologies
- Space Science and Extraterrestrial Life
Swedish Institute of Space Physics
2009-2024
Lund University
1994-1996
Abstract. In this paper, we analyse in detail two famous space weather events; a railway problem on 13–14 July 1982 and power blackout 30 October 2003. Both occurred Sweden during very intensive storms each of them few years after the sunspot maximum. This paper provides description conditions Sun solar wind leading to GIC events ground. By applying modelling techniques introduced developed our previous also calculate horizontal geoelectric field at Earth's surface southern as well flowing...
Sweden has experienced many geomagnetically induced current (GIC) events in the past, which is obviously due to high‐latitude location of country. The largest GIC, almost 300 A, was measured southern earthing lead a 400 kV transformer neutral during magnetic storm on 6 April 2000. On 30 October 2003, city Malmö at coast suffered from power blackout caused by leaving 50,000 customers without electricity for about 20–50 min. We have developed model that enables calculation GIC Swedish grid....
Abstract Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect near‐Earth space into a single parameter. Most best‐known calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created predict values these indices, often using solar wind measurements upstream Earth input variables to calculation. This document reviews current state geomagnetic methods used assess their ability...
Abstract. An artificial feed-forward neural network with one hidden layer and error back-propagation learning is used to predict the geomagnetic activity index (Dst) hour in advance. The Bz-component ΣBz, density, velocity of solar wind are as input network. trained on data covering a total 8700 h, extracted from 25-year period 1963 1987, taken NSSDC base. performance examined test data, not included training set, which covers 386 h includes four different storms. Whilst predicts initial...
We here present a model for real time forecasting of the geomagnetic index Dst . The consists recurrent neural network that has been optimized to be as small possible without degrading accuracy. It is driven solely by hourly averages solar wind magnetic field component B z , particle density n and velocity V which means does not rely on observed In an evaluation based more than 40,000 hours data, it shown this smaller errors other models currently in operational use. A complete description...
We have developed neural network models that predict Kp from upstream solar wind data. study the importance of various input parameters, starting with magnetic component Bz, particle density n, and velocity V then adding total field B By component. As we also notice a seasonal UT variation in average include functions day-of-year UT. Finally, as is global representation maximum range geomagnetic over 3-hour intervals conclude sudden changes can big effect on Kp, even though it value....
Abstract. We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include density n, velocity V and southward component Bz interplanetary magnetic field. is not included in implement an explicit functional relationship between solar wind geomagnetic disturbance, including both direct time-delayed non-linear relations. In this study we especially consider influence varying...
Empirical models are developed to provide 10–30-min forecasts of the magnitude time derivative local horizontal ground geomagnetic field (|dBh/dt|) over Europe. The driven by ACE solar wind data. A major part work has been devoted search and selection datasets support model development. To simplify problem, but at same capture sudden changes, 30-min maximum values |dBh/dt| forecast with a cadence 1 min. Models tested both without use SWEPAM plasma It is shown that generally increases in...
The use of time delay feed‐forward neural networks to predict the hourly values ionospheric F 2 layer critical frequency, f 0 , 24 hours ahead, have been examined. measurements per day are reduced five coefficients with principal component analysis. A line these is then used as input a network. Also included in 10.7 cm solar flux and geomagnetic index Ap . network trained measured data from 1965 1985 at Slough station validated on an independent validation set same for periods 1987–1990...
Abstract. The local ground geomagnetic field fluctuations (Δ B) are dominated by high frequencies and 83% of the power is located at periods 32 min or less. By forming 10-min root-mean-square (RMS) Δ B a major part this variation captured. Using measured induced currents (GIC), from grid transformer in Southern Sweden, it shown that standard deviation GIC may be computed linear model using RMS X Y Brorfelde (BFE: 11.67° E, 55.63° N), Denmark, Uppsala (UPS: 17.35° 59.90° with correlation...
Abstract We have investigated the consequences of extreme space weather on ion outflow from polar ionosphere by analyzing solar storm that occurred early September 2017, causing a severe geomagnetic storm. Several X‐flares and coronal mass ejections were observed between 4 10 September. The first shock—likely associated with ejection—hit Earth late 6 September, produced sudden commencement, began initial phase It was followed second shock, approximately 24 hr later, initiated main...
Previous studies have demonstrated a close relationship between the time derivative of horizontal geomagnetic field vector (dH/dt) and geomagnetically induced currents (GIC) at nearby location in power grid. Similarly, high correlation exists GIC local geoelectric (E), typically modelled from measured magnetic field.
Abstract Rosenqvist and Hall (2019), https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018SW002084 developed a proof‐of‐concept modeling capability that incorporates detailed 3D structure of Earth's electrical conductivity in geomagnetically induced current estimation procedure (GIC‐SMAP). The model was verified based on GIC measurements northern Sweden. study showed southern Sweden is exposed to stronger electric fields due combined effect low crustal the influence surrounding coast....
Abstract. High-frequency ( ≈ minutes) variability of ground magnetic fields is caused by ionospheric and magnetospheric processes driven the changing solar wind. The varying induce electrical that cause currents to flow in man-made conductors like power grids pipelines. Under extreme conditions geomagnetically induced (GIC) may be harmful grids. Increasing our understanding events thus important for solar-terrestrial science space weather. In this work 1-min resolution time derivative...
Abstract Models that predict the Kp and Dst indices are evaluated using solar wind data at L1. The models consist of ensembles neural networks have been developed ACE Level 2 from period 1998–2015. use is motivated by difficulty generating functions generalize well in regions input‐output space poorly sampled, which typically occurs during stronger events. Since launch DSCOVR spacecraft, providing measurements about August 2016, new independent become available to test models. for almost...
Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs Elman, gated unit (GRU), and long short-term memory (LSTM). take solar wind data as inputs to predict Dst index. index summarizes complex processes into a single time series. models trained tested using five-fold cross-validation based on hourly resolution OMNI dataset from years 1995–2015. plasma (particle density speed), vector magnetic fields, year, day....
In the domain of space science, numerous ground-based and space-borne data various phenomena have been accumulating rapidly, making analysis scientific interpretation challenging. However, recent trends in application artificial intelligence (AI) shown to be promising extraction information or knowledge discovery from these extensive sets. Coincidentally, preparing for use as inputs AI algorithms, referred AI-readiness, is one outstanding challenges leveraging science. Preparation AI-ready...
Predictions of the daily solar wind velocity ( V ) at 1 AU from flux tube expansion factor ƒ s are examined with radial basis function neural networks. The is calculated potential field model, using Wilcox Solar Observatory magnetograms, source surface placed 2.5 radii. time series extend over 20 years 1976 to 1995 and consist approximately 3000 values . correlation between monthly averages 1/ƒ 0.57, independent assumed Sun‐Earth travel τ. However, for drops 0.38 τ = 5 days. Even adjusting...
Abstract. The 7-10 November 2004 period contains two events for which the local ground magnetic field was severely disturbed and simultaneously, solar wind displayed several shocks negative Bz periods. Using empirical models 10-min RMS at Brorfelde (BFE, 11.67° E, 55.63° N), Denmark, are predicted. recurrent neural networks with plasma data as inputs. predictions show a good agreement during 7 November, up until around noon on 8 after become significantly poorer. correlations between...