scholarly journals Extreme value analysis of the time derivative of the horizontal magnetic field and computed electric field

2016 ◽  
Vol 34 (4) ◽  
pp. 485-491 ◽  
Author(s):  
Peter Wintoft ◽  
Ari Viljanen ◽  
Magnus Wik
Keyword(s):  
Magnetic Fields ◽  
Time Derivative ◽  
Extreme Value ◽  
Power Grids ◽  
Extreme Value Analysis ◽  
Geomagnetically Induced Currents ◽  
Horizontal Magnetic Field ◽  
Value Analysis ◽  
Electrical Fields ◽  
Ground Magnetic

Abstract. High-frequency ( ≈  minutes) variability of ground magnetic fields is caused by ionospheric and magnetospheric processes driven by the changing solar wind. The varying magnetic fields induce electrical fields that cause currents to flow in man-made conductors like power grids and pipelines. Under extreme conditions the geomagnetically induced currents (GIC) may be harmful to the power grids. Increasing our understanding of the extreme events is thus important for solar-terrestrial science and space weather. In this work 1-min resolution of the time derivative of measured local magnetic fields (|dBh∕dt|) and computed electrical fields (Eh), for locations in Europe, have been analysed with extreme value analysis (EVA). The EVA results in an estimate of the generalized extreme value probability distribution that is described by three parameters: location, width, and shape. The shape parameter controls the extreme behaviour. The stations cover geomagnetic latitudes from 40 to 70° N. All stations included in the study have contiguous coverage of 18 years or more with 1-min resolution data. As expected, the EVA shows that the higher latitude stations have higher probability of large |dBh∕dt| and |Eh| compared to stations further south. However, the EVA also shows that the shape of the distribution changes with magnetic latitude. The high latitudes have distributions that fall off faster to zero than the low latitudes, and upward bounded distributions can not be ruled out. The transition occurs around 59–61° N magnetic latitudes. Thus, the EVA shows that the observed series north of  ≈ 60° N have already measured values that are close to the expected maxima values, while stations south of  ≈ ° N will measure larger values in the future.

scholarly journals Relation between substorm characteristics and rapid temporal variations of the ground magnetic field

2006 ◽  
Vol 24 (2) ◽  
pp. 725-733 ◽  
Author(s):  
A. Viljanen ◽  
E. I. Tanskanen ◽  
A. Pulkkinen
Keyword(s):  
Magnetic Field ◽  
Time Derivative ◽  
Sunspot Cycle ◽  
Temporal Variations ◽  
Field Vector ◽  
Geomagnetically Induced Currents ◽  
Horizontal Magnetic Field ◽  
Auroral Substorm ◽  
Induced Currents ◽  
Ground Magnetic

Abstract. Auroral substorms are one of the major causes of large geomagnetically induced currents (GIC) in technological systems. This study deals with different phases of the auroral substorm concerning their severity from the GIC viewpoint. Our database consists of 833 substorms observed by the IMAGE magnetometer network in 1997 (around sunspot minimum) and 1999 (rising phase of the sunspot cycle), divided into two classes according to the Dst index: non-storm (Dst>-40 nT, 696 events) and storm-time ones (Dst<-40 nT, 137 events). The key quantity concerning GIC is the time derivative of the horizontal magnetic field vector (dH/dt) whose largest values during substorms occur most probably at about 5 min after the onset at stations with CGM latitude less than 72 deg. When looking at the median time of the occurrence of the maximum dH/dt after the expansion onset, it increases as a function of latitude from about 15 min at CGM lat=56 deg to about 45 min at CGM lat=75 deg for non-storm substorms. For storm-time events, these times are about 5 min longer. Based on calculated ionospheric equivalent currents, large dH/dt occur mostly during the substorm onset when the amplitude of the westward electrojet increases rapidly.

scholarly journals Characteristics of variations in the ground magnetic field during substorms at mid latitudes

2009 ◽  
Vol 27 (9) ◽  
pp. 3421-3428 ◽  
Author(s):  
K. L. Turnbull ◽  
J. A. Wild ◽  
F. Honary ◽  
A. W. P. Thomson ◽  
A. J. McKay
Keyword(s):  
Magnetic Field ◽  
Transmission Lines ◽  
Power Transmission ◽  
Time Derivative ◽  
High Time Resolution ◽  
Power Transmission Lines ◽  
Geomagnetically Induced Currents ◽  
Horizontal Magnetic Field ◽  
Ground Magnetic ◽  
The Uk

Abstract. Substorms are known to cause geomagnetically induced currents (GIC) in power transmission lines through variations in the ground magnetic field. An improved knowledge and understanding of how the different phases of substorms affect the ground magnetic field will ultimately help to better understand how GIC arise. Although usually associated with high latitude power transmission networks, GIC potentially pose a risk to mid latitude networks such as the UK's National Grid. Using a list of substorm expansion phase onsets derived from auroral observations by the IMAGE-FUV satellite, this study examines 553 individual onsets. In order to cover mid latitudes, ground magnetometer data from the UK Sub-Auroral Magnetometer Network (SAMNET) are exploited. These high time resolution (5 s) data are used to study the ground magnetic field for an hour after onset, in particular the time derivative of the horizontal magnetic field, H. The data covers the period from 2000 to 2003 (just after solar maximum). Results are compared with a previous study of magnetic field variations at higher latitudes, using data with a much lower (1 min) cadence during substorms identified from geomagnetic indices during a period just after solar minimum.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

&lt;p&gt;To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically &gt;1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years &amp;#8211; Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document