scholarly journals TheKpindex and solar wind speed relationship: Insights for improving space weather forecasts

Space Weather ◽  
2013 ◽  
Vol 11 (6) ◽  
pp. 339-349 ◽  
Author(s):  
Heather A. Elliott ◽  
Jörg-Micha Jahn ◽  
David J. McComas
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Weather Forecasts

scholarly journals Several special conditions in the solar wind fora supersubstorm appearance.

2020 ◽  
Author(s):  
I.V. Despirak ◽  
◽  
A.A. Lubchich ◽  
N.G. Kleimenova ◽  
◽  
...  
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Data Base ◽  
Space Weather ◽  
Large Scale ◽  
Dynamic Pressure ◽  
Solar Wind Speed ◽  
Weather Conditions ◽  
The Other ◽  
Solar Wind Parameters

Analysis of the space weather conditions associated with supersubstorms (SSS) was carried out. Two magnetic storms, on 11 April and on 18 April 2001 have been studied and compared. During the first storm, there were registered twoevents of the supersubstorms with intensity of the SML index ~2000-3000 nT, whereas during the second storm there were observed two intense substorms with SML ~ 1500 nT. Solar wind conditions before appearance of the SSSs and intense substorms were compared. For this purpose, the OMNI data base, the catalog of large-scale solar wind phenomena and the data from the magnetic ground-based stations of the SuperMAG network (http://supermag.jhuapl.edu/) were combined. It was shown that the onsets of the SSS event were preceded by strong jumps in the dynamic pressure and density of the solar wind, which were observed against the background of the high solar wind speed and high values of the southern ВZcomponent of the IMF. Comparison with the usual substorms showed thatsome solar wind parameters were higher before SSSs, then before usual substorms: the dynamic pressure, the speed and the magnitude of IMF. On the other hand, the PC index values was the same for these all substorms, that leads to the conclusion about the possible independence of SSS appearance on the level of solar energy penetrated to the magnetosphere.

scholarly journals Timing of the solar wind propagation delay between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Gb Model

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents  are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

scholarly journals Star Wars? Space Weather and Electricity Market: Evidence from China

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5281
Author(s):  
Tong Wu ◽  
Zhe You ◽  
Mengqi Gong ◽  
Jinhua Cheng
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Electricity Market ◽  
Power Plants ◽  
Consumption Rate ◽  
Solar Wind Speed ◽  
Electricity Consumption ◽  
Coal Consumption ◽  
Geomagnetic Index

This paper aims to investigate the impact of space weather on China’s electricity market. Based on data products provided by NOAA and the National Energy Administration in China, this paper uses solar wind velocity as a solar weather indicator and the disturbance storm time index as a magnetospheric weather indicator to match monthly Chinese electricity market data over 10 years. Based on a VAR model, we found that (1) space weather increases the demand for electricity in China, and solar wind speed and the geomagnetic index increase the electricity consumption of the whole of Chinese society, as space weather mainly increases the electricity consumption of the secondary and industrial sectors. (2) The geomagnetic index significantly promotes power station revenue. (3) Space weather is associated with increased energy consumption. The geomagnetic index significantly increases the coal consumption rate of fossil power plants in China, but the solar wind speed has nothing to do with the coal consumption rate of fossil power plants.

scholarly journals Responses and Periodic Variations of Cosmic Ray Intensity and Solar Wind Speed to Sunspot Numbers

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Jacob Oloketuyi ◽  
Yu Liu ◽  
Amobichukwu Chukwudi Amanambu ◽  
Mingyu Zhao
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Phase Relationship ◽  
Cosmic Ray ◽  
Strong Negative Correlation ◽  
Cosmic Ray Intensity ◽  
Sunspot Numbers ◽  
Daily Data ◽  
Wide Range

To investigate the periodic behaviour and relationship of sunspot numbers with cosmic ray intensity and solar wind speed, we present analysis from daily data generated from 1995 January to 2018 December. Cross-correlation and wavelet transform tools were employed to carry out the investigation. The analyses confirmed that the cosmic ray intensity correlates negatively with the sunspot numbers, exhibiting an asynchronous phase relationship with a strong negative correlation. The trend in cosmic ray intensity indicates that it undergoes the 11-year modulation that mainly depends on the solar activity in the heliosphere. On the other hand, the solar wind speed neither shows a clear phase relationship nor correlates with the sunspot numbers but shows a wide range of periodicities that could possibly be connected to the pattern of coronal hole configuration. A number of short and midterm variations were also observed from the wavelet analysis, i.e., 64–128 and 128–256 days for the cosmic ray intensity, 4–8, 32–64, 128–256, and 256–512 days for the solar wind speed, and 16–32, 32–64, 128–256, and 256–512 days for the sunspot numbers.

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