Prediction of local geomagnetic activity on the example of data of “Lviv” Magnetic Observatory
Рубрика:
1Vlasov, DI, 1Parnowski, AS 1Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, Kyiv, Ukraine |
Space Sci. & Technol. 2021, 27 ;(1):78-84 |
https://doi.org/10.15407/knit2021.01.078 |
Язык публикации: Ukrainian |
Аннотация: For the first time in world practice, predictive models were constructed for X, Y, Z geomagnetic elements. Based on these models, the prediction was made with 3 hours lead time using data of the “Lviv” magnetic observatory. The properties of models are as follows: observatory — LVV, рredicted values — XYZ; lead time — 3 hours; correlation coefficients’ averaged measurement data — 0.824 (X), 0.811 (Y), 0.804 (Z); prediction efficiency — 0.816 (X), 0.803 (Y), 0.801 (Z); skill score — 0.115 (X), 0.095 (Y), 0.099 (Z). The developed models were tested in the Main Center of Special Monitoring, and they were found to meet the Basic Requirements for operational predictive models.
|
Ключевые слова: local geomagnetic activity, regression modeling, space weather |
References:
1. Forecast verification methods. URL: http://www.cawcr.gov.au/projects/verification/ (Last accessed: 09.09.2019).
2. International Real-time Magnetic Observatory Network (INTERMAGNET). URL http://www.intermagnet.org (Last accessed: 09.09.2019).
3. OMNIWeb online database. URL: https://omniweb.gsfc.nasa.gov/html/ow_data.html (Last accessed: 09.09.2019).
4. World Data Center for Geomagnetism, Kyoto. URL: http://wdc.kugi.kyoto-u.ac.jp/ (Last accessed: 09.09.2019).
5. Fisher R. А. (1954). Statistical methods for research workers. London: Oliver and Boyd.
6. Hudson D. (1964). Statistics lectures on elementary statistics and probability. Geneva.
7. Billings S. A. (2013). Nonlinear system identification. Wiley.
8. King J. H., Papitashvili N. E. (2004). Solar Wind Spatial Scales in Comparisons of Hourly Wind and ACE Plasma and Magnetic Field Data. J. Geophys. Res., 110, A02209
2. International Real-time Magnetic Observatory Network (INTERMAGNET). URL http://www.intermagnet.org (Last accessed: 09.09.2019).
3. OMNIWeb online database. URL: https://omniweb.gsfc.nasa.gov/html/ow_data.html (Last accessed: 09.09.2019).
4. World Data Center for Geomagnetism, Kyoto. URL: http://wdc.kugi.kyoto-u.ac.jp/ (Last accessed: 09.09.2019).
5. Fisher R. А. (1954). Statistical methods for research workers. London: Oliver and Boyd.
6. Hudson D. (1964). Statistics lectures on elementary statistics and probability. Geneva.
7. Billings S. A. (2013). Nonlinear system identification. Wiley.
8. King J. H., Papitashvili N. E. (2004). Solar Wind Spatial Scales in Comparisons of Hourly Wind and ACE Plasma and Magnetic Field Data. J. Geophys. Res., 110, A02209
https://doi.org/10.1029/2004JA010804.
9. Machol J. L., Reinard A. A., Viereck R. A., Biesecker D. A. (2013). Identification and replacement of proton-contaminated real-time ACE solar wind measurements. Space Weather, 11, № 7, 434—440.
9. Machol J. L., Reinard A. A., Viereck R. A., Biesecker D. A. (2013). Identification and replacement of proton-contaminated real-time ACE solar wind measurements. Space Weather, 11, № 7, 434—440.
https://doi.org/10.1002/swe.20070.
10. Parnowski A. S. (2009). Regression modeling method of space weather prediction. Astrophys. and Space Sci., 323, № 2, 169—180.
10. Parnowski A. S. (2009). Regression modeling method of space weather prediction. Astrophys. and Space Sci., 323, № 2, 169—180.
https://doi.org/10.1007/s10509-009-0060-4 [arXiv:0906.3271].
11. Parnowski A. S. (2011). Regression modelling of geomagnetic activity. J. Phys. Studies, 15, № 2, 2002.
11. Parnowski A. S. (2011). Regression modelling of geomagnetic activity. J. Phys. Studies, 15, № 2, 2002.
12. Parnowski A. S., Polonska A. Yu. (2012). Regression modelling of the interaction between the solar wind and the terrestrial magnetosphere. J. Phys. Studies, 16, № 1/2, 1002.
13. Press W. H., Teukolsky S. A., Vetterling W. T., Flannery B. P. (1992). Numerical Recipes in FORTRAN. The Art of Scientific Computing. 2nd Ed. Cambridge: Cambridge Univ. Press.
14. Reay S., Herzog D., Alex S., Kharin E., McLean S., Nosé M., et al. (2011). Magnetic Observatory Data and Metadata: Types and Availability. Geomagnetic Observations and Model, 149—181.
14. Reay S., Herzog D., Alex S., Kharin E., McLean S., Nosé M., et al. (2011). Magnetic Observatory Data and Metadata: Types and Availability. Geomagnetic Observations and Model, 149—181.
15. Semeniv O., Polonska A., Parnowski A. (2014). Operational geomagnetic forecast service. Bull. Taras Shevchenko Nat. Univ. Kyiv. Ser. Astronomy, № 51, 23—24.
16. Sumaruk T, Sumaruk Yu. (2007). The New Index of Geomagnetic Activity. Publications of the Institute of Geophysics Polish Academy of Sciences. Monographic volume C-99(398), 374—382.
16. Sumaruk T, Sumaruk Yu. (2007). The New Index of Geomagnetic Activity. Publications of the Institute of Geophysics Polish Academy of Sciences. Monographic volume C-99(398), 374—382.