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Ionospheric response characteristics and PPP accuracy analysis at different latitudes during strong geomagnetic storms

Chen Dongjie, Hu Zhenluan, Wang Fu, Gou Yanmei, Zhang Ting

Acta Geophysica

2024

Vol. 72, no. 3

2097--2106

To investigate the response of ionosphere to strong geomagnetic storms at different latitudes, this paper focuses on the response characteristics of global ionospheric ROTI at different latitudes during the September 2017 strong geomagnetic storms based on global ROTI maps and GPS dual-frequency observation data, and explores the PPP accuracy of GPS stations at each latitude during two different types of strong geomagnetic storms. The results show that (i) the daily mean ROTI values during the September 2017 strong geomagnetic storms show a spatial distribution of higher in the global region in the high-latitude region of North America, the northern European region and the whole Antarctic region, and lower in other regions. (ii) The highest ROTI time mean value in the high-latitude belt, with a peak value of 0.30 TECU/min, exceeds twice the peak value of the global time mean value; within the mid-latitude belt, the ROTI time mean value in the northern hemisphere is larger, with values exceeding three times those of the Southern Hemisphere. (iii) During the strong geomagnetic storm in September 2017, the PPP results of high-latitude GPS stations were affected to a greater extent, and the mean value of positioning error in the vertical direction exceeded 2.0 m. (iv) During the strong geomagnetic storm in November 2021, the changing trend of the mean value of PPP positioning error at different latitudes was consistent with that of 2017, but the mean value of positioning error and RMS at high latitudes in 2021 was higher than those in 2017.

The impact of green finance development on ecological protection based on machine learning

Zhang Ting

Ecological Chemistry and Engineering. S

2023

Vol. 30, nr 1

103--110

In the context of today’s green development, it is the core task of the financial sector at all levels to enhance the utilisation of resources and to guide the high-quality development of industries, especially to channel funds originally gathered in high-pollution and energy-intensive industries to sectors with green and high-technology, to achieve the harmonious development of the economy and the resources and environment. This paper proposes a green financial text classification model based on machine learning. The model consists of four modules: the input module, the data analysis module, the data category module, and the classification module. Among them, the data analysis module and the data category module extract the data information of the input information and the green financial category information respectively, and the two types of information are finally fused by the attention mechanism to achieve the classification of green financial data in financial data. Extensive experiments are conducted on financial text datasets collected from the Internet to demonstrate the superiority of the proposed green financial text classification method.