Speaker
Description
The analysis of low-latitude magnetograms, essential for space weather monitoring, is generally affected by the presence of noise. Our initial investigation focused on quantifying this impact by applying the Discrete Wavelet Transform (DWT) to data from South American geomagnetic stations. The results demonstrated that common noise features, such as Gaussian white noise, compromise the identification of high-frequency phenomena like Pc5 geomagnetic pulsations. This finding established the need for a storm index that is inherently resilient to such interference. To address this challenge, we evaluated the Wavelet-Based Index of Storm Activity (WISA), a high-resolution (1-minute) alternative to the Dst index. Using data from the INTERMAGNET, EMBRACE, and AMBER networks, the index was calculated with the iWISA algorithm. The main focus of our experiment was to systematically substitute the San Juan (SJG) station with observatories located in South America. Crucially, to validate the index's robustness, we compared the WISA results calculated from signals with and without a preliminary noise-filtering treatment. The results confirm the dual effectiveness of the WISA index. First, by comparing the two approaches, we verified that WISA produces consistent results, demonstrating that its methodology, based on the Maximal Overlap Discrete Wavelet Transform (MODWT), acts as an intrinsic filter, ensuring a reliable measurement of geomagnetic disturbance even in the presence of noise. Second, the index proved to be an efficient quantifier of equatorial storm activity, equivalent to the Dst/SYM-H, and sensitive to the electrodynamic peculiarities of the South American region. We conclude that WISA is a robust and efficient tool, ideal for both characterizing magnetic disturbances and handling inherent signal noise, thereby simplifying the processing chain and increasing the reliability of regional space weather studies.
