Temperature and pressure variability in mid-latitude low atmosphere and stratosphere-ionosphere coupling

Abstract

This study presents the continuation of our previous analysis of variations of atmospheric and space weather parameters above Iberian Peninsula along two years near the 24 th solar cycle maximum. In the previous paper (Morozova et al., 2017) we mainly discussed the first mode of principal component analysis of tropospheric and lower stratospheric temperature and pressure fields, which was shown to be correlated with lower stratospheric ozone and anti-correlated with cosmic ray flux. Now we extend the investigation to the second mode, which suggests a coupling between the stratosphere and the ionosphere.This second mode, located in the low and middle stratosphere (and explaining ~7% of temperature and ~3% of geopotential height variations), showed to be statistically significantly correlated with variations of the middle stratosphere ozone content and anti-correlated with variations of ionospheric total electron content. Similar co-variability of these stratospheric and ionospheric parameters was also obtained with the wavelet cross-coherence analysis.To investigate the role of atmospheric circulation dynamics and the causal nature of the found correlations, we applied the convergent cross mapping (CCM) analysis to our series. Strong evidence for the stratosphere-ionosphere coupling were obtained for the winter 2012-2013 that is characterized by the easterly QBO phase (quasi-biennial oscillations of the direction of the stratospheric zonal winds) and a strong SSW (sudden stratospheric warming event). Further analysis (for the three-year time interval 2012-2015) hint that SSWs events play main role in emphasizing the stratosphere-ionosphere coupling. IntroductionThe ionosphere is a partially ionized layer of the Earth's atmosphere located between the upper mesosphere (~ 60 km) and the lower exosphere (~ 900 km), where the UV and XR radiations and energetic particles of solar and cosmic origin are the main ionization sources. Understanding of variations of ionospheric parameters is not only scientifically important but also necessary from the practical and technological points of view. Variations in ionospheric plasma densities change conditions for the radio signal propagation and, consequently, affect the functioning of the satellite based communication, surveillance, and navigation systems (e.g., Kumar and Parkinson, 2017). In this paper we focus on the variations of the ionospheric conditions above the Iberian Peninsula middle latitudinal region.The ionosphere shows coupling both with the underlying neutral atmosphere and the overlying magnetosphere (Chapman and Bartels, 1951;Kazimirovsky and Kokourov, 1991). In its turn, electric currents running in the ionosphere cause variations of the ground measured geomagnetic field, e.g., well known "solar quiet" (Sq) daily variations (Chapman and Bartels, 1951;Matsushita, 1968;Yamazaki et al., 2016).The most widely used parameters characterizing ionospheric variable conditions are the total electron content (TEC, i.e. total number of electrons in a column of air of 1 m 2...

PCA-MRM Model to Forecast TEC at Middle Latitudes

“…Preliminary tests of the PCA-MRM models as well as the results of [20,22] were used to define the set of space weather parameters (SWp) that are used as predictors for TEC variations. In particular, we prefer to use the Mg II index over the more often used F10.7 index [22]; also, the test models that used local K COI showed slightly better performance compared to ones that used the global Kp index.…”

“…Preliminary tests of the PCA-MRM models as well as the results of [20,22] were used to define the set of space weather parameters (SWp) that are used as predictors for TEC variations. In particular, we prefer to use the Mg II index over the more often used F10.7 index [22]; also, the test models that used local K COI showed slightly better performance compared to ones that used the global Kp index. The separation of the number of flares of different classes (C, M and N) was proposed to find out what is more important for a TEC model: the total number of flares, the number of the most abundant flares (C-class or less) or the number of moderate flares (M class); since in 2015 there was only one short-living X-class flare, the influence of the X flares on the model's performance was not studied.…”

Variabilidad climática (precipitación, temperatura y humedad relativa) para la gestión hídrica del departamento del Casanare, Colombia

“…A su vez, a partir del análisis espacio-temporal se puede desde, la precipitación ayudar en el monitoreo de sequías e inundaciones (Torres-Batlló y Martí-Cardona, 2020;Arias et al, 2021), la temperatura conocer el desarrollo de calentamiento o disminución de la temperatura en la estratósfera (da Silva-Júnior et al, 2020;Morozova et al, 2020), y la humedad relativa entender los cambios de humectación o secado en función del contenido de vapor atmosférico (Hu et al, 2018;Wei et al, 2021). Por lo tanto, la combinación de lo anterior, permitiría identificar escenarios que ocasionen disminución o aumento de las variables en estudio en función del tiempo (Beltrán y Díaz, 2020), e influir en la toma de decisiones al no planificar bajo escenarios de incertidumbre (Castro-García y Sosa-Rico, 2017;Ryazanova et al, 2021), lo que, implícitamente aumentaría el riesgo en el territorio.…”

Variations of TEC Over Iberian Peninsula in 2015 Due to Geomagnetic Storms and Solar Flares

“…The results of the data analysis can be used to develop regional empirical models allowing prediction of ionospheric response to different forcings, for example, geomagnetic storms. In particular, the method called principal component analysis (PCA) showed good potential for the analysis and modeling of ionospheric regular variations and disturbances (Chen et al, 2015; Li et al, 2019; Morozova et al, 2019). The advantage of the regional models (e.g., Hu & Zhang, 2018; Mukhtarov et al, 2018; Petry et al, 2014; Tebabal et al, 2019; Tsagouri et al, 2018) over the global ones is in their better reflection of specific local behavior of the ionosphere.…”

“…principal component analysis (PCA) showed good potential for the analysis and modeling of ionospheric regular variations and disturbances (Chen et al, 2015;Li et al, 2019;Morozova et al, 2019). The advantage of the regional models (e.g., Hu & Zhang, 2018;Mukhtarov et al, 2018;Petry et al, 2014;Tebabal et al, 2019;Tsagouri et al, 2018) over the global ones is in their better reflection of specific local behavior of the ionosphere.…”