Relation of Atmosphere Surface Pressure Fluctuations with Geomagnetic and Cosmic Factors
Mikhail B. Bogdanov, Alexandr V. Fedorenko
Abstract
We present results of the digital spectral analysis of the time series containing the daily samples of the atmosphere surface pressure measured at Saratov city from Jan 1, 1995 to Dec 31, 1999. We calculated also cross correlation functions between the pressure fluctuations and the planetary geomagnetic indices, Ap and Cp, and cross correlation function between these fluctuations and the cosmic-ray intensity. Two peaks detected in the calculated power spectrum are related, probably, with the atmosphere tidal waves. The relation of the pressure fluctuations with the Ap index of the geomagnetic activity is statistically insignificant. The cross correlation function between the pressure fluctuations and the Cp index has the maximal value 0.044+-0.023 at the pressure time delay 4 days. The statistically significant negative correlation is discovered between the surface pressure fluctuations and the cosmic-ray intensity. The cross correlation function has two minima: at the zero time delay with the value -0.068+-0.023, and at the pressure time delay 12 days with the value -0.087+-0.023. The negative correlation between the pressure and the cosmic-ray intensity is observed at the pressure time delay as long as 17 days and can be explained by the so-called condensation mechanism of an interaction of the cosmic rays with the atmosphere.
Create a lesson
Related papers
Variable Masking for Climate Emulation in ACE
Alexey Yermakov, Troy Arcomano, Jeremy McGibbon et al.
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models
Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad Alipour
Building and calibrating a 3Di subgrid flood model for Hanoi, without a surveyed terrain model, drainage data, or rain gauges
Minh Hoa Nguyen, Mai Anh Tran
Improved Precipitation Variability in the Community Atmosphere Model through Implementation of a Neural-Network Deep Convection Scheme
Xavier J. Levine, Griffin Mooers, Jack W. Atkinson et al.
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625 High-Resolution IMD Gridded Precipitation Data
Bipin Kumar, Rajib Chattopadhyay, Harsh P. Bachhav et al.
Generative modelling for multivariate spatio-temporal downscaling of monthly climate fields
Yu Huang, Shangshang Yang, Sebastian Bathiany et al.