Journal of Atmospheric and Solar-Terrestrial Physics最新文献

The purpose of this paper is to investigate temporal variations in the northward, X, eastward, Y, and downward, Z, components of the geomagnetic field recorded during the October 14, 2023 annular solar eclipse, which main features include its annularity, the eclipse occurrence from local dawn to local dusk, its magnitude variation from 0.30 to 0.86, and the longest ever-observed path across the mainland of the Americas, covering latitudes from ∼65°N to 12°S. The analysis was made possible due to the data on temporal variations in the northward, X, eastward, Y, and downward, Z, components of the geomagnetic field collected at thirteen International Real-time Magnetic Observatory Network magnetometer stations (https://imag-data.bgs.ac.uk/GIN_V1/GINForms2). The solar eclipse acted to cause non-sinusoidal and quasi-sinusoidal perturbations having temporal durations of 180–240 min in all geomagnetic field components on a global scale (∼8000 km). The X-component experienced the largest perturbations attaining 10–20 nT, and the Z-component underwent the smallest disturbances. The quasi-sinusoidal perturbation amplitude did not exceed 5–6 nT, and the period most often showed variations within 15–40 min. The magnetic effect exhibited a tendency to increase with solar eclipse magnitude, while the magnitude of the effect has been shown to be significantly dependent on geographic coordinates, local time, ionospheric state, and the patterns of ionospheric currents as well. During the solar eclipse, the electron density depletion was estimated to be ∼0.10 to ∼0.40–0.60 when the eclipse obscuration A_max varied from 19% to 82%. The movement of the lunar shadow was accompanied by the generation of atmospheric gravity waves with period of ∼10–80 min and by electron density perturbations with amplitudes of the order of 0.01–0.03. The estimates made on the assumption that the magnetic effect is due to the ionospheric current disruptions show good agreement with the observations.

This study investigates the dynamics of a significant dust storm that occurred in Algeria in March 2022, employing data derived from the Sentinel-5P and CALIPSO satellite instruments. We examine the Aerosol Absorbing Index (AAI) to detect n absorbing aerosols, with a focus on desert dust, and analyze the attenuation coefficient. Additionally, we employ the HYSPLIT trajectory analyze to study dust transport and MERRA-2 to examine wind patterns wind. The key findings unveil a detailed trajectory of a prominent dust storm in Algeria in March 2022. The Aerosol Absorbing Index (AAI) effectively identifies absorbing aerosols, particularly desert dust, through thorough analyses of dust trajectory and wind patterns; augmenting these findings, CALIPSO satellite data has provided a detailed vertical profile of aerosols within the dust plume, emphasizing spatial and altitudinal extents. This research significantly contributes to advancing scientific discussions on atmospheric dynamics in arid regions and enhances our understanding and forecasting capabilities related to Saharan dust storm initiation and trajectory.

Characteristic variations in the Greenland isotope temperature data over the last 1000 years and in the meteorological temperature measurements collected from Central England during the past four centuries have been analyzed. We take advantage of the continuous wavelet transform to analyze the simultaneous occurrence of temperature variations of different time scales. We assess the extent to which these phenomena can be compared when examining two different northern hemisphere locations at different time scales. Among the long-term variations, we focus on the cooling at the turn of the 18th century, which occurred slightly later in Greenland than in central England, and the warming observed at present. On the short time scale, the range under study is limited to times of the order of 5-10 years. It has been found that it is on these scales that temperature variations in the two locations are relatively consistent, with a cross-correlation coefficient as high as 0.6 for timescales of the order of 9 years. The main solar activity cycle also falls within the interval of significant correlations. It is shown that despite the absence of direct correlation between temperature and solar activity, the time dependence of the wavelet cross-correlation coefficient of the two temperature series on the scale of 11 years reproduces the long-term variations of solar activity.

An anomalous increase in the level of Very Low Frequency (VLF, 3–30 kHz) and Extremely Low Frequency (ELF, 3–3000 Hz) radio noise and the rate of VLF atmospherics was registered during the explosive eruption of the Tonga volcano on January 15, 2022 at the Akademik Vernadsky station (65.246°S; 64.257°W) about 8870 km from the volcano. At the peak activity around 5 UT, the number of atmospherics in 2-min intervals increased by almost 15 times compared to the period preceding the eruption. At this point, the estimated rate reached 360 VLF atmospherics per second. At the same time, an increase in the power spectral density of the magnetic field by 5–9 times was observed in both the ELF and VLF ranges. After 40 min, only on ELF an increased peak lasting ∼10 min was observed, comparable in magnitude to the main peak. According to the Worldwide Lightning Location Network (WWLLN), increased thunderstorm activity was concentrated very close to the volcano during this period. This discrepancy between the intensities of ELF and VLF radiation suggests a significant difference in the parameters of currents in lightning discharges occurring in the area of the volcano vent and in the area of the volcanic ash plume.

Predicting rainfall is essential for many applications, including agriculture, hydrology, and disaster management. In this work, we undertake a comparison examination of various machine learning models to forecast rainfall based on meteorological data. The target variable in this study is rainfall, and the dataset used includes characteristics like temperature, relative humidity, wind speed, and wind direction. The following seven machine learning models were assessed: Support Vector Regression (SVR), Multivariate adaptive regression splines (MARS), Random Forest Regression, and Deep Neural Network with Historical Data (DWFH), Haar Wavelet Function, Decision Tree and Discrete wavelet Transform (DWT). Data preprocessing, which includes standardisation and lagging to capture temporal dependencies, comes first in the analysis phase. A wavelet transformation is also used to capture complex patterns in the data. Each model is tested on a different test set after being trained on a subset of the dataset. The results are assessed using the Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), focusing on the RMSE and MSE values for better comparison across models. Our findings reveal that the DWFH model achieved an RMSE of 0.0138807 mm and MSE of 0.000193 mm², demonstrating their effectiveness in predicting rainfall. The Random Forest and SVR models also provided competitive results. This study highlights the importance of selecting an appropriate machine learning model for rainfall prediction and the significance of preprocessing techniques in improving model performance. These insights can aid decision-makers in choosing the most suitable model for their specific application, contributing to more accurate rainfall predictions and enhanced decision support systems.

Astronomical sites have to be selected according to many factors whereas the geographic location of the site and the quality of the atmosphere above the site play an important role in the decision process. The following factors were chosen to create layers 1907 northern and 235 southern observatories: CC (cloud coverage), PWV (precipitable water vapor), AOD (aerosol optical depth), VWV (vertical wind velocity), and HWV (horizontal wind velocity). To estimate the astronomical importance of the sites, DEM (digital elevation model) and LAT (latitude of observatory location) layers were also included. In addition to the variations or trends, a complete statistical analysis was carried out for all factors to investigate the potential correlations between the factors. There is a clear difference between the northern and southern hemispheres. The exchange of meteorological seasons between hemispheres is also compliant with factors. The geographical locations of most of the observatories were found to be “not suitable”. There seem to be no apparent long-term variations and/or patterns in all factors.

Sea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Türkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5^ox0.5°) monthly SST dataset spanning the years 1970–2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 °C, 20.2 °C, 17.0 °C, and 16.6 °C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 °C, 21.9 °C, 18.1 °C, and 18.8 °C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.