Journal of Atmospheric and Solar-Terrestrial Physics最新文献

Studies concerning solar eclipses have been rising significantly, yet, different circumstances during their occurrence provide uniqueness to every study. This paper studies the ionospheric and meteorological response to the total solar eclipses of August 21, 2017, March 20, 2015 and August 1, 2008. The ionospheric total electron content (TEC) was calculated from the signals beamed by the dual-frequency Global Positioning System (GPS) satellites and accessed from University NAVSTAR Consortium (UNAVCO) data archive. Similarly, the data of meteorological parameters were accessed from the historical climate archive of respective countries. The TEC drop of ∼2–7 TECU with a lag of ∼15–30 min is observed at varying latitudes which correspond with the findings of numerous past research. We analyzed the data of 15 stations under ∼100% obscuration to rule out the varying effects of different obscuration rates. Yet, given the turbulent nature of climate, we found varying changes at observed locations. A good relationship, however, was observed in 8 of the stations, where temperature drop ranged from 0.4 °C to 6.11 °C, and rise in relative humidity ranged from 0 to ∼77%. Wind speed has shown the most turbulent behavior. Their change was largely impacted by the eclipse on 5 of the stations, while the local factor was dominant on the others. In spite of this, the stations under observation showed distinct responses to the ionospheric change during the total solar eclipses, thus demonstrating the relation of meteorological parameters with eclipses.

This paper presents a description and characterization of the lightning activity over the central and northern portions of the Colombian territory, which was divided into nine subregions for the analysis, between 2007 and 2016. The lightning activity and parameters within the study areas display a high level of temporal and spatial variation; however, these variations are overshadowed when the analysis is conducted for larger areas. In the northern regions, the monthly lightning activity follows a unimodal distribution, whereas in the central and southern regions, it follows a bimodal distribution. The majority of the lightning activity occurs during the afternoon and evening (local time). Regarding the lightning peak current, the polarity percentage was observed to fluctuate slightly between 80 and 20 percent for negative and positive flashes respectively. In certain regions, this percentage varies only in isolated cases and cannot be interpreted as a rule. Positive flashes have a multiplicity of 1 stroke per flash, whereas negative flashes have a multiplicity between 1 and 2 strokes per flash. For most subregions, the Cloud-to-Ground Lightning Flash Density (CGLFD) displays large variations with maximum values above 30 flashes km⁻² year⁻¹. CGLFD and altitude correlations did not exhibit a uniform pattern, however, in certain regions, the CGLFD and altitude display an inverse relationship. Additionally, a comparative analysis of the lightning parameters found in this study is performed with the values reported at other latitudes.

The occurrence of some natural hazards in the troposphere may lead to creation of Internal Gravity Waves (IGWs). These waves transfer energy from the lower troposphere to upper layers, and to the ionosphere. When these IGWs reach the ionosphere, they create significant variations in the ionospheric parameters. Therefore, they have considerable effects on performance of Global Navigation Satellite Systems (GNSS) receivers. In this study, we used double-frequency measurements of GNSS ground-based stations from GEONET network in New Zealand to detect the IGWs created by the tsunami induced from the 2022 Tonga volcanic eruption. In addition to GNSS measurements, FORMOSAT-7/COSMIC-2 (F7/C2) data, and SWARM data were also used to study these IGWs. It is known that many of the IGWs have horizontal phase speeds faster than that of the tsunami. As the volcanic-originated IGWs spread in cone-shape pattern, it is possible to detect these fast IGWs in the ionosphere earlier than the tsunami waves, reaching the tide gauges or DART buoys. In our study, we could detect the first IGWs at the New Zealand GNSS stations, 2 h earlier than the first registration of the tsunami waves at tide gauges and DART buoys near the New Zealand peninsula, which is located approximately 1.600 km from the Tonga Volcano. It can be concluded that IGWs can be used to warn tsunamis faster than the current early-warning systems, which make use of tide gauges and DART buoys. Furthermore, the spatial variations in ionospheric electron density (IED) were investigated using F7/C2 RO data. The results show that the volcanic-originated IGWs cause reduction in the IED peak value and altitude. The results of IED derived from F7/C2 and SWARM were in good agreement.

To reveal the spatial-temporal characteristics of atmospheric pollution during dust events on the North Slope of the Tianshan Mountains (NSTM), this study conducted a joint detection experiment from April to June from 2019 to 2021 at Shihezi, using satellite remote sensing, a microwave radiometer, meteorological sensors, and environmental monitors. Using long-term detection data from the aforementioned equipment, this study analyzed the characteristics of meteorological elements, Aerosol Optical Depth (AOD), PM, and gaseous pollutants. The main findings are as follows: The dust particles from the two severe pollution dust events on April 9, 2020, and May 1, 2021, on the NSTM originated from the Gurbantünggüt Desert and were transported along the northwest direction, leading to a significant increase in AOD (averaging 1.36 g/m²) and dust column mass concentration (averaging 1.58 g/m²). During the two dust storms, the PM₁₀ concentration peaks reached 2536.5 μg/m³ and 1804.5 μg/m³, respectively. The average relative humidity (RH) was less than 30%, the average wind speed was more than 6 m/s, and the average visibility (V) was less than 1000 m. Moreover, during the dust events from April to June, the temperature and relative humidity were higher in June, but the wind speed was lower. The vertical thermodynamic interactions of the dust storms were stronger than those of the blowing and floating dust storms. Finally, the relationship between PM₁₀ and V was fitted using the following equation: $V=11326.8166e^{-0.0015{PM}_{10}}$.This study provides scientific support for the prediction of dust storms on the NSTM.

Turbulence in the Mesosphere and Lower Thermosphere (MLT) region is responsible for vertical mixing and transport of constituents and heat and the formation of the turbo-pause. A study of turbulence at the Andes Lidar Observatory (ALO) by Philbrick et al. (2021) found, for 25 nights of lidar observations, the probability of Ri < 1/4 decreased with altitude above 100 km, whereas the power in turbulence increased. The objective of this study is to understand the atmospheric process responsible for the observed increase in turbulence power with altitude. Conventionally turbulence is caused by instabilities due to convection (Ri < 0), and Kelvin-Helmholtz Instabilities (KHI), 0 < Ri < 1/4. These criteria are based on laminar flow, a waveless basic state. However, wave-modulated states requiring Floquet theory may dominate the MLT region and can generate instabilities and turbulence under more stable conditions (Ri > 1/4, Klostermeyer, 1990). It was determined in this study the probability of Ri > 1/4 to be >70% at 105 km, consistent with parametric instability (PI) where large tidal induced wind shears and gravity wave presence are contributing factors.

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

Lightning studies are highly focused on spatial and temporal variability in various scales but very limited studies are focused on dominant spatial modes of variability. This study intends to identify the possible spatial modes of climate variability of lightning over India during different seasons and relate them to regional and large-scale climate modes. Empirical orthogonal function analysis of lightning has been carried out and the first three orthogonally independent modes are considered in order to retrieve the maximum variance explained by each mode. To understand the role of remote and local teleconnections on the lightning flash rate (LFR) variability, we have analyzed two Pacific Ocean modes (El Niño Southern Oscillation; ENSO, Pacific Decadal Oscillation; PDO) and two Indian Ocean modes (Indian Ocean Dipole; IOD and Bay of Bengal (BOB) meridional Sea Surface Temperature (SST) gradient). First mode is positively correlated with the warm phase of ENSO and PDO whereas second and third modes are negatively correlated with the warm phase of ENSO and PDO during pre-monsoon, post-monsoon and winter. Reverse is true for the monsoon season due to the shift in walker cell caused by the changes in the location of the heat sources and sinks. A strong positive correlation of IOD and BOB meridional SST gradient with first mode, suggests the vital role of nearby Indian Ocean in explaining the typical lightning flashes over India due to the enhanced zonal and meridional circulation, thereby moisture supply to the Indian subcontinent. The impact of Nino-3.4, IOD and BOB meridional SST gradient on lightning over India further suggest the role of SST in local and remote influence on lightning variability through the distribution and transport of heat and moisture.

The knowledge of raindrop size distribution (DSD) is crucial for understanding the microphysical processes involved with the precipitation. Different empirical relationships established with DSD parameters, like radar reflectivity– rainfall rate (Z–R) relationships and shape–slope (μ–Ʌ) relationships, can progress the rainfall estimation algorithms and cloud modeling simulations. In the present study, long-term (2018–2021) measurements of a Laser Precipitation Monitor (LPM) disdrometer installed at the National Institute of Technology, Rourkela, India is used to investigate the DSD characteristics of pre-monsoon (March–May) rainfall. Along with the disdrometer data, auxiliary parameters like convective available potential energy (CAPE), total column water vapor (TCWV), vertical profiles of temperature and relative humidity from reanalysis data sets of ECMWF (European Centre for Medium-Range Weather Forecasts) fifth-generation reanalysis (ERA5) are also used in this study. Based on standardized rainfall anomaly, the pre-monsoon precipitation days are classified into strong, moderate, and weak rainy days, and they contributed to 58.69%, 32.7%, and 8.61% of total rainfall, respectively. The average DSD indicated noteworthy variations among strong, moderate, and weak rainy days with maximum (minimum) concentration of raindrops in strong (weak) rainy days. The mean value of rain rate (R), normalized intercept parameter (N_w), and mass-weighted mean diameter (D_m) is maximum during days of strong rainfall. Strong rainy days showed high-value CAPE, TCWV and vertical profile of relative humidity. The majority of R is contributed by moderate-sized raindrops with a significant difference in the Z–R and μ–Λ relationships among three types of rainy days.

Cirrus clouds play a crucial role in regulating the Earth’s radiation budget, and this paper aims to contribute to a deeper understanding of their optical and geometrical properties over the Arabian Sea (AS) and Bay of Bengal (BoB) regions using CALIPSO data. Comprehensive statistics are derived, encompassing mean values of cirrus cloud top, base altitude, geometrical thickness, cloud optical depth, and temperature. Over the AS region, the mean values are 15.10 ± 1.50 km, 12.63 ± 1.75 km, 2.52 ± 1.37 km, 0.4 ± 0.58, and -62.30 ± 10.6 (°C), respectively. For the BoB region, the corresponding values are 15.43 ± 1.51 km, 12.72 ± 1.74 km, 2.71 ± 1.46 km, 0.49 ± 0.67, and -64.35 ± 10.7 (°C). A larger spread in optical depth and a higher frequency of occurrence for both cirrus and subvisible cirrus (SVC) clouds were observed over BoB compared to AS. Additionally, the study delves into SVC cloud characteristics, emphasizing their thinness and higher base altitudes compared to cirrus clouds. This comprehensive investigation contributes valuable insights into the distinctive properties of cirrus and SVC clouds in these regions, enhancing our knowledge of atmospheric processes and their implications for climate modelling and predictions.

Ionosphere is one of the main error sources of global navigation satellite system (GNSS) precise positioning, and affecting communicate services such as communication, broadcasting, and radar positioning. Total electron content (TEC) is a key parameter to characterize the state of the ionosphere. Establishing a high-precision TEC model and making accurate predictions can effectively improve positioning accuracy and improve communication quality. The traditional TEC model has limited ability to describe the changes of TEC under extreme conditions such as magnetic storms. Based on the temporal convolution network (TCN) model, this paper conducts experiments on TEC grid data in six low latitude regions and six mid latitude regions, and compares them with Long short term memory (LSTM), gated recurrent units (GRU) and bidirectional long short term memory (BiLSTM) models. Results show that the mean average error (MAE) of TCN (1.2385 TECU) is lower in most areas compared with LSTM (1.2727 TECU), GRU (1.2602 TECU) and BiLSTM (1.2767 TECU). And the TCN model shows better performance in the mid latitude regions (0.8778 TECU) than low latitude regions (1.5992 TECU). Then, this paper takes 1st October to 31st December 2021. as an example to calculate the prediction accuracy of the TCN model in the magnetic quiet period and the magnetic storm period. During the sample time, there were 4 weak geomagnetic storms, 1 strong geomagnetic storm, and there was a continuous long magnetic resting period at the same time, with a variety of different geomagnetic activities. The results show that the MAE distribution of the TCN model is more concentrated in the magnetostatic period, and the model error in the mid latitude region is normally distributed between -4-4.5 TECU. During the magnetic storm period, the TCN model has the lowest proportion of errors exceeding 5 TECU, and the proportions in the mid latitude and low latitude regions are 2.8% and 10.4%, respectively, which are better than the comparison model. Finally, we discuss the performance of short-term TEC prediction and the possible causes of obvious errors. The accuracy of the TCN model reaches 1.07 TECU, which is better than the long-term prediction result (1.24 TECU), and the accuracy is the best among the four models. After the detection of TEC anomaly disturbance, we believe that the obvious errors in the three experimental grids in north america are related to hurricane ELSA.