Journal of Atmospheric and Solar-Terrestrial Physics最新文献

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.

The temporal variations of cosmic-ray intensity, measured by ground-based detectors at various latitudes, longitudes, and altitudes, are related to the geophysical and solar phenomena. The latter are interplanetary coronal mass ejections and fast solar wind from coronal holes, which cause interplanetary magnetic field (IMF) abrupt variations near Earth. Interacting with the magnetosphere, they cause worldwide sudden decreases (Forbush decreases, FDs) of intensity followed by gradual recovery. The amplitude of the flux depletion depends on the type and energy of the registered particle, which in turn depends on geographical coordinates and the detector's energy threshold and selective power. SEVAN particle detector network with nodes in Europe and Armenia selects three types of particles that demonstrate coherent depletion and recovery and correspond to different energy galactic protons interacting with disturbed magnetospheric plasmas.

On November 3–4, 2021, an interplanetary coronal mass injection (ICME) hit the magnetosphere, sparking a strong G3-class geomagnetic storm and auroras as far south as California and New Mexico. All detectors of the SEVAN network have registered an (FD) of ≈5% depletion in a 1-min time series of count rates. Approaching the maximum solar activity cycle, large variations of the particle flux intensity were registered on February 27, March 23, 2023, and March 24, 2024.

In this work, we present measurements of these FDs performed on mountain altitudes on Aragats (Armenia), Lomnicky Stit (Slovakia), Mileshovka (Czechia), and at sea level DESY (Hamburg, Germany). We compared FD measurements made by SEVAN detectors and neutron monitors located on Aragats and Lomnicky Stit and made a correlation analysis of FD registration at different locations.

In the last decade, new algorithmic positioning techniques have been developed for Global Navigation Satellite Systems (GNSS). These have brought a new focus on high accuracy applications which do not combine multiple frequencies to remove ionospheric errors (i.e. PPP-RTK, Fast-PPP). Not only do these algorithms focus on improvements in the position domain but also in acquiring the positioning solution as fast as possible. In this work, capabilities of different global ionospheric models are assessed, analyzing both the Ionospheric delay accuracy and the associated model uncertainty. Accurate model uncertainties are crucial for reducing the convergence time in uncombined filters, and to guarantee unbiased convergence in the first place. The assessment is done using an uncombined navigation filter with different ionospheric models: GPS ICA, IGS vTEC (vertical Total Electron Content) maps (IGSG, CODG and UQRG), two realizations of the ESA-UGI (Voxel and Multi-Layer), the Madrigal TEC, and the Spire Global vTEC maps. To quantify the model uncertainties without the use of a reference ionospheric model, global maps of an uncertainty inflation factor are computed to show the inflation required to produce optimal filter convergence. These maps demonstrate that some models are too optimistic in the reporting of their own uncertainty estimates, requiring an uncertainty factor up to 10 times the quoted value.

We report our observations and data analysis of ionospheric effects during the total solar eclipse over the South-East Asia-Pacific region on 9 March 2016. Here we present observations of spatio-temporal changes in the total electron content (TEC) distribution in the areas traversed by the eclipse. TEC reductions of 10-14 TECU were observed over the eastern part of Indonesia and over Guam. In the surveilled areas, TEC reductions due to solar extreme ultraviolet (EUV) obstruction during the eclipse were more prominent at coordinate points located further east, closer to the point of greatest eclipse duration in the middle of the Pacific. In addition, we also discuss observations of medium-scale traveling ionospheric disturbances (MSTIDs) associated with the passage of this solar eclipse. Signatures of eclipse-related TIDs were seen in the TEC perturbation (TECP) signals from several GPS receiver stations in eastern part of Indonesia and in the Doppler signals from ionosonde measurements at Guam. These MSTIDs were observed at F-region heights with a period of 30–45 min, Doppler velocity amplitude of 15 m/s, and TECP fluctuations of 0.3–0.4 TECU.

The escalating concerns surrounding urban air pollution's impact on both the environment and human health have prompted increased attention from researchers, policymakers, and citizens alike. As such, this study addresses growing concerns about urban air pollution's impact on the environment and human health, emphasizing the need for early, high-resolution PM2.5 pollutant measurements. Utilizing Google Earth Engine (GEE) machine learning algorithms, our study evaluates six models over four years in Tehran and Tabriz. Inputs include satellite imagery, meteorological data, and pollutant measurements from air quality stations. Four models—Histogram Gradient Boosting, Random Forest, Extreme Gradient Boosting, and Ada Boosted Decision Trees—outperform Support Vector Machine and Linear Regression. The selected model, a combination of decision tree algorithms and Ada Boost, achieves a notable correlation coefficient of 79.8% and an RMSE of 0.271 g/m3. This superior performance enables the generation of high-resolution (30-m) PM2.5 estimates for the two cities. The study's comprehensive approach, involving various data sources and advanced machine learning techniques, contributes a valuable method for accurate PM2.5 assessment. The findings hold significance for urban air quality management and provide a potential framework for generating detailed PM2.5 datasets based on Landsat images.

The troposphere, the lowest and closest layer of the atmosphere, is where all meteorological events take place. The tropospheric — lower stratospheric (TLS) temperature trend is determined using linear regression and is essential to comprehending the consequences of climate change in the future. In this article, we explored the long-term temperature variabilities and trends of TLS (1–25 km) temperature and its responses by natural drivers such as El Nino southern oscillation (ENSO), solar flux (SF), quasi-biannual oscillation (QBO), Indian ocean dipole (IOD), and aerosol indexes (AI) using monthly averaged zonal mean COSMIC satellite and ground — based Radiosonde (RS) observations for the period of 2006 – 2020 over tropical station Addis ($9^0$ N, $38.8^0$ E) and subtropical station Cairo ($30.03^0$ N, $31.23^0$ E). The tropopause is located at the tropical station Addis at 17 km with a temperature of 190–194 K, and for the subtropical station Cairo, it is located at 15 km with a temperature of 201 K, which supports the decrement of tropopause height from the tropics to the subtropics with a slight increase in temperature. The two main oscillations in the TLS region can be seen by using the wavelet analysis technique: the semiannual oscillation (SAO) and the annual oscillation (AO), with the AO being especially strong in the lower troposphere. Furthermore, Morlet wavelet analysis on cold-point tropopause temperature CPTt displays AO and cold point tropopause height CPTh reveals a QBO-like signal. The TLS region has positive peaks at heights of 7, 21, 22, 13, and 4 km for the Addis station, and at 15, 19, 25, 15, and 16 km for the Cairo station in response to natural drivers such as ENSO, SF, QBO, IOD, and aerosol. Lag analyses demonstrate a one-month delay for all natural forcings, except for oceanic indices and SSF, up to three months below the tropopause (below 15 km). There is a noticeable 3 to 4 months lag in every oscillation above the tropopause. A warming trend in the tropospheric region and a cooling trend in the UTLS regions are revealed by MLR trend analysis. In contrast to the subtropical Cairo station, which has the highest warming rate of 0.38 K/decade at 2 km and the maximum cooling rate of −0.2 K/decade at 10 km, the tropical Addis station has the highest cooling rate of −0.38 K/decade at 12 km and the highest warming rate of 0.28 K/decade at 3 km. Our trend findings are consistent with previous research.