Journal of Atmospheric and Solar-Terrestrial Physics最新文献

Atmospheric fluctuation can be seen everywhere. This study focuses on the record-breaking increase of O₃ concentration during the summer in some sensitive areas in recent years. The findings indicate that in the vicinity of the East Asian continent near western Pacific ocean, when the atmospheric conditions are stable or neutral, it is conducive to the maintenance and propagation of atmospheric oscillations near the height of the pollutant mixed layer (H_PML). Accompanied by the "peak-trough" effect of external gravity wave oscillations, due to the abundant water vapor of the cloud system (there are low pressure or typhoon disturbances in summer) near the large-scale cloud belt at the edge of the subtropical high in the western Pacific, the bright temperature at cloud top shows "light and dark changes" on satellite images, forming a wave-like cloud system. The novelty of this study lies in the fact that atmospheric fluctuations near the H_PML is not only related to the known aggravation of heavy rainfall, but also leads to the additional value-added effect of aerosols. Under static atmospheric conditions, the impact of atmospheric fluctuations near the H_PML on additional rise of O₃ concentration helps us to deepen our understanding of the so-called "entrained ozone (EZ) effect" in the atmosphere. Due to the external gravity waves, the concentration of O₃ increased further. Diurnal variations of solar zenith angle and H_PML are key meteorological factors influencing the significant increase in near-surface O₃ concentration entrainment. The formation mechanism of solar photochemical O₃ is further deepened and supplemented by analyzing the record-breaking increase of O₃ concentration in summer observed in recent years.

Ionospheric modelling is one of the major tools to study the behavior of the ionosphere. Ionospheric models have been useful in predicting the true state of the ionosphere particularly in regions where Global Positioning System (GPS) are not readily available. This research paper aims to study the longitudinal variations and the effects of local time on the total electron content (TEC) recorded in two different sectors (Asia and America) during the ascending, maximum and descending phases of solar cycle 24 (2011–2017) and also to compare its values to IRI-2016, IRI-Plas2017 and NeQuick-2 models in order to evaluate their performances. An hourly interval profile computed on seasonal basis were used to study the behaviors of TEC diurnally and seasonally. A monthly interval error profile plotted on annual basis was also used to investigate the deviations of the models from the GPS values. Our results showed that the peak values of TEC in the Asian and American sectors were recorded around the dawn,06:00UT (13:00LT) and dusk, 18:00UT (15:00LT) respectively. We also affirmed from our results that seasonal/winter anomalies were recorded in all the phases of the solar cycle in both sectors. Equinoctial Asymmetry was also observed to be predominant during different phases of the solar cycle in both sectors except during ascending and descending phases in the Asian and American sectors respectively. Out of the 168 months of data collated for this study, only 162 months of data were available. The IRI-2016, IRI-Plas2017 and NeQuick-2 models have 11.7%, 23.5% and 64.8% better performance in all the months under consideration. Therefore, the NeQuick-2 model had the best performance in both the Asian and American sectors. Finally, from the results of our statistical analysis, Mean Absolute Error (MAE) has ∼3 TECU lower than the Root Mean Square Error (RMSE) values in both sectors and in all the solar cycle phase. Hence, MAE can evaluate the performance of ionospheric models better than RMSE.

The DC global electric circuit (GEC) distributes charge in the lower atmosphere by current flow between “generator regions” (thunderstorms and rain clouds) and “load regions” (distant conductive air), with a timescale defined by circuit properties. Previously, the load has only been modelled by assuming fair weather (FW) conditions, neglecting cloud. As stratiform clouds cover ∼30 % of the Earth's surface, load resistance has been added to represent them, considered to provide semi fair weather (semi-FW) conditions. This increases the GEC timescale by 9 % for stratocumulus, or 33 % for stratus at a lower level. Including mutual capacitance between the outer charged layer and an electrode representing stratocumulus clouds increases the timescale by 35 %, to 8.6 min. These modelled results - the first including the semi-FW aspects - are demonstrated to be consistent with experimentally determined timescales of the real GEC, of between 7 and 12 min, derived from volcanic lightning variations associated with the May 2011 Grímsvötn eruption in Iceland. Accounting for semi-FW circumstances improves the modelled representation of the natural global circuit. Further, the GEC timescale is comparable with cloud droplet charging timescales in the updrafts of extensive layer clouds, suggesting its possible relevance to the microphysical behaviour of stratiform (layer) clouds in the climate system.

The effects of aerosols (i.e., CCN) on the lifecycle of a well-documented mesoscale cloud cluster (CC) over the Indian peninsula are investigated in this study. The WRF model coupled with a sophisticated spectral bin microphysics scheme is employed to simulate the observed cloud system under various CCN scenarios. The CCN sensitivity experiments were carried out using three different background CCN concentrations (250, 1000, and 3000 cm⁻³). The prevailing environment featured a relatively dry mid-level. The introduction of more aerosols weakened the convection and moistened the upper troposphere in the initiation and mature phases. The major impacts of the enhanced aerosols include the dissipation of shallow clouds, a decrease in the number of convective cells and their overall coverage, enhanced convective organization in the early phase(s) of the lifecycle, enhancement of the cloud-free area, etc. The key microphysical changes due to enhanced aerosols are the increase in cloud liquid water, presence of numerous smaller cloud droplets, enhancement of condensation and evaporation, formation of smaller ice crystals, reduced snow mass and reduction in the aggregation process, high graupel mass and number and a reduction in graupel size, fewer raindrops with slight enhancement in raindrop size, etc. Cloud growth is significantly limited in the high aerosol scenarios due to large evaporation favored by a relatively dry environment and no invigoration effect is noted. A significant reduction in the rainfall (and associated rainfall-type) from isolated convective cores is noted due to high aerosols, especially in the initiation and mature phases. The eventual impact on the surface precipitation is a decrease in overall rainfall in the enhanced aerosol scenarios, with suppression of heavy rain. The study indicates that in a dry environment, the microphysical changes in various CCN scenarios cumulatively lead to macrophysical changes, which are found to be the primary controller of the overall surface rainfall associated with the CC.

Long-term trends in the evolution of ionospheric plasma at Hyderabad (17.38${}^{\circ }$N, 78.48${}^{\circ }$E; 8.52${}^{\circ }$N Magnetic Latitude), a near-equatorial anomaly (EIA) crest region of the Indian ionospheric sector, have been studied using 9 years of vertical Total Electron Content (TEC) data from 2004 to 2009 and 2011 to 2013 using global positioning satellites (GPS) measurements. The study examined the mean diurnal, monthly, seasonal, and yearly variations of TEC during geomagnetic quiet days in different seasons from 2004 to 2013. The findings reveal that the daytime TEC at the anomaly crest region exhibits semi-annual variations throughout the study period, while midnight TEC shows semi-annual variation only during the high solar activity years of 2011–2013. The winter anomaly was observed in 2004 and 2006. The study also assessed the performance of the International Reference Ionosphere (IRI) 2016 model in reproducing GPS TEC variability at the equatorial crest region. The diurnal and seasonal variation patterns in IRI-TEC show a good correlation with GPS TEC. However, the IRI 2016 model tends to overestimate TEC values during low solar activity conditions (2006–2009) but represents TEC variations reasonably well during high solar activity periods (2011–2013). Nevertheless, the IRI model fails to capture the wide plateau-like structure in the peak TEC, typically occurring between 1200–1600 IST at Hyderabad. Additionally, IRI-TEC consistently indicates very low TEC values during the early morning hours, whereas GPS-TEC measurements suggest a significant presence of plasma density. The study suggests a strong influence of the solar cycle on TEC variations at Hyderabad, evident from the positive correlation (R${}^2=$ 0.71) with the F10.7 cm index. This characteristic is also well represented by the IRI 2016 model.

Pc5 geomagnetic pulsations can accelerate electrons in the radiation belts, which can pose adverse threats to both astronauts and satellites in space. The estimation of Pc5 waves in space is crucial to radiation belt dynamics studies and will help mitigate these challenges. Here, we explore the advantages of the Feed-forward Neural Network (FFNN) and Random Forest (RF) algorithm for effective estimation of Pc5 geomagnetic pulsations observed in space at geostationary orbit during solar cycle 23. The dataset used in this study is the vector magnetic field measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) and the solar wind parameters: $B_z$ and $V_x$ component of the solar wind in the Geocentric Solar Ecliptic (GSE) coordinate system, proton density, flow pressure, and plasma beta obtained from the OMNI Web database during part of solar cycle 23. Pc5 geomagnetic pulsations were extracted from the toroidal component of the magnetic field time series using a bandpass Butterworth filter. The continuous wavelet transform (CWT) was utilized to study the characteristics of the extracted wave in the time-frequency domain for its validation. The validated Pc5 events were used as the target in the model's development, with the solar wind parameters as the inputs. In addition to the solar wind parameters, we included an attribute of the magnetic field time series as an input variable in the model. The dataset is carefully divided to ensure effective training and testing of the models. Finally, we trained both models using the same inputs and targets and explored their estimation abilities. The model was tested during the maximum, descending, and minimum phases of solar cycle 23. Both the FFNN and RF models have a similar estimation, with average cross-correlation score (R) values of 0.74 and 0.73 and corresponding average root mean squared error (RMSEs) of 0.16 nT and 0.67 nT, respectively. The model was deployed to investigate the response of Pc5 waves during three storm days in each testing year. The machine learning (ML) model outputs showed good coherence with the observed Pc5 waves. To validate the models, we studied the correlation between the estimated Pc5 events with the Kp index, and a good correlation was seen to exist between both events. This validates the good performance of the developed models. This work will aid in the study of radiation belt dynamics and the construction of electron depletion regions in the radiation belt.

– Development of magnetospheric substorm from standpoint of energy evolution in geomagnetic tail is considered. In this approach, geomagnetic tail is taken to be an open thermodynamic system (OTS) with infinite number of conserved energy links to external space environment. General self-consistent theory of energy evolution in OTS was presented earlier. In contrast to existing models of magnetospheric activity, presented model suggests believing that shaping of geomagnetic activity is controlled by the system nature of geomagnetic tail. From this angle, energy profile of disturbance is determined by particular qualities of energy development in OTS. Intercoupling between the system factors of the tail and flow of raw energy from space sources can produce different forms of magnetospheric activity including substorms. As an example, suggested system mechanism is considered for interpretation of the classic energy profile of isolated magnetospheric substorm, comparison with some experimental features of substorm is also provided.

A new VLF (Very low Frequency) receiver has been developed by the Peruvian Space Agency (CONIDA) for space weather studies. The receiver has been designed based on a Red Pitaya board which performs an SDR (Software Defined Radio) to digitize, process and store the signal. The receiver is composed of a vertical antenna, a preamplifier to filter and amplify the incoming VLF signals from several transmitters located around the world. The receiver is able to cover a bandwidth from 1 up to 50 kHz and it has been developed in such a way as to be cost-effective, autonomous and solar-powered, making it suitable for installation in multiple locations with different geographic conditions. We show the performance of the receiver, the typical daily pattern of the lower ionosphere for the NAA VLF signal, as observed in Peru, and the first solar flares observed. The VLF amplitude curves recorded are validated by comparing them with data from SAVNET (The South American VLF Network) receiver installed in Peru. In a first effort to investigate the impact of solar flares on the lower ionosphere, we conducted a statistical analysis between VLF amplitude perturbations and 1–8 Å solar X-rays flux provided by GOES satellites, resulting in a linear relationship.

We compare Joule Heating rates as derived from ground-based magnetic field and all-sky camera data, from Low Earth Orbit satellite data (ESA Swarm) and from a MHD simulation (GUMICS-5) with each other in a case study of an auroral arc system. The observational estimates of Joule Heating rates provide information on regional scales and with high spatial resolution (10–100 km). Their comparison with global MHD results is conducted for a quiet time interval of a few minutes, just before a magnetic substorm. Analysis of the ground-based observations yields electric field with dominating North-South component pointing towards the arcs and having maxima values in the range 20–35 V/km. Combining these values with Pedersen conductance estimates from optical data (5–10 S) yields Joule Heating rates in the range 2.5–3.5 mW/m${}^2$. Swarm electric field measurements are consistent in their direction and intensity with the ground-based estimates. They also show that heating is increased particularly in the region where the conductance is low. The total amount of Joule heating in the area between the Swarm A and C satellite footprints while crossing the all-sky camera field of view is estimated to be 46 MW and the total amount energy dissipation during the 80 s overflight is around 3.6 GJ (1000 kWh). GUMICS-5 estimate of the peak Joule Heating in the magnetic local time sector of the arc system is smaller than that from the ground-based data with a factor of 2.9. Comparisons of GUMICS-5 results with Space Weather Modeling Framework (SWMF), shows that the latter gives on average larger heating rates being thus more consistent with our regional observations. However, both MHD-codes yield smaller Joule Heating rates around the time of the arcs and during the following substorm than the CTIP-e code. CTIP-e has a more detailed description of ionosphere-thermosphere interactions than the MHD-codes and its convection electric field is enhanced with a randomly varying additional component mimicking small scale structures. GUMICS-SWMF comparisons of global Joule Heating patterns in the Northern polar area reveal that the two simulations have significant differences in their spatial distribution of heating rates. Main cause for these deviations is the difference in the derivation of ionospheric Pedersen conductance. Our results emphasize the fact that future estimates of the global energetics in the magnetosphere–ionosphere–thermosphere system require better knowledge on ionospheric conductivities, both by new measurement concepts and by better understanding on the background physics controlling conductivity variations.

Increasing lightning fatalities over India is a concerning subject. Especially, it is pretty crucial over North-Eastern part of the country where lightning is extremely frequent. Given the complex nature of the problem, machine learning can be an excellent option in such forecasting scenarios. However, such dynamic processes seek proper transparency of the model. The current work attempts to devise a model for short range prediction (one month ahead) of lightning density based on primary atmospheric parameters from satellite data with a lead time of one month over North –Eastern and Eastern part of the country. Random Forest regression seems to outperform other models explored, with a R² of 0.86 and an MAE of 0.0071. The interpretation of the model output using SHAP index reveals that 2 m temperature at previous two months and CAPE and K-index at previous month has a positive impact on the output of the model whereas, instantaneous surface heat flux of previous month and two month prior K-index has an inhibiting effect on model's output. The use of machine learning techniques for atmospheric predictions without the shed of the black box can be of importance to the scientific community. Such studies especially over lightning prone tropical regions can be crucial in meteorological forecasting applications.