Atmosphere

Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal wind. The maximum wind speed occurs in the relatively lower altitudes during daytime and is delayed in the relatively higher altitude after sunset in a diurnal cycle. In the summer season, the altitudes appear earlier than in other seasons via the dominant solar radiation effect during daytime, and the altitude after sunset increases up to 160 m. However, the maximum wind speed in the winter season occurs irregularly among altitudes, and it is lower than that in the summer season. This can be attributed to the increase in the mean wind speed in the diurnal cycle caused by the strong northwestern wind in the winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal winds. These results are expected to contribute to site selection criteria for wind farms. Full article

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO₂ concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications. Full article

In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR and (Δ(SSR)) is conducted against high-quality reference surface measurements from 1193 Global Energy Balance Archive (GEBA) and 66 Baseline Surface Radiation Network (BSRN) stations. For the first time, the FORTH-RTM Δ(SSR) was evaluated over an extended period of 35 years and with a spatial resolution of 0.5° × 0.625°. The RTM uses state-of-the-art input products such as MERRA-2 and ISCCP-H and computes 35-year-long monthly SSR and GDB, which are compared to a comprehensive dataset of reference measurements from GEBA and BSRN. Overall, the FORTH-RTM deseasonalized SSR anomalies correlate satisfactorily with either GEBA (R equal to 0.72) or BSRN (R equal to 0.80). The percentage of agreement between the sign of computed GEBA and FORTH-RTM Δ(SSR) is equal to 63.5% and the corresponding percentage for FORTH-RTM and BSRN is 54.5%. The obtained results indicate that a considerable and statistically significant increase in SSR (Brightening) took place over Europe, Mexico, Brazil, Argentina, Central and NW African areas, and some parts of the tropical oceans from the early 1980s to the late 2010s. On the other hand, during the same 35-year period, a strong and statistically significant decrease in SSR (Dimming) occurred over the western Tropical Pacific, India, Australia, Southern East China, Northern South America, and some parts of oceans. A statistically significant dimming at the 95% confidence level, equal to −0.063 Wm⁻² year⁻¹ (or −2.22 Wm⁻²) from 1984 to 2018 is found over the entire globe, which was more prevalent over oceanic than over continental regions (−0.07 Wm⁻² year⁻¹ and −0.03 Wm⁻² year⁻¹, statistically significant dimming at the 95% confidence level, respectively) in both hemispheres. Yet, this overall 35-year dimming arose from alternating decadal-scale changes, consisting of dimming during 1984–1989, brightening in the 1990s, turning into dimming over 2000–2009, and brightening during 2010–2018. Full article

Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, during a strict, moderate, and post-COVID-19 period of 28 months (March 2020–June 2022). The purpose of this study is to monitor and analyze the relationship between criteria air pollutants (SO₂, particulate matter (PM 10 and 2.5), CO, O₃, and NO₂) through a Haz-Scanner 6000 and mobile van (ambient air quality monitoring station) over nine towns in Lahore. The results showed significantly lower concentrations of pollutants during strict lockdown which increased during the moderate and post-COVID-19 lockdown periods. The post-COVID-19 period illustrates a significant increase in the concentrations of SO₂, PM₁₀, PM_2.5, CO, O₃, and NO₂, in a range of 100%, 270%, 500%, 300%, 70%, and 115%, respectively. Major peaks (pollution concentration) for PM₁₀, PM_2.5, NO₂, and SO₂ were found during the winter season. Multi-linear regression models show a significant correlation between PM with NO₂ and SO₂. The ratio of increase in the PM concentration with the increasing NO₂ concentration is nearly 2.5 times higher than SO₂. A significant positive correlation between a mobile van and Haz-Scanner was observed for CO and NO₂ data as well as ground-based observation and satellite data of SO₂, NO₂, and CO. During the strict COVID-19 lockdowns, the reduction in the vehicular and industrial exhaust significantly improved the air quality of nine towns in Lahore. This research sets the ground for further research on the quantification of total emissions and the impacts of vehicular/industrial emissions on human health. Full article

By analyzing the mass concentrations and compositions of atmospheric PM_2.5 in Shaoxing from December 2019 to February 2020, the characteristics of carbon-containing components, water-soluble ions and metal elements were obtained. NO₃⁻, OC, SO₄²⁻ and NH₄⁺ were the main components of PM_2.5 in winter. The OC/EC ratio was 3.27, which proved the existence of SOC. The proportion of SOC in OC was 47.3%, which showed that secondary sources made a significant contribution. The values of OC/EC and NO₃⁻/SO₄²⁻ indicated that vehicle exhaust emissions also made a significant contribution to PM_2.5. Trace elements of Na, Ca, K and Cd had higher enrichment factor values and were enriched due to human activities. Finally, PM_2.5 sources analysis was performed by the positive matrix factorization model. The results showed that secondary inorganic salts (49.3%), motor vehicles and industrial sources (21.3%) and dust sources (17.0%) were the important sources of PM_2.5 pollution. Full article

The study presents seismogenic ULF (ultra-low-frequency) wave effects, as observed at our own new magnetic observatory at Asahi (geographic coordinates: 35.770° N, 140.695° E) in Chiba Prefecture. Our target earthquake (EQ) is a huge one offshore of Fukushima prefecture (37.353° N, 141.603° E) with a magnitude (M) of 7.4, which occurred at 20.59 h on November 21 UT, 2016. As a sampling frequency of 1 Hz was chosen for our induction magnetometer, we could detect both ULF wave effects: ULF radiation from the lithosphere, and the ULF depression effect, indicative of lower ionospheric perturbations. Observing the results of polarization analyses, we detected clear enhancements in ULF (frequency = 0.01–0.03 Hz) lithospheric radiation 14 days, 5 days, and 1 day before the EQ, and also observed a very obvious phenomenon of ULF (0.01–0.03 Hz) depression just 1 day prior to the EQ, which is regarded as the signature of lower ionospheric perturbations. These findings suggest that pre-EQ seismic activity must be present in the lithosphere, and also that the lower ionosphere was very much perturbed by the precursory effects of the Fukushima EQ. These new observational effects from our station have been compared with our previous investigations on different seismogenic topics for the same EQ, including the ULF observations at another magnetic observatory at Kakioka, belonging to the Japan Meteorological Agency (JMA), about 50 km north of our Asahi station, subionospheric VLF/LF propagation data (Japanese and Russian data), AGW (Atmospheric gravity wave) activity in the stratosphere, and satellite observation of particle precipitations. We have found that seismogenic anomalies of different parameters tend to happen just around the EQ day, but mainly before the EQ, and have found the chain-like tendency of the effects of the lithosphere, which seem to propagate upwards the lower ionosphere. Finally, we will try to gain a better understanding of the physical phenomena or mechanisms of the lithosphere–atmosphere–ionosphere coupling (LAIC) process during the EQ preparation phase. Full article

Tropical cyclones have long been known to be powered by turbulent enthalpy fluxes from the ocean’s surface and slowed by turbulent momentum fluxes into the surface. Here, we review evidence that the development and structure of these storms are also partially controlled by turbulence in the outflow near the storm’s top. Finally, we present new research that shows that tropical cyclone-like, low-aspect-ratio vortices are most likely in systems in which the bottom heat flux is controlled by mechanical turbulence, and the top boundary is insulating. Full article

High-resolution reanalysis data are an effective way to evaluate cloud water resources (CWRs). Based on ERA5 reanalysis data and gridded observed precipitation data, combined with the diagnostic quantification method of cloud water resource (CWR-DQ), we analyze and evaluate the CWRs and their distribution characteristics in the Huaihe River Basin from 2011 to 2021. Moreover, we compare and evaluate the CWRs of two typical precipitation processes in summer and winter. The results show that the annual total amount of atmospheric hydrometeor (GM_h) in the Huaihe River Basin is approximately 1537.3 mm. The precipitation (P_s) is 963.5 mm, the cloud water resource (CWR) is 573.8 mm, and the precipitation efficiency of hydrometeor (PE_h) is 62.4%. The CWR in the Huaihe River Basin shows a slow increasing trend from 2011 to 2021.The monthly variations in P_s, CWR, and PE_h show a single peak distribution. The spatial horizontal distributions of the gross mass of water vapor (GM_v), GM_h, and P_s in the Huaihe River Basin are zonal, and the values decrease with increasing latitude. In summer, the hydrometeors are mainly distributed in the middle layer (between 600 and 350 hPa). The hydrometeors in spring, autumn, and winter are mainly below 500 hPa. Two cases reveal that GM_v, the condensation from water vapor to hydrometeors (C_vh), GM_h, P_s, and PE_h in the summer case are significantly higher compared to those in the winter case, while the CWRs are similar. The results are helpful for proposing rational suggestions for the Huaihe River Basin and to provide some beneficial reference for the development of CWRs. Full article

In recent years, the concentrations of PM_2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM_2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (O_X = O₃ + NO₂). The concentrations of the primary and secondary components of PM_2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM_2.5 and the secondary inorganic components (SO₄²⁻, NO₃⁻, NH₄⁺, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM_2.5 concentrations and SNA in Beijing was investigated. The results revealed that O_X concentrations reached their peak in 2016 and reached their lowest point in 2019. The O_X concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM_2.5 fraction were higher and accounted for a higher proportion of the total PM_2.5 mass concentrations. In the PM_2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant O_X in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM_2.5 concentration. Based on a relationship analysis of SOR, NOR, and O_X, it was inferred that the relationship between O_X and SOR and the relationship between O_X and NOR were both nonlinear. Therefore, when establishing PM_2.5 control strategies in Beijing in the future, the impact of the AOC on PM_2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM_2.5 pollution. Full article

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (T_m) modeling, this paper proposed four machine learning (ML)-based T_m models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based T_m models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (T_s), water vapor pressure (E_s), and atmospheric pressure (P_s) were identified as crucial influencing factors after analyzing their correlations to the T_m. The ML-based T_m models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based T_m models. Experimental results show that the overall accuracy of the LightGBM-based T_m model is superior to the SVM, CART, and RF-based T_m models under different temporal variations. The mean RMSE of the daily LightGBM-based T_m model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based T_m model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based T_m model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based T_m model is also smaller than that of the other ML-based T_m models. Therefore, the LightGBM-based T_m model can provide more accurate T_m and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region. Full article

With increasing computational power, the regional climate modeling community is moving to higher resolutions of a few kilometers, named convection-permitting (CP) simulations. This study aims to present an assessment of precipitation metrics simulated with the non-hydrostatic regional climate model RegCM-4.7.1 at CP scale for a decade-long period (2001–2010) for Bulgaria. The regional climate simulation at 15 km grid spacing uses ERA-Interim (0.75° × 0.75°) re-analysis as the driving data and parametrized deep convection. The kilometer-scale simulation at 3 km horizontal grid spacing is nested into regional climate simulation using parametrized shallow convection only. The CP simulation is evaluated against daily and hourly datasets available for the selected period and is compared with the coarser resolution driving simulation. The results show that the model represents well the spatial distribution of mean precipitation at the regional and kilometer scale for the territory of Bulgaria. However, the CP_RegCM_3km model produces too much precipitation over the mountains and shows the largest biases in the summer season (above 100%). At the daily scale, improvements are found in CP simulation for precipitation wet-day intensity and extreme precipitation in the autumn and for wet-day frequency in the summer. At the hourly scale, the kilometer-scale simulation improved the performance of wet-hour precipitation intensity in all seasons compared with coarse-resolution simulation (−23% vs. −46% in MAM; −10% vs. −37% in JJA; −47% vs. −53% in SON; −54% vs. −62% in DJF) and extreme precipitation in the autumn (−7% vs. −51%) and winter (−34% vs. −58%). The representation of wet-hour frequency was improved by CP_RegCM_3km in all seasons, except summer (−3.1% vs. −6.7% in spring; 0.5% vs. −3.8% in autumn and −7.7% vs. −11.5% in winter). Full article

Conventional methods for monitoring ammonia (NH₃) emissions from livestock farms have several challenges, such as a poor environment for measurement, difficulty in accessing livestock, and problems with long-term measurement. To address these issues, we applied various neural network models for the long-term prediction of NH₃ concentrations from sow farms in this study. Environmental parameters, including temperature, humidity, ventilation rate, and past records of NH₃ concentrations, were given as inputs to the models. These neural network models took the encoder or the feature extracting parts from the representative deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer, to encode temporal patterns of time series. However, all of these models adopted dense layers for the decoder to format the task of long-term prediction as a regression problem. Due to their regression nature, all models showed a robust performance in predicting long-term NH₃ concentrations at a scale of weeks or even months despite there being a relatively short period of input signals (a few days to a week). Given one week of input, LSTM showed the minimum mean absolute errors (MAE) of 1.83, 1.78, and 1.87 ppm for the prediction of one, two, and three weeks, respectively, whereas Transformer performed best with a MAE of 1.73 ppm for a four-week prediction. In the long-term estimation of spanning months, LSTM showed the minimum MAEs of 1.95 and 1.90 ppm when trained on predicting two and three weeks of windows. At the same condition, Transformer gave the minimum MAEs of 1.87 and 1.83 when trained on predicting one and four weeks of windows. Overall, the neural network models can facilitate the prediction of national-level NH₃ emissions, the development of mitigation strategies for NH₃-derived air pollutants, odor management, and the monitoring of animal-rearing environments. Further, their integration of real-time measurement devices can significantly prolong device longevity and offer substantial cost savings. Full article

Air pollution has emerged as a pressing global issue in recent decades. While criteria pollutants and greenhouse gases contribute to the problem, this article explicitly addresses hazardous air pollutants (HAPs). This work estimates the country-wide cumulative human health impacts from exposure to HAPs. Kuwait is used as the case study due to data availability and non-fragmentation of data. At present, the evaluation of multi-pathway human health risks arising from exposure to HAPs is incomplete, as indirect pathways have not been considered. Furthermore, only a few HAPs, such as benzene, have established ambient air quality standards specifically intended to safeguard human health, leaving many HAPs unregulated. This study considers several pathways (both direct and indirect) and various environmental media (air, water, plants, soil, and animal tissue). The findings indicate that cumulative health risks in the coastal air quality zone are within acceptable limits but are notably higher when compared to the other air quality zones. For cancer risks, only the Ahmadi Hospital, with a cancer risk of 1.09 × 10⁻⁵ for the resident adult exposure scenario, slightly exceeds the acceptable risk level of 1 × 10⁻⁵. The proposed methodology integrates the results from a country-wide emissions inventory composed of different air quality zones, air dispersion and deposition modeling, multi-pathway transport-and-fate analysis, exposure quantification, and health risk and hazard characterization. It also extends and adapts EPA methodologies initially designed for hazardous waste combustion facilities to additional emission sources and provides a case study for a region seldom subjected to such human health risk assessments. Full article

Five overcast marine stratocumulus cases during the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) aircraft field campaign were selected to examine aerosol and cloud condensation nuclei (CCN) properties with cloud influence. The Aitken- and accumulation-mode aerosols contributed approximately 70% and 30% of the total aerosols, respectively. The aerosol properties before and after periods of drizzle were investigated using in situ measurements during one case. Sub-cloud drizzle processes impacted accumulation-mode aerosols and CCN distribution. There was a nearly linear increase in CCN number concentration (N_CCN) with supersaturation (S) during the ‘before drizzle’ period, but this was not true for the ‘after drizzle’ period, particularly when S > 0.4%. Using the hygroscopicity parameter (κ) to quantitatively investigate the chemical cloud-processing mechanisms, we found that higher κ values (>0.4) represent cloud-processing aerosols, while lower κ values (<0.1) represent newly formed aerosols. When the supersaturation is less than the Hoppel minimum (0.22%), cloud processing is dominant, whereas sea-spray aerosols are dominant contributors to CCN activation when S exceeds 0.22% but is less than 0.32%, the effective supersaturation threshold. Sea salt is considered a non-cloud-processing aerosol and is large and hygroscopic enough to form cloud droplets. Full article

Ozone, an important atmospheric constituent, affects various processes in the troposphere–stratosphere region and significantly contributes to climate and environmental change. The advancement of meteorological satellite technology has enabled the deployment of ozone detection instruments in space, providing accurate and global satellite ozone data in all weather conditions. This study employs scientometric methods, such as collaboration analysis, co-citation analysis, and keyword co-occurrence analysis to investigate the current status, trends, and future directions of satellite ozone research, with a broader search scope and more objective results compared with a manual review. Analyzing a dataset of 5320 bibliographic records from the WoS core collection database reveals the key intellectual frameworks shaping this field during the period from 2005 to 2023. The findings indicate that leading nations, like the United States, Germany, France, and China, along with their respective institutions and authors, spearhead satellite ozone research. Collaborative partnerships between the United States and European countries play a crucial role in advancing research efforts. Moreover, 20 distinct co-citation clusters identify the knowledge framework within the field, demonstrating a consistent progression over time. The focus has expanded from satellite ozone observation instruments to encompass broader areas, such as atmospheric pollution and environmental conditions, with “air quality” emerging as a prominent research area and future trend. Based on these insights, four major research directions are proposed: understanding atmospheric pollution mechanisms, improving ozone detection technologies, utilizing satellite ozone data for weather, and climate phenomena. This study aims to assist scholars by providing a comprehensive understanding of the developmental trajectory of satellite ozone research. Its results can serve as a valuable reference for researchers to identify relevant publications and journals efficiently. Policymakers can also utilize this systematic review as a structured point of reference. Full article

This paper compares the ideas contained in the main papers published on climate change since World War II to arrive at a suggested consensus of our present knowledge regarding climatic changes and their causes. Atmospheric carbon dioxide is only suggested as a cause in one theory, which, despite its wide acceptance by Politicians, the media, and the Public, ignores the findings in other studies, including the ideas found in the Milankovitch Cycles. It also does not explain the well-known NASA map of the changes between the global 1951–1978 and the 2010–2019 mean annual temperatures. The other theories by Oceanographers, Earth scientists, and Geographers fit together to indicate that the variations in climate are the result of differential solar heating of the Earth, resulting in a series of processes redistributing the heat to produce a more uniform range of climates around the surface of the Earth. Key factors are the shape of the Earth and the Milankovitch Cycles, the distribution of land and water bodies, the differences between heating land and water, ocean currents and gateways, air masses, and hurricanes. Low atmospheric carbon dioxide levels during cold events could result in too little of this gas to support photosynthesis in plants, resulting in the extermination of most life on Earth as we know it. The 23 ka Milankovitch cycle has begun to reduce the winter insolation received at the surface of the atmosphere in the mid-latitudes of the Northern Hemisphere starting in 2020. This results in extreme weather as the winter insolation reaching the surface of the atmosphere in the higher latitudes of the Northern Hemisphere decreases while the summer air temperatures increase. It heralds the start of the next glaciation. A brief outline is given of some of the climatic changes and consequences that may be expected in western Canada during the next 11.5 ka. Full article

It is increasingly recognized that the generic climate state is a macroscopic manifestation of a nonequilibrium thermodynamic (NT) system characterized by maximum entropy production (MEP)—a generalized second law. Through a minimal tropical/polar-band model, I show that MEP would propel low clouds to polar bands to symmetrize the planetary albedo, a remarkable observation that may now be explained. The prognosed polar albedo is consistent with the current observation, which moreover is little altered during the ice age of more reflective land and the early Triassic period of symmetric land, suggesting its considerable stability through Earth’s history. Climate models have not replicated the observed albedo symmetry and, given the potency of MEP in propelling clouds, it is suggested that to improve climate models, a higher premium be placed on resolving eddies—thereby encapsulating the NT—than detailed cloud physics. Full article

Plasma irregularity in the equatorial and low-latitude ionosphere, which leads to ionospheric scintillation, can threaten the operation of radio-based communication and navigation systems. A method for forecasting scintillation activity is still pending. In this study, we examined the performance of ionospheric parameters, including the critical frequency (foF2), peak height of the F2-layer (hmF2), scale height (Hm) and virtual height (h’F), around local sunset from ground-based ionosonde observations, and also the characteristics of Equatorial Ionization Anomaly (EIA) derived from Gravity Recovery and Climate Experiment (GRACE) observations in equinoctial months (March–April and September–October) during high solar activities (2012–2013) at a low-latitude station at Sanya (18.3° N, 109.6° E; dip lat.: 12.8° N), China. Furthermore, the simplified linear growth rate of Rayleigh–Taylor (R–T) instability inferred from ionosonde measurements and EIA strength derived from GRACE observations were used to estimate the day-to-day occurrence of post-sunset scintillation. The results indicate that it is not adequate to determine whether scintillation in a low-latitude region would occur or not based on one ionospheric parameter around sunset. The simplified growth rate of R–T instability can be a good indicator for the day-to-day occurrence of scintillation, especially in combination with variations in EIA strength. An index including the growth rate and EIA variations for the prediction of the post-sunset occurrence of irregularity and scintillation is proposed; the overall prediction accuracy could be about 90%. Our results may provide useful information for the development of a forecasting model of the day-to-day variability of irregularities and scintillation in equatorial and low-latitude regions. Full article