Low Visibility Events Research Articles

Prediction of Low-Visibility Events by Integrating the Potential of Persistence and Machine Learning for Aviation Services

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.

A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic

This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable artificial intelligence (XAI) refers to the transparency of AI systems in providing explanations for their predictions and decision-making processes, and contribute to improve prediction accuracy and enhance trust in AI systems. The focus of this paper relies on the interpretability challenges in ordinal classification problems within weather forecasting. Ordinal classification involves predicting weather phenomena with ordered classes, such as temperature ranges, wind speed, precipitation levels, and others. To address this challenge, a novel and general explicable forecasting framework, that combines inductive rules and fuzzy logic, is proposed in this work. Inductive rules, derived from historical weather data, provide a logical and interpretable basis for forecasting; while fuzzy logic handles the uncertainty and imprecision in the weather data. The system predicts a set of probabilities that the incoming sample belongs to each considered class. Moreover, it allows the expert decision-making process to be strengthened by relying on the transparency and physical explainability of the model, and not only on the output of a black-box algorithm. The proposed framework is evaluated using two real-world weather databases related to wind speed and low-visibility events due to fog. The results are compared to both ML classifiers and specific methods for ordinal classification problems, achieving very competitive results in terms of ordinal performance metrics while offering a higher level of explainability and transparency compared to existing approaches.

Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method

Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013–2019, but an unexpected deterioration (approximately 1 km yr−1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.

The application study of the revised IMPROVE atmospheric extinction algorithm in atmospheric chemistry model focusing on improving low visibility prediction in eastern China

Low visibility event, as a disastrous weather, has great impacts on traffic and transportation, aircraft, and people's daily life, etc. Timely and accurate forecasts of low visibility events are urgently needed and meaningful. The reasonable algorithm of atmospheric extinction in atmospheric chemistry models is the basis for quantitatively predicting low visibility. The revised IMPROVE algorithm (RIMP) of atmospheric extinction is incorporated into the chemistry-weather interacted model GRAPES_Meso5.1/CUACE CW V1 to improve the prediction of low visibility events (LVEs) in the urban agglomerations in eastern China, which is compared with the original IMPROVE algorithm (OIMP) used in this model. The study results show that the RIMP effectively reduces the overestimation of low visibility prediction by OIMP in general, leading to a decrease of root-mean-square errors (RMSEs) and an increase of Threat Score (TS) of visibility <3 km, 5 km, and 10 km overall both at regional and city scales in varying degrees due to its more detailed processing of aerosols' size, optical feature and hygroscopic growth; The improvements of visibility prediction of LVEs by RIMP depends on the combined contribution of high relative humidity (RH) and PM2.5 instead of single high RH or PM2.5. The relative contributions of RH and PM2.5 concentration on different levels of low visibility are different in Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions due to their different RH and PM2.5, which leads to the different improvement of RIMP in the two regions. The larger improvements by RIMP occur for visibility <5 km in BTH, while in YRD, the larger improvements by RIMP occur for visibility <10 km and >5 km. Moreover, the improvements by RIMP were more evident with higher RH conditions in both regions. The uncertainty created by the extinction algorithm is one important factor of the multiple factors affecting LVEs prediction; accurate modeling of high RH near saturation is also very important for LVEs prediction.

Efficient prediction of fog-related low-visibility events with Machine Learning and evolutionary algorithms

Low visibility events are a severe problem for road transport, causing accidents and major economic losses. Their accurate prediction may help prevent these problems. For that purpose, machine and deep learning techniques have been applied for fog prediction using in situ meteorological data and persistence variables as baseline predictors. These techniques have been evaluated for different prediction time-horizons: 1 h, 3 h and 6 h. The effect of including data extracted from ERA5 Reanalysis as predictive variables has been studied. A database, covering 23 months, has been used, which contains visibility and other meteorogical variables measured in Mondoñedo, Galicia, Spain. A 222000 km2 region around Mondoñedo has been delimited. Thus, a proposed iterative forward selection algorithm based on evolutionary algorithms has been applied to determine the optimal variables and nodes in the region for each regressor model. Both Differential Evolution and Particle Swarm Optimization have been used as optimization algorithms, and an improvement of up to 17.3% with respect to the baseline databases have been obtained. Finally, an analysis of the most frequently selected variables by the evolutionary algorithms has been conducted, leading us to conclude which variables and geographical nodes provide better information to the prediction models.

Deep learning ensembles for accurate fog-related low-visibility events forecasting

In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem.

Extreme Low-Visibility Events Prediction Based on Inductive and Evolutionary Decision Rules: An Explicability-Based Approach

In this paper, we propose different explicable forecasting approaches, based on inductive and evolutionary decision rules, for extreme low-visibility events prediction. Explicability of the processes given by the rules is in the core of the proposal. We propose two different methodologies: first, we apply the PRIM algorithm and evolution to obtain induced and evolved rules, and subsequently these rules and boxes of rules are used as a possible simpler alternative to ML/DL classifiers. Second, we propose to integrate the information provided by the induced/evolved rules in the ML/DL techniques, as extra inputs, in order to enrich the complex ML/DL models. Experiments in the prediction of extreme low-visibility events in Northern Spain due to orographic fog show the good performance of the proposed approaches.

Enhanced nitrate contribution to light extinction during haze pollution in Chengdu: Insights based on an improved multiple linear regression model

In recent years, the annual mean concentration of PM2.5 has decreased in Chengdu, China; however, atmospheric visibility has not improved accordingly. Low-visibility events occurred even when the PM2.5 mass concentrations were below the national ambient air quality secondary standard (daily mean concentration, 75 μg/m3). In this study, the non-linear relationship between PM2.5 and visibility was analyzed under different NO3− mass fractions in PM2.5 based on 2-year field observation data. The results indicated that NO3− formation contributed to particulate pollution events and reduced atmospheric visibility. Multiple linear regression was used to propose a localized reconstruction equation for the light-scattering coefficient. According to the maximum likelihood estimation method and log-transformed residuals, the mass scattering coefficients (MSEs) of organic matter (OM), NH4NO3, and (NH4)2SO4 in Chengdu were 7.42, 3.83, and 3.80, respectively. OM and NH4NO3 contributed to more than 50% of the light-extinction coefficient (bext). NH4NO3 was the main pollutant causing the substantial increase in bext. Chengdu has a high relative humidity (annual mean 70%), and under such conditions, the contribution of NH4NO3 to bext was considerably enhanced through hygroscopic growth and heterogeneous reactions. This study estimated the localized MSEs of OM, NH4NO3, and (NH4)2SO4 in Chengdu and emphasized that effective control measures to reduce nitrate and its precursors could simultaneously ameliorate air quality and visibility in humid regions with poor atmospheric visibility.

Fog-Haze Transition and Drivers in the Coastal Region of the Yangtze River Delta.

Low-visibility events (LVEs) are severe weather phenomena that are closely linked with anthropogenic pollution, which negatively affects traffic, air quality, human health, and the environment. This study conducted a two-month (from October to December 2019) continuous measurement campaign on Chongming Island in Shanghai to characterize the LVEs transition and its drivers. The LVEs accounted for 38% of the time during the campaign, of which mist accounted for 14%, fog–haze for 13%, haze for 6%, and fog for 5%. The fog and mist mainly occurred from midnight to early morning, while haze mostly occurred during the daytime. Different LVEs were interdependent and transitioned from one to another. Fog generally turned into haze after sunrise, while haze turned into fog after sunset. Their formation and evolution were caused by the combined impacts of meteorological conditions and aerosol particles. It was found that temperature difference was the dominant meteorological factor driving the evolution of LVEs. Within the short term, cooling led to a greater increase in relative humidity than humidification. Radiative cooling during the night promoted the formation of fog and mist. During fog and mist events, cloud condensation nuclei (CCN) were mainly internally mixed due to the impact of fog droplet removal and aqueous/heterogeneous aerosol reactions occurring under high humidity. Increased CCN concentration appeared to increase the fog droplet number and liquid water content in fog events. Overall, conditions of high humidity and high particle loading were conducive to LVEs, whereas conditions of sufficient water vapor at a low particle level and sufficient particles at a low humidity level also caused LVEs. This study provided insights into LVEs classification, evolution scheme, and aerosol roles from a micro point of view. The findings could be useful for improving forecasts of local radiative fog and other LVEs.

Joint increase of aerosol scattering efficiency and aerosol hygroscopicity aggravate visibility impairment in the North China Plain

China's “Blue Sky Action Plan” aimed at tremendous improvements in atmospheric visibility. While stringent emission control policies have substantially brought down PM2.5 mass concentrations, visibility improved much less than expected due to non-linear responses of visibility changes to PM2.5 reductions. In this study, we used long-term continuous humidified nephelometer system measurements of multi-wavelength aerosol scattering coefficients in both dry state and controlled relative humidity conditions in the North China Plain during spring and summer to attempt disentanlge the non-linear relationsips between visibility and PM2.5 mass.Aerosol scattering efficiency, optical hygroscopicity and air relative humidity are key factors for relating PM2.5 mass to visibility. It was found that aerosol volume scattering efficiencies (VSEs) were highly correlated (r > 0.8) with aerosol scattering coefficients. The continuous decrease of aerosol scattering Ångström exponent during pollution episodes revealed dominant contributions of secondary aerosol formation to aerosol size growth and mass accumulation, explaining aerosol VSE increases. Moreover, the optical hygroscopicity parameter κsca that describes the aerosol light scattering enhancement abilities through water uptake increased jointly with VSE and aggravated the visibility degradation during middle to final stages of pollution episodes. Thus, low visibility events (<3 km) only occurred when VSE and κsca were at their highest levels. The contribution of aerosol water to visibility degradation increased as visibility decreased, and contributed dominantly to visibility degradation under extremely low visibility conditions (<1 km). However, under hazy visibility conditions (3–10 km), which occurred most frequently, both aerosol water and scattering efficiency enhancement played significant roles. For setting up more efficient emission control strategies targeting on visibility improvement, our results highly encourage more future research on the linkages between secondary aerosol formation mechanisms and co-variations of aerosol scattering efficiency and aerosol hygroscopicity on the NCP.

Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns.

Visibility and aerosol optical depth (AOD) characterization, and their relationship with PM10 and local and synoptic meteorology, were studied for January-March in 2014 and 2015 over Bangkok. Visibility degradation intensifies in the dry season as compared to the wet season due to increase in PM10 and unfavorable meteorological conditions. The average visibility is lower in January and February as compared to the other months. Relatively higher AOD in March despite lower PM10 is attributed to the synergetic effect of moderate relative humidity, secondary aerosols, elevated aerosol layer due to summertime convection, and biomass burning. Larger variability in visibility and PM10 in winter months is due to more synoptic weather fluctuations while AOD shows similar variability for all months attributed partly to fires. Higher PM10 and moderate-to-high relative humidity cause lower visibility in the morning while it improves in afternoon as PM10 and relative humidity decrease. AOD is higher in the afternoon as compared to that in the morning and evening as it is less sensitive to diurnal change in aerosols and meteorology at the surface level. Visibility and AOD relationships with PM10 are dependent on relative humidity. Weaker winds lead to lower visibility, higher PM10, and higher AOD irrespective of wind direction. Stronger winds improve visibility and decrease PM10 for all directions while AOD is higher for all directions except eastern and northeastern. The back-trajectory results show that the transport of pollutant and moist air is coupled with the synoptic weather and influence visibility and AOD. Two low-visibility events were investigated. The first event is potentially caused by the combined effect of local emissions and their accumulation due to stagnant weather conditions, secondary aerosols, and forest fires in the nearby regions. The second event can be attributed to the local emission and fires in the nearby area with hygroscopic growth of aerosols due to moist air from the Gulf of Thailand. Based on these findings, some policy implications have also been given.

Machine learning regression and classification methods for fog events prediction

Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and low-visibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction.

Research on the usability of different machine learning methods in visibility forecasting

Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and, thereby mitigate haze pollution. However, it is not easy to accurately predict the low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine, k-nearest neighbor, random forest, as well as several deep learning methods, on the visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) Among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) RNN LSTM, and GRU methods performs almost equally well on short-term visibility forecasts(i.e. 1h, 3h, and 6h); (3) A classical machine learning method (i.e. the SVM) performs well in mid- and long-term visibility forecasts; (4) The machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g. of 72h).

Forecasting and identifying the meteorological and hydrological conditions favoring the occurrence of severe hazes in Beijing and Shanghai using deep learning

Abstract. Severe haze or low-visibility events caused by abundant atmospheric aerosols have become a serious environmental issue in many countries. A framework based on deep convolutional neural networks containing more than 20 million parameters called HazeNet has been developed to forecast the occurrence of such events in two Asian megacities: Beijing and Shanghai. Trained using time-sequential regional maps of up to 16 meteorological and hydrological variables alongside surface visibility data over the past 41 years, the machine has achieved a good overall performance in identifying haze versus non-haze events, and thus their respective favorable meteorological and hydrological conditions, with a validation accuracy of 80 % in both the Beijing and Shanghai cases, exceeding the frequency of non-haze events or no-skill forecasting accuracy, and an F1 score specifically for haze events of nearly 0.5. Its performance is clearly better during months with high haze frequency, i.e., all months except dusty April and May in Beijing and from late autumn through all of winter in Shanghai. Certain valuable knowledge has also obtained from the training, such as the sensitivity of the machine's performance to the spatial scale of feature patterns, that could benefit future applications using meteorological and hydrological data. Furthermore, an unsupervised cluster analysis using features with a greatly reduced dimensionality produced by the trained HazeNet has, arguably for the first time, successfully categorized typical regional meteorological–hydrological regimes alongside local quantities associated with haze and non-haze events in the two targeted cities, providing substantial insights to advance our understandings of this environmental extreme. Interesting similarities in associated weather and hydrological regimes between haze and false alarm clusters or differences between haze and missing forecasting clusters have also been revealed, implying that factors, such as energy-consumption variation and long-range aerosol transport, could also influence the occurrence of hazes, even under unfavorable weather conditions.

Current challenges of improving visibility due to increasing nitrate fraction in PM2.5 during the haze days in Beijing, China

The annual mean PM2.5 mass concentration has decreased because of the stringent emission controls implemented in Beijing, China in recent years, whereas the nitrate NO3– mass fraction in PM2.5 increases gradually. Low-visibility events occur frequently even though PM2.5 pollution has been mitigated significantly, with the daily mean PM2.5 mass concentration mostly less than 75 μg/m3. In this study, the non-linear relationship was analyzed between atmospheric visibility and PM2.5 based on chemical composition from a two-year field observation. Our results showed that NO3– became the main constituent of PM2.5, especially during the haze pollution episodes. A localized parameterization scheme was proposed between the atmospheric extinction coefficient (σext) and major chemical constituents of PM2.5 by multiple linear regression (MLR). The contribution of NO3– to σext increased with increasing air pollution, and NO3– became the most important contributor for PM2.5 above 75 μg/m3. The visibility decreased with increasing NO3– mass fraction for the same PM2.5 mass concentration when PM2.5 was above 20 μg/m3. The hygroscopicity of PM2.5 increased with increasing mass fraction of hygroscopic NO3–. These results stressed the importance of reducing particulate NO3– and its precursors (for instance, NH3) through effective emission control measures as well as the tightening of PM2.5 standards to further improve air quality and visibility in Beijing.

Statistical Analysis and Machine Learning Prediction of Fog-Caused Low-Visibility Events at A-8 Motor-Road in Spain

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8, within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road.

Long-term variation characteristics and influencing factors of low-visibility events on the coast of China

Low-visibility events (LVEs, e.g., haze, fog, mist) in China have comprised a high proportion of bad weather phenomena in past decades. In this study, the long-term (1980–2017) trend of LVEs over the Chinese coastland and its influencing factors were examined using in-situ meteorological and reanalysis data as well as climatic indices data. A stricter definition of LVEs was proposed to investigate the events occurring on a short sub-day time scale. Higher frequencies of LVEs in ports were possibly induced by local ship emissions. The numbers of annual fog and haze days exhibited almost opposite temporal variation trends from 1980 to 2017, and the transition of haze days towards fog days occurred around 2004. Notably, the coexistence or conversion of fog and haze events was always found within one day. Our analysis showed that the dominant factor influencing haze towards fog days was not aerosol change in low atmosphere, especially in the last decade, but relative humidity (RH) variation, which was affected by air temperature (T), sea temperature (SST), wind field, planetary boundary layer height (PBLH), and climate factors. From the perspective of LVE long-term changes, higher temperatures promote haze event occurrence, temperature inversion favors pollutant and moisture accumulation, high wind speeds significantly dissipate both fog and haze, and lower boundary layer accumulate water vapor effectively, enhancing the conversion of haze to fog. The influence of sea temperature indices on low-visibility days (LVDs) was found strongest. The temperature difference between sea and air would determine whether the formation of fog was caused by turbulent cooling or thermal uplift. The effects of atmospheric circulation on LVDs were manifold, mainly reflected in water vapor transport, pollutant transport and dissipation. The results can provide insights into long-term variations of LVEs and serve as a reference for accurately predicting small-scale fog and haze.

Impact of water vapor content on visibility: Fog-haze conversion and its implications to pollution control

Atmospheric water vapor content, which reflects the total change of water vapor in the whole atmosphere, has an important effect on the atmospheric visibility (VIS). To investigate the variation of the VIS in Beijing during the past decades and to address the key factors affecting the VIS, the relationships of the VIS with the PM2.5 concentrations, the relative humidity (RH) and the total column water vapor (TCWV) have been explored in this study. The evolution characteristics of these factors in the fog-haze conversion process during the low-visibility (LVIS, VIS <10 km) events are also analyzed. Based on the relations between the TCWV and VIS, a novel method to distinguish haze and fog has been developed and employed in this study. The results show that the frequency of LVIS increases with the increasing of RH, TCWV and PM2.5 concentration. Under dry conditions (RH ≤ 40%), the VIS variation is mainly affected by the PM2.5 concentrations. Under moderate RH conditions (86% > RH > 40%), the VIS is more influenced by TCWV and RH. Under high RH and extremely low visibility conditions (RH > 86% and VIS <1 km), the water vapor is absorbed by aerosols, resulting in the conversion of haze to fog. This process leads to sharp decreases in TCWV and VIS, but an increase in PM2.5 concentrations simultaneously. This study provides a meaningful approach to identify the VIS evolution under the variations of RH, TCWV and PM2.5, which is helpful to the forecasting and early warning of LVIS events.

Mechanism for synoptic and intra-seasonal oscillation of visibility in Beijing-Tianjin-Hebei region

Based on the station observation data from 1980 to 2016, the atmospheric circulation anomalies affecting the synoptic and intra-seasonal oscillation (ISO) of visibility in the Beijing-Tianjin-Hebei (BTH) region were investigated. Results show that the synoptic variability of visibility over the BTH region was affected by Rossby waves moving from west to east. When the BTH region is on the eastern flank of a trough or the western flank of a ridge in the mid-troposphere, the anomalous southerly wind in the mid- and low troposphere played a key role in reducing the visibility, possibly by weakening the wind speed of the prevailing northwesterly winter monsoon, enhancing the stability in the boundary layer, and increasing relative humidity. The continuous low-visibility days usually began when surface high-pressure system moved eastward from East Asia continent into the sea, and it terminates when the next cold high-pressure system invades the BTH region from the west. The essential atmospheric circulation anomalies affecting the intra-seasonal variability of visibility in the BTH region are the intra-seasonal seesaw between Siberian High (SH) and Aleutian Low (AL). An anomalous southerly wind appears over the BTH region when the Siberian High and the Aleutian Low are weaker at intra-seasonal time scale, and it results in intra-seasonal low-visibility events possibly through reduced total wind speed, enhanced static stability, and enhanced relative humidity as well. No propagating feature of the intra-seasonal atmospheric circulation anomalies is identified.

Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.