Search Results (46)

This study evaluated the performance of the operational GK2A (GEO-KOMPSAT-2A) fog detection algorithm (GK2A_FDA) using ground-based visibility meter data from 176 stations across South Korea from 2021 to 2023. According to the verification method using the nearest pixel and 3 × 3 neighborhood pixel approaches to the visibility meter, the 3-year average probability of detection (POD) is 0.59 and 0.70, the false alarm ratio (FAR) is 0.86 and 0.81, and the bias is 4.25 and 3.73, respectively. POD is highest during daytime (0.72; bias: 7.34), decreases at night (0.57; bias: 3.89), and is lowest at twilight (0.52; bias: 2.36). The seasonal mean POD is 0.65 in winter, 0.61 in spring and autumn, and 0.47 in summer, with August reaching the minimum value, 0.33. While POD is higher in coastal areas than inland areas, inland regions show lower FAR, indicating more stable performance. Over-detections occurred regardless of geographic location and time, mainly due to the misclassification of low-level clouds and cloud edges as fog. Especially after sunrise, the fog dissipated and transformed into low-level clouds. These findings suggest that there are limitations to improving fog detection levels using satellite data alone, especially when the surface is obscured by clouds, indicating the need to utilize other data sources, such as objective ground-based analysis data. Full article

Swimming goggles still face numerous challenges in practical use, including deterioration and failure of anti-fog coatings, residual water marks on lens surfaces, and relatively short service life in complex environments. When swimming outdoors during winter, goggles also present an icing problem. To address these problems and enhance the performance of swimming goggles, this study employs a combination of plasma cleaning and mechanical spraying methods, utilizing HB-139 SiO₂ to modify the surface of goggle lenses, thereby fabricating lenses with superhydrophobic properties. The changes in lens surfaces before and after friction and immersion treatments were characterized using three-dimensional profilometry and scanning electron microscopy, further investigating the hydrophobic, drag-reducing, wear-resistant, and anti-icing properties of the lenses. Experimental results demonstrate that SiO₂ can enhance the hydrophobic, drag-reducing, durability, and anti-icing performance of the lenses. Under standard conditions, the contact angle of modified samples reached 162.33 ± 3.15°, representing a 48.77 ± 2.15% improvement over original samples. Under friction conditions, modified samples exhibited a 45.86 ± 2.53% increase in contact angle compared to original samples, with Sa values decreasing by 58.64 ± 3.21%. Under immersion conditions, modified samples showed a 54.37 ± 2.44% increase in contact angle relative to original samples. The modified samples demonstrated excellent droplet bouncing performance at temperatures of −10 °C, 10 °C, and 30 °C. De-icing efficiency improved by 14.94 ± 2.37%. Throughout the experimental process, SiO₂ demonstrated exceptional hydrophobic, drag-reducing, durability, and anti-icing capabilities. This establishes a robust foundation for the exemplary performance of swimming goggles in both training and competitive contexts. Full article

To prevent traffic accidents caused by icy roads in winter and damage to roads resulting from repeated freeze–thaw cycles, this paper proposes an optimized design plan for slow-release anti-icing fog seal. The effects of the dosages of slow-release anti-icing agent, water-based epoxy resin modifier, and penetrant on the ice- and snow-melting properties, mechanical properties, and penetration properties of the fog seal were investigated. Based on single-factor experiments, a Box–Behnken model was established, and the response surface method was employed to optimize the design of the fog seal. Subsequently, wear resistance was assessed using an accelerated loading test, while anti-skid performance was evaluated through the British pendulum test and the sand patch test. The results indicate that the optimal ratio for the slow-release anti-icing fog seal is 13% slow-release anti-icing agent, 20% water-based epoxy resin modifier, and 12% penetrant. This material demonstrated excellent ice- and snow-melting performance as well as good wear and skid resistance in testing, providing valuable insights for the application of the slow-release anti-icing agent in new pavement maintenance techniques. Full article

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities. Full article

Northern and southern fog events are identified over eastern China across 40 winters from 1981 to 2021. By performing composite analysis on these events, this study reveals that the formation of fog events is controlled by both dynamic and thermodynamic processes. The fog events were induced by Rossby wave trains over the Eurasian continent, leading to the development of surface wind and pressure anomalies, which favor the formation of fog events. The Rossby wave trains in northern and southern fog events are characterized by their occurrence in northern and southern locations, respectively, with different strengths. The water vapor fluxes that contribute to the enhancement of the northern fog events originate from the Yellow Sea and the East China Sea, whereas the southern fog events are characterized by water vapor from the East China Sea and the South China Sea. In both northern and southern fog events, dew point depression and positive A and K index anomalies are found in northern and southern regions of eastern China, which are indicative of supersaturated air and the unstable atmospheric saturation from the low to the middle troposphere, thus providing favorable conditions for the establishment of fog events in northern and southern regions of eastern China. Full article

Fog is a weather phenomenon with visibility below 1 km. Fog heavily influences ground and air traffic, leading to accidents and delays. The main goal of this study is to use two machine-learning (ML) techniques—the random forest (RF) and long short-term memory (LSTM) models—to estimate visibility using 11 meteorological parameters. Several meteorological elements related to fog are investigated, including pressure, temperature, wind speed, and direction. The seasonal cycle shows that fog in Sofia has a peak in winter, but a small secondary peak in spring was found in this study. Fog occurrence has a tendency to decrease during the studied period, with the peak of fog observations being shifted towards the higher visibility range. The input parameters in the models are day of year, hour, wind speed, wind direction, first-cloud-layer coverage, first-cloud-layer base height, temperature, dew point, dew-point deficit, pressure, and fog stability index (FSI). The FSI and dew-point deficit are evaluated as the most important input parameters by the RF model. Post-processing was performed with double linear regression for the correction of the predictions by the models, which led to a significant improvement in performance. Both models were found to describe the complexity of fog well. Full article

The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling fog (CF) dominated by southeasterly wind in spring and summer. CF is thicker than EF due to different planetary boundary layer (PBL) structures. For EF, the middle and low troposphere are affected by dry and cold air, while CF is affected by warm and moist air below 850 hPa. When EF formed, downdrafts and a positive vertical gradient of the pseudo-equivalent potential temperature indicate stable PBL, surface heat flux is upward from sea to atmosphere and surface wind diverges near the air–sea interface. When CF formed, these characteristics are reversed. Fog is significantly affected by sea–land–atmosphere interactions. The moisture source is mainly from surface fluxes released by the Yellow Sea in the case of EF, while it is from moist air at low latitudes and local land transpiration in the case of CF. The difference in temperature between the sea surface and surface air changes from the range of 0–8 K for EF but from −4–0 K for CF. Full article

In years with unfavorable weather, winter frost during the blossoming season can play a significant role in reducing fruit yield and impacting the profitability of cultivation. The mango Naomi cultivar Mangifera indica L. has a low canopy that is severely affected by the effects of frost stress. As a result of the canopy being exposed to physiological problems, vegetative development is significantly inhibited. The current investigation aimed to study the influence of spraying nitric oxide and fogging spray systems on Naomi mango trees grafted on ‘Succary’ rootstock under frost stress conditions. The treatments were as follows: nitric oxide (NO) 50 and 100 μM, fogging spray system, and control. In comparison to the control, the use of nitric oxide and a fogging system significantly improved the leaf area, photosynthesis pigments of the leaf, the membrane stability index, yield, and physical and chemical characteristics of the Naomi mango cultivar. For instance, the application of 50 μM NO, 100 μM NO, and the fogging spray system resulted in an increase in yield by 41.32, 106.12, and 121.43% during the 2020 season, and by 39.37, 101.30, and 124.68% during the 2021 season compared to the control, respectively. The fogging spray system and highest level of NO decreased electrolyte leakage, proline content, total phenolic content, catalase (CAT), peroxidases (POX), and polyphenol oxidase (PPO) enzyme activities in leaves. Furthermore, the number of damaged leaves per shoot was significantly reduced after the application of fogging spray systems and nitric oxide in comparison to the control. Regarding vegetative growth, our results indicated that the fogging spray system and spraying nitric oxide at 100 μM enhanced the leaf surface area compared to the control and other treatments. A similar trend was noticed regarding yield and fruit quality, whereas the best values were obtained when the fogging spray system using nitric oxide was sprayed at a concentration of 100 μM. The application of fogging spray systems and nitric oxide can improve the production and fruit quality of Naomi mango trees by reducing the effects of adverse frost stress conditions. Full article

The paper investigates weather-related fatalities over the territory of the Czech Republic in the 100-year period from 1921 to 2020. The unique database, created from documentary evidence (particularly newspapers), includes, for each deadly event, information about the weather event, the fatality itself, and related circumstances. A total of 2729 fatalities were detected during the 100-year period and were associated with various weather categories including frost (38%), convective storms (19%), floods (17%), fog (11%), snow and glaze ice (8%), windstorms (5%), and other inclement weather (2%). A detailed analysis was performed for each individual category. Fatalities occurred throughout the country, with a main maximum in winter (January) and a secondary maximum in summer (July), corresponding to the occurrence of extreme weather. Deaths were mainly interpreted as direct, caused by freezing to death/hypothermia or drowning, and occurred in the afternoon and at night in open countryside or on rivers and water bodies. Males outnumbered females, and adults outnumbered children and the elderly. Hazardous behavior was more frequent than non-hazardous behavior among victims. The information on fatalities and the structure of their characteristics strongly reflects historical milestones of the country, political and socioeconomic changes, as well as changes in lifestyle. Although important weather effects were observed on the deadliest events, the character of the data did not allow for clear evidence of the effects of long-term climate variability. Full article

Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel approach, the Adaptive Outlier Removal filter on range Image (AORI), which combines a projection image from LiDAR point clouds with an adaptive outlier removal filter to remove snow particles. Our research aims to analyze the characteristics of LiDAR and propose an image-based approach derived from LiDAR data that addresses the limitations of previous studies, particularly in improving the efficiency of nearest neighbor point search. Our proposed method achieves outstanding performance in both accuracy (>96%) and processing speed (0.26 s per frame) for autonomous driving systems under harsh weather from raw LiDAR point clouds in the Winter Adverse Driving dataset (WADS). Notably, AORI outperforms state-of-the-art filters by achieving a 6.6% higher F1 score and 0.7% higher accuracy. Although our method has a lower recall than state-of-the-art methods, it achieves a good balance between retaining object points and filter noise points from LiDAR, indicating its promise for snow removal in adverse weather conditions. Full article

Sea fog is one of the main types of dangerous weather affecting offshore operations. The sea surface temperature (SST) has an important influence on the water vapor content and intensity of sea fog. In order to study the impact of SST on local relative humidity and atmospheric visibility, a sea fog episode that occurred over the Yellow Sea and Bohai Sea on 21 January 2013 was investigated through observational data, reanalysis data, and Weather Research and Forecasting (WRF) simulation. The results show that the influence of SST on the distribution of sea fog with different properties is inconsistent. Based on the time-varying equation of relative humidity, the changes in the advection, radiation, and turbulence effects on the relative humidity with respect to SST are explored through control and sensitivity experiments. The results show that the advection effect plays a decisive role in the generation and dissipation stages of sea fog. The increase (decrease) in SST weakens (strengthens) the radiative cooling and relative humidity. The contribution magnitude of advection effect to relative humidity is 10⁻⁵, while those of radiation and turbulence are 10⁻⁶ and 10⁻⁷, respectively. The atmospheric visibilities in the Bohai Sea and northern Yellow Sea decrease with increasing SST, which are mainly affected by the positive turbulence effect; whereas the atmospheric visibility in the central and southern Yellow Sea increases with SST, which is mainly influenced by the combined effects of U-direction advection, radiation, and turbulence. The stability related to boundary layer height plays an important role in water vapor condensation. Full article

The relatively low light intensity during autumn–winter or early spring and inclement weather such as rain or fog may lead to extended production periods and decreased quality of greenhouse-grown tomato seedlings. To produce high-quality tomato seedlings rapidly, the influences of supplementary lights with different spectra on the morphological and physiological traits of tomato seedlings were measured in a greenhouse. Supplemental lighting with the same daily light integrals (DLI) of 3.6 mol m⁻²d⁻¹ was provided by white (W) light-emitting diodes (LEDs), white plus red (WR) LEDs, and red plus blue (RB) LEDs, respectively, and tomato seedlings grown under only sunlight irradiation were regarded as the control. Our results demonstrate that raised DLI by supplementary light improved the growth and development of greenhouse-grown tomato seedlings, regardless of the spectral composition. Under conditions with the equal DLI, the tomato seedlings grown under supplementary WR LEDs with a red to blue light ratio (R:B ratio) of 1.3 obtained the highest values of the shoot and root fresh weights, net photosynthetic rate, and total chlorophyll content. The best root growth and highest root activity of tomato seedlings were also found under the supplementary WR LEDs. Supplementary WR LEDs remarkably increased the stem firmness of the greenhouse-grown tomato seedlings, and increased the starch content in the leaves of greenhouse-grown tomato seedlings compared to the control. However, statistically significant differences did not occur in the sucrose, carotenoid contents, superoxide dismutase (SOD), and catalase (CAT) activities among the different supplemental lighting treatments. In conclusion, supplemental LED lighting could promote the growth and development of greenhouse-grown tomato seedlings grown under insufficient sunlight conditions. In addition, WR LEDs could obtain tomato seedlings with a higher net photosynthetic rate, higher root activity, and higher starch content compared with other treatments, which could be applied as supplementary lights in greenhouse-grown tomato seedlings grown in seasons with insufficient light. Full article

One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts. Full article

Reduction in visibility (Vis) due to fog is one of the deadliest severe weather hazards affecting aviation and public transportation. Nowcasting/forecasting of Vis reduction due to fog using current models is still problematic, with most using some type of empirical parameterization. To improve the models, further observational studies to better understand fog microphysics and seasonal variability are required. To help achieve these goals, the seasonal and microphysical characteristics of different fog types at Cold Lake airport (CYOD), Alberta, Canada were analyzed using hourly and sub-hourly METAR data. Microphysical and meteorological measurements obtained using the DMT Fog Monitor FM-120 and the Vaisala PWD22 were examined. The results showed that radiation fog (RF) dominates at CYOD in summer while precipitation, advection and cloud-base-lowering fogs mostly occur in fall and winter. All fog types usually form at night or early morning and dissipate after sunrise. The observed dense fog events (Vis < 400 m) were mainly caused by RF. The observed mean fog particle spectra ($\mathrm{n}\left(\mathrm{D}\right)$) for different fog types and temperatures showed bimodal n(D) (with two modes near 4 μm and 17–25 μm; the maximum total number concentration (${\mathrm{N}}_{\mathrm{d}}$) was 100 cm⁻³ and 20 cm⁻³, respectively, corresponding to each mode). Parameterizations of Vis as a function of liquid water content ($\mathrm{LWC}$) and ${\mathrm{N}}_{\mathrm{d}}$ were developed using both the observed Vis and calculated Vis based on $\mathrm{n}\left(\mathrm{D}\right)$. It was found that the observed Vis was higher than the calculated Vis for warm fog with $\mathrm{LWC}$ > 0.1 gm⁻³ and most of the mass was contributed by the large drops. Based on the observed Vis, the relative error of the visibility parameterization as a function of both $\mathrm{LWC}$ and ${\mathrm{N}}_{\mathrm{d}}$ (32%) was slightly lower than that (34%) using $\mathrm{LWC}$ alone for warm fogs. Full article

Light Detection And Ranging (LiDAR) is an important technology integrated into self-driving cars to enhance the reliability of these systems. Even with some advantages over cameras, it is still limited under extreme weather conditions such as heavy rain, fog, or snow. Traditional methods such as Radius Outlier Removal (ROR) and Statistical Outlier Removal (SOR) are limited in their ability to detect snow points in LiDAR point clouds. This paper proposes an Adaptive Group of Density Outlier Removal (AGDOR) filter that can remove snow particles more effectively in raw LiDAR point clouds, with verification on the Winter Adverse Driving Dataset (WADS). In our proposed method, an intensity threshold combined with a proposed outlier removal filter was employed. Outstanding performance was obtained, with higher accuracy up to 96% and processing speed of 0.51 s per frame in our result. In particular, our filter outperforms the state-of-the-art filter by achieving a 16.32% higher Precision at the same accuracy. However, our method archive is lower in recall than the state-of-the-art method. This clearly indicates that AGDOR retains a significant amount of object points from LiDAR. The results suggest that our filter would be useful for snow removal under harsh weathers for autonomous driving systems. Full article