راضیه پهلوان

 The presence of fog reduces the horizontal visibility to less than 1000 meters and disrupts air transport services and can make it impossible for aircraft to land and take off. Climatology of fog can help better diagnosis and prediction of fog. In this study, METAR data from 2005 to 2020 were used to detect fog events at Rasht Airport and according to the classification algorithm of Tardif and Rasmussen (2007), the types of fog events were determined. Then the fog climate was studied during the period. The results showed that in terms of frequency, the most common type of fog at Rasht Airport was radiation fog with 58.84% and Cloud Base Lowering (CBL) fog with 26.74% of all fog occurrences. The rarest type of fog was advection fog with 3.49% of all occurrences at this Airport during 16 years of study. These results are consistent with Tajbakhsh (2015).In terms of the duration of the fog occurrence, due to the long and heavy rains in this station, the radiation fog event was the longest type of fog events. Also, the duration of CBL fog event was shorter than other types of fog events. The second type of fog in terms of duration of occurrence was advection fog, which can be caused by the slow movement of the synoptic system to transfer moist air from the sea to the land (Tardif and Rasmussen, 2007). Advection and radiation fogs had minimum visibility of less than 100 meters for 50% of occurrences. Radiation fog had the lowest concentration so that only 50% of radiation fog events had visibility less than 400 meters.Precipitation fog had the lowest concentration so that only 50% of precipitation fog events had visibility less than 400 meters. The highest incidence of fog during the study years was at 00 GMT. This stems from the fact, the radiative cooling is the strongest in the hours before sunrise. The strong reduction of the temperature increases the relative humidity near to 100%. This trend of changes in the number of hours with fog event during the day and night is consistent with the results of studies by Tajbakhsh (2015) at Rasht Airport, Cséplő, et al. (2019) in Hungary, and Tardif and Rasmussen (2007) in New York. The monthly distribution of radiation fog showed that this type of fog event often occurs in autumn and winter to early spring (October to April). The minimum frequency of radiation fog was also observed in May to September. The maximum monthly frequency of advection fog was seen in early spring (March) and its minimum frequency was seen in April, June to September and November. Previous studies have shown that in early spring, with the increase of air temperature on land, the water temperature is lower than the air temperature on land and the conditions for the formation of advection fog are available (Roach, 1995; Cho, et al., 2000; Taylor, 1917; Klein and Hartmann, 1993). The monthly distribution of CBL fog showed that there was no occurrence of CBL fog in July to September. This type of fog starts in late autumn and continues until mid-spring. Boundary layer cooling is the most important process that causes fog in spring, while winter occurrences can be caused due to large-scale atmospheric systems (Tardif and Rasmussen, 2007). The monthly distribution of precipitation fog showed that the most occurrences are in winter season (January and February). Precipitation fog didn’t occurred in April to October. Since precipitation fog depends on large-scale factors (Tardif and Rasmussen, 2007), this type of fog is more common in autumn and winter. In terms of annual changes in the occurrence of fog, there was no significant trend in the number of fog hours during the studied years. In terms of fog concentration, the number of semi-dense fog events (visibility equal or more than 100 meters and less than 500 meters) at Rasht Airport was higher than fog (visibility equal or more than 500 meters and less than 1000 meters) and dense fog (visibility less than 100 meters) in all months. Also, most fog events had a minimum visibility of 100 meters and then 200 meters .

Ultraviolet radiation is defined as electromagnetic radiation with wavelengths in the range of 200-400 nm and is divided into three different bands. UVC is related to the wavelength from 200 to 280 nm, while UVB is related to the wavelength ranging from 280 to 315 nm and UVA is related to the wavelength from 315 nm to the visible level (400 nm). Ultraviolet radiation has beneficial effects such as making vitamin D and disinfecting effects. On the other hand, it causes harm such as burns and skin cancer, and damage to the eyes and immune system. Predicting the amount of UV radiation based on the UV index can be of great help to people's health. In this study, the tropospheric ultraviolet-visible (TUV) model was used to predict the UVI index. This model requires ozone, whiteness, and Aerosol Optical Depth (AOD) to forecast UVI. WACCM model data was used for ozone and whiteness column values from the ozone data of the GFS and AOD global forecasting systems. 612 case studies in the whole year of 2020 were selected from each of the 12 months of the year from different parts of the country. GFS, WACCM, and OMI data were extracted for the mentioned dates and interpolated at the desired points. Because OMI data is available locally at noon everywhere, case studies have been selected for noon. Then the interpolated values along with the length, width, and height of the points were given as input to the TUV model, and the UVI value was predicted. Due to the lack of access to the actual value of UVI in the country, OMI data was assumed as observational data and used to compare with the predicted value. Conventional statistical measures ME, MAE, RMSE, and Pearson correlation coefficient were used to validate the prediction value with observational data. The results showed that in January, February, April, November, and December, which are the coldest months of the year and the day length is shorter and the sun is less intense, so the error rate is lower than in other months (warm months of the year). However, in general, the forecast is very accurate. So that in all selected study cases, the values of ME, MAE, RMSE, and R are 0.16, 0.85, 1.13, and 0.93, respectively, which indicates the high accuracy of the forecast. The results also showed that the forecast error has a linear relationship with the AOD value. Thus, the higher the AOD value, the more negative the forecast error and underestimated forecast value.In the warmer months of the year, the length of the day is longer and the intensity of the sun's radiation is higher, resulting in more errors. The amount of error is also related to the amount of light depth of the particles; the greater the AOD, the greater the error. The correlation coefficient diagram also showed that there is a high correlation between the forecast and observation values. This research is the first research in the field of forecasting the UV index in the country and has had satisfactory results.

Climatology study of fog at Mashhad airportAbstract Low horizontal visibility caused by fog can affect air traffic and in some cases is the main cause of air accidents. Fog is a condition in which water droplets or ice crystals in the air layer near the Earth's surface reduce horizontal visibility to less than 1000 meters. Fog is one of the major causes of flight delays and accidents. In fact, fog is the second hazardous weather event affecting aviation activities (Gultepe et al., 2019). The effects of fog in the aviation industry can cost hundreds of millions of dollars due to flight delays and cancelations (Gultepe et al., 2017). Fog event at Mashhad airport has repeatedly delayed or canceled flights. Therefore, study the climatology of the fog event at this airport helps to better understand this phenomenon and improve fog forecasting. For this purpose, all types of fog events were detected and analyzed using the hourly observation data of METAR during the statistical period from 2001 to 2020 and based on Tardif and Rasmussen (2007) algorithm. Then the fog climate was studied during the period. The results showed that CBL fog is the most common type of fog in terms of frequency with 43.94% of all fog occurrences at Mashhad Airport. Precipitation fog is also the rarest type of fog among fog types with 22.72% of all fog occurrences. Advection fog, which is formed under the influence of the marine environment, was not observed at this airport. Given that radiation fog usually forms at night and usually dissipates after sunrise, this type of fog has been the longest-lasting type of fog at the airport during 16 years of study. Also, the duration of CBL fog was the shortest one compared to other types of fog events.The minimum visibility of radiation and CBL fog events at this airport was lower than other types of fog. Also, precipitation fog had the lowest concentration compared to other types of fog events, which is consistent with the results of Tardif and Rasmussen (2007). The most reports of fog occurrence at Mashhad Airport during the studied years was at midnight and before sunrise, which could be due to the radiation cooling at night and before sunrise, which reaches its maximum (Hoch et al., 2011; Cséplő et al., 2019; Zouzoua et al., 2021; Wærsted et al., 2017). This result is consistent with the study of Cséplő et al. (2019) and Tardif and Rasmussen (2007). Tajbakhsh (2015) has also reported the maximum occurrence of fog at 21, 00 and 03 UTC using 20-years synoptic data at Mashhad Airport. Also, the frequency of fog occurrence decreases rapidly after 03 UTC.The monthly distribution of fog types in Mashhad Airport showed that radiation fog events often occur in winter and CBL fog events often occur from late autumn to mid-spring. Boundary layer cooling is the most important process that causes fog event in spring, while winter fog occurrences can be related to large-scale atmospheric systems (Tardif and Rasmussen, 2007). The maximum monthly frequency of precipitation fog event at this airport is in the winter. The fog events in the autumn can also be related to these weather systems. Since precipitation fog events depend on large-scale factors (Tardif and Rasmussen, 2007), the maximum occurrence of this type of fog is observed in winter. In general, the frequency of fogs in December and January is higher than other months of the year. This result is consistent with Tajbakhsh (2015).In terms of annual changes in fog occurrences, there was no significant trend in the number of fog hours during the studied years (2001-2020). In terms of fog concentration, in all months of the year, the number of semi-dense fog events with minimum visibility of 100 to 500 meters was more than other types. Also, the number of fog events with minimum visibility of 100 meters and then 200 meters had the highest number. It shows the importance of climate investigation and prediction of the fog event at this airport from the point of view of the aviation industry, because dense fog events cause difficulties in the landing and takeoff of airplanes. Keywords: Fog climatology, Fog type, Radiation fog, Precipitation fog, CBL fog.

The presence of fog reduces the horizontal visibility to less than 1000 meters and disrupts air transport services and can make it impossible for aircraft to land and take off. Climatology of fog can help better diagnosis and prediction of fog. In this study, METAR data from 2011 to 2020 were used to detect fog events at Ardebil Airport and according to the classification algorithm of Tardif and Rasmussen (2007), the types of events were determined. Then the fog climate was studied during the period. The results showed that the most common type of fog at Ardebil Airport is radiation fog and the rarest type is precipitation fog. In terms of duration of fog event, radiation fog was the longest fog event and precipitation fog was the shortest fog event. The highest incidence of fog during the study years was at 3 and 4 GMT. The monthly distribution of radiation fog showed that events often occurred in autumn and early winter (September to December). In terms of annual changes in the occurrence of fog, there was no significant trend in the number of fog hours during the studied years, except for the last three years which had a decreasing trend.

In this study, numerical simulation of two advection and radiation fog events at Ardabil Airport in January 2015 was performed using the Weather Research and Forecasting (WRF) model and SW99 visibility algorithm. Six Planetary Boundary Layer (PBL) schemes including YSU, MYJ, ACM2, MYNN2.5, MYNN3 and QNSE were used to evaluate the sensitivity of fog simulation to the PBL schemes. The results show that the simulation of these two fog events is sensitive to PBL. Also, due to the importance of accurate prediction of 2-m relative humidity, temperature and dew point temperature and 10-m wind speed in fog forecasting, the sensitivity of simulation of these variables to PBL scheme was investigated. The results showed that the simulation of advection fog event was successfully performed using most of PBL schemes mentioned above. YSU, ACM2 and MYNN2.5 schemes performed better in simulation of advection fog. The QNSE scheme was not successful in simulating the advection fog event. In radiant fog simulation, most PBL schemas were not able to simulate the moisture required to form fog at the time of fog occurrence.Most PBL schemes were not able to simulate the moisture required for formation of fog at the time of the radiation fog event. A few hours before the onset of the radiation fog event, the WRF model with most schemes simulated the visibility reduction due to the fog. Generally, QNSE and MYJ schemes performed worse than other schemes in simulating temperature, dew point temperature, relative humidity and wind speed.

In this study it has been attempted to simulate the occurrence of radiation and CBL fog during late December 2015 in Orumiyeh, Arak, Bushehr, Zahedan, Hamedan and Shahrekord airports, using the mesoscale WRF model. To simulate radiation and CBL fogs, the output of the WRF model with five different configurations as presented in table 1 was used, and then four visibility calculation algorithms were applied to the output of the model to find the best configuration and visibility calculation algorithm for the prediction of the considered fog events. The WRF model version 3.9.1 with Lambert conformal projection, using two nested domains with 16-km and 4-km grid spacing was used for the simulation. Based on the results of some previous studies (e.g. Lin et al., 2017; Van der Velde et al., 2010; Roman-Casc´on et al., 2012), because of the sensitivity of the fog prediction to cloud microphysics, planetary boundary layer and long-wave radiation schemes, five different configurations of the WRF model with varying physical parametrization schemes were implemented. In the first series of experiments, the five mentioned configurations of the model were run using 28 vertical levels. Then four horizontal visibility calculation algorithms including SW99 (Stoelinga and Warner,1999), FSL (Doran et al., 2009), G2009 (Gultepe et al., 2009) and RUC (Benjamin et al., 2004) were applied on the model output to calculate the horizontal visibility separately for each model output and for six synoptic stations in the domain. It is seen that, since the amount of liquid water content (LWC) of the cloud with all model configurations is zero or nearly zero, the calculated horizontal visibility was much greater than 10 km for all stations. In general, the model showed poor results for the simulation of relative humidity in the boundary layer and thus the occurrence of fog. Since some previous studies (e.g. Yang and Gao, 2016; Philip et al., 2016; Tardif, 2007) have emphasized the importance of high vertical resolution to resolve the main fog formation processes, in the next series of experiments the number of vertical levels in the model was increased from 28 to 32, such that 11 vertical levels were considered from the ground level up to 200 m above ground. The model was then implemented with the same five configurations as in the first experiments. Examining the results revealed that the model found the skill of recognizing moisture and fog in most cases, and predicted 5 out of 6 cases of CBL and 2 out of 4 cases of radiation fogs. For the verification the outputs for the predictions of fog events the calculated visibilities were compared with the verifying observational data and hit rate and equitable threat score were calculated. The evaluation indicated that the configuration 2 with 32 vertical levels combined with SW99 and G2009 algorithms performed better for the prediction of visibility for stations considered here. It can also be said that increasing the number of vertical levels close to the surface is of great importance in improving the quality of fog forecasting, because high vertical resolution is required to realistically represent the vertical structure and magnitude of the radiative cooling in the first few meters of the atmosphere, and thus obtain more accurate forecasts of radiation fog. Using high vertical resolution in simulations, results in an acceptable increase of liquid water content (LWC) and thus improves the accuracy of fog prediction. The results also show that the model's skill to predict CBL fog is higher than that of radiation fog in this case study. By comparing the simulated and observational two meter temperature values, it was observed that the simulated air temperature is overestimated, especially for the radiation fog. Previous researches have also shown a positive bias in predicted air temperature at two-meter height in radiation fog conditions, which is probably due to the inability of the model to simulate the actual radiative cooling associated with fog conditions in the first few meters of the atmosphere (Roman-Casc´on et al., 2019).

Air pollution is a major problem in urban life and populated cities. The important issue to contrast and reduce this problem is to estimate the level of air pollution. We can use satellite sensors according to their application for this act. The suitable sensor is MODIS that is aboard the Terra and Aqua satellites. MODIS is the best choice for estimating the level of air pollution especially aerosols, because of its numerous spectrum channels, narrow spectral bands, complete daily coverage of the earth, free of charge and availability of data. In this study, we use 20 air quality stations of Tehran for a period of 85 days (25 Sep to 19 Dec 2012) thatmeasures air pollution hourly. These stations are scattered in Tehran irregularly, in some areas, it has high density, and in some areas, there is no station for measurement. Thus, satellites are good supplements for measuring pollution, due to their spatial and temporal coverage of earth. According to this issue, in this study, we evaluated estimation accuracy of PM10 and PM2.5 mass concentration using MOD04 and MYD04 products. Results show that there is a linear relationship between aerosol optical depth and PM10 and PM2.5 (that are measured at ground stations). Results show good correlation (R) 0.67 to 0.81 between satellite data and ground data. AOD act better in estimating PM10 than PM2.5 mass concentration.