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Öğe Development of visibility equation based on fog microphysical observations and its verification using the WRF model(SPRINGER HEIDELBERG, 2022) Wagh, Sandeep; Kulkarni, Rachana; Lonkar, Prasanna; Parde, Avinash N.; Dhangar, Narendra G.; Govardhan, Gaurav; Sajjan, Veeresh; Debnath, Sreyashi; Gültepe, İsmailThe campaign mode observational program 'Winter Fog Experiment' (WiFEX) was set up at the Indira Gandhi International Airport (IGIA), New Delhi, during the winter months of 2016-17 and 2017-18. Using the WiFEX data, in this study, we examine the microphysical structure of fog formed in a polluted environment and attempt to predict visibility (V-is) using the fog index approach. The examination of eleven fog events demonstrates that the mean droplet concentration (up to 674.94 #/cm(-3)) and liquid water content (LWC, up to 0.29 g m(-3)) are high in dense fog cases (V-is < 200 m). The droplet spectrum shows bi-modal distribution and dominance of smaller droplets in the 3-7 mu m range. For most fog cases, the droplet spectrum extends up to 50 mu m. The mature phase of the fog depicts a relatively increased population of droplets in the higher-sized bins, highlighting the formation of larger droplets. Moreover, we found that V-is is inversely related to the liquid water content and the fog droplet number concentration. Fog index-based visibility parameterization has been developed to diagnostically compute visibility for the different categories of fog events, namely category-IIIB (CAT-IIIB) and category-IIIC (CAT-IIIC), using the meteorological variables. Out of 14 CAT-IIIB and 19 CAT-IIIC fog events, the 'WiFEX-in' could predict seven CAT-IIIB and 12 CAT-IIIC fog events, respectively. However, significant under-prediction was evident for the total CAT-IIIB fog hours and over-prediction for the total CAT-IIIC fog hours. It is found that the observed and predicted fog hour differences were related to the errors in the fog onset, dissipation, and magnitude of predicted liquid water content during CAT-IIIB and CAT-IIIC events and the same are discussed.Öğe Enhanced secondary aerosol formation driven by excess ammonia during fog episodes in Delhi, India(Elsevier Ltd, 2022) Acharja, Prodip; Ali, Kaushar; Ghude, Sachin Dinkar; Sinha, Vinayak; Sinha, Baerbel; Kulkarni, Rachana G.; Gültepe, İsmail; Rajeevan, M.The Indo-Gangetic Plain (IGP) has high wintertime fine aerosol loadings that significantly modulate the widespread fog formation and sustenance. Here, we investigate the potential formation of secondary inorganic aerosol driven by excess ammonia during winter fog. Physicochemical properties of fine aerosols (PM1 and PM2.5) and trace gases (HCl, HONO, HNO3, SO2, and NH3) were simultaneously monitored at hourly resolution using Monitor for AeRosols and Gases in Ambient air (MARGA-2S) for the first time in India. Results showed that four major ions, i.e., Cl?, NO3?, SO42?, and NH4+ contributed approximately 97% of the total measured inorganic ionic mass. The atmosphere was ammonia-rich in winter and ammonium was the dominant neutralizer with aerosol neutralization ratio (ANR) close to unity. The correlation between ammonium and chloride was ?0.8, implying the significant formation of ammonium chloride during fog in Delhi. Thermodynamical model ISORROPIA-II showed the predicted PM1 and PM2.5 pH to be 4.49 ± 0.53, and 4.58 ± 0.48 respectively which were in good agreement with measurements. The ALWC increased from non-foggy to foggy periods and a considerable fraction of fine aerosol mass existed in the supermicron size range of 1–2.5 ?m. The sulfur oxidation ratio (SOR) of PM1, PM2.5 reached up to 0.60, 0.75 in dense fog and 0.74, 0.87 when ambient RH crossed a threshold of 95%, much higher than non-foggy periods (with confidence level of ?95%) pointing to enhanced formation of secondary aerosol in fog.Öğe Updated Trewartha climate classification with four climate change scenarios(John Wiley and Sons Inc, 2022) Valjarevi?, Aleksandar; Milanovi?, Miško; Gültepe, İsmail; Filipovi?, Dejan; Luki?, TinThe Updated Trewartha climate classification (TWCC) at global level shows the changes that are expected as a consequence of global temperature increase and imbalance of precipitation. This type of classification is more precise than the Köppen climate classification. Predictions included the increase in global temperature (T in °C) and change in the amount of precipitation (PA in mm). Two climate models MIROC6 and IPSL-CM6A- LR were used, along with 4261 meteorological stations from which the data on temperature and precipitation were taken. These climate models were used because they represent the most extreme models in the CMIP6 database. Four scenarios of climate change and their territories were analysed in accordance with the TWCC classification. Four scenarios of representative concentration pathway (RCP) by 2.6, 4.5, 6.0 and 8.5 W/m2 follow the increase of temperature between 0.3°C and 4.3°C in relation to precipitation and are being analysed for the periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100. The biggest extremes are shown in the last grid for the period 2081–2100, reflecting the increase of T up to 4.3°C. With the help of GIS (geographical information systems) and spatial analyses, it is possible to estimate the changes in climate zones as well as their movement. Australia and South East Asia will suffer the biggest changes of biomes, followed by South America and North America. Climate belts to undergo the biggest change due to such temperature according to TWCC are Ar, Am, Aw and BS, BW, E, Ft and Fi. The Antarctic will lose 11.5% of the territory under Fi and Ft climates within the period between 2081 and 2100. The conclusion is that the climates BW, Bwh and Bwk, which represent the deserts, will increase by 119.8% with the increase of T by 4.3°C. The information, practices and views in this article are those of the author(s) and do not necessarily reflect the opinion of the Royal Geographical Society (with IBG). © 2022 Royal Geographical Society (with the Institute of British Geographers).Öğe Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications(MDPI, 2021) Cordeiro, Fabricio Magalhães; Franca, Gutemberg Borges; Neto, F.L.A.; Gültepe, İsmailThis work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc ) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Öğe Weather aerodynamic adaptation for autonomous vehicles: a tentative framework(CANADIAN SCIENCE PUBLISHING, 2022) Hangan, H.; Agelin-Chaab, M.; Gültepe, İsmail; Elfstrom, G.; Komar, J.While autonomous vehicles (AVs) are potentially the future of transportation, one of the main issues that need to be addressed is their behaviour and response to adverse weather conditions. Herein, we proposed a research frame to understand and mitigate the impact of weather stressors (wind, rain, snow, ice, and fog) on AVs. A recently launched initiative to design and engineer an indigenous Canadian road vehicle served as a background for this intended framework. The proposed frame consists of (i) on-road testing and numerical computational fluid dynamics (CFD) simulations to derive statistically significant critical weather conditions (weather design cases, WDCs) and (ii) simulation of these weather conditions in the ACE climatic wind tunnel at Ontario Tech University, Canada, to (iii) identify adaptive controls to minimize the effects of the WDCs on vehicles improving their aerodynamics, safety, and sensor functionality. This framework is intended to (i) provoke discussions among the AV industry and research stakeholders in Canada and elsewhere and (ii) provide a context for future research in related areas such as AV aerodynamics, maneuverability, weather impacts (e.g., wind, rain, snow, ice, and fog), sensors, and soiling.
