"International Journal of Geography, Geology and Environment"

Author(s): Mamta Yadav, Pushpa Saroja, Aneesh Wanjare, Anand Yadav, Sachin Kumar Sahu and Devendra Pradhan

Abstract: This study investigates fog estimation potential and performance of presently available AI/ML models on Four years (2021-2024) hourly METAR data is used for model running, training and testing like XGboost, Random Forest and also deep learning models LSTM, CNN and Transformer using current weather observation of AMO Bhopal. Fog is very crucial for air transport and airport traffic management. During winter season as the atmospheric stability starts increasing and temperatures starts falling severe fog episodes start appearing in North India and some parts of Central India. The fog prediction skills of tree based models like Random forest and XGBoost are superior to deep learning models. Meteorological wind and temperature data of upper air revealed key insights of inversion layer and variation of its depth with high humidity supports dense fog formation on ground. It further supports the atmospheric stability restrict vertical motion and ambient air gets more chance get condense by coming in contact with more cold solid bodies and hydrophilic CCNs. As air quality get poorer wit increase of AQI index and pollutants play variable role for visibility changes. In Our case after analysis found that PM2.5 and PM10 played a dominant role in visibility reduction during moderate to dense fog days. Winter season is known for poor air quality as stable atmosphere no weather dense fog episode visibility reduction is dominated by PM particles and if weather has occurred then increased RH and diurnal variation of temperature with lag of 24-48 hours dense fog may occur.

DOI: 10.22271/27067483.2025.v7.i12a.459

Pages: 40-52 | Views: 191 | Downloads: 105

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