India experiences different types of seasons each year due to its position on the globe. Seasonal and spatial scale-based variation in weather conditions and topographical features of the region result in various climate of that specific region. Measured weather parameters are analyzed and variations can be seen with respect to the desired time resolution. However, measured parameters represents micro-meteorology and space-based extrapolation is difficult in the manners of topography, land use and its physics. Simulation-based mesoscale weather parameters can be analyzed and variation for short period can be seen. Ahmedabad city of Gujarat State in India has been taken as a study area for analysis of meteorological parameters. Weather parameters were obtained by simulating weather research and forecasting model using two way nested for three domains and evaluated with micrometeorological tower observations in the previous study. Various weather parameters such as temperature, rainfall, humidity, wind speed, wind direction and cloud cover were studied with respect to 3 hourly intervals for 3 months (January, February and March) for the year of 2015. A statistical analysis including average, correlation and standard deviation was carried out for the parameters to study the weather for the city. It was found that the all parameters have maximum deviation from 12 noon to 18 p.m. except wind in a day over the period. More variations were seen between February 27, 2015 and March 02, 2015 for all the parameters, which is majorly due to the season change from the retreating winter to the onset of summer. This study can help to understand the change in micro and mesoscale meteorology based on atmospheric earth system.
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We extend our sincere gratitude to Dr. Prashant Kumar from Space Applications Centre, Indian Space Research Organization, Ahmedabad, Gujarat, India for providing relevant data.
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Kumar, A., Dhakhwa, S. & Kumar, M. Statistical analysis of high-resolution mesoscale meteorology derived from weather research and forecasting model. Model. Earth Syst. Environ. 7, 235–245 (2021). https://doi.org/10.1007/s40808-020-00875-x
- Statistical analysis