Insight into seasonal aerosol concentrations and meteorological influence over a flood affected area using satellite data and statistical model simulation

Abstract

Aerosol particles in the atmosphere are one of the many factors that might affect floods. One of India's most flood‐prone regions, Muzaffarpur district in Bihar, has been chosen as the study area. The time period considered for the satellite data on meteorological influence is from the year 2000 to 2021. The correlation coefficient between past forecasts and its confirming data is often used to assess the effectiveness of weather and climate forecasting systems. Therefore, changes in correlation can be utilized to gauge advancements in forecasting ability. In this study, the relationship between aerosol optical depth (AOD) 550 nm, temperature, relative humidity, wind speed, PM2.5 and precipitation are carried out with correlation co‐efficient. The average rainfall throughout the monsoon was found to be 162.5 mm, during the period 2000–2021. The descriptive statistical data for seasonal data were determined for AOD (550 nm), PM2.5, wind speed, relative humidity, and temperature values. It is inferred that there is a positive correlation between precipitation and AOD for the monsoon season. Hence the fitting of meteorological parameter AOD for the monsoon season are done with six different probability distributions Gamma (3P), Logistics, Lognormal (3P), Normal, Rayleigh (2P) and Weibull (3P). The parameters of these distributions are evaluated using the Maximum Likelihood Estimation and the goodness of fit is tested using the test statistics Kolmogorov Smirnov and Anderson Darling test. Among the six discussed distributions, Weibull (3P) distribution provides the best fit to model the meteorological data from the satellite, the flood prediction would be obtained from the model proposed.