Identification and Classification of Sea Surface Pressure Anomalies in the North Indian Ocean

Abstract In the northern basin of the Indian Ocean, including the Bay of Bengal and the Arabian Sea pre-monsoon (March–May) and post-monsoon (October–December) seasons (defined as the seasons before and after the summer monsoon season, Atmospheric conditions create a favorable environment for the formation and strengthening of tropical storms, which can cause human and financial damage if they reach the shores of the Indian Ocean. Various parameters are effective in creating and amplifying tropical storms, such as sea level pressure, sea surface temperature and wind speed. These parameters deepen with the development of the storm and become normal before the storm as the storm weakens. Surface pressure is a quantity that develops and weakens with the growth cycle of a tropical cyclone, and the most important feature of a tropical cyclone is the formation of closed compression lines around the center of low pressure, Which makes it possible to identify anomalies using the tools of statistics and probability and pressure of the rotating center. Statistical or percentage statistical technique has been used to identify low pressure centers.Mean sea level pressure data on the North Indian Ocean were available over a 41-year period, and one of the challenges of using a percentile or percentage on this data is the existence of temporal and spatial dimensions in the data. To overcome the problem of temporal and spatial dimensions of data and define a threshold value for anomalies that includes both time and place dimensions: A) Calculate the percentage index on spatial data in the North Indian OceanB) Calculation of percentage index in time dimension from spatial percentage indicators calculated The important point in identifying the anomalies of the North Indian Ocean is that the best monthly threshold is 97.5% with the identification of 94.5%, which was able to detect the most anomalies in the North Indian Ocean. Therefore, it is observed that the best result in identifying turbulence and rotational disorders by percentage thresholds is the monthly threshold of 97.5%, which is obtained by calculating the percentage threshold by temporal and spatial dimensions