Tropical cyclones (TCs) are extreme weather events that may result in enormous loss of life and property especially for countries surrounding the North Indian Ocean (NIO) rim. In this study, a regional scale spatial statistical model has been developed to define the relationship between TCs and sea surface temperature (SST)/tropical cyclone heat potential (TCHP) for the period 1979 to 2017 over the NIO region. The spatial model employed here tessellates the NIO basin with hexagonal grids (area of each hexagon is approximately 213,961 km²) in order to analyze the relationship between cyclone intensity and the two predictors-SST and TCHP. The role of SST and TCHP contribution to the NIO cyclone intensity is determined by using a geographically weighted regression (GWR) method. This study postulates that a hexagon with positive coefficient signifies a direct relationship between the cyclone intensity and the predictors, for example, between SST (TCHP) and the Bay of Bengal (Arabian Sea) TCs. Based on robust model verification and test of significance, it is attributed that the spatial model using GWR has the potential to model NIO cyclone intensity, particularly using SST as the predictor for Bay of Bengal and TCHP for the Arabian Sea.

热带气旋 (TC) 是极端天气事件，可能导致巨大的生命和财产损失，尤其是对北印度洋 (NIO) 边缘的国家而言。本研究建立了一个区域尺度的空间统计模型来定义1979-2017年蔚来地区TCs与海面温度（SST）/热带气旋热势（TCHP）之间的关系。这里使用的空间模型用六边形网格对 NIO 盆地进行细分（每个六边形的面积约为 213,961 km ²) 以分析气旋强度与两个预测因子——SST 和 TCHP 之间的关系。SST 和 TCHP 对 NIO 气旋强度的贡献是通过使用地理加权回归 (GWR) 方法确定的。该研究假设具有正系数的六边形表示气旋强度和预测因子之间的直接关系，例如 SST (TCHP) 和孟加拉湾 (阿拉伯海) TC 之间的关系。基于稳健的模型验证和显着性检验，认为使用 GWR 的空间模型具有模拟 NIO 气旋强度的潜力，特别是使用 SST 作为孟加拉湾的预测因子和 TCHP 的阿拉伯海。