Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean

被引：2

作者：

Espejo, A. ^{[1
]}

Mendez, F. J. ^{[1
]}

Diez, J. ^{[1
]}

Medina, R. ^{[1
]}

AL-Yahyai, S. ^{[2
]}

机构：

[1] Univ Cantabria, Environm Hydraul Inst, IH Cantabria, C Isabel Torres 5,Parque Cient & Tecnol Cantabria, Santander 39011, Spain

[2] Mazoon Elect Co, Dept Informat Technol, Muscat, Oman

来源：

JOURNAL OF FLOOD RISK MANAGEMENT | 2016年 / 9卷 / 04期

关键词：

Clustering algorithm; sea-surface temperature; seasonal forecast; tropical cyclones; WAVE CLIMATE; EL-NINO; VARIABILITY; INTENSITY; IMPACT;

D O I：

10.1111/jfr3.12197

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Coastal flooding due to tropical cyclones (TCs) is a major concern in low-lying areas of the North Indian Ocean. In the past few years, there have been devastating TCs (e.g. Nargis and Layla in the Bay of Bengal and Gonu in the Arabian Sea) that have galvanised efforts to better understand and forecast such events. Here, we focus on the role of sea-surface temperature (SST) as a predictor of TC intensification. We develop a statistical downscaling model that identifies patterns of large-scale SST associated with TC activity in terms of occurrence rate and expected tracks. Finally, we use the Climate Forecast System v2 to construct a probabilistic forecast of TC activity for the coming months/season. We demonstrate that this approach is an integral component of a very early TC warning system.

引用

页码：379 / 389

页数：11

共 28 条

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