4.3 Article

An Overview of the China Meteorological Administration Tropical Cyclone Database

Journal

JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
Volume 31, Issue 2, Pages 287-301

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JTECH-D-12-00119.1

Keywords

Data processing; Tropical cyclones; Databases; North Pacific Ocean

Funding

  1. National Basic Research Program of China [2012CB956003]
  2. National Natural Science Foundation of China [41075071]
  3. China National Special Funding Project for Meteorology [GYHY201006008]

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The China Meteorological Administration (CMA)'s tropical cyclone (TC) database includes not only the best-track dataset but also TC-induced wind and precipitation data. This article summarizes the characteristics and key technical details of the CMA TC database. In addition to the best-track data, other phenomena that occurred with the TCs are also recorded in the dataset, such as the subcenters, extratropical transitions, outer-range severe winds associated with TCs over the South China Sea, and coastal severe winds associated with TCs landfalling in China. These data provide additional information for researchers. The TC-induced wind and precipitation data, which map the distribution of severe wind and rainfall, are also helpful for investigating the impacts of TCs. The study also considers the changing reliability of the various data sources used since the database was created and the potential causes of temporal and spatial inhomogeneities within the datasets. Because of the greater number of observations available for analysis, the CMA TC database is likely to be more accurate and complete over the offshore and land areas of China than over the open ocean. Temporal inhomogeneities were induced primarily by changes to the nature and quality of the input data, such as the development of a weather observation network in China and the use of satellite image analysis to replace the original aircraft reconnaissance data. Furthermore, technical and factitious changes, such as to the wind-pressure relationship and the satellite-derived current intensity (CI) number-intensity conversion, also led to inhomogeneities within the datasets.

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