News Release

China Meteorological Administration improves tropical cyclone forecasting accuracy

Peer-Reviewed Publication

Institute of Atmospheric Physics, Chinese Academy of Sciences

A decade of progress in TC forecasting by the China Meteorological Administration (2013–2022)

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A decade of progress in TC forecasting by the China Meteorological Administration (2013–2022)

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Credit: Hong Wang

Tropical cyclones (TCs) are among the most destructive natural hazards in East Asia, posing severe threats to coastal communities, infrastructure, and economic stability. Accurate TC track and intensity forecasts are critical for effective disaster risk reduction.

A recent study published in Atmospheric and Oceanic Science Letters presents a decade-long assessment of TC forecasting performance by the China Meteorological Administration (CMA), offering valuable insights into both progress and remaining challenges in operational prediction.

Based on the TC forecast data issued by the CMA from 2013 to 2022, this study demonstrates consistent improvements in both track and intensity prediction accuracy. Notably, track forecast errors have exhibited a marked reduction, particularly at longer lead times. Similarly, intensity forecasts have shown significant advancements, with errors in maximum sustained wind speed and minimum sea level pressure displaying a pronounced downward trend. Specifically, the maximum sustained wind speed errors for 120-hour forecasts decreased at an average rate of 0.26 m s⁻¹ per year.

The evaluation identifies consistent patterns in forecast bias. Track errors are generally smaller for stronger TCs and larger for rapidly weakening ones. In terms of intensity forecasts, a systematic bias is observed: weaker TCs tend to be overestimated, while stronger TCs are more likely to be underestimated. Moreover, forecast errors are unevenly distributed across different regions.

By providing a systematic evaluation of forecast errors across annual trends, regional distributions, and intensity and intensity-variability category dependencies, this work establishes a benchmark for future forecast system development. The findings can guide targeted improvements in modeling, observations, and data assimilation, contributing to more reliable TC forecasts in the western North Pacific.


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