Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Shiqiu Peng x
  • Journal of Atmospheric and Oceanic Technology x
  • All content x
Clear All Modify Search
Yuhang Zhu, Yineng Li, and Shiqiu Peng

Abstract

The track and accompanying sea wave forecasts of Typhoon Mangkhut (2018) by a real-time regional forecasting system are assessed in this study. The real-time regional forecasting system shows a good track forecast skill with a mean error of 69.9 km for the forecast period of 1–72 h. In particular, it predicted well the landfall location on the coastal island of South China with distance (time) biases of 76.89 km (3 h) averaging over all forecasting made during 1–72 h and only 3.55 km (1 h) for the forecasting initialized 27 h ahead of the landfall. The sea waves induced by Mangkhut (2018) were also predicted well by the wave model of the forecasting system with a mean error of 0.54 m and a mean correlation coefficient up to 0.94 for significant wave height. Results from sensitivity experiments show that the improvement of track forecasting skill for Mangkhut (2018) are mainly attributed to application of a scale-selective data assimilation scheme in the atmosphere model that helps to maintain a more realistic large-scale flow obtained from the GFS forecasts, whereas the air–sea coupling has slightly negative impact on the track forecast skill.

Restricted access
Shiqiu Peng, Yineng Li, and Lian Xie

Abstract

A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.

Full access