LAUSR

The present study uses the nonlinear singular vector (NFSV) approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting (WRF) model for tropical cyclone (TC) intensity forecasts. For nine selected TC cases, the NFSV-tendency perturbations of the WRF model, including components of potential temperature and/or moisture, are calculated when TC intensities are forecasted with a 24-hour lead time, and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts. The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time, and their dominant energies concentrate in the middle layers of the atmosphere. Moreover, such structures do not depend on TC intensities and subsequent development of the TC. The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs, makes larger contributions to the TC intensity forecast uncertainty. Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model, even if using a WRF with coarse resolution. 利用非线性强迫奇异向量方法, 对 9 个台风个例的强度预报进行了研究, 识别了台风强度 24 小时预报的敏感要素和敏感区域. 结果表明: (1) 相较其它区域, 台风强度预报不确定性对台风内核区的温度变化更为敏感; (2) 相较其它高度, 台风强度预报的不确定性对对流层中低层 (800-600 hPa) 的温度变化更为敏感; (3) 上述敏感要素和区域对不同强度的台风个例的依赖性不显著. 不仅如此, 根据非线性强迫奇异向量揭示的敏感要素和敏感区域对 WRF 模式进行相应矫正, 能够显著提高台风强度预报技巧.