Scholarlink

<p>Short-term sea surface temperature (SST) forecasting is an essential operational task around China seas. However, the capability of short-term SST forecast from the dynamical numerical model for China seas has not been fully evaluated so far. We assessed the short-term SST forecast skill using a global eddy-resolving ocean forecast system, i.e., the LICOM Forecast System version 1.0 (LFS v1.0) for China seas in 2022 against satellite SST. Results show that LFS v1.0 was able to forecast the short-term SST variation in the study area. The SST with 1-, 7-, and 15-d lead time well captured the observed SST with average pattern correlation coefficient (PCC) of 0.94, 0.93, and 0.92 throughout 2022, the annual mean bias of the forecasted SST of 0.08, −0.16, and −0.33 °C, and the average root mean square error (RMSE) of 0.61, 0.72, and 0.90 °C, respectively. Geographically, the forecast RMSE with 1-d lead time in China seas increased from south to north, and the values were 0.41 °C in South China Sea (SCS) and 1.31 °C in the Bohai Sea (BS). In addition, LFS v1.0 showed better forecast SST abilities in the SCS and East China Sea (ECS) than those in the Yellow Sea and BS. In the ECS and SCS, the forecasted SST was less influenced by the ocean bottom topography due to accurately simulated ocean circulations like Kuroshio. The RMSEs of the SST forecasted by LFS v1.0 displayed seasonal variations, smaller in the area from the middle of boreal August to the middle of boreal December, and larger in boreal late spring and early summer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An assessment on short-term sea surface temperature forecast in China seas based on a global eddy-resolving dynamical forecast system

  • 期刊论文

  • 发表日期：

    2025年10月29日

查看全文
  • Huan Xu,
  • Pengfei Lin,
  • Xuhua Cheng,
  • Hailong Liu,
  • Weipeng Zheng,
  • Zipeng Yu,
  • Yongqiang Yu,
  • Yiwen Li,
  • Tao Zhang

摘要

Short-term sea surface temperature (SST) forecasting is an essential operational task around China seas. However, the capability of short-term SST forecast from the dynamical numerical model for China seas has not been fully evaluated so far. We assessed the short-term SST forecast skill using a global eddy-resolving ocean forecast system, i.e., the LICOM Forecast System version 1.0 (LFS v1.0) for China seas in 2022 against satellite SST. Results show that LFS v1.0 was able to forecast the short-term SST variation in the study area. The SST with 1-, 7-, and 15-d lead time well captured the observed SST with average pattern correlation coefficient (PCC) of 0.94, 0.93, and 0.92 throughout 2022, the annual mean bias of the forecasted SST of 0.08, −0.16, and −0.33 °C, and the average root mean square error (RMSE) of 0.61, 0.72, and 0.90 °C, respectively. Geographically, the forecast RMSE with 1-d lead time in China seas increased from south to north, and the values were 0.41 °C in South China Sea (SCS) and 1.31 °C in the Bohai Sea (BS). In addition, LFS v1.0 showed better forecast SST abilities in the SCS and East China Sea (ECS) than those in the Yellow Sea and BS. In the ECS and SCS, the forecasted SST was less influenced by the ocean bottom topography due to accurately simulated ocean circulations like Kuroshio. The RMSEs of the SST forecasted by LFS v1.0 displayed seasonal variations, smaller in the area from the middle of boreal August to the middle of boreal December, and larger in boreal late spring and early summer.