Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI
Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI
复制标题
DOI：
10.1175/2008waf2007099.1
复制
发表时间：
2009-04
影响因子：
2.9
通讯作者：
S. Camargo;A. Barnston
中科院分区：
文献类型：
--
作者：
S. Camargo;A. Barnston
自2003年初以来，国际气候与社会研究所一直在发布几个海洋盆地的试验性季节性热带气旋活动预报。在本文中，用于获得这些预测的方法进行了描述和预测性能进行评估。这些预测是基于低分辨率气候模式ECHAM4.5中检测和跟踪的热带气旋特征。模拟技巧的模型使用历史观测到的海表温度（SST）在几十年来，以及与SST异常持续从上个月的观测，进行了讨论。这些模拟技能相比，纯粹基于统计的后报作为预测最近观察到的SST的技能。对于最近6年的时间，在此期间，实时预测，技能的原始模型输出相比，主观修改的概率预测实际发布。尽管从一个盆地到另一个变化，动态和统计预测方法的后报技能的水平被发现，总体而言，是相当温和的，但统计上显着的水平大致相当。动力学预报需要统计后处理（校准），以与统计模型竞争，在某些情况下上级。技能水平只会随着交货时间的增加而缓慢下降，最多可达2 - 3个月。在最近的实时预测期间，发布的预测比原始模型输出具有更高的概率技能，这是由于预测者主观消除了模型预测中的“过度自信”偏差。展望了今后改进动力热带气旋预报的前景。
The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone‐like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month’s observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued. Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2‐3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters’ subjective eliminationof the ‘‘overconfidence’’ bias in the model’s forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.