AI 中文总结
该研究利用2023年夏季大量后报数据，通过改进的检测和跟踪框架识别热带气旋，用球形傅里叶神经算子生成后报数据，重现异常扩散幂律，得出其对热带气旋轨迹和登陆位置预测性的影响。
AI 中文摘要
我们利用2023年夏季的大量后报数据（HENS）来研究热带气旋（TCs）是否遵循普通布朗扩散或异常扩散。已从每个气旋起始和终止之间最短路径的轨迹波动推断出实际TCs的异常扩散。我们用HENS重现了连接空间位置和时间的相同异常扩散幂律。此外，我们表明，自起始以来单个TC在HENS中的位置方差随时间遵循标度律，在某些情况下对应于TC在背景大气流中的弹道运动。这一判定得益于对34个单独TC的数千个似是而非但与事实相反的重现所确定的特殊统计数据。HENS由7424个15天的后报数据组成，这些数据于2023年6月1日至8月31日每天从观测到的大气条件开始，使用欧洲中期天气预报中心（ECMWF）的ERA5气象再分析数据生成。后报数据是使用英伟达基于机器学习的天气和气候模拟器球形傅里叶神经算子（SFNO）生成的。我们使用一种改进的Tempest Extremes检测和跟踪框架来识别HENS中的热带气旋，并调整一次性参数，以相对于2023年夏季观测到的TCs的国际最佳轨道存档（IBTrACS）记录，将误报和漏报数量降至最低。我们最后得出了我们的发现对TC轨迹和登陆位置提前几天到几周的预测性的影响。
英文摘要
We examine whether tropical cyclones (TCs) obey ordinary Brownian or anomalous diffusion using a huge ensemble (HENS) of hindcasts for summer 2023. Anomalous diffusion has been inferred for actual TCs from the fluctuations in their tracks from the shortest paths between the initiation and termination of each cyclone. We reproduce the same anomalous diffusion power laws connecting spatial position and time using HENS. In addition, we show that the variance in the position of a single TC across HENS since initiation follows a scaling law with time that, in some cases, corresponds to ballistic motion of the TC through the background atmospheric flow. This determination was enabled by the exceptional statistics determined from thousands of plausible yet counterfactual recreations of 34 individual TCs. HENS consists of 7424 15-day hindcasts initiated from observed atmospheric conditions each day from June 1, 2023 to August 31, 2023 using the ECMWF ERA5 meteorological reanalysis. The hindcasts were generated using NVIDIA's Spherical Fourier Neural Operator (SFNO) machine-learning-based weather and climate emulator. We identify tropical cyclones in HENS using a variant of the Tempest Extremes detection and tracking frameworks for TCs with adjustments to the disposable parameters to minimize the numbers of false positives and negatives relative to the International Best Track Archive for Climate Stewardship (IBTrACS) records for TCs observed in summer 2023. We conclude with the implications of our findings for the predictability of TC tracks and landfall locations on lead times of days to weeks.
