用红外卫星影像自动定位台风中心，精度媲美专业数据。
Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery
- 基于高时频红外影像序列，通过深度学习修正初始位置偏差。
- 对所有系统平均误差26.6公里，强飓风仅14.6公里，接近专业数据水平。
- 实时输出不确定性估计，适合业务化每10分钟运行一次。
确定热带气旋（TC）表面环流中心位置——“中心定位”——是台风预报的关键第一步，影响路径、强度和结构的评估。尽管自动化方法有所发展，但仅有ARCHER-2一种投入业务运行，且其最佳性能依赖于常不可得的微波或散射计数据。本文提出一种名为GeoCenter的深度学习算法，仅使用静止红外（IR）卫星图像（10分钟频率，低延迟<10分钟），不依赖任何其他数据。该算法处理以初步估计为中心的9通道红外图像时间序列（最多4小时），纠正平均48公里、有时超过100公里的位置偏差。在独立测试集上，对所有系统平均/中位/均方根误差分别为26.6/22.2/32.4公里；对热带系统为24.7/20.8/30.0公里；对2–5级飓风为14.6/12.5/17.3公里。这些结果与使用微波或散射计数据的ARCHER-2相当，优于仅用红外数据的版本。此外，GeoCenter生成150个校准良好的中心位置预测，实现有效不确定性量化。所有输入数据均可实时获取，支持每10分钟一次的业务化部署。
原文摘要 · Abstract (English)
Determining the location of a tropical cyclone's (TC) surface circulation center -- "center-fixing" -- is a critical first step in the TC-forecasting process, affecting current/future estimates of track, intensity, and structure. Despite a recent increase in automated center-fixing methods, only one such method (ARCHER-2) is operational, and its best performance is achieved when using microwave or scatterometer data, which are often unavailable. We develop a deep-learning algorithm called GeoCenter; besides a few scalars in the operational Automated Tropical Cyclone Forecasting System, it relies only on geostationary infrared (IR) satellite imagery, which is available for all TC basins at high frequency (10 min) and low latency (< 10 min) during both day and night. GeoCenter ingests an animation (time series) of IR images, including 9 channels at lag times up to 4 hours. The animation is centered at a "first guess" location, offset from the true TC-center location by 48 km on average and sometimes > 100 km; GeoCenter is tasked with correcting this offset. On an independent testing dataset, GeoCenter achieves a mean/median/RMS (root mean square) error of 26.6/22.2/32.4 km for all systems, 24.7/20.8/30.0 km for tropical systems, and 14.6/12.5/17.3 km for category-2--5 hurricanes. These values are similar to ARCHER-2 errors with microwave or scatterometer data, and better than ARCHER-2 errors when only IR data are available. GeoCenter also performs skillful uncertainty quantification, producing a well calibrated ensemble of 150 TC-center locations. Furthermore, all predictors used by GeoCenter are available in real time, which would make GeoCenter easy to implement operationally every 10 min.
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