Christophersen Hui, Sippel Jason, Aksoy Altug, Baker Nancy L
US Naval Research Laboratory, Marine Meteorology Division, Monterey, CA, USA.
NOAA/Atlantic Oceanographic and Meteorological Laboratory, Hurricane Research Division, Miami, Florida, USA.
Ann N Y Acad Sci. 2022 Nov;1517(1):25-43. doi: 10.1111/nyas.14873. Epub 2022 Aug 17.
In this review, data assimilation (DA) techniques used for tropical cyclones (TCs) are briefly overviewed. The strength and weakness of variational methods, ensemble methods, hybrid methods, and particle filter methods are also discussed. Several global numerical weather prediction models and their corresponding DA systems frequently used for TC forecasting and verification are described first. The DA research and development efforts in the operational regional model from the National Centers for Environmental Prediction's Hurricane Weather Research and Forecasting are then discussed in greater detail. Focused remarks on TC observations from reconnaissance, ground-based radar, enhanced satellite-derived atmospheric motion vectors and all-sky satellite radiances and their impacts on TC analyses and forecasts are addressed. Recent TC DA advancements and challenges on better use of observations and more advanced DA methods for TC application are also briefly reviewed.
在本综述中，简要概述了用于热带气旋（TC）的资料同化（DA）技术。还讨论了变分方法、集合方法、混合方法和粒子滤波方法的优缺点。首先介绍了几种常用于TC预报和验证的全球数值天气预报模型及其相应的DA系统。然后更详细地讨论了美国国家环境预报中心飓风天气研究与预报业务区域模式中的DA研发工作。重点讨论了来自侦察机、地基雷达、增强型卫星衍生大气运动矢量和全天空卫星辐射的TC观测及其对TC分析和预报的影响。还简要回顾了TC DA在更好地利用观测资料和采用更先进的DA方法用于TC应用方面的最新进展和挑战。
