Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons

Abstract

This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager (AGRI) radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation (DA) method along with the WRF model. A channel-sensitive cloud detection scheme based on the particle filter (PF) algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual (MMR) algorithm and another traditional cloud mask–dependent cloud detection scheme. Results show that both channel-sensitive cloud detection schemes are effective, while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel. In general, the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances. Moreover, it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon, including the temperature, moisture, and dynamical conditions. The typhoon track forecast skill is improved with AGRI radiance DA, which could be explained by better simulating the upper trough. The impact of assimilating AGRI radiances on typhoon intensity forecasts is small. On the other hand, improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields, albeit the improvements are limited.

摘要

本文采用WRF预报模式和WRFDA同化系统中的3DVar方法,考察同化FY-4A卫星多通道扫描成像辐射计(AGRI)两个水汽通道的晴空辐射率资料对西北太平洋三个登陆台风个例预报的影响。本研究发展了基于粒子滤波(PF)算法的通道依赖云检测算法,并将该算法与一种基于变分思想的多元最小残差(MMR)云检测算法以及传统的云覆盖产品依赖的云检测算法分别展开了对比。结果表明,PF和MMR两种通道依赖的云检测方案都是有效的,而PF方案较MMR方案能够为相同通道保留更多的扫描点。总的来说,额外同化AGRI资料能对台风的分析和预报带来正效果。一方面,同化AGRI资料更好地模拟出高空低槽,提高了台风路径预报能力,但对台风强度预报的影响较小。另一方面,同化AGRI资料对热力场和湿度场的预报误差都略有减小,最终得到更好的降水预报。其中,使用PF云检测方法的AGRI同化试验的分析场能够更好地描述台风的温度、湿度和动力条件。