Tropical cyclone (TC) is an extreme event in the coupled atmosphere and ocean system, which is commonly regarded as a natural disaster in human society (Emanuel, 2003; Zhang et al., 2009). As the top class of TC, each super typhoon is worth comprehensively studying to improve the present understanding of TC events.
Typhoon observations are primarily achieved by satellites (Guan et al., 2017; Yue et al., 2018; Jin et al., 2019). Satellite is an important reconnaissance platform for the determination of best track (Cassity and Colgan, 1973). Generally, at the cyclogenesis phase, cloud patterns in satellite imagery indicate the existence of TC. Later after TC reaches tropical storm intensity, the TC intensity changes are correlated with cloud features (Velden et al., 2006). Based on multi-platform reconnaissance datasets, interpolating in the temporal and spatial grids lead to quantitative estimation of TC position, size, and intensity (Knaff et al., 2010).
Satellite-retrieved sea surface temperature (SST), surface wind, and precipitation data are frequently used to describe TC events. Indeed, these three parameters all contribute to the TC energy balance. First, SST determines the air-sea turbulent heat flux (latent and sensible heat fluxes), and warmer SST provides favorable conditions for TC development (Cione, 2015; Sun et al., 2019). Second, surface wind is a realization of the mechanical energy transferred from the TC. Surface wind and surface turbulent heat flux play different roles in TC dynamics, and they are the sink and source of TC energy, respectively. Third, the precipitation represents the latent heat release from water vapor and supplies considerable energy for the development of a TC (Adler and Rodgers, 1977). In light of these knowledge, satellite products provide indispensable information for the study and operational forecast of TC events.
SST can be retrieved from visible infrared and microwave satellite sensors (Kishtawal, 2016). As an infra sensor, the advanced very high resolution radiometer (AVHRR) has high precision, but the data are limited to cloud-free conditions. Passive microwave sensors, such as the special sensor microwave/imager (SSM/I) and the tropical rainfall measuring mission (TRMM) microwave imager (TMI), are not subject to the cloud coverage restriction and are supposed to have all-weather working capability. After in situ calibration, blended SST data (Reynolds et al., 2007) are suitable for operational weather monitoring (Fu et al., 2018).
For surface wind data, microwave radar scatterometers, such as Seasat, European remote-sensing-1 (ERS-1), European remote-sensing-2 (ERS-2), Quick Scatterometer (QuickSCAT), SeaWinds, and advanced scatterometer (ASCAT), can measure surface wind vectors (Hawkins and Black, 1983; Jones et al., 1999). However, scatterometers have proved problematic in rainy conditions, and the relationship between wind and the received signal is considerably weak in high-wind regimes; thus, scatterometers result in local deficiencies in observation fields. Further, so-called saturation leads to the underestimation of wind speed in high winds when using the empirical relationship in moderate wind conditions (Fu et al., 2018). Occasionally, scatterometers happen to scan the center of a TC and can describe the spatial structure of the TC core (Kishtawal, 2016). Passive microwave sensors, like SSM/I, TMI, and global precipitation measurement (GPM) microwave imager (GMI), are used to retrieve precipitation data. Since there is a close relationship between the rain rate and microwave radiation (or brightness temperature), precipitation is well measured from space platforms (Adler and Rodgers, 1977). Active microwave sensors, such as TRMM Precipitation Radar, provide three-dimensional rain rates during a TC, and this information is critical to the three-dimensional initial conditions of model-based weather forecasting. However, there is still room to improve the retrieval of remotely sensed observations on the core structure of typhoons. Obstacles to achieving a better understanding of these events include suboptimal temporal and spatial resolution, as well as the shielding effect of clouds. Furthermore, the calibration and evaluation of satellite data on marine surfaces are challenging because of insufficient in situ observations. Surface drifters and profiling floats are too sparse to resolve the horizontal structure of a TC.
In fact, in situ ocean observations remain largely based on chance. On the other hand, satellite and in situ observations are being increasingly integrated into data assimilation systems, in which a numerical model is used to pursue consistent datasets. The output data are referred to as analysis or reanalysis. One of the important applications of analysis/reanalysis data is their use as the initial conditions for numerical forecast and hindcast simulation. It is doubtless that the information from satellite and in situ observations is the backbone of reanalysis datasets, but the numerical model and data assimilation method also affect the analysis results. Among the limitations of satellite and in situ observations is the little information they yield about super typhoons (Guan et al., 2014; Cao et al., 2018; He et al., 2018). This restricts the reliability of atmospheric and oceanic reanalysis. Therefore, for super typhoon events, it is crucial to perform integrated studies that cover satellite data, in situ observations, and related analysis and reanalysis. The results of such research can serve as preliminary knowledge on a specific super typhoon and pave the road for the potential next-step model simulation (Li et al., 2016; Zhao et al., 2017; Sun et al., 2019).
The western North Pacific (WNP) experiences the highest occurrence of TCs in the world (Pun et al., 2011). On average, there are 16 typhoons per year, and they mainly occur in summer and autumn (Webster et al., 2005). The upper ocean response to tropical cyclones involves many mechanisms, including near-inertial current, near-inertial internal wave, sea surface cooling, vertical mixing, and subsurface upwelling (Price, 1981). When the moving speed of typhoon is faster than the phase speed of first baroclinic mode in the upper ocean (Chang et al., 2013), near-inertial currents were induced after the passage of typhoon, meanwhile, near-inertial internal waves were generated at the base of mixed-layer, and the corresponding mechanic energy propagated downward (Nilsson, 1995; Chen et al., 2015). Enhanced vertical mixing is related to the near-inertial current in the near surface, which is relatively strong on the right-hand side of a typhoon track as a result of the resonance effect. Subsurface upwelling is induced by Ekman transport.
Meanwhile, sea surface cooling significantly affects the air-sea sensible and latent heat fluxes and therefore has a negative feedback on TC intensity (Wada et al, 2014; Cione, 2015; Wada et al., 2018). The WNP is also characterized by mesoscale eddies, which modulate SST and affect the development of a TC (Qiu, 1999). The rapid intensification of a number of typhoons has been found to be related to warm core eddies (Hong et al., 2000; Shay et al., 2000).
Studying the characteristics of a specific super typhoon is of great scientific significance and a step toward understanding super typhoons in general. This paper focuses on super typhoon Meranti, which was a widely-impact disaster to Batanes, Taiwan, and Fujian in September 2016. This study aims to describe the basic features of super typhoon Meranti, investigate the ocean response to Meranti using multi-satellite and in situ ocean measurements, and evaluate the performance of related atmospheric and ocean reanalysis. In the next section we briefly introduce the data we used, the results are shown in Section 3. In Section 4 we discuss the performance of reanalysis datasets and how the pre-existing warm eddy modulate the typhoon-induced SST cooling, and the conclusions are given in the last section.