Ningbo Meteorological Observatory, Ningbo, Zhejiang, China
The background fields for meso-scale regional models are often obtained from the analysis of large-scale or global assimilation systems. Because of the coarse resolution in such large-scale systems, the TC vortex in the initial analysis is often too weak to simulate severe TC and to represent the process of TC rapid intensification. The commonly used bougs vortex schemes in global models are usually start with a mature symmetric vortex, which is unsuitable for the asymmetric features of TC under the condition of significant vertical wind shear. A method of vortex initialization constructed by model integration, which is very similar to the GFDL scheme, has already been utilized in meso-scale model experiments (Liu et al., 1997). In this method, the TC center is searched first, and then the humidity profile around the TC vortex is adjusted (usually enhanced symmetrically). Employing a bogus vortex from the numerical analysis or forecast data, the GFDL vortex relocation scheme constructs the model’s initial vortex by integrating the numerical model with the relocated TC vortex. In this way, the bogus vortex interacts with the background field under the dynamical and thermodynamic constraints of a numerical model, which ultimately possesses asymmetric features consistent to the real TC vortex through the forcing of large-scale circulation. Most importantly, with the use of humidity forcing, the TC intensity and radius of maximum wind speed (RMW) were improved extensively (Liu et al., 1997).
A recent study by Wang (2009) demonstrated the importance of humidity and latent heat release in the outer spiral rainbands in simulating the size and intensity of TC. The humidity distribution around TC vortex could adjust the downdraft intensity and SLP gradient by latent heating and cooling, and finally control the TC inner-core’s size and intensity (Wang, 2009). In association with the humidity, the wind-induced surface heat exchange (WISHE; Emanuel, 1986; Rotunno and Emanuel, 1987) mechanism plays a critical role in the rapid intensification of TC. Particularly, as the vortical hot towers (VHTs) associated with TC deep convection play an important role in TC rapid intensification (Hendricks et al., 2004), it is critical to represent the deep convection and its moisture structure correctly for successful TC intensity prediction.
In the present study, a physical initialization scheme is proposed to improve TC simulation by enhancing the TC humidity profile in deep convection region, through humidity nudging and the use of Fengyun 2 (FY2) cloud-top brightness temperature data.
The nudging scheme developed by Orlandi et al. (2010) is implemented here for the simulation of TC. It is hypothesized that the middle- and low-level air masses within the TC deep-convection regions are nearly saturated during the nudging procedure. The nudging scheme compares brightness temperature evaluated by the Community Radiative Transfer Model (CRTM) in the WRF model and that observed from FY2 satellite in high temporal (1 h) and high spatial (0.1°×0.1°) resolutions so that it can detect the activity of convection in the WRF model. The nudging amplitude of the specific humidity profile within the TC deep-convection regions is then tuned. The humidity profile for non-deep-convection areas can be modified through the horizontal advection of the model. Through the continuous humidity nudging, the humidity profile in the initial condition can be modified. Most importantly, the numerical simulation of TC deep convection is expected to be improved, and so does the prediction skill of TC intensity. The deep-convection areas are determined by a criterion of the CTBT smaller than -43 ℃, which is the same as that used by Davolio and Buzzi (2004) and Hendon and Wood-berry (1993). Within these deep-convection areas, the CTBT from the FY2 infrared Channel 1 at 10.3-11.3 μm is assimilated through the nudging scheme to modify the mixing ratio at each model level k and each grid point. The nudging scheme compares the CTBT evaluated by CRTM with that observed by satellite, and then according to the development of convection, the model specific humidity profile is modified appropriately through the following procedure.
where is the saturation mixing ratio, which is evaluated with respect to liquid water above 0 ℃, while below 0 ℃, a mixed-phase cloud is considered. is a weighting function used in determining the vertical profile modified by the nudging procedure. It is set to 1 bellow 400 hPa and to 0 from 400 hPa up to the top of the model. Parameter is the nudging relaxation time, and the common actions of and the nudging period determine the intensity of nudging. Based on many test experiments, the values selected in the present paper is 1 h for and 30 min for . The intensity of the moisture profile modification depends on the parameter . In the control simulation (Exp CTL), if the CTBT from the CRTM is higher than the satellite observation, that means the convection in the model is less intense is set to 1 and the humidity profile is modified toward a saturation state. In the opposite situation, the model has produced unrealistic convection already, so is set to 0.25 to enhance the humidity profile with a weaker intensity. The comparison between the CTBT from the CRTM and that from the satellite observation is performed every 30 min while the satellite data is updated once per hour. The nudging is carried out iteratively until the SLP difference between the bogus vortex and that of the observation at T=0 becomes less than 5 hPa (fig. 1).
After 24 h of humidity nudging, the TC bogus vortex bred by the WRF model approaches the observed TC vortex. Firstly, the three-dimensional (3D) data of the bogus vortex (u, v, h, t, slp) is picked up within the range of TC vortex circulation defined by the maximum radius of gale-force wind. Then, the bogus vortex is embedded into the background fields and relocated according to the observed position of TC at T= 0.