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Qin Xu
,
Li Wei
,
Yi Jin
,
Qingyun Zhao
, and
Jie Cao

Abstract

This paper proposes a new method to properly define and accurately determine the vortex center of a model-predicted tropical cyclone (TC) from a dynamic perspective. Ideally, a dynamically determined TC vortex center should maximize the gradient wind balance or, equivalently, minimize the gradient wind imbalance measured by an energy norm over the TC vortex. In practice, however, such an energy norm cannot be used to easily and unambiguously determine the TC vortex center. An alternative yet practical approach is developed to dynamically and unambiguously define the TC vortex center. In this approach, the TC vortex core of near-solid-body rotation is modeled by a simple parametric vortex constrained by the gradient wind balance. Therefore, the modeled vortex can fit simultaneously the perturbation pressure and streamfunction of the TC vortex part (extracted from the model-predicted fields) over the TC vortex core area (within the radius of maximum tangential wind), while the misfit is measured by a properly defined cost function. Minimizing this cost function yields the desired dynamic optimality condition that can uniquely define the TC vortex center. Using this dynamic optimality condition, a new method is developed in the form of iterative least squares fit to accurately determine the TC vortex center. The new method is shown to be efficient and effective for finding the TC vortex center that accurately satisfies the dynamic optimality condition.

Full access
Wei Sun
,
Zhiquan Liu
,
Guiting Song
,
Yangyang Zhao
,
Shan Guo
,
Feifei Shen
, and
Xiangming Sun

Abstract

To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).

Open access
Gong Shang
,
Zhiwei Zhang
,
Shoude Guan
,
Xiaodong Huang
,
Chun Zhou
,
Wei Zhao
, and
Jiwei Tian

Abstract

Diapycnal mixing in the South China Sea (SCS) is commonly attributed to the vertical shear variance (S2) of horizontal ocean current velocity, but the seasonal modulation of the S2 is still poorly understood due to the scarcity of long-term velocity observations. Here, this issue is explored in detail based on nearly 10-year-long ADCP velocity data from a mooring in the northern SCS. We find that the S2 in the northern SCS exhibits significant seasonal variations at both the near-surface (90–180 m) and sub-surface (180–400 m) layers, but their seasonal cycles and modulation mechanisms are quite different. For the near-surface layer, the S2 is stronger in late summer, autumn, and winter but weaker in spring and early summer, while in the sub-surface layer, it is much stronger in winter than other seasons. Further analysis suggests that in the near-surface layer, the stronger S2 in autumn and winter is primarily caused by typhoons-induced near-inertial internal waves (NIWs) and the large sub-inertial velocity shear of the baroclinic mesoscale eddies, respectively. With respect to the sub-surface layer, the enhanced wintertime S2 is primarily associated with the “inertial chimney” effect of anticyclonic eddies, trapping wind-forced downward-propagating NIWs and significantly increasing the near-inertial shear at the critical layer. The findings in this study highlight the potentially important roles of mesoscale eddies and NIWs in modulating the seasonality of upper-ocean mixing in the northern SCS. This modulation is attributed not only to the strong shear of these features but also to their interactions.

Restricted access
Guangzhen Jin
,
Haidong Pan
,
Qilin Zhang
,
Xianqing Lv
,
Wei Zhao
, and
Yuan Gao

Abstract

As an effective tool to distinguish different tidal components, classical tidal current harmonic analysis has been widely used to obtain harmonic parameters of internal tidal currents. However, harmonic parameters cannot exactly reveal the motion of internal tides, as the irregular temporal variations for internal tides are significant in many regions of the world’s oceans. An enhanced harmonic analysis (EHA) algorithm based on the independent point scheme and cubic spline interpolation is presented in this paper to obtain harmonic parameters with temporal variations for different tidal constituents of internal tides. Moreover, this algorithm is applied to analyze 14 months of current data obtained from a mooring located on the continental shelf in the northeastern region of the South China Sea. The obvious irregular temporal variations for the four principal constituents—M2, K1, S2, and O1—of internal tides in this region are indicated. It is hoped that this algorithm might present a brand-new view for researchers to investigate the irregular temporal motions of internal tides.

Full access
Hui Sun
,
Wei Zhao
,
Qingxuan Yang
,
Shuqun Cai
,
Xinfeng Liang
, and
Jiwei Tian

Abstract

Internal waves can transfer energy from large-scale to microscale processes; however, the spectra of these waves remain poorly known. A method that combines modal harmonic decomposition and maximum-likelihood method is proposed in this study to estimate four-dimensional internal wave spectrum using limited mooring observations. Using this method, a four-dimensional internal wave spectrum was obtained for the first time based on the mooring measurements collected during the South China Sea (SCS) Internal Wave Experiment in July 2014. The spectrum was then validated by comparing with the spectrum based on Fourier analysis and with the modified Garrett–Munk internal wave spectrum, respectively. The power of the internal wave spectrum decreased obviously with increasing frequency and wavenumber, with a falloff rate of ω −2 beyond tidal frequencies, and with falloff rates of k h 2 and k z 2.5 for horizontal and vertical wavenumbers, respectively. In addition, at a fixed frequency and vertical wavenumber, the propagation direction and phase speed of internal waves can be obtained through the four-dimensional spectrum. In summary, we verified the feasibility of estimating four-dimensional internal wave spectrum using limited mooring observations in this study, and the method we proposed should be applicable to other regions where such mooring observations are available.

Full access
Yuxin Zhao
,
Dequan Yang
,
Wei Li
,
Chang Liu
,
Xiong Deng
,
Rixu Hao
, and
Zhongjie He

Abstract

A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.

Full access
Shuyi S. Chen
,
Wei Zhao
,
Mark A. Donelan
, and
Hendrik L. Tolman

Abstract

The extreme high winds, intense rainfall, large ocean waves, and copious sea spray in hurricanes push the surface-exchange parameters for temperature, water vapor, and momentum into untested regimes. The Coupled Boundary Layer Air–Sea Transfer (CBLAST)-Hurricane program is aimed at developing improved coupling parameterizations (using the observations collected during the CBLAST-Hurricane field program) for the next-generation hurricane research prediction models. Hurricane-induced surface waves that determine the surface stress are highly asymmetric, which can affect storm structure and intensity significantly. Much of the stress is supported by waves in the wavelength range of 0.1–10 m, which is the unresolved “spectral tail” in present wave models. A directional wind–wave coupling method is developed to include effects of directionality of the wind and waves in hurricanes. The surface stress vector is calculated using the two-dimensional wave spectra from a wave model with an added short-wave spectral tail. The wind and waves are coupled in a vector form rather than through the traditional roughness scalar. This new wind–wave coupling parameterization has been implemented in a fully coupled atmosphere–wave–ocean model with 1.67-km grid resolution in the atmospheric model, which can resolve finescale features in the extreme high-wind region of the hurricane eyewall. It has been tested in a number of storms including Hurricane Frances (2004), which is one of the best-observed storms during the CBLAST-Hurricane 2004 field program. This paper describes the new wind–wave coupling parameterization and examines the characteristics of the coupled model simulations of Hurricane Frances (2004). Observations of surface waves and winds are used to evaluate the coupled model results.

Full access
Chao Wang
,
Liguang Wu
,
Jun Lu
,
Qingyuan Liu
,
Haikun Zhao
,
Wei Tian
, and
Jian Cao

Abstract

Understanding variations in tropical cyclone (TC) translation speed (TCS) is of great importance for islands and coastal regions since it is an important factor in determining TC-induced local damages. Investigating the long-term change in TCS was usually subject to substantial limitations in the quality of historical TC records, but here we investigated the interannual variability in TCS over the western North Pacific (WNP) Ocean by using reliable satellite TC records. It was found that both temporal changes in large-scale steering flow and TC track greatly contributed to interannual variability in the WNP TCS. In the peak season (July–September), TCS changes were closely related to temporal variations in large-scale steering flow, which was linked to the intensity of the western North Pacific subtropical high. However, for the late season (October–December), changes in TC track played a vital role in interannual variability in TCS while the impacts of temporal variations in large-scale steering were weak. The changes in TC track were mainly contributed by the El Niño–Southern Oscillation (ENSO)-induced zonal migrations in TC genesis locations, which make more or fewer TCs move to the subtropical WNP, thus leading to notable changes in the basinwide TCS because of the much greater large-scale steering in the subtropical WNP. The increased influence of TC track change on TCS in the late season was linked to the greater contrast between the subtropical and the tropical large-scale steering in the late season. These results have important implications for understanding current and future variations in TCS.

Free access
Wei Zhao
,
Zhongmin Hu
,
Qun Guo
,
Genan Wu
,
Ruru Chen
, and
Shenggong Li

Abstract

Understanding the atmosphere–land surface interaction is crucial for clarifying the responses and feedbacks of terrestrial ecosystems to climate change. However, quantifying the effects of multiple climatic factors to vegetation activities is challenging. Using the geographical detector model (GDM), this study quantifies the relative contributions of climatic factors including precipitation, relative humidity, solar radiation, and air temperature to the interannual variation (IAV) of the normalized difference vegetation index (NDVI) in the northern grasslands of China during 2000 to 2016. The results show heterogeneous spatial patterns of determinant climatic factors on the IAV of NDVI. Precipitation and relative humidity jointly controlled the IAV of NDVI, illustrating more explanatory power than solar radiation and air temperature, and accounting for higher proportion of area as the determinant factor in the study region. It is noteworthy that relative humidity, a proxy of atmospheric aridity, is as important as precipitation for the IAV of NDVI. The contribution of climatic factors to the IAV of NDVI varied by vegetation type. Owing to the stronger explanatory power of climatic factors on NDVI variability in temperate grasslands, we conclude that climate variability may exert more influence on temperate grasslands than on alpine grasslands. Our study highlights the importance of the role of atmospheric aridity to vegetation activities in grasslands. We suggest focusing more on the differences between vegetation types when addressing the climate–vegetation relationships at a regional scale.

Free access
Meilin Zhu
,
Lonnie G. Thompson
,
Huabiao Zhao
,
Tandong Yao
,
Wei Yang
, and
Shengqiang Jin

Abstract

Glacier changes on the Tibetan Plateau (TP) have been spatially heterogeneous in recent decades. The understanding of glacier mass changes in western Tibet, a transitional area between the monsoon-dominated region and the westerlies-dominated region, is still incomplete. For this study, we used an energy–mass balance model to reconstruct annual mass balances from October 1967 to September 2019 to explore the effects of local climate and large-scale atmospheric circulation on glacier mass changes in western Tibet. The results showed that Xiao Anglong Glacier is close to a balanced condition, with an average value of −53 ± 185 mm water equivalent (w.e.) yr−1 for 1968–2019. The interannual mass balance variability during 1968–2019 was primary driven by ablation-season precipitation, which determined changes in the snow accumulation and strongly influenced melt processes. The interannual mass balance variability during 1968–2019 was less affected by ablation-season air temperature, which only weakly affected snowfall and melt energy. Further analysis suggests that the southward (or northward) shift of the westerlies caused low (or high) ablation-season precipitation, and therefore low (or high) annual mass balance for glaciers in western Tibet. In addition, the average mass balance for Xiao Anglong Glacier was 83 ± 185, −210 ± 185, and −10 ± 185 mm w.e. yr−1 for 1968–90, 1991–2012, and 2013–19, respectively. These mass changes were associated with the variations in precipitation and air temperature during the ablation season on interdecadal time scales.

Full access