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Lingjing Zhu, Jiming Jin, and Yimin Liu

Abstract

In this study, we investigated the effects of lakes in the Tibetan Plateau (TP) on diurnal variations of local climate and their seasonal changes by using the Weather Research and Forecasting (WRF) Model coupled with a one-dimensional physically based lake model. We conducted WRF simulations for the TP over 2000–10, and the model showed excellent performance in simulating near-surface air temperature, precipitation, lake surface temperature, and lake-region precipitation when compared to observations. We carried out additional WRF simulations where all the TP lakes were replaced with the nearest land-use types. The differences between these two sets of simulations were analyzed to quantify the effects of the TP lakes on the local climate. Our results indicate that the strongest lake-induced cooling occurred during the spring daytime, while the most significant warming occurred during the fall nighttime. The cooling and warming effects of the lakes further inhibited precipitation during summer afternoons and evenings and motivated it during fall early mornings, respectively. This study lays a solid foundation for further exploration of the role of TP lakes in climate systems at different time scales.

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Yao Ha, Zhong Zhong, Yimin Zhu, and Yijia Hu

Abstract

The contribution of barotropic energy conversion to tropical cyclone (TC) activity over the western North Pacific (WNP) during warm and cold phases of El Niño–Southern Oscillation (ENSO) is investigated by separating TC vortices from reanalysis data and using a linearized eddy kinetic energy tendency equation. By comparing the characteristics of TC disturbances with synoptic-scale disturbances, it is found that the modulation of ENSO on the WNP TC intensity is presented more objectively by using TC kinetic energy (EKETC) than eddy kinetic energy (EKE). Barotropic energy conversion (KmKe) into TC disturbances (KmKeTC) is an effective indicator in detecting the barotropic energy source of low-level cyclone genesis and maintenance during the ENSO cycle. However, its dynamical processes play different roles. Shear in large-scale zonal wind and convergence in large-scale meridional wind provide direct barotropic energy source for TC genesis, but make effects in different regions of the WNP. In contrast, convergence in large-scale zonal and shear in large-scale meridional wind exert little influence on TC genesis during ENSO.

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Yijia Hu, Yimin Zhu, Zhong Zhong, and Yao Ha

Abstract

The prediction of mei-yu onset date (MOD) in the middle and lower reaches of the Yangtze River valley (MLYRV) is an important and challenging task for those making seasonal climate predictions in China. In this paper, the atmospheric and oceanic conditions in the preceding winter and spring related to MOD are analyzed. It is found that the MOD is associated with the intensity of the Ural high and the East Asian trough in high latitudes, with the intensity of the upper-level westerly jet in middle latitudes, and with the contrast of land–sea temperature and pressure in the preceding winter and spring, which are proxies for the intensity of the East Asian winter monsoon (EAWM). It is suggested that the intensity of the EAWM is the most crucial factor affecting the MOD. Years with an early MOD usually correspond to strong EAWMs in the preceding winter, and vice versa. The EAWM can affect the MOD by influencing the East Asian summer monsoon (EASM) through tropical ocean–atmosphere and tropical–extratropical interactions. Based on the above analysis, a physics-based statistical forecast model is established using multivariable linear regression techniques. The hindcast of MOD during the 13 yr from 1998 to 2010 is carried out to evaluate the performance of this forecast model. The MOD can be predicted successfully in 8 out of the 13 yr. The forecast model predicts the MOD in the years with strong mei-yu intensity more accurately than in those with weak mei-yu intensity, especially for cases of extreme flooding. This is useful in the prevention of flooding disasters.

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Xiaoying Chen, Aiguo Song, Jianqing Li, Yimin Zhu, Xuejin Sun, and Hong Zeng

Abstract

It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

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