Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East...Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia.Therefore,this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model.Firstly,the predictors,which are the inputs of the model,are analysed based on three characteristics:the first is the statistical discipline of the WPSH index anomalies corresponding to the three types of typhoon paths;the second is the correspondence of distributions between sea surface temperature,850 hPa zonal wind(u),meridional wind(v),and 500 hPa potential height field;and the third is the numerical sensitivity experiment,which reflects the evident impact of variations in the physical field around the typhoon to the WPSH index.Secondly,the model is repeatedly trained through the backward propagation algorithm to predict the WPSH index using 2011–2018 atmospheric variables as the input of the training set.The model predicts the WPSH index after 6 h,24 h,48 h,and 72 h.The validation set using independent data in 2019 is utilized to illustrate the performance.Finally,the model is improved by changing the CNN2D module to the DeCNN module to enhance its ability to predict images.Taking the 2019 typhoon“Lekima”as an example,it shows the promising performance of this model to predict the 500 hPa potential height field.展开更多
Radiation fog seriously threatens traffic safety,human health,and the development of low-altitude economy in North China.However,there are still great uncertainties in the numerical forecasting of radiation fog,and tu...Radiation fog seriously threatens traffic safety,human health,and the development of low-altitude economy in North China.However,there are still great uncertainties in the numerical forecasting of radiation fog,and turbulence parameterization is one of the key uncertainty sources.In this study,the spatiotemporal variations of turbulent diffusion were analyzed by using the 5-level tower turbulence observations in Tianjin during four radiation fog episodes from 2016 to 2019.Based on the observation analysis,an improvement to the minimum eddy diffusivity of heat(K_(hmin))in the Yonsei University(YSU)scheme was implemented in the Weather Research and Forecasting(WRF)model.K_(hmin) is the minimum threshold of the heat eddy diffusion coefficient(K_(h))defined in the model(0.01 m^(2) s^(-1) in YSU scheme)to avoid the physically unrealistic situations where the model calculates zero turbulent atmosphere under strong stability conditions.However,observations in this study revealed that the 10th percentile of K_(h) at night was mostly lower than 0.01 m^(2) s^(-1),indicating that the model may overestimate the nighttime turbulent diffusion during the fog episodes.Sensitivity experiments demonstrated that changes in K_(hmin) significantly altered the simulation of no cturnal boundary layer(NBL)structure,while exerting a rather ne gligible impact during the daytime.Reducing K_(hmin)resulted in a lower surface temperature and a stronger inversion,thereby facilitating the formation of radiation fog.When K_(hmin) was reduced from 0.01 to 0.0001 m^(2) s^(-1),the nighttime Threat Score(TS)and Probability of Detection(POD)for fog forecasting increased by 0.029 and 0.053,respectively.Conversely,increasing K_(hmin) led to a weaker inversion and the dissipation of fog.This study highlights the importance of K_(hmin) in the planetary boundary layer(PBL)scheme for simulating fog,and also provides a novel perspective for improving fog forecasting.Specifically,a smaller K_(hmin) value may be more appropriate for simulating radiation fog.展开更多
The spring persistent rains （SPR） over southeastern China （SEC） is a synoptic and climatic phenomenon that is unique in East Asia. Sufficient evidence proves that it results from the mechanical and thermal effects...The spring persistent rains （SPR） over southeastern China （SEC） is a synoptic and climatic phenomenon that is unique in East Asia. Sufficient evidence proves that it results from the mechanical and thermal effects of the giant Tibetan Plateau （TP）, but its temporal span and spatial distribution are not clear at present. A climatological analysis of the NCEP/NCAR circulation and sensible heat data shows that at the 13th pentad of the solar year （lst pentad of March） there are remarkable increases in the sensible heating over the main and southeastern part of the TP, the southwesterly velocity over the southeastern flank of the TP and SEC, and rainfall over SEC, indicating the onset of the SPR. However, after the 27th pentad of the solar year （3rd pentad of May）, these variables, except for the sensible heating over the main part of the TP, decrease rapidly. The ridge line of the subtropical high in the mid-low troposphere over the South China Sea （SCS） slopes northward to the SCS and the SCS monsoon instead of southward as before breaks out, indicating the end The rain belt center over SEC shifts of the SPR. Hence, it is reasonable to define the SPR temporal span from the 13th to 27th pentad of the solar year. Data analysis and numerical sensitivity experiments show that, although the warm and cold airs converge at about 30°N in the SPR period, the distribution and intensity of the SPR rain belt are obviously influenced by the topography of the Nanling and Wuyi Mountains （NWM）. The mountains can block and lift cold and warm airs, strengthening frontogenesis and rainfall. As a result, the axis of the SPR rain belt is superposed over that of the mountain range. Accordingly, the spatial distribution of the SPR extends over most of the SEC, more specifically, to the south of the middle and lower reaches of the Yangtze River （30°N）, and to the east of 110°E.展开更多
文摘Seasonal location and intensity changes in the western Pacific subtropical high(WPSH)are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia.Therefore,this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model.Firstly,the predictors,which are the inputs of the model,are analysed based on three characteristics:the first is the statistical discipline of the WPSH index anomalies corresponding to the three types of typhoon paths;the second is the correspondence of distributions between sea surface temperature,850 hPa zonal wind(u),meridional wind(v),and 500 hPa potential height field;and the third is the numerical sensitivity experiment,which reflects the evident impact of variations in the physical field around the typhoon to the WPSH index.Secondly,the model is repeatedly trained through the backward propagation algorithm to predict the WPSH index using 2011–2018 atmospheric variables as the input of the training set.The model predicts the WPSH index after 6 h,24 h,48 h,and 72 h.The validation set using independent data in 2019 is utilized to illustrate the performance.Finally,the model is improved by changing the CNN2D module to the DeCNN module to enhance its ability to predict images.Taking the 2019 typhoon“Lekima”as an example,it shows the promising performance of this model to predict the 500 hPa potential height field.
基金Supported by the National Natural Science Foundation of China(42205092,42205009,and 42105084)Applied Foundational Research Project of Tianjin(22JCQNJC00370)+3 种基金Scientific Research Project of Tianjin Meteorological Bureau(202309ybxm04 and 202306ybxm02)Open Project of Tianjin Key Laboratory of Oceanic Meteorology(2024TKLOM04)Nanjing Institute of Meteorological Science and Technology Innovation Arctic Pavilion Open Research Fund(BJG202404)Science and Technology Collaborative Innovation Fund of Bohai Rim Region(QYXM202112 and QYXM202202)。
文摘Radiation fog seriously threatens traffic safety,human health,and the development of low-altitude economy in North China.However,there are still great uncertainties in the numerical forecasting of radiation fog,and turbulence parameterization is one of the key uncertainty sources.In this study,the spatiotemporal variations of turbulent diffusion were analyzed by using the 5-level tower turbulence observations in Tianjin during four radiation fog episodes from 2016 to 2019.Based on the observation analysis,an improvement to the minimum eddy diffusivity of heat(K_(hmin))in the Yonsei University(YSU)scheme was implemented in the Weather Research and Forecasting(WRF)model.K_(hmin) is the minimum threshold of the heat eddy diffusion coefficient(K_(h))defined in the model(0.01 m^(2) s^(-1) in YSU scheme)to avoid the physically unrealistic situations where the model calculates zero turbulent atmosphere under strong stability conditions.However,observations in this study revealed that the 10th percentile of K_(h) at night was mostly lower than 0.01 m^(2) s^(-1),indicating that the model may overestimate the nighttime turbulent diffusion during the fog episodes.Sensitivity experiments demonstrated that changes in K_(hmin) significantly altered the simulation of no cturnal boundary layer(NBL)structure,while exerting a rather ne gligible impact during the daytime.Reducing K_(hmin)resulted in a lower surface temperature and a stronger inversion,thereby facilitating the formation of radiation fog.When K_(hmin) was reduced from 0.01 to 0.0001 m^(2) s^(-1),the nighttime Threat Score(TS)and Probability of Detection(POD)for fog forecasting increased by 0.029 and 0.053,respectively.Conversely,increasing K_(hmin) led to a weaker inversion and the dissipation of fog.This study highlights the importance of K_(hmin) in the planetary boundary layer(PBL)scheme for simulating fog,and also provides a novel perspective for improving fog forecasting.Specifically,a smaller K_(hmin) value may be more appropriate for simulating radiation fog.
基金Supported by the National "973" program under Grant No.2006CB403600the National Natural Science Foundation of China under Grant Nos.40475027,40220503,and 40523001
文摘The spring persistent rains （SPR） over southeastern China （SEC） is a synoptic and climatic phenomenon that is unique in East Asia. Sufficient evidence proves that it results from the mechanical and thermal effects of the giant Tibetan Plateau （TP）, but its temporal span and spatial distribution are not clear at present. A climatological analysis of the NCEP/NCAR circulation and sensible heat data shows that at the 13th pentad of the solar year （lst pentad of March） there are remarkable increases in the sensible heating over the main and southeastern part of the TP, the southwesterly velocity over the southeastern flank of the TP and SEC, and rainfall over SEC, indicating the onset of the SPR. However, after the 27th pentad of the solar year （3rd pentad of May）, these variables, except for the sensible heating over the main part of the TP, decrease rapidly. The ridge line of the subtropical high in the mid-low troposphere over the South China Sea （SCS） slopes northward to the SCS and the SCS monsoon instead of southward as before breaks out, indicating the end The rain belt center over SEC shifts of the SPR. Hence, it is reasonable to define the SPR temporal span from the 13th to 27th pentad of the solar year. Data analysis and numerical sensitivity experiments show that, although the warm and cold airs converge at about 30°N in the SPR period, the distribution and intensity of the SPR rain belt are obviously influenced by the topography of the Nanling and Wuyi Mountains （NWM）. The mountains can block and lift cold and warm airs, strengthening frontogenesis and rainfall. As a result, the axis of the SPR rain belt is superposed over that of the mountain range. Accordingly, the spatial distribution of the SPR extends over most of the SEC, more specifically, to the south of the middle and lower reaches of the Yangtze River （30°N）, and to the east of 110°E.
