This study aims to validate and improve the universal evaporation duct (UED) model through a further analysis of the stability function (ψ). A large number of hydrometeorological observations obtained from a tower platform near Xisha Island of the South China Sea are employed, together with the latest variations in ψ function. Applicability of different ψ functions for specific sea areas and stratification conditions is investigated based on three objective criteria. The results show that, under unstable conditions, ψ function of Fairall et al. (1996) (i.e., Fairall96, similar for abbreviations of other function names) in general offers the best performance. However, strictly speaking, this holds true only for the stability (represented by bulk Richardson number RiB) range ?2.6 ? RiB < ?0.1; when conditions become weakly unstable (?0.1 ? RiB < ?0.01), Fairall96 offers the second best performance after Hu and Zhang (1992) (HYQ92). Conversely, for near-neutral but slightly unstable conditions (?0.01 ? RiB < 0.0), the effects of Edson04, Fairall03, Grachev00, and Fairall96 are similar, with Edson04 being the best function but offering only a weak advantage. Under stable conditions, HYQ92 is the optimal and offers a pronounced advantage, followed by the newly introduced SHEBA07 (by Grachev et al., 2007) function. Accordingly, the most favorable functions, i.e., Fairall96 and HYQ92, are incorporated into the UED model to obtain an improved version of the model. With the new functions, the mean root-mean-square (rms) errors of the modified refractivity (M), 0–5-m M slope, 5–40-m M slope, and the rms errors of evaporation duct height (EDH) are reduced by 21.65%, 9.12%, 38.79%, and 59.06%, respectively, compared to the classical Naval Postgraduate School model.
To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction. 相似文献
High quality marine source rock (HQMSR) is the key prerequisite for medium to large hydrocarbon accumulations. However, the forming mechanism remains unclear. On the basis of the in-vestigation for the geodynamic setting of the Middle-Upper Yangtze during the Early Cambrian in dif-ferent spatial scales and the analysis of trace elements, the main controlling factors of the development of high quality marine source rock are discussed, with specific consideration of the burial rate of the organic matter. The ... 相似文献
