The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, having a significant impact on medium-range weather forecasts and climate integrations. To pursue this further, a moderately stratified Arctic case is simulated by nineteen single-column turbulence schemes. Statistics from a large-eddy simulation intercomparison made for the same case by eleven different models are used as a guiding reference. The single-column parameterizations include research and operational schemes from major forecast and climate research centres. Results from first-order schemes, a large number of turbulence kinetic energy closures, and other models were used. There is a large spread in the results; in general, the operational schemes mix over a deeper layer than the research schemes, and the turbulence kinetic energy and other higher-order closures give results closer to the statistics obtained from the large-eddy simulations. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored. 相似文献
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.
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. 相似文献
The constitutive theory on the viscoelastoplasticity and damage of frozen soil is based on the continuous mechanics and thermodynamics. The basic principles of the theory, dissipation potential function and damage model are presented. The constitutive theory explains the mechanical properties of frozen soils under complicated stresses, especially under high confining pressures which make frozen soil harden and soften. The agreement between the calculated results by the constitutive theory and the experimental results of triaxial creep of frozen soil is seen to be very good. 相似文献