季节分型下一种面向风电功率日前预测的深度自适应滤波框架

深入研究数值天气预报风速的波动性对提高风电功率日前预测精度具有重要意义。首先提出一种深度自适应滤波框架,对于数值天气预报风速,采用引入相对熵的变分模态分解算法,经分解产生多个模态分量后,基于非局部均值去噪算法对其中的噪声分量滤波,随后将其与有效分量重构得到去噪后的序列;在此基础上按季节分型,将去噪后的数值天气预报风速序列作为输入,在备选模型库中由验证集选用该季节最适合的风速-功率转化模型,并对测试集进行风电功率预测。选用中国东北某风电场进行算例分析,相较于其他分解算法,所提方法在不同季节的预测准确率可提升0.25%~1.58%,即季节分型下的深度自适应滤波框架可有效提高风电功率预测精度。

分县逐日滚动MOS预报模式研制及其应用

本文介绍了江苏分县逐日滚动 1～ 5天时效的 MOS预报的研制 ,包括降水的概率 MOS法和量级 MOS法 ,预报量设置、季节分型和样本选择 ,因子的构造及其加工 ,概率统计模型的选择及其因子预选、筛选。最后介绍了 MOS工具在省台

Modeling Seasonal Variations of Subsurface Chlorophyll Maximum in South China Sea

In the South China Sea(SCS), the subsurface chlorophyll maximum(SCM) is frequently observed while the mechanisms of SCM occurrence have not been well understood. In this study, a 1-D physical-biochemical coupled model was used to study the seasonal variations of vertical profiles of chlorophyll-a(Chl-a) in the SCS. Three parameters(i.e., SCM layer(SCML) depth, thickness, and intensity) were defined to characterize the vertical distribution of Chl-a in SCML and were obtained by fitting the vertical profile of Chl-a in the subsurface layer using a Gaussian function. The seasonal variations of SCMs are reproduced reasonably well compared to the observations. The annual averages of SCML depth, thickness, and intensity are 75 ± 10 m, 31 ± 6.7 m, and 0.37 ± 0.11 mg m-3, respectively. A thick, close to surface SCML together with a higher intensity occurs during the northeastern monsoon. Both the SCML thickness and intensity are sensitive to the changes of surface wind speed in winter and summer, but the surface wind speed exerts a minor influence on the SCML depth; for example, double strengthening of the southwestern monsoon in summer can lead to the thickening of SCML by 46%, the intensity decreasing by 30%, and the shoaling by 6%. This is because part of nutrients are pumped from the upper nutricline to the surface mixed layer by strong vertical mixing. Increasing initial nutrient concentrations by two times will increase the intensity of SCML by over 80% in winter and spring. The sensitivity analysis indicates that light attenuation is critical to the three parameters of SCM. Decreasing background light attenuation by 20% extends the euphotic zone, makes SCML deeper(~20%) and thicker(12% – 41%), and increases the intensity by over 16%. Overall, the depth of SCML is mainly controlled by light attenuation, and the SCML thickness and intensity are closely associated with wind and initial nitrate concentration in the SCS.

Impacts of Vegetation on the Intraseasonal Oscillation Simulated by the Community Atmosphere Model (CAM3)

The influences of vegetation on intraseasonal oscillation （ISO） were examined using the Community Atmosphere Model version 3 （CAM3）. Two 15-year numerical experiments were completed： the first was performed with a realistic vegetation distribution （VEG run）, and the second was identical to the VEG run except without land vegetation （NOVEG run）. Generally speak- ing, CAM3 was able to reproduce the spatial distribution of the ISO, but the ISO intensity in the simulation was much weaker than that observed in nature： the 1SO has a relatively much stronger signal. A comparison of the VEG run with the NOVEG run revealed that the presence of vegetation usually produces a weak ISO. The vegetation effects on ISO intensity were significant over West Africa and South Asia, especially in the summer half-year. Vegetation also plays an important role in modulating ISO propagation. The eastward propagation of the ISO in the VEG run was clearer than that in the NOVEG run over the West African and Maritime Continent regions. The northward propagation of the ISO in the VEG run was more consistent with observation than that in the NOVEG run.

Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast

Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature(SST) over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial.