全球可再生能源资源的总储量及其时空分布对于其开发极为重要,目前一般使用气候模式进行全球可再生能源资源评估。然而,精细化的全球可再生能源资源评估面临计算量大、计算资源有限带来的计算时间过长的问题。文章提出了一种基于云计算...全球可再生能源资源的总储量及其时空分布对于其开发极为重要,目前一般使用气候模式进行全球可再生能源资源评估。然而,精细化的全球可再生能源资源评估面临计算量大、计算资源有限带来的计算时间过长的问题。文章提出了一种基于云计算的全球可再生能源资源评估方法,通过远程交互并行网格嵌套计算、作业优化和控制方案,在云中心和云平台的子节点间合理地分配了WRF(Weather Research and Forecast)气候模式的计算任务,并实现了节点间计算文件的高效传输和管理,提高了可再生能源资源评估的计算效率,显著缩短了计算时间,有利于全球能源互联网的开发布局研究。展开更多
精准的风速预报是风电功率预测的基础,风场的预报评估是提升风电功率预测水平的有效途径之一。基于中尺度数值天气预报模式(weather research and forecast,WRF),采用风险评分、相关系数及均方根误差等定量分析指标,结合西北电网数值预...精准的风速预报是风电功率预测的基础,风场的预报评估是提升风电功率预测水平的有效途径之一。基于中尺度数值天气预报模式(weather research and forecast,WRF),采用风险评分、相关系数及均方根误差等定量分析指标,结合西北电网数值预报结果,开展6种大气边界层参数化方案的适用性研究。结果表明:YSU(Yonsei University)方案预报的地面纬向风平均相关系数最高为0.87、均方根误差最低仅1.0 m/s,且地面经向风、850 hPa和500 hPa高度场、以及500 hPa风速预报效果均最优,是提升西北电网风场预报的最优边界层方案。本研究为西北电网风场(以及其他气象要素)预报效果的提升指明了方向。展开更多
The daily FY2 E Sea Surface Temperature(SST) data from China National Satellite Meteorological Center(NSMC) was evaluated and compared with the Optimum Interpolation Sea Surface Temperature(OISST) data from US Nationa...The daily FY2 E Sea Surface Temperature(SST) data from China National Satellite Meteorological Center(NSMC) was evaluated and compared with the Optimum Interpolation Sea Surface Temperature(OISST) data from US National Oceanic and Atmospheric Administration(NOAA) over Northwest Pacific Ocean(NPO) in this study. The results show that the distribution of FY2 E SST is close to OISST in tropical region over NPO, especially in typhoon active season, but the value of FY2 E SST is a little lower than that of OISST in tropical ocean, with the absolute deviation 1℃ lower and the relative deviation about 6% lower. The correlation coefficient between monthly FY2 E SST and monthly OISST is as high as 0.7, which passes the t-test at a significance level of 0.01. Based on the evaluation result, the merged SST_(FY)over NPO is calculated using a weighting function. Besides, Tropical Cyclone Heat Potential(TCHP_(FY)) is calculated and combined with the simulated sea temperature profile. From three years operational tests in NSMC, the merged SST_(FY)and TCHP_(FY)are shown to be good indexes in monitoring and predicting the intensity of tropical cyclones(TCs) over NPO.展开更多
文摘全球可再生能源资源的总储量及其时空分布对于其开发极为重要,目前一般使用气候模式进行全球可再生能源资源评估。然而,精细化的全球可再生能源资源评估面临计算量大、计算资源有限带来的计算时间过长的问题。文章提出了一种基于云计算的全球可再生能源资源评估方法,通过远程交互并行网格嵌套计算、作业优化和控制方案,在云中心和云平台的子节点间合理地分配了WRF(Weather Research and Forecast)气候模式的计算任务,并实现了节点间计算文件的高效传输和管理,提高了可再生能源资源评估的计算效率,显著缩短了计算时间,有利于全球能源互联网的开发布局研究。
文摘精准的风速预报是风电功率预测的基础,风场的预报评估是提升风电功率预测水平的有效途径之一。基于中尺度数值天气预报模式(weather research and forecast,WRF),采用风险评分、相关系数及均方根误差等定量分析指标,结合西北电网数值预报结果,开展6种大气边界层参数化方案的适用性研究。结果表明:YSU(Yonsei University)方案预报的地面纬向风平均相关系数最高为0.87、均方根误差最低仅1.0 m/s,且地面经向风、850 hPa和500 hPa高度场、以及500 hPa风速预报效果均最优,是提升西北电网风场预报的最优边界层方案。本研究为西北电网风场(以及其他气象要素)预报效果的提升指明了方向。
基金Science and Technology Foundation of State Grid Corporation of ChinaNational Natural Science Founda tion of China(41575045,41205030,41175046)Basic Research Fund of the Chinese Academy of Meteorological Sciences(2017Z013)
文摘The daily FY2 E Sea Surface Temperature(SST) data from China National Satellite Meteorological Center(NSMC) was evaluated and compared with the Optimum Interpolation Sea Surface Temperature(OISST) data from US National Oceanic and Atmospheric Administration(NOAA) over Northwest Pacific Ocean(NPO) in this study. The results show that the distribution of FY2 E SST is close to OISST in tropical region over NPO, especially in typhoon active season, but the value of FY2 E SST is a little lower than that of OISST in tropical ocean, with the absolute deviation 1℃ lower and the relative deviation about 6% lower. The correlation coefficient between monthly FY2 E SST and monthly OISST is as high as 0.7, which passes the t-test at a significance level of 0.01. Based on the evaluation result, the merged SST_(FY)over NPO is calculated using a weighting function. Besides, Tropical Cyclone Heat Potential(TCHP_(FY)) is calculated and combined with the simulated sea temperature profile. From three years operational tests in NSMC, the merged SST_(FY)and TCHP_(FY)are shown to be good indexes in monitoring and predicting the intensity of tropical cyclones(TCs) over NPO.
