Recently, a new high-resolution daily downscaled data-set derived from 21 CMIP5 model simulations has been released by NASA, called ＇NASA Earth Exchange Global Daily Downscaled Projections＇ （NEX-GDDP）. In this stu...Recently, a new high-resolution daily downscaled data-set derived from 21 CMIP5 model simulations has been released by NASA, called ＇NASA Earth Exchange Global Daily Downscaled Projections＇ （NEX-GDDP）. In this study, the performance of this data-set in simulating precipitation extremes and long-term climate changes across China are evaluated and compared with CMIP5 GCMs. The results indicate that NEX-GDDP can successfully reproduce the spatial patterns of precipitation extremes over China, showing results that are much closer to observations than the GCMs, with increased Pearson correlation coefficients and decreased model relative error for most models. Furthermore, NEX-GDDP shows that precipitation extremes are projected to occur more frequently, with increased intensity, across China in the future. Especially at regional to local scales, more information for the projection of future changes in precipitation extremes can be obtained from this high-resolution data-set. Most importantly, the uncertainties of these projections at the regional scale present significant decreases compared with the GCMs, making the projections by NEX-GDDP much more reliable. Therefore, the authors believe that this high-resolution data-set will be popular and widely used in the future, particularly for climate change impact studies in areas where a finer scale is required.展开更多
The projection of China＇s near- and long-term future climate is revisited with a new-generation statistically down- scaled dataset, NEX-GDDP （NASA Earth Exchange Global Daily Downscaled Projections）. This dataset p...The projection of China＇s near- and long-term future climate is revisited with a new-generation statistically down- scaled dataset, NEX-GDDP （NASA Earth Exchange Global Daily Downscaled Projections）. This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and re- markably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved： （1） the climatology in the past as compared against the ob- servations; （2） more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and （3） much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modem climate change for diverse earth science communities.展开更多
在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving ...在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving Average Model),在常规的自然环境模态分析算法的基础上构造出一种快速求解NEx T-ARMA模型的算法进行桥梁模态识别。相比于传统的环境激励模态参数计算方法,该算法不但降低了传统算法的复杂度,而且采用了反馈的方式提高了计算精度。采用ANSYS建立有限元模型并搭建简易实验系统分别对该算法进行仿真验证和实验验证,验证结果表明,该算法能够有效地在自然激励下提取出桥梁结构的各阶模态,其中对前三阶固有频率的识别相对误差降到1%左右。展开更多
基金jointly supported by the National Key Research and Development Program of China[grant number2016YFA0602401]the External Cooperation Program of Bureau of International Co-operation(BIC)+1 种基金Chinese Academy of Sciences[grant number 134111KYSB20150016]the National Natural Science Foundation of China[grant number 41421004]
文摘Recently, a new high-resolution daily downscaled data-set derived from 21 CMIP5 model simulations has been released by NASA, called ＇NASA Earth Exchange Global Daily Downscaled Projections＇ （NEX-GDDP）. In this study, the performance of this data-set in simulating precipitation extremes and long-term climate changes across China are evaluated and compared with CMIP5 GCMs. The results indicate that NEX-GDDP can successfully reproduce the spatial patterns of precipitation extremes over China, showing results that are much closer to observations than the GCMs, with increased Pearson correlation coefficients and decreased model relative error for most models. Furthermore, NEX-GDDP shows that precipitation extremes are projected to occur more frequently, with increased intensity, across China in the future. Especially at regional to local scales, more information for the projection of future changes in precipitation extremes can be obtained from this high-resolution data-set. Most importantly, the uncertainties of these projections at the regional scale present significant decreases compared with the GCMs, making the projections by NEX-GDDP much more reliable. Therefore, the authors believe that this high-resolution data-set will be popular and widely used in the future, particularly for climate change impact studies in areas where a finer scale is required.
基金Supported by the National Natural Science Foundation of China(41130105,41130962,and 41005035)Beijing Young Elite Foundation(YETP0005)
文摘The projection of China＇s near- and long-term future climate is revisited with a new-generation statistically down- scaled dataset, NEX-GDDP （NASA Earth Exchange Global Daily Downscaled Projections）. This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and re- markably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved： （1） the climatology in the past as compared against the ob- servations; （2） more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and （3） much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modem climate change for diverse earth science communities.
文摘在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving Average Model),在常规的自然环境模态分析算法的基础上构造出一种快速求解NEx T-ARMA模型的算法进行桥梁模态识别。相比于传统的环境激励模态参数计算方法,该算法不但降低了传统算法的复杂度,而且采用了反馈的方式提高了计算精度。采用ANSYS建立有限元模型并搭建简易实验系统分别对该算法进行仿真验证和实验验证,验证结果表明,该算法能够有效地在自然激励下提取出桥梁结构的各阶模态,其中对前三阶固有频率的识别相对误差降到1%左右。
