为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于...为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于WRF模式中各种参数的特点,设计4种参数方案进行计算,采用双层网格嵌套达到降尺度模拟,为了高精度解析大气边界层过程,在竖直方向、近地面1.5km高度内加密为15层,提取康定站计算结果与观测结果进行比较.结果表明,WRF模式的计算结果符合康定市气候特征,气象要素的相关系数均高于0.5;风吹雪发生概率从高到低的区域依次为康定市东部和南部边缘的贡嘎山区,内部的大雪山段,以及位于101.7°E~102.0°E位置处的铁路线路,概率分别为19%、14%和12%,其他区域的概率低于4%.展开更多
WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层...WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层)以及次网格地形方案对新疆北部不同地形地区风场模拟结果的影响。结果表明:每组试验均能模拟出风速的变化趋势;陆面过程RUC(rapid update cycle)方案和微物理Lin(Purdue Lin)方案对平原地区模拟结果较好,陆面过程Noah方案和微物理WSM6(WRF single moment 6 class)方案对山区地形模拟结果较好,且对于平原和山谷地形,次网格地形方案对模拟地区均能起到较好的修正作用。展开更多
The micaceous weathered granitic soil(WGS)is frequently encountered in civil engineering worldwide,unfortunately little information is available regarding how mica affects the physico-mechanical behaviors of WGS.This ...The micaceous weathered granitic soil(WGS)is frequently encountered in civil engineering worldwide,unfortunately little information is available regarding how mica affects the physico-mechanical behaviors of WGS.This study prepares reconstituted WGS with different mica contents by removing natural mica in theWGS,and then mixes it with commercial mica powders.The geotechnical behavior as well as the microstructures of the mixtures are characterized.The addition of mica enables the physical indices of WGS to be specific combinations of coarser gradation and high permeability but high Atterberg limits.However,high mica content in WGS was found to be associated with undesirable mechanical properties,including increased compressibility,disintegration,and swelling potential,as well as poor compactability and low effective frictional angle.Microstructural analysis indicates that the influence of mica on the responses of mixtures originates from the intrinsic nature of mica as well as the particle packing being formed withinWGS.Mica exists in the mixture as stacks of plates that form a spongy structure with high compressibility and swelling potential.Pores among the plates give the soil high water retention and high Atterberg limits.Large pores are also generated by soil particles with bridging packing,which enhances the permeability and water-soil interactions upon immersion.This study provides a microlevel understanding of how mica dominates the behavior of WGS and provides new insights into the effective stabilization and improvement of micaceous soils.展开更多
Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantil...Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.展开更多
文摘为给通过地形复杂,缺少气象资料的西南艰险山区的铁路研究风吹雪易发性提供方法,以位于该区域的四川省康定市为例,采用中尺度数值天气预报模式(The Weather Research and Forecasting Mode,WRF)对该区域气象要素的时空分布进行模拟.基于WRF模式中各种参数的特点,设计4种参数方案进行计算,采用双层网格嵌套达到降尺度模拟,为了高精度解析大气边界层过程,在竖直方向、近地面1.5km高度内加密为15层,提取康定站计算结果与观测结果进行比较.结果表明,WRF模式的计算结果符合康定市气候特征,气象要素的相关系数均高于0.5;风吹雪发生概率从高到低的区域依次为康定市东部和南部边缘的贡嘎山区,内部的大雪山段,以及位于101.7°E~102.0°E位置处的铁路线路,概率分别为19%、14%和12%,其他区域的概率低于4%.
文摘WRF(weather research and forecasting)模式中参数化方案的选择与近地面风场的仿真模拟结果关系密切。为解决新疆北部不同地形地区风场模拟准确性的问题,采用WRF中尺度气象模式,探究4类参数化方案(边界层、微物理、陆面过程、近地面层)以及次网格地形方案对新疆北部不同地形地区风场模拟结果的影响。结果表明:每组试验均能模拟出风速的变化趋势;陆面过程RUC(rapid update cycle)方案和微物理Lin(Purdue Lin)方案对平原地区模拟结果较好,陆面过程Noah方案和微物理WSM6(WRF single moment 6 class)方案对山区地形模拟结果较好,且对于平原和山谷地形,次网格地形方案对模拟地区均能起到较好的修正作用。
基金The financial supports of the National Natural Science Foundation of China(Grant No.42177148)the opening fund of State Key Laboratory of Geohazard Prevention and Geo-environment Protection(Grant No.SKLGP 2023K011)Postdoctoral Research Project of Guangzhou(Grant No.20220402)are gratefully thanked.
文摘The micaceous weathered granitic soil(WGS)is frequently encountered in civil engineering worldwide,unfortunately little information is available regarding how mica affects the physico-mechanical behaviors of WGS.This study prepares reconstituted WGS with different mica contents by removing natural mica in theWGS,and then mixes it with commercial mica powders.The geotechnical behavior as well as the microstructures of the mixtures are characterized.The addition of mica enables the physical indices of WGS to be specific combinations of coarser gradation and high permeability but high Atterberg limits.However,high mica content in WGS was found to be associated with undesirable mechanical properties,including increased compressibility,disintegration,and swelling potential,as well as poor compactability and low effective frictional angle.Microstructural analysis indicates that the influence of mica on the responses of mixtures originates from the intrinsic nature of mica as well as the particle packing being formed withinWGS.Mica exists in the mixture as stacks of plates that form a spongy structure with high compressibility and swelling potential.Pores among the plates give the soil high water retention and high Atterberg limits.Large pores are also generated by soil particles with bridging packing,which enhances the permeability and water-soil interactions upon immersion.This study provides a microlevel understanding of how mica dominates the behavior of WGS and provides new insights into the effective stabilization and improvement of micaceous soils.
基金supported by the National Natural Science Foundation of China (Project No.42375192)the China Meteorological Administration Climate Change Special Program (CMA-CCSP+1 种基金Project No.QBZ202315)support by the Vector Stiftung through the Young Investigator Group"Artificial Intelligence for Probabilistic Weather Forecasting."
文摘Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.
