Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
作为天气系统的主要组成部分,三维云仿真在军事、航空等领域都起着重要作用.目前主流的边界体积层次结构(Bounding Volume Hierarchy,BVH)在处理形状不均匀且体积较大的云时存在渲染效率低下的问题,为此提出一种基于优化BVH算法的云产...作为天气系统的主要组成部分,三维云仿真在军事、航空等领域都起着重要作用.目前主流的边界体积层次结构(Bounding Volume Hierarchy,BVH)在处理形状不均匀且体积较大的云时存在渲染效率低下的问题,为此提出一种基于优化BVH算法的云产品渲染方法.将WRF(Weather Research and Forecasting,天气研究与预报)模型网格点中的数据作为云基元,利用Z-order Hilbert曲线对其进行空间排序,结合云基元密度优化BVH算法,提高计算效率.提出ONS(Overlapping Node Sets,重叠节点结构)降低数据存取耗时.优化BVH算法能够减少不必要的光线和三角形面之间的相交测试次数,并解决边界体无效重叠问题.仿真实验显示,SAH(Surface Area Heuristic,表面积启发式)成本较同类最优算法可提升15.6%,EPO(Effective Partial Overlap,有效重叠部分)可提升10%,构建时间减少100%以上,在任意云场景中优化BVH算法的计算效率较同类算法都有显著提高,表明其能实现WRF云产品的快速渲染.展开更多
在人类活动加重气候变暖的背景下,极端水文气象事件发生概率增加。数值模式作为研究水循环和极端水文事件的有效工具,已在全球范围内得到广泛应用。为深入理解气候变化背景下全球陆地水循环时空演变规律,揭示大气-陆面-水文互馈机制,大...在人类活动加重气候变暖的背景下,极端水文气象事件发生概率增加。数值模式作为研究水循环和极端水文事件的有效工具,已在全球范围内得到广泛应用。为深入理解气候变化背景下全球陆地水循环时空演变规律,揭示大气-陆面-水文互馈机制,大气-陆面-水文耦合过程模拟研究已成为国际大气、水文等学科研究的热点之一。本文首先回顾和梳理了大气-陆面-水文耦合模式的发展历程,阐明了大气-陆面-水文耦合模式WRF-Hydro(Weather Research and Forecasting Model Hydrological modeling system)的优势,并系统总结了WRF-Hydro模式的主要敏感性参数分析及模式在对地表径流、土壤湿度、能量水分循环以及相关大气和水文过程等方面的应用。最后探讨WRF-Hydro大气-陆面-水文耦合模式未来发展趋势,提出应着眼于发展有效的尺度转换方案、完善参数化方案以及开展流域内大气、水文变量时空分布高分辨率模拟等方面,以期系统提升耦合模式对大气、陆面过程及水文过程的刻画能力。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
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
基于福建省冬半年沿海和港湾岛屿自动站的逐时极大风观测资料和WRF（Weather Research and Forecast）、EC（European Centre for Medium-Range Weather Forecasts）细网格以及T639（TL639L60）三种模式预报的10 m风场资料，将模式预报...基于福建省冬半年沿海和港湾岛屿自动站的逐时极大风观测资料和WRF（Weather Research and Forecast）、EC（European Centre for Medium-Range Weather Forecasts）细网格以及T639（TL639L60）三种模式预报的10 m风场资料，将模式预报的风向风速与观测资料进行对比检验，结果表明：福建省沿海冬半年大风的盛行风向以东北风为主，大风的时空分布极为不均，沿海风力的脉动性、跳跃性、局地性突出。从三种模式对风速风向的模拟效果来看， WRF和EC细网格的预报效果较好，有可参考性， T639可参考性不高。对于风速，模式预报结果相比实况极大风速偏小，港湾岛屿代表站风速的平均绝对误差均小于沿海代表站，预报平均误差由沿海向内陆逐渐减小，由中部向南北逐渐减小。风向相比风速的预报效果要差， WRF和EC细网格的风向预报误差在45°-50°，有一定的参考意义；港湾岛屿代表站风向的平均绝对误差大于沿海代表站，以浮标站的误差最大。当观测风速出现7级及以上风速时，若对大风进行分级检验，则较低风速的预报平均绝对误差小于较高风速；风向预报的平均绝对误差也大大降低，且误差都在45°以内，具有良好的参考性。展开更多
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘作为天气系统的主要组成部分,三维云仿真在军事、航空等领域都起着重要作用.目前主流的边界体积层次结构(Bounding Volume Hierarchy,BVH)在处理形状不均匀且体积较大的云时存在渲染效率低下的问题,为此提出一种基于优化BVH算法的云产品渲染方法.将WRF(Weather Research and Forecasting,天气研究与预报)模型网格点中的数据作为云基元,利用Z-order Hilbert曲线对其进行空间排序,结合云基元密度优化BVH算法,提高计算效率.提出ONS(Overlapping Node Sets,重叠节点结构)降低数据存取耗时.优化BVH算法能够减少不必要的光线和三角形面之间的相交测试次数,并解决边界体无效重叠问题.仿真实验显示,SAH(Surface Area Heuristic,表面积启发式)成本较同类最优算法可提升15.6%,EPO(Effective Partial Overlap,有效重叠部分)可提升10%,构建时间减少100%以上,在任意云场景中优化BVH算法的计算效率较同类算法都有显著提高,表明其能实现WRF云产品的快速渲染.
文摘在人类活动加重气候变暖的背景下,极端水文气象事件发生概率增加。数值模式作为研究水循环和极端水文事件的有效工具,已在全球范围内得到广泛应用。为深入理解气候变化背景下全球陆地水循环时空演变规律,揭示大气-陆面-水文互馈机制,大气-陆面-水文耦合过程模拟研究已成为国际大气、水文等学科研究的热点之一。本文首先回顾和梳理了大气-陆面-水文耦合模式的发展历程,阐明了大气-陆面-水文耦合模式WRF-Hydro(Weather Research and Forecasting Model Hydrological modeling system)的优势,并系统总结了WRF-Hydro模式的主要敏感性参数分析及模式在对地表径流、土壤湿度、能量水分循环以及相关大气和水文过程等方面的应用。最后探讨WRF-Hydro大气-陆面-水文耦合模式未来发展趋势,提出应着眼于发展有效的尺度转换方案、完善参数化方案以及开展流域内大气、水文变量时空分布高分辨率模拟等方面,以期系统提升耦合模式对大气、陆面过程及水文过程的刻画能力。
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。
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
文摘基于福建省冬半年沿海和港湾岛屿自动站的逐时极大风观测资料和WRF（Weather Research and Forecast）、EC（European Centre for Medium-Range Weather Forecasts）细网格以及T639（TL639L60）三种模式预报的10 m风场资料，将模式预报的风向风速与观测资料进行对比检验，结果表明：福建省沿海冬半年大风的盛行风向以东北风为主，大风的时空分布极为不均，沿海风力的脉动性、跳跃性、局地性突出。从三种模式对风速风向的模拟效果来看， WRF和EC细网格的预报效果较好，有可参考性， T639可参考性不高。对于风速，模式预报结果相比实况极大风速偏小，港湾岛屿代表站风速的平均绝对误差均小于沿海代表站，预报平均误差由沿海向内陆逐渐减小，由中部向南北逐渐减小。风向相比风速的预报效果要差， WRF和EC细网格的风向预报误差在45°-50°，有一定的参考意义；港湾岛屿代表站风向的平均绝对误差大于沿海代表站，以浮标站的误差最大。当观测风速出现7级及以上风速时，若对大风进行分级检验，则较低风速的预报平均绝对误差小于较高风速；风向预报的平均绝对误差也大大降低，且误差都在45°以内，具有良好的参考性。
