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排序方式: 共有303条查询结果,搜索用时 15 毫秒
1.
Jacob A. Zwart Samantha K. Oliver William David Watkins Jeffrey M. Sadler Alison P. Appling Hayley R. Corson-Dosch Xiaowei Jia Vipin Kumar Jordan S. Read 《Journal of the American Water Resources Association》2023,59(2):317-337
Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making. 相似文献
2.
介绍了中山市空气质量多模式预报系统。对2015年3月—2016年2月的预报效果评估表明:系统可以较好地反映污染物的变化趋势但仍存在系统性偏差。通过对排放源清单的调整优化及对模式干沉降模块的改进使模式系统的偏差显著减小。总结并建立中山市污染天气案例库,发现在典型污染天气形势下,除O3-8 h外,其他污染物均值均能达到空气质量二级标准。未来将从2个方面提高中山市空气质量预报预警系统的实用性,包括逐步实现污染源排放清单的动态及时更新和提高极端气象条件下O3污染预报能力。 相似文献
3.
为了验证经度、纬度和时差参数对AERMOD模式预测结果是否有影响,假设在某气象条件下存在一个点源,利用AERMOD模式计算该点源排放的污染物的浓度分布.保持气象条件和污染源参数不变,仅改变点源所在地的经度、纬度或时差,重新利用AERMOD模式进行了预测,比较预测结果的差异.结果表明,经度、纬度和时差参数对白天时段行星边界层参数的计算值有影响,受影响的参数包括地表热通量、摩擦速度、Monin-Obukhov长度、对流速度尺度、温度尺度和混合高度.经度、纬度和时差的不同取值对AERMOD计算出的污染物小时平均浓度最大值和浓度分布影响明显.根据AERMOD公式的描述和能量变化得出,经度、纬度和时差的改变引起了项目所在地白天太阳高度角的变化,进而改变了地表热通量.地表热通量的改变,造成了行星边界层参数的变化,最终影响了浓度分布的计算值. 相似文献
4.
5.
Ingrid Arocho William Rasdorf Joseph Hummer Phil Lewis 《International Journal of Sustainable Engineering》2017,10(2):123-134
The construction process contributes to pollutant emissions, particularly through the operation of diesel- and gasoline-powered equipment. In the past decade, a series of investigations were undertaken to quantify these emissions for a variety of non-road construction equipment performing different activities and undergoing different duty cycles, and a model to estimate quantities of six types of pollutant was developed. This paper uses that model to estimate emissions for four street and utility construction projects which no one has done previously. We combined information from company records with standard construction industry manuals to estimate total emissions for the projects and to examine the pollution patterns and magnitudes. The street construction projects all had similar emission profiles with a large peak at the beginning and a steady output of emissions throughout the duration of the project. For example, in two of the projects studied, half of all CO2 emissions were produced before the projects were 40% completed. Results showed that demolition and earthwork are the activities with the largest contribution. The equipment types with the largest contribution are backhoes, front-end loaders, bulldozers and trenchers. Trenchers, for example, produced 30% of all emissions on the projects on which they were used. 相似文献
6.
7.
G20峰会空气质量保障机制的构建 总被引:2,自引:2,他引:0
为进一步做好G20峰会空气质量保障工作,在大气污染联防联控机制的基础上,建立G20峰会空气质量保障机制,形成专项工作小组,按照空气质量保障组织框架运行,为精准保障提供空气质量预报、污染态势特征及评估、措施执行情况及效果、污染溯源等管控技术支撑。2016年9月,G20峰会期间杭州市空气质量总体优良,8月24日至9月7日G20峰会前后杭州市PM_(2.5)、PM_(10)、SO_2、NO_2、CO、O_3等污染物浓度同比2015年分别下降40%、40%、37%、56%、22%、7%,在该机制的推动下空气质量保障工作圆满完成。 相似文献
8.
Caleb A. Buahin Nikhil Sangwan Cassandra Fagan David R. Maidment Jeffery S. Horsburgh E. James Nelson Venkatesh Merwade Curtis Rae 《Journal of the American Water Resources Association》2017,53(2):300-315
One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods. 相似文献
9.
基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案 总被引:1,自引:0,他引:1
针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考. 相似文献
10.
Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River 下载免费PDF全文
Dominique R. Bourdin Dave Campbell Roland B. Stull Tobi Gardner 《Journal of the American Water Resources Association》2015,51(2):502-512
Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust. 相似文献