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61.
利用NCEP/NCAR500hPa高空再分析资料插补得到青藏高原1950—2000年50个海拔在3000m以上台站500hPa完整可靠的月平均温度序列。基于青藏高原地面气温和其上空500hPa温度有着密切关系,设计了这50个台站地面月平均温度序列的回归方案,用回归估计值将近50a各台站的现有月平均气温记录进行补齐、延长,得到重建的高原台站完整连续的地面月平均气温资料,并对回归效果进行独立样本实验。重建的青藏高原地面气温资料能够真实地反应高原实际气温的变化特征。 相似文献
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广东省是我国温泉出露最多的省份之一。在温泉附近建立温泉宾馆,是利用温泉资源大力发展本地旅游业的有效途径之一。本文利用新型NR 667A连续测氡仪测量了广东省4处温泉宾馆室内外空气中氡浓度以及室内温泉水氡浓度。温泉水氡浓度变化范围为53 4~292 5Bq L,室外氡浓度范围是17~48Bq m3,而客房内平均氡浓度水平比没有用温泉水时高出10 9%~813%,浴室内氡浓度比没有用温泉水洗浴时高出13 8%~516%。在宾馆室内使用温泉水有较高的氡转换系数。某些宾馆室内氡浓度超标,可能对宾馆工作人员造成潜在健康危害,应采取一些管理措施或技术手段降低其暴露时间或程度。 相似文献
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利用遥感综合分析西风引导气流与地形对沙尘运移路径的影响 总被引:2,自引:7,他引:2
沙尘暴的发生受大气环流、地表状况、降雨的影响,还受到局部地区地形的影响。一次规模较大的沙尘暴过程,沙尘可以从蒙古国和我国西部沙源地输送到我国东部、韩国、日本乃至夏威夷、美国西海岸。中日亚洲沙尘暴ADEC项目对亚洲沙尘暴的起沙、传输和降落的运行机制已经作了深入的研究,并建立了亚洲沙尘暴的数值模拟系统。本文以影响北京地区的沙尘暴事件为例,利用遥感技术,综合DEM地形数据和地面实测数据分析西风引导气流和地形对沙尘运移路径影响,将MODIs影像数据和DEM地形数据以及地面观测站点实测数据相结合,进行综合分析,结果表明在一次沙尘暴过程中,沙尘在运移过程中的运移路径明显地受到西风引导气流、沙尘粒子自然沉降规律以及局部地形的影响,要预防(减少)北京地区的沙尘暴仅仅作好北京地区的生态环境建设是不够的,加强北京周边地区,尤其是张家口地区、官厅水库库区及库区周围地区的生态环境建设尤为重要。 相似文献
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沙尘天气等对西安市空气污染影响的研究 总被引:7,自引:10,他引:7
通过对西安市1981—2000年TSP、SO2和NOx年平均浓度资料,1998—2000年周报和日报环境监测资料以及相应的地面、高空常规气象观测资料的统计分析,研究了该市空气污染的时间变化特点以及沙尘天气等几种气象条件对其浓度变化的影响。结果表明:(1)颗粒污染物(TSP和PM10)是西安市的首要污染物,其次是SO2。1981—2000年期间,TSP年平均浓度降低了75%,SO2年平均浓度降低了77%,NOx年平均浓度总体上变化不大;这三种污染物月平均浓度的年变化都呈单周期型,冬季1月份最高,夏季最低(TSP是7月份最低,SO2和NOx是8月份最低)。(2)2001年春季3~4月份沙尘天气的频繁发生,使西安市空气污染日出现全年的第二个多发期(23d·月-1),这有别于正常年份仅在冬季1月份出现一个浓度峰值的特点;强沙尘暴天气过程会使西安市PM10浓度在非常短的时间内提高3倍左右,造成严重的颗粒物污染。(3)西安市冬半年出现轻度污染以上级别的几率明显大于夏半年。影响西安市的地面天气系统可归纳为12类,当受不同天气系统控制时,其污染状况会有较大差异。(4)西安市一年四季都有逆温存在,100m平均逆温强度为0.90℃;全年以低层逆温出现日数最多,但冬季贴地逆温出现日数最多,厚度最厚,强度最大,是造成西安市冬季空气污染严重的最重要气象因素之一。(5)西安 相似文献
66.
本文针对CAPPS2.0系统在业务化自动运行过程中的一些问题,提供了一套具体可行的解决方案,实现了CAPPS系统的全自动运行,具有很高的实用价值。 相似文献
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XU Jianming XU Xiangde LIU Yu DING Guoan CHEN Huailiang HU Jiangkai ZHANG Jianchun WU Hao LI Weiliang HE Jinhai YANG Yuanqin WANG Jiahe 《中国科学D辑(英文版)》2005,48(Z2)
Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area". 相似文献
69.
Three dimensional numerical simulation of residential building on shrink–swell soils in response to climatic conditions 下载免费PDF全文
Shrink–swell soils can cause distresses in buildings, and every year, the economic loss associated with this problem is huge. This paper presents a comprehensive system for simulating the soil–foundation–building system and its response to daily weather conditions. Weather data include rainfall, solar radiation, air temperature, relative humidity, and wind speed, all of which are readily available from a local weather station or the Internet. These data are used to determine simulation flux boundary conditions. Different methods are proposed to simulate different boundary conditions: bare soil, trees, and vegetation. A coupled hydro‐mechanical stress analysis is used to simulate the volume change of shrink–swell soils due to both mechanical stress and water content variations. Coupled hydro‐mechanical stress‐jointed elements are used to simulate the interaction between the soil and the slab, and general shell elements are used to simulate structural behavior. All the models are combined into one finite element program to predict the entire system's behavior. This paper first described the theory for the simulations. A site in Arlington, Texas, is then selected to demonstrate the application of the proposed system. Simulation results are shown, and a comparison between measured and predicted movements for four footings in Arlington, Texas, over a 2‐year period is presented. Finally, a three‐dimensional simulation is made for a virtual residential building on shrink–swell soils to identify the influence of various factors. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
70.
Mohammad M. Sohrabi Rohan Benjankar Daniele Tonina Seth J. Wenger Daniel J. Isaak 《水文研究》2017,31(9):1719-1733
Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data. 相似文献