乌兰察布市人工测墒与自动土壤水分仪测墒结果分析

通过对察右中旗2011年人工观测与GStar-I C型自动土壤水分仪观测的数据进行对比,分析两种观测方法存在的差异,为自动土壤水分监测仪投入业务化运行提供一定的科学依据。     

自动站与人工观测降水量差异原因分析

分析引起自动站与人工观测降水量差异的原因,并提出解决问题的着眼点。     

甘肃民勤自动站与人工站气象观测数据差异分析

从自动站与人工站观测方式的区别人手,对民勤国家基准气候站观测的数据进行整理与对比分析得出：（1）2种观测方式数据序列中,本站气压2a平均差值为0．1hPa,差值变幅在～0．3～0．5hPa;气温2a平均差值-0．1℃,差值变幅在-0．1～0．0℃之间;相对湿度2a平均差值为-1％,差值变幅在一4％～2％之间;2min平均风速2a平均差值为0．5m／s,差值变幅在0．3～0．7m／s之间,10min平均风速2a平均差值为0．4m／s,差值变幅在0．4～0．5m／s之间;地面温度2a平均差值为0．6℃,差值变幅在0．0～1．2℃之间。本站气压、气温、相对湿度、风向风速、地温差值虽然不固定,但对历史资料的序列连续性影响不显著;（2）各要素中差值最大的是地面最高温度,2a平均差值为1．8oC,差值变幅在-1．7～4．3℃之间;（3）自动站的观测结果比人工观测更真实、准确、科学,更接近大气中的实际情况。     

人工站与自动站风的自动观测资料差异分析

通过对侧风仪的构造及采集方法的差异分析,采用广西2004～2008年4个基准气候观测站人工站与自动站风的同步自动观测资料作对比分析,分析结果表明:人工站与自动站风的自动观测资料存在差异。短时风人工站与自动站差异比较大,平均风时间越长风速差异率就减少,风向相符率越大。风大时,人工站的观测数据比自动站大;10分钟平均风向风速人工站与自动站比较接近;极大风风速一致率及风向相符率比较低,说明瞬时风人工站与自动站差异最大。差异的原因主要是观测仪器制作材料不相同及观测方法不一致所造成。     

自动站与人工观测常规气象要素差异对比分析

利用青海省海西州所属气象站人工与自动平行观测资料,统计分析气温、气压、相对湿度、风要素观测差异,探讨差异产生的原因。结果表明:自动站与人工观测常规气象要素均存在一定的差异,自动站气温、风普遍高于人工站观测值,气压、相对湿度自动站观测大多低于人工观测值,没有一个站各要素偏差都在评估允许范围内的;观测仪器结构与观测原理的不同、观测时空差异、观测方式的不同是造成偏差的主要原因。     

自动站与人工站降水量差异原因浅析及仪器维护

对2008年的逐日降水进行对比分析,发现自动站和人工站观测的降水量存在差值,且差值有正,有负,发现其差值随降水强度的变化而变化,分析其造成差异的原因.     

大连自动站与人工站观测数据的差异对比分析

利用2005—2006年大连自动气象站与人工站观测的温度、气压、降水、相对湿度、0 cm地温和风速风向等资料,对其差值、粗差率、一致率和风向相符率等进行对比分析。结果表明：自动站与人工站气温、本站气压、降水和0 cm地温观测差值较小,其准确度能够满足日常业务使用;而相对湿度、10 min与2 min风速和风向相符率观测差值较大,距离正常业务使用有一定的距离。因观测数据采集方法不同、感应器所处的环境不同、观测仪器原理不同、观测样本不同、观测时间的差异和人为因素的影响是造成对比观测差异的主要原因。     

自动站与人工观测降水量差值及原因分析

1引言通过对北安市自动站与人工观测对比分析,发现自动站降水量与人工观测降水量数据存在差异,根据这些差异分析其出现差异的原因,观测员逐步掌握差异的规律,以便在工作中取得更准确     

内蒙古自动站与人工观测数据差异对比分析

利用2006年内蒙古自治区共36个自动站和人工站平行观测资料,对观测得到的气象要素进行了统计和评估。通过分析气温、气压、相对湿度、风等要素的对比差值及其标准差、对比差值的频次分布和空间分布等,探讨自动站与人工观测数据差异的可能原因。结果表明：内蒙古自动观测与人工观测各气象要素均存在一定的差异,对所有台站平均而言,其差异均在自动站差值允许范围之内。产生差异的原因：观测仪器结构与观测原理差异、观测时空差异、观测方式差异等,在将人工观测数据与自动观测数据连续使用时,需要根据当地情况进行适当订正,以确保资料的连续性。     

自动站与人工观测降水量差值及原因分析

通过自动站与人工站在降水量观测差值的对比，对差值原因进行了分析。     

陕西自动观测与人工观测相对湿度差异分析

利用陕西省97个地面自动气象站和人工站相对湿度观测数据,研究人工观测与自动观测相对湿度对比差值的大小,对比差值的日、月变化规律以及与所在气候区域的关系,同时研究影响对比差值大小的主要原因。结果表明：自动观测比人工观测的日平均相对湿度平均偏低2.28%,对比差值的标准差为3.07%,自动站与人工站对比差值地域差异不明显,但存在日、月际变化;观测时间不一致并不是造成自动站与人工站日、月平均值差异的主要原因,其大小主要与相对湿度大小有关。7.5%的自动站月平均相对湿度有与历史长序列月平均值相比有显著性差异。     

Trends and scales of observed soil moisture variations in China

A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981-1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air (TDSA) are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.     

Assimilation of screen-level observations by variational soil moisture analysis

Summary Inaccurate specification of soil moisture contents can result in forecast errors up to several degrees centigrade. Since direct measurements are rarely available, a variational method has been developed that assimilates synoptic measurements of 2 m-temperature in order to specify the moisture contents of the two soil layers of the Local Model at Deutscher Wetterdienst. The analyzed values minimize a cost functional that expresses the differences between model forecast and observed screen-level temperatures. The minimization is performed highly efficiently and only two additional forecasts are required but neither tangent linear nor adjoint. Background state and background error covariance matrix are updated at each analysis step in a Kalman-filter-like cycled scheme, which takes a model error into account. The soil moisture assimilation shows improved 2 m-temperature forecasts in case of high radiative forcing by up to 3 °C for small areas in the presented 6-week trial run. It proved stability and robustness for general weather conditions and has become operational at DWD for the LM on 14 March 2000. Received August 21, 2000 Revised March 26, 2001     

江苏省土壤湿度观测资料与再分析资料对比分析研究

利用江苏省2010—2015年的60个站点土壤湿度观测资料,对欧洲中心ERA-Interim再分析资料(ERA)和美国宇航局再分析资料(MERRA)的两套土壤湿度数据在江苏地区的可靠性进行了评估。结果表明:相比于ERA再分析资料,MERRA较好地再现出江苏省次表层年平均土壤湿度的空间分布特征,但是两种资料的次表层和深层土壤湿度的数值均小于观测。ERA和MERRA基本都能揭示出江苏省次表层土壤湿度的季节变化特征,但是深层土壤湿度与观测仍有较大差距。在时间演变方面,ERA次表层土壤湿度与站点观测在研究时段内较为接近,EOF分析揭示出1979—2016年江苏省次表层土壤湿度存在区域一致型与南北偶极型两个主要的年代际变率模态。但是对于深层土壤湿度时间演变而言,两种再分析资料都与观测有较大的差距。总体而言,再分析资料的次表层土壤湿度与站点观测较为接近,但是由于再分析资料陆面模式中地下水等影响深层土壤湿度的关键过程刻画较为简单,使得深层土壤湿度与观测有较大的差距。     

Use of a land surface model to evaluate the observed soil moisture of grassland at the Tongyu reference site

A land surface model driven by the continuous three-year observed meteorological data with a time interval of 30 minutes at the Tongyu station, a reference site of the Coordinated Enhanced Observing Period （CEOP）, was used to evaluate the observation bias of soil moisture （SM） data and analyze the variation of SM at different time scales. The saline-alkaline soil of the grassland at the Tongyu site makes the measured SM too high, especially in boreal summer of 2003-05. The simulated annual mean SM has the lowest value in 2004 and its three-year variation corresponds to the change of precipitation, whereas the observation shows the increasing trend from 2003 to 2005. Compared to the variation range between -60% and 40% for the anomaly percentage of the simulated daily mean SM during May-October of 2004, the measured data show the higher values more than 40%. The magnitude of the variation trend of the observed daily mean SM in 2003 and 2005 is generally consistent with the simulation. The largest deficiency for the soil moisture observation of the grassland is the overestimated value in the drought year with less precipitation. The simulated monthly mean SM has the lowest value in March due to the large contribution of evaporation relative to precipitation and this phenomenon can not be reproduced in the observation.     

土壤水分条件对亚麻生长发育的影响

采用随机区组设计，三次重复，研究了土壤湿度对亚麻生长发育的影响，在生育期间定期取样，分别测定干物重、CAT、POD活性及叶绿素含量，并在收获后分析种子包衣对高度、工艺长度、萌果数、分枝数、原茎数、纤维重及出麻率的影响。结果表明：高水份条件利于亚麻的生长发育，能有效提高CAT和POD的活性，增加抗病抗害能力，促进干物质的积累，在一定程度上提高了亚麻出麻率。     

上海自动站气温资料的空间质量控制与特征分析

运用Barnes插值方法和2011年7月1日00时至2012年6月30日23时(北京时间)逐时自动站气温资料,对Barnes插值方法的空间一致性质量控制进行研究,研究表明该方法在上海及华东地区有一定的适用性,同时分析了代表性区域的资料特征。结果表明:非降水日情况下,陆地站点和海边站点的气温具有显著的日变化特征,09时与18时左右两者之间差异最小;白天(夜晚),陆地(海边)站点气温高于海边(陆地)站点,最高(最低)气温差异约为0.74℃(0.79℃),多数时刻海陆差异显著,同时海陆气温日变化曲线不同的原因与日照、风向风速相关。此外,自动站资料能够充分反映出有天气过程时气象要素的变化特征,并能体现城市热岛效应。     

Climatic soil moisture deficit - climate and soil data integration in a GIS

This paper discusses a GIS based implementation of a model for soil droughtiness assessment evaluating the impact of possible climate change. It focuses, in particular, on the development of a methodology for mapping Available Water Capacity. An assessment of the Soil Drought Susceptibility for Scotland in the year 2030 is made and illustrated with maps and derived statistics.     

Mapping surface soil moisture with microwave radiometers

Summary Water stored in the soil serves as a reservoir for the evapotranspiration (ET) process on land surfaces, therefore knowledge of the soil moisture content is important for partitioning the incoming solar radiation into latent and sensible heat components. There is no remote sensing technique which directly observes the amount of water in this reservoir, however microwave remote sensing at long wavelengths (>10 cm) can give estimates of the moisture stored in the surface 5-cm layer of the soil. This approach is based on the large dielectric contrast between water and dry soil, resulting in emissivity changes from 0.96 for a dry smooth soil to less than 0.6.In this paper, basic relationships between soil moisture and emissivity are described using both theory and observations from various platforms. The ability of the approach to be extended to large regions has been demonstrated in several aircraft mapping experiments, e.g., FIFE, Monsoon 90, Washita 92 and HAPEX Sahel. Some results from Monsoon 90 are presented here. Applications of these soil moisture maps in runoff prediction, rainfall estimation, determining the direct evaporation from the soil surface and serving as a boundary condition for soil profile models are presented.With 10 Figures