东亚-太平洋型季节内演变和维持机理研究

利用850hPa的纬向风异常建立一个逐候东亚-太平洋(East Asian Pacific,EAP)型指数,研究其季节内演变特征,发现东亚-太平洋型经向波列是东亚夏季风季节内变化的主要模态.其演变过程为:扰动首先出现在北太平洋中部,并通过正压不稳定过程从基本气流中获得能量而发展,在高层罗斯贝波能量向南频散,激发热带对流异常和赤道罗斯贝波,并相互锁相,因赤道罗斯贝波受β效应影响而共同向西移动.热带对流和环流异常在菲律宾附近达到最强,此时在东亚沿岸出现经向三极型波列,此后中低纬度异常继续向西北方向移动,使降水异常在长江流域能维持较长时间.东亚-太平洋型在东亚发展和维持有以下原因:首先,菲律宾暖水上空的对流和低层环流之间存在正反馈;其次,由于海陆热力差异导致暖大陆和冷海洋之间存在特殊的纬向温度梯度和北风垂直切变,东亚-太平洋型在经向上有向北倾斜的斜压结构,能通过斜压能量转换从平均有效位能中获得能量,同时,也能从经向温度梯度的平均有效位能中获得能量.     

青藏和伊朗高原热力异常与北疆夏季降水的关系

青藏高原和伊朗高原热力异常对其周边地区天气气候有重要影响,已有研究多关注东部季风区,而对干旱区关注较少.针对这一不足,利用美国国家环境预测中心/美国国家大气研究中心(NCEP/NCAR)再分析月平均资料和北疆43站降水资料,分析了1961-2007年5月青藏高原和伊朗高原地表感热异常与北疆夏季降水的关系.奇异值分解(SVD)分析发现,5月青藏高原地表感热与北疆夏季降水呈负相关,伊朗高原为正相关.青藏高原和伊朗高原感热异常的大尺度对比,要比仅考虑单一高原的感热异常与北疆夏季降水有更密切的联系.定义了一个热力差异指数来表征这种地表感热异常的对比程度,相关分析发现,当5月伊朗高原地表感热偏强,青藏高原地表感热偏弱时,500hPa中亚上空和贝加尔湖上空分别为异常气旋和反气旋环流,在二者共同作用下,新疆上空盛行异常的偏南气流,有利于低纬度的暖湿气流北上,形成有利于降水的环流形势,同时越赤道索马里急流偏强,低纬度水汽被接力输送至中亚和新疆地区,为降水的发生提供了有利的水汽条件.进一步分析发现,青藏高原热力异常主要影响中高层大气环流,伊朗高原则主要影响水汽通量输送.     

STUDY ON CLIMATIC FEATURES OF SURFACE TURBULENT HEAT EXCHANGE COEFFICIENTS AND SURFACE THERMAL SOURCES OVER THE QINGHAI-XIZANG PLATEAU

Using monthly mean of surface turbulent heat exchange coefficients calculated based on datafrom four automatic weather stations(AWS)for thermal equilibrium observation in July 1993—September 1996 and of surface conventional measurements,an empirical expression is establishedfor such coefficients.With the expression,the heat exchange coefficients and the components ofsurface thermal source are computed in terms of 1961—1990 monthly mean conventional data from148 stations over the Qinghai-Xizang(Tibetan)Plateau(QXP)and its adjoining areas,and the1961—1990 climatic means are examined.Evidence suggests that the empirical expression is capable of showing the variation of the heatexchange coefficient in a climatic context.The monthly variation of the coefficients averaged overthe QXP is in a range of 4×10~(-3)-5×10~(-3).The wintertime values are bigger in the mountainsthan in the valleys and reversal in summer.Surface effective radiation and sensible heat are thedominant factors of surface total heat.In spring surface sensible heat is enhanced quickly,resulting in two innegligible regions of sensible heat,one in the west QXP and the other innorthern Tibet.with their maximums emerging in different months.In spring and summersensible heat and surface effective radiation are higher in the west than in the east.The effectiveradiation peaks for the east in October—December and the whole QXP and in June and October forthe west.The surface total heat of the plateau maximizes in May.minimizes in December andJanuary,and shows seasonal variation more remarkable in the SW compared to the eastern part.Inthe SW plateau the total heat is much more intense than the eastern counterpart in all the seasonsexcept winter.Under the effect of the sensible heat,the total heat on the SW plateau starts toconsiderably intensify in February,which leads to a predominant heating region in the west,withits center experiencing a noticeable westward migration early in summer and twice pronouncedweakening in July and after October.However,the weakening courses are owing to differentcauses.The total heat over the north of QXP is greatly strengthened in March.thus generatinganother significant thermal region in the plateau.     

南海-热带东印度洋海温年际变化与南海季风爆发关系的初步分析

对５月东亚至热带东印度洋表面温度距平主要特征向量场的分析表明，以苏门答腊为中心的热带海洋温度异常与南海季风爆发有密切关系。当该海域海温较常年偏暖（冷）时，南海季风爆发往往迟（早），它可能是通过影响中南半岛与其南方热带海洋之间经向热力差异的变化来实现的。分析了从冬到夏南海－热带东印度洋海温距平主要特征向量场的时空演变，末夏初以苏门答腊为中心的热带海温距平场特征可以追溯到冬季南海海温场的变化，后者与南     

土壤热异常影响地表能量平衡的个例分析和数值模拟

The statistical relationship between soil thermal anomaly and short-term climate change is presented based on a typical case study. Furthermore, possible physical mechanisms behind the relationship are revealed through using an off-line land surface model with a reasonable soil thermal forcing at the bottom of the soil layer.In the first experiment, the given heat flux is 5 W m-2 at the bottom of the soil layer (in depth of 6.3 m)for 3 months, while only a positive ground temperature anomaly of 0.06℃ can be found compared to the control run. The anomaly, however, could reach 0.65℃ if the soil thermal conductivity was one order of magnitude larger. It could be even as large as 0.81℃ assuming the heat flux at bottom is 10 W m-2. Meanwhile, an increase of about 10 W m-2 was detected both for heat flux in soil and sensible heat on land surface, which is not neglectable to the short-term climate change. The results show that considerable response in land surface energy budget could be expected when the soil thermal forcing reaches a certain spatial-tem poral scale. Therefore, land surface models should not ignore the upward heat flux from the bottom of the soil layer. Moreover, integration for a longer period of time and coupled land-atmosphere model are also necessary for the better understanding of this issue.     

发动机燃烧室内壁材料热环境的气动热试验模拟技术研究

利用超声速矩形湍流导管和等离子电弧加热器模拟了发动机燃烧室内流和高超声速飞行器外壁面外流热环境,进行了平板表面冷壁热流测量和燃烧室内壁材料考核试验。结果表明:由于辐射换热的影响,在选取的两个典型来流条件下,发动机燃烧室内流热环境下的冷壁热流比外流热环境下的高出21%和40%,但是冷壁热流的增量基本相当,约为0.70~0.80MW/m²。随着冷壁热流的增加,辐射换热产生的热流增量的影响力会逐渐减小。材料考核时,相同配方的C/SiC复合材料在内流热环境下的表面温度高出约400℃,背面温度高出约90℃,这种差异对于发动机燃烧室内壁面材料考核至关重要,必须在材料考核试验中加以考虑。      

1951-2013年浙江热量资源变化研究

基于1951—2013年浙江71个常规气象站逐日气温资料和1971—2013年土温资料,采用M-K检验、气候倾向率等气候统计诊断方法,分析浙江地区气温、地温、农业界限温度等热量资源要素的变化规律及气候变化下浙江热量资源区划的年代际变化特征,以期为充分利用气候资源、优化农业结构和作物引种提供科学依据。研究结果表明：1）1951—2013年浙江四季和年平均气温变化幅度分别为0.24 ℃·10a-1(P0.01)、0.10 ℃·10a-1(P0.05)、0.14 ℃·10a-1(P0.01)、0.14 ℃·10a-1(P0.05)和0.18 ℃·10a-1(P0.05),突变起始年分别为1997、2005、2002、1993和1999年;2）1951—2013年浙江平均极端最高、最低气温的气候倾向率分别为0.10 ℃·10a-1(P0.01)和0.30 ℃·10a-1(P0.10);3）土温与农业密切相关,0—20 cm土温与日平均温度通过0.01显著性相关;4）气候变暖导致浙江农业界限温度(0 ℃、5 ℃、10 ℃、15 ℃、20 ℃)初日提前、终日延后、持续天数和积温增加,农业界限温度初日、终日、持续天数和积温的气候倾向率平均值分别为-3.2～-1.5 d·10a-1、0.3～1.6 d·10a-1、2.8～4.1 d·10a-1、95.7～107.3 ℃·d·10a-1;5）1951—2013年浙江初霜延迟、终霜提前、无霜期增加,初霜、终霜和无霜期气候倾向率平均为-3.62 d·10a-1、1.32 d·10a-1和4.97 d·10a-1;6）气候变暖导致浙江热量资源区划变化显著,2001—2010年淳安建德浦江义乌诸暨嵊州天台三门线以南地区≥10 ℃积温超过5600 ℃·d。     

基于SuperMap的空间大数据可视化技术研究与应用

人类正从IT时代走向DT时代,诸多的技术手段使大数据处理技术能够从大规模数据中分析出相关模式或趋势,让我们对海量数据的价值挖掘充满了期待.当可视化呈现让大数据的潜力达到最大时,以往未被观察到的现象或趋势很容易被发现,用户能够快速地获得更多信息,发现他们所需要的价值.因此,如何最大化地呈现大数据中隐含的价值变得尤为关键.本文将重点研究空间大数据的可视化方法,充分发挥数据可视化的作用,帮助用户挖掘隐藏在空间大数据中的价值.     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

基于城市地表温度与归一化植被指数对北京市建成区热环境分析

针对研究城市热环境的过程中,利用归一化植被指数(NDVI)进行地表温度(LST)反演,再将LST和NDVI结合说明地物变迁与城市热环境的影响的现状,利用Landsat-8多时相遥感影像、高分辨率影像、公开GIS等多源数据,通过人工交互判读和量化统计分析,实现了2013—2017年北京建成区NDVI变化及其对地表热环境影响分析,再对分析结果进行差值拟合评价。对NDVI阈值分割按照大小为LC1、LC2、LC3、LC4,对LST分为高温区(TH)、常温区(TR)和低温区(TL)。结果表明,2013年至2017年:1)建成区的平均NDVI增加0.03,其中LC1增加1.0%,LC2减少11.6%,LC3区域减少1.7%,LC4区域增加12.3%。2)建成区平均LST增加2.55 K,TH百分比增加0.6%;TR百分比减少1.1%,TL百分比增加0.5%。3)NDVI相对增加,地表温度相对下降以及NDVI相对减少,地表温度相对上升占60%,NDVI相对下降,地表温度相对下降以及NDVI相对增加,地表温度相对上升的占40%。