基于卫星和再分析数据的大气水循环变量比较和分析

发布时间：2018-09-14 18:55

【摘要】：水循环是指地气系统的水在太阳辐射和重力作用下以蒸发、降水和径流等方式进行的周而复始的运动过程,它所涉及的大气物理量包括大气中的水汽,云中的水凝物及降水等。水循环既受气候系统的制约,又反过来通过影响大气环流以及气候变化对气候系统进行反馈,因此研究水循环相关物理量将有助于我们更加深刻地认识地气系统,以及更准确地评估未来气候变化。本文所涉及的水循环变量主要包括两大类,即水凝物和水汽。对于前者,我们选择了目前较为常用的几种水凝物数据,包括卫星观测资料ISCCP、MODIS和CloudSat,以及再分析资料CFSR和ERA,对中国及其周边地区水凝物的气候态水平及垂直分布特征以及水凝物的季节变化进行了比较研究,并对不同资料之间水凝物的不确定性进行评估;对于后者,我们主要是利用热带测雨卫星TRMM搭载的微波成像仪(TMI)多年水汽反演结果,对热带洋面上的水汽日变化特征进行研究。结果表明:在总的水凝物含量方面,MODIS、ERA和CFSR三种资料都显示出较高的一致性,无论是在描述整个中国及周边地区的水平分布特征和主要变化模态,还是在描述不同区域的月变化方面。其中,MODIS数据的绝对数值和变化幅度都最大,CFSR在陆地上与其他资料相差不大,而ERA则是三者中较小。与之相比,尽管ISCCP也能捕捉到水凝物总量的水平分布和区域月变化的一些主要特征,但在一些具体细节和时间与空间的相关性方面与其他三种数据存在一定差别,且绝对数值和变化幅度都是最小的。在液态水含量方面,几种数据间的一致性更高。但是,两套观测数据MODIS和ISCCP的绝对值要明显低于再分析资料ERA和CFSR。特别地,ERA的液水含量占总水凝物的比例最大,且值都较CFSR更高。上述差异导致在低纬洋面和青藏高原南侧等气候敏感区域,LWP的"不确定性"高达60%以上,这需要引起足够的重视。对于冰水含量,不同数据间无论是分布形式还是具体数值都存在明显差异,因此除云贵川地区外,其他区域的"不确定性"都较大。其中MODIS数据的值最高,CFSR次之,而ERA冰水含量占总水凝物比例最低,其绝对数值也最低。研究水凝物的季节分布以及季节变化特征可以发现,中国大部分地区夏季的云水路径比冬季高,中国及其周边地区水凝物季节变化特征显著,不同资料在反映云水产品季节分布时大致相似,但观测资料ISCCP、MODIS,再分析资料CFSR、ERA在反应IWP的高值中心有显著不同。不同区域水凝物分布受地形、大气环流、水汽输送等因素影响,地域特征明显。对于水凝物的垂直分布,中国南方地区的液态水含量则明显高于北方。再分析资料与CPR的观测结果相比,ERA的液态水含量偏高而CFSR的液态水含量整体偏低,且高值中心与观测资料的高值中心存在差异。对于冰水含量的垂直分布,再分析资料CFSR、ERA的冰层都较CPR更加深厚,且模式资料较观测资料的值也有差异,表明模式有可能高估了中国地区云的热动力学过程,模式的冰云参数化方案与观测相比仍存在较大的不确定性。总而言之,观测资料ISCCP、MODIS、CPR和再分析资料ERA、CFSR在反映中国及其周边地区的水凝物时基本相似,但由于卫星仪器的不同,且不同模式选用的参数化方案不尽相同,不同资料之间反映的水凝物的值也有较大差异。事实上需再次强调的是,不同的水凝物数据各自均存在可能的误差来源(其中部分甚至是无法克服的),例如基于星载被动光谱类传感器的观测结果无法获取冰云下的云水信息,基于云雷达的云水和冰水划分标准值得商榷,不同模式的参数化方案和下垫面处理方式存在不确定性等。因此,本文的目的并非通过比较来明确哪种数据的水凝物结果更好,而仅是指出不同数据间可能存在的差异,以便在使用相应数据进行分析时对其"不确定性"程度有所认识,从而更好地估计云的辐射效应,以及理解其在气候变化中所扮演的角色。另一方面,对于水循环的另一个重要变量水汽的研究结果表明,在水汽分布方面,热带地区洋面的水汽分布呈现由赤道向两极递减的趋势,水汽主要集中在印度洋面孟加拉湾、印度尼西亚附近海域,太平洋面暖池地区以及赤道辐合带。在秘鲁沿岸、美国西海岸等水汽下沉区,水汽的值较低。再分析资料和观测反演产品在反映水汽的整体分布情况时一致性很高。对反演产品水汽的日变化进行周期性分析后可发现,洋面上大部分区域水汽的日变化普遍具有很好的周期性,以24小时或12小时为主。我们进一步利用反演的水汽资料对洋面上几个典型区域的水汽日变化特征进行了分析,结果表明,洋面上水汽普遍具有很好的周期性,大部分区域水汽的日变化都在当地时傍晚或更晚些的时候达到峰值。此外,这些区域的水汽日变化可能受到海温、风速、以及太阳短波辐射的影响。
[Abstract]:Water cycle refers to the cyclic movement of water in the geo-atmospheric system by means of evaporation, precipitation and runoff under the action of solar radiation and gravity. The atmospheric physical quantities involved include water vapor in the atmosphere, water condensate in clouds and precipitation. Water cycle is constrained by both the climate system and, in turn, by affecting atmospheric circulation. The water cycle variables involved in this paper mainly include two categories, i.e. water condensate and water vapor. Several kinds of water condensate data, including ISCCP, MODIS and Cloud Sat, and reanalysis data CFSR and ERA, were used to compare the horizontal and vertical distribution characteristics of climatic state and seasonal variation of water condensate in China and its surrounding areas, and to evaluate the uncertainty of water condensate between different data. In the latter case, we mainly use the Microwave Imager (TMI) carried by TRMM to study the diurnal variation characteristics of water vapor over the tropical ocean. The results show that MODIS, ERA and CFSR data show high consistency in describing the whole China and the whole week. The absolute value and amplitude of variation of MODIS data are the largest, CFSR on land is not much different from other data, and ERA is relatively small. In contrast, although ISCCP can also capture the horizontal distribution of the total amount of water condensate. There are some main features of regional monthly variations, but there are some differences in some specific details and spatial and temporal correlations with the other three types of data, and the absolute values and amplitudes of variations are minimal. Especially, the liquid water content of ERA is higher than that of CFSR. The above differences lead to more than 60% uncertainty of LWP in climate sensitive areas such as the low latitude ocean surface and the southern side of the Qinghai-Tibet Plateau, which needs to be paid enough attention. In addition to Yunnan-Guichuan area, the "uncertainty" of other areas is large, in which MODIS data is the highest, followed by CFSR. ERA ice water content accounts for the lowest proportion of total condensate, and its absolute value is also the lowest. It is found that the cloud-water path in summer is higher than that in winter in most parts of China. The seasonal variation of water condensate in China and its surrounding areas is remarkable. Different data are similar in reflecting the seasonal distribution of cloud-aquatic products, but the observed data ISCCP, MODIS, reanalysis data CFSR, ERA are significantly different in the high value centers of IWP. The distribution is affected by topography, atmospheric circulation, water vapor transport and other factors. The liquid water content in southern China is obviously higher than that in northern China for the vertical distribution of water condensate. Regarding the vertical distribution of ice water content, the ice layers of CFSR and ERA are deeper than that of CPR, and the values of model data are also different from those of observation data, indicating that the model may overestimate the thermodynamic process of cloud in China. The ice cloud parameterization scheme of the model is still larger than that of observation. All in all, the observed data ISCCP, MODIS, CPR and reanalysis data ERA, CFSR are basically similar in reflecting the condensate in China and its surrounding areas, but because of the different satellite instruments and the different parameterization schemes used in different models, the values of the condensate reflected by different data are also quite different. Again, it is emphasized that there are possible sources of errors (some of which are even insurmountable) for different water condensate data, such as the inability to obtain cloud-water information under ice clouds based on observations from spaceborne passive spectral sensors, the criteria for cloud-water and ice-water partitioning based on cloud radar are questionable, and the parameterization of different models is questionable. Therefore, the purpose of this paper is not to determine which data is better for the condensate results, but only to point out the possible differences between the different data, so as to understand the degree of uncertainty in the analysis of the corresponding data, so as to better estimate the radiation of the cloud. On the other hand, studies of water vapor, another important variable in the water cycle, show that the water vapor distribution over the tropical ocean is decreasing from the equator to the poles, with water vapor mainly concentrated in the Bay of Bengal, the Indian Ocean, and Indonesia. Nearby waters, warm pools in the Pacific Ocean and equatorial convergence zones. Water vapor values are low along the Peruvian coast, the western coast of the United States and other water vapor subsidence zones. The diurnal variation of water vapor in some regions is generally of good periodicity, mainly 24 hours or 12 hours. The diurnal variation characteristics of water vapor in several typical regions on the ocean surface are analyzed by using the inverted water vapor data. The results show that the water vapor on the ocean surface generally has good periodicity, and the diurnal variation of water vapor in most regions is all. In addition, diurnal variations of water vapor in these regions may be affected by sea temperature, wind speed, and solar short-wave radiation.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P339

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