利用Alg@line系统对波罗的海监测数据进行统计分析的方法和结果

V. Rozhkov, E. Litina, S. Kaitala, Y. Klevantsov, E. Zakharchuk
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引用次数: 0

摘要

该报告介绍了在Alg@line项目框架内实施的海水温度和盐度测量的统计分析。该信息的特殊性在于,测量是在5米深度进行的,时间离散性约为20秒,空间分辨率为200-250米。在统计分析中,我们将把期望位形空间中的实现作为集合。本研究使用的是赫尔辛基- 贝克航线的测量数据;航行具有准规律性:平均航行时间约为26小时,航段长度L=1132 km,部分区域航速为随机变量,航行时间有季节性和年际变化。由于巡航的“规律性”,在模式空间上可以将“空间场不均匀性及其时间变异性”的集合分解为代数场不均匀性及其变异性的多周期性(日、天气、季节和年际范围)。二维空间(ri, ti)在一维空间中的降维是通过依赖ri=cti实现的,其中(ri, ti)是固定的,c -船速-是随机变量。它可以用于数据分析理论的几乎周期性相关随机过程(Dragan, Rozhkov, Yayorskiy)。海洋过程韵律学的概率分析方法。Gidrometeoizdat, 1987)。在报告中,“节律”的概念是根据巡航的“规律性”和水的昼夜温度变化来使用的,因此每天的节律应该在天文时间内进行分析。随选择的概率模型不同,随机性具有不同的含义。概率模型可以表示为:ξ(r, t) = Σak(t)φk(r),其中ak(t) -随机过程,φk(r) -基。分析结果在报告中以以下形式呈现:典型的邮轮ts图,空间ts趋势,温度日节奏参数,考虑其季节性调制的天气变率参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods and results of statistical analysis of Baltic sea monitoring data obtained by Alg@line system
The report presents a statistical analysis of sea water temperature and salinity measurements, implemented in framework of Alg@line project. The specificity of this information is that the measurements are performed at a depth 5 m with temporal discreteness about 20 sec and spatial resolution 200-250 m. In statistical analysis we will take as ensemble of realizations in space of desired configuration. Measurement data obtained from route Helsinki-Lübeck were used in this work; the cruises are quasiregular: their average duration is about 26 hours, the sections length L=1132 km, vessel speed on some areas is a random variable, sailing schedule have seasonal and inter-annual changes. Due to cruises “regularity”, in pattern space it becomes possible to split the ensemble of “spatial field inhomogeneity and its temporal variability” into algebraic field inhomogeneity and polycyclicity of its variability (in daily, synoptic, seasonal and inter-annual ranges). The dimensionality reduction of two-dimensional space (ri, ti) in one-dimensional space is achieved due to dependence ri=cti, where (ri, ti) are fixed, c - ship speed - is the random variable. It enables to use for data analysis the theory of almost periodically correlated random processes (Dragan, Rozhkov, Yayorskiy. Methods of probabilistic analysis of oceanographic processes rhythmics. Gidrometeoizdat, 1987). In the report the concept “rhythmics” is using in terms of cruises “regularity” and diurnal temperature variation of water, hence the daily rhythm should be analyzed in the astronomical time. Stochasticity has different meaning depending on selected probabilistic model. Probabilistic model can be represented as: ξ(r, t) = Σak(t)φk(r), where ak(t) - stochastic process, φk(r) - basis. The analysis results are presented in the report in the form: TS-diagrams typical for cruises, spatial TS-trends, parameters of the temperature daily rhythmic, synoptic variability parameters, considering its seasonal modulation.
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