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首页> 外文期刊>Forstwissenschaftliches Centralblatt >From discretely located to spatially interpolated forest meteorologicaldata - reconstruction of missing values by approximate estimation offorest meteorological data [German]
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From discretely located to spatially interpolated forest meteorologicaldata - reconstruction of missing values by approximate estimation offorest meteorological data [German]

机译：从离散的位置到空间内插的森林气象数据-通过对森林气象数据的近似估计来重建缺失值[德语]

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摘要

In the German state Rheinland-Pfalz the Forstliche Versuchsanstalt Rheinland-Pfalz acquires forest relevant data at 31 weather stations. Despite sophisticated measuring techniques data gaps often occur, so that it becomes indispensable to develop new approximation methods to close those gaps. Such methods require a spatial distribution of meteorological data, changing at short temporal intervals, from measurements taken at discrete locations, including a proper error estimation. For this purpose, two geomathematically funded deterministic approaches are presented in this article. Realistic approx imations of missing data as well as smoothing of error-affected data can be achieved by a multivariate spline interpolation and smoothing method, taking into account the spherical curvature of the earth as well as the real topography. This multivariate interpolation method also enables us to produce maps of climatological data. Following a different approach, neural networks determine missing data by utilising measurements acquired in the past, thereby neglecting topographical factors. This article presents error analyses for the estimation of missing data of daily mean air temperature by means of various error types (e.g. mean absolute error). These analyses and comparisons with other studies show that both approaches are suitable to solve the problem of closing data gaps.
机译：在德国莱茵兰-普法尔茨州，Forstliche Versuchsanstalt莱茵兰-普法尔茨州在31个气象站获取了与森林有关的数据。尽管采用了先进的测量技术，但经常会出现数据间隙，因此，开发新的近似方法以弥合这些间隙变得必不可少。此类方法需要从离散位置进行的测量（包括适当的误差估计）在短时间间隔内变化的气象数据的空间分布。为此，本文介绍了两种由数学方法资助的确定性方法。考虑到地球的球面曲率以及真实的地形，可以通过多元样条插值和平滑方法来实现丢失数据的逼真的近似以及受错误影响的数据的平滑。这种多元插值方法还使我们能够生成气候数据图。按照不同的方法，神经网络通过利用过去获得的测量值来确定丢失的数据，从而忽略了地形因素。本文介绍了通过各种误差类型（例如平均绝对误差）估算每日平均气温缺失数据的误差分析。这些分析和与其他研究的比较表明，这两种方法都适合解决缩小数据缺口的问题。

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