Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach
Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach
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亚热带山区降水数据的概率降尺度：两步法
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
K. Born
中科院分区：
文献类型：
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作者：
R. Haas;K. Born
抽象的。在这项研究中，一个两步的概率降尺度方法进行了介绍和评估。该方法是典型的应用降水观测在亚热带山区环境的高阿特拉斯在摩洛哥。面临的挑战是要处理复杂的地形、严重倾斜的降水分布以及空间和时间数据量稀少的情况。在该方法的第一步中，大规模的预测和本地观测的分布之间的传递函数。其目的是预测累积分布函数的参数从已知的数据。第二步利用当地地形信息对观测数据的分布参数进行多元线性回归，以进行站点间插值。通过结合这两个步骤，实现了在调查区域的每个点处的预测。这两个步骤及其组合通过交叉验证和将可用数据集拆分为训练子集和验证子集进行评估。由于估计的分位数和概率为零日降水量，这种方法被认为是足够的应用，即使在困难的地形环境和低数据可用性的地区。
Abstract. In this study, a two-step probabilistic downscaling approach is introduced and evaluated. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the High Atlas in Morocco. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data, both spatial and temporal. In the first step of the approach, a transfer function between distributions of large-scale predictors and of local observations is derived. The aim is to forecast cumulative distribution functions with parameters from known data. In order to interpolate between sites, the second step applies multiple linear regression on distribution parameters of observed data using local topographic information. By combining both steps, a prediction at every point of the investigation area is achieved. Both steps and their combination are assessed by cross-validation and by splitting the available dataset into a trainings- and a validation-subset. Due to the estimated quantiles and probabilities of zero daily precipitation, this approach is found to be adequate for application even in areas with difficult topographic circumstances and low data availability.