挪威上空高分辨率月降水气候学：空间内插方法的评估
High-resolution monthly precipitation climatologies over Norway: assessment of spatial interpolation methods
摘要：
1981-2010年挪威大陆上空的月降水量气候资料分辨率为1 km。通过应用多线性局部回归克里金法（MLRK），在规则网格上对观测到的台站法线进行插值。统计方法的目的是模拟主要地理特征，如纬度，经度，海拔和海近对降水场的影响，在当地的尺度。MLRK由两个步骤组成，（i）通过基于地理信息的多线性局部回归计算背景降水场，然后（ii）应用克里金插值来调整场，以便更好地拟合站残差（即，观察到的法线和背景场之间的差异）。通过用留一法重建台站法线，并与其他插值方法进行比较，评价了模型的插值精度。在挪威偏远山区的观测覆盖率差，促使我们考虑数值模式产生的降水场。事实上，数值模型输出为MLRK的评估提供了一个参考场，该参考场不依赖于台站密度，尽管它不如观测准确。具体来说，使用了分辨率为2.5 km的区域气候模拟，该模拟基于全球再分析ERA Interim的动态降尺度，适用于2003-2016年。除了MLRK验证，数值模式场和观测气候的组合进行了研究，结果表明，这种综合方法提供了更准确的高分辨率的气候在全国。数值背景介绍了有价值的信息，在偏远地区和山区，而在现场数据校正模型的偏差。
英文摘要：
Monthly precipitation climatologies at 1 km resolution have been produced over the Norwegian mainland for 1981-2010. The observed station normals are interpolated over a regular grid by applying a multi-linear local regression kriging (MLRK). The statistical method aims at modeling the influence of the main geographical features, such as: latitude, longitude, elevation and sea nearness on the precipitation field at a local scale. The MLRK is composed of two steps, (i) a background precipitation field is computed through a multi-linear local regression based on the geographical information, then (ii) a kriging interpolation is applied to adjust the field so to better fit the station residuals (i.e., the difference between the observed normals and the background field). The interpolation accuracy is evaluated by reconstructing the station normals with a leave-one-out approach and by comparing the model performances with those of other interpolation methods. The poor observation coverage over remote and mountainous regions in Norway has motivated us to consider precipitation fields produced by numerical models. In fact, numerical model output provides a reference field for the evaluation of MLRK that is not dependent on the station density, though it is not as accurate as the observations. Specifically, a regional climate simulation with a resolution of 2.5 km, based on the dynamical downscaling of the global reanalysis ERAInterim and available for the time period 2003-2016, has been used. In addition to the MLRK validation, the combination of numerical model fields and observed climatologies has been investigated and the results show that this integrated approach provides more accurate high-resolution climatologies over the Country. The numerical background introduces valuable information over remote and mountainous regions, whereas in-situ data correct for model biases.
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