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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics
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Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics

机译：使用创新性能指标对每月土地表面温度模式进行大型陆地表面模型的空间验证

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摘要

Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High-Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.
机译：陆地表面模型（LSM）是增强过程理解的关键工具，并提供对陆地水圈及其大气耦合的预测。分布的LSM在每个网格细胞上预测水文状态和助水管，例如陆地表面温度（LST）或实际蒸发（AET）。 LST观测可通过卫星遥感平台广泛使用，可实现LSM的全面空间验证。尽管LST数据的可用性很大，但大多数验证研究依赖于简单的小区到细胞比较，因此不要认为真正的空间模式信息。本研究的核心新颖性是开发和应用两种创新的空间绩效指标，即经验正交功能（EOF）和连接分析，以通过三个LSM（Mosaic，NoAh，可变渗透容量（VIC））验证预测的LST模式在连续的美国。 LST验证数据集源自全球高分辨率红外辐射测量检测器30年。度量是偏差不区分大小写，这是一个重要的特征，以便真正验证空间模式。 EOF分析评估了空间变异性和模式季节性，并在温暖的月份和寒冷的月份到马赛克和诺亚，证明了更好的性能。此外，可以通过与空气温度强烈相关的单个图案来捕获超过75％的LST可变性。连接性分析评估了模式的均匀性和平滑度。 LSM最可靠地预测温暖月份的冷LST模式，反之亦然。最后，在通量塔部位研究AET和LST之间的耦合，并与LSM进行比较以解释所确定的LST缺点。

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