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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：使用创新的性能指标针对月度地表温度模式对大型地表模型进行空间验证

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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）是增强对过程的了解并提供对陆地水圈及其大气耦合的预测的关键工具。分布式LSM预测每个网格单元的水文状态和通量，例如地表温度（LST）或实际蒸散量（aET）。 LST观测可通过卫星遥感平台广泛使用，该平台可对LSM进行全面的空间验证。尽管LST数据的可用性很高，但是大多数验证研究都依赖于简单的单元间比较，因此并未考虑真正的空间模式信息。这项研究的核心新颖之处在于开发和应用了两个创新的空间性能指标，即经验正交函数（EOF）和连通性分析，以通过三个LSM（马赛克，诺亚，可变渗透能力（VIC））验证预测的LST模式。在邻近的美国。 LST验证数据集来自30年来全球高分辨率红外辐射测深仪的检索结果。度量标准对偏差不敏感，这是真正验证空间格局的重要功能。 EOF分析评估了空间变异性和模式季节性，并在温暖月份证明了对VIC的性能更好，在寒冷月份证明了对马赛克和Noah的性能。此外，可以通过与空气温度高度相关的单个模式捕获超过75％的LST变异性。连通性分析评估图案的均匀性和平滑性。 LSM在预测温暖月份的冷LST模式时最为可靠，反之亦然。最后，在通量塔位置对aET和LST之间的耦合进行了研究，并与LSM进行了比较，以解释所发现的LST缺点。

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