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首页> 外文期刊>Journal of hydrometeorology >A multimodel analysis, validation, and transferability study of global soil wetness products
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A multimodel analysis, validation, and transferability study of global soil wetness products

机译：全球土壤湿度产品的多模型分析，验证和转移性研究

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Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land-atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.
机译：研究表明，多模式集合预报可通过大气和海洋环流模型对气候预测技术提供系统的改进。但是，对于地表部分（天气和气候预测模型的重要下边界）所做的工作很少。在这项研究中，作者检查和评估了几种方法，这些方法结合了从未耦合的地表模型计算和耦合的陆-气模型重新分析中得出的各个全球土壤湿度产品，以进行整体分析。根据相关系数和均方根误差（RMSE）测得的技能，对照全球土壤水分数据库（GSMDB）的观察结果对分析进行了验证。还进行了初步的可转移性研究，以研究在两个特定区域（伊利诺伊州和中国中东部）以及在这两个气候和土地使用相似的区域之间转移集合回归参数的可行性。结果表明，如果有足够的验证数据，则可以使用季节相关的线性回归来提高任何单独的土壤湿度模型仿真的技能。可以通过更复杂的集成方法（例如，回归调整后的多模型集合均值分析和回归调整后的多模型分析）来进一步提高技能。但是，就模拟土壤湿度异常而言，所有涉及回归的集成方案通常都无法帮助提高技能得分。在没有校准数据的情况下，在表示土壤湿度及其异常的任何位置上，多个土壤湿度产品的简单算术平均均值通常比最佳单个模型好或更好。从伊利诺伊州或中国中东部数据集的一个站点子集到另一个站点的可转移性给出了令人满意的结果。但是，即使在相似的气候条件和土地覆盖下，在不同地区之间转移回归权重时，结果也很差。这样的练习可以帮助我们更好地理解各种集成技术的优点和局限性，并可以从实用的角度出发，朝着创建与模型无关的最佳分析方向发展。

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