Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
复制标题
使用聚类进行基于超分辨率集合的山区积雪再分析
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Westermann
中科院分区：
文献类型：
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作者：
J. Fiddes;Kristoffer Aalstad;S. Westermann
抽象的。高地貌景观的空间变异性是巨大的，基于网格的模型不能以空间分辨率运行来明确表示重要的物理过程。这妨碍了对水可获得性或大规模流动危险等重要问题当前和未来演变的评估。在这里，我们提出了一种新的处理链，它将有效的亚格子方法与降尺度工具和数据同化方法相结合，目的是改进对无资料流域多时空尺度上的地面过程的数值模拟。这种方法的新奇之处在于，虽然我们由于集合模拟而增加了1-2个数量级的计算成本，但与显式模拟高分辨率网格相比，我们节省了4-5个数量级。这种方法使得大时空尺度的数据同化成为可能。此外，该方法仅利用免费可用的全局数据集，因此能够全局运行。我们使用这种同化MODIS雪盖产品的方法在估计不同尺度的雪高度和雪水当量方面取得了显着的改进。我们建议，这是一种适合于各种业务和研究应用的方法，在这些应用中，地面模型需要在稀疏或不存在的地面观测中大规模运行，并具有同化地球观测任务检索的各种变量的灵活性。
Abstract. Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost due to ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.