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Assimilation ofMODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4

机译:通过数据同化研究试验台和社区土地模型第4版对MODIS雪盖进行同化

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To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (23°-45°N). Only minimal modifications are made in the higher-middle (45°-66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.
机译:为了改善社区土地模型第4版(CLM4)中的积雪估计,通过数据同化研究试验台(DART)将中等分辨率成像光谱仪(MODIS)的积雪分数(SCF)吸收到社区土地模型第4版(CLM4)中。 CLM4和DART之间的接口是一种灵活,可扩展的地面数据同化方法。该数据同化系统具有较大的集合(80个成员)大气强迫,可促进基于集合的土地数据同化。我们使用40个随机选择的强制成员来驱动40个CLM成员,以平衡计算成本和数据同化性能。调整了DART中的参数本地化距离,以优化全局范围内的数据同化性能。雪水当量(SWE)和雪深通过集合调整卡尔曼滤波器进行调整,特别是在SCF变化较大的区域。相对于MODIS SCF的预测SCF的均方根误差已大大降低。在DJF(12月-1月-2月)中,MODIS和CLM4之间的差异在中低纬度(23°-45°N)得到了广泛改善。在中高纬度(45°-66°N)和高纬度地区仅进行了最小的修改,部分原因是当积雪接近100%时模型与观测值之间的一致性。在某些地区,它还揭示出与MODIS相比,CLM4建模的积雪缺乏异质特征。在MAM(3月-4月-5月)中,对雪的调整向极移,主要是由于雪线向北移动(即,SCF不确定性最大且SCF同化影响最大)。数据同化的有效性也随植被类型的不同而变化,在森林区域的混合表现和在草地上的表现始终如一,这在一定程度上可以用模型集合中SCF和SWE之间关系的线性来解释。将更新后的积雪深度与加拿大气象中心(CMC)的数据进行了比较。在密集监视的区域中,CMC和CLM4之间的差异通常会减小。

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