Xue Yuan, Houser Paul R, Maggioni Viviana, Mei Yiwen, Kumar Sujay V, Yoon Yeosang
George Mason University, Fairfax, VA, United States.
Hydrological Sciences Laboratory, NASA/GSFC, Greenbelt, MD, United States.
Front Earth Sci (Lausanne). 2019;7. doi: 10.3389/feart.2019.00115. Epub 2019 May 22.
Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0-10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10-40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.
为了确定亚洲高山地区（HMA）的水文变化情况，本研究探索使用美国国家航空航天局（NASA）陆地信息系统中开发的高分辨率（1公里）陆地数据同化（DA）框架，该框架采用诺亚多参数化陆面模型（Noah-MP），并由来自《现代时代回顾分析用于研究和应用》第2版数据的气象边界条件驱动。进行了两组不同的DA实验：（1）同化卫星衍生的积雪覆盖图（MOD10A1）；（2）同化2007年至2008年NASA测量的景观冻融产品。通过与现有的基于遥感的雪水当量产品和地面雪深测量结果进行比较，评估积雪覆盖同化的性能。例如，在与地面雪深测量结果的比较中，大多数站点（14个中的13个）由于雪数据同化，拟合优度统计略有改善，但只有4个具有统计学意义。此外，与基于卫星的陆地表面温度产品（MOD11A1和MYD11A1）的比较表明，冻融数据同化在均方根误差（RMSE）方面（在某些网格单元）最多可将误差降低0.58 K，在绝对偏差方面（相对于仅模型模拟）最多可降低0.77 K。在与沿喜马拉雅山脉的三个地面土壤温度测量结果的比较中，0至10厘米土壤温度的偏差和RMSE分别平均降低了10%和7%。土壤估计顶层的改善也传播到更深的土壤层，其中10至40厘米土壤温度的偏差和RMSE分别平均降低了9%和6%。然而，在与中低海拔地面表面温度测量结果的比较中，未观察到冻融数据同化系统在统计上有显著的技能差异。因此，所提出的两种数据同化方案显示出改善整个亚洲高山地区雪量、地表温度和土壤温度状态预测能力的潜力，但仍需要更多的地面测量，特别是在高海拔地区，以便记录这两种数据同化方案带来的更具统计学意义的改善。
