Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?
Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?
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DOI：
10.1029/2019jd030823
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
C. He;Fei Chen;M. Barlage;Changhai Liu;A. Newman;W. Tang;K. Ikeda;R. Rasmussen
中科院分区：
文献类型：
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作者：
C. He;Fei Chen;M. Barlage;Changhai Liu;A. Newman;W. Tang;K. Ikeda;R. Rasmussen
准确的降水估算对于模拟季节性积雪演变至关重要。我们利用多参数化（Noah‐MP）陆地表面模式（由对流允许（4‐km）天气研究与预报（WRF）模式驱动的降水强迫）和四个广泛使用的高分辨率数据集（基于原位测量的统计插值数据集），对2013水年美国西部（WUS）山区的高分辨率（4‐km）积雪进行了模拟并进行了评估。这5个数据集的降水量存在显著差异，特别是在WUS山脉的西部和北部，显著影响模拟雪水当量（SWE）和雪深（SD），但对积雪分数（SCF）和地表反照率的影响相对有限。WRF通常捕获观测到的降水模式，并在WUS山脉的西部和北部获得总体上表现最好的SWE和SD，在那里，统计插值的数据集导致降水、SWE和SD被低估。在WUS内部山区，所有数据集都一致低估降水，造成SWE和SD的显著负偏差，其中WRF降水驱动的结果表现平均。进一步的分析表明，在不同降水数据集驱动的模拟中，WUS山脉的SCF和地表反照率存在系统的正偏差，偏差模式和量级相似，这表明迫切需要改进Noah‐MP积雪物理。该研究强调，在典型的ENSO中性年，特别是在观测稀缺地区，对流允许的建模和适当的配置可以为WUS山区的高分辨率积雪模拟提供体面的降水提供附加价值。
Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.