首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >What Causes the Unobserved Early-Spring Snowpack Ablation in Convection-Permitting WRF Modeling Over Utah Mountains?
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What Causes the Unobserved Early-Spring Snowpack Ablation in Convection-Permitting WRF Modeling Over Utah Mountains?

机译:是什么导致了犹他州山脉上空允许对流的WRF模型中未观察到的早春积雪消融?

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摘要

Accurate prediction of snowpack evolution and ablation is critical to supporting weather and hydrological applications. Convection-permitting modeling has been shown to well capture observed snowpack evolution over many western United States (U.S.) mountain ranges, but some significant ablation biases still remain. In this study, we conduct process-level snowpack analyses of a widely used convection-permitting (4-km) weather research and forecasting (WRF) modeling product (WRF4km) for the contiguous U.S. to understand the mechanisms causing its unobserved early-spring snow ablation over Utah mountains. Analyses across Utah Snowpack Telemetry (SNOTEL) sites show that the unobserved snowpack ablation during mid-February to late-March in WRF4km is driven by multiple strong melting events. The melting results from the enhanced downward sensible heat flux to snowpack and enhanced ground solar radiation absorption, with generally larger contributions from the former before early March and from the latter after early March. The enhanced downward sensible heat flux to snowpack is mainly due to the enhanced surface heat exchange coefficient induced by high surface wind speeds. The enhanced ground solar radiation absorption is driven by both enhanced surface downward solar radiation and strong melting-induced snow cover reduction that is caused by deficiencies in Noah-MP snow-related parameterizations used in WRF4km. The substantial snow cover reduction during melting decreases surface albedo and hence triggers a positive albedo feedback that further accelerates melting. Our analyses reveal possible deficiencies in WRF and Noah-MP (e.g., canopy processes and snow albedo) and shed light on future directions for model improvements.
机译:准确预测积雪的演变和消融对于支持天气和水文应用至关重要。允许对流的建模已被证明可以很好地捕捉美国西部许多山脉上观测到的积雪演变,但仍然存在一些明显的消融偏差。在这项研究中,我们对美国本土广泛使用的允许对流(4 公里)天气研究和预报 (WRF) 建模产品 (WRF4km) 进行了过程级积雪分析,以了解导致其未观察到的早春积雪消融的机制犹他州山脉。犹他州积雪遥测 (SNOTEL) 站点的分析表明,2 月中旬至 3 月下旬在 WRF4km 未观察到的积雪消融是由多个强烈融化事件驱动的。融化是由于积雪的感热通量增加和地面太阳辐射吸收增强所致,前者在3月初之前的贡献普遍较大,后者在3月初之后的贡献更大。积雪的向下显热通量增强主要是由于高地表风速引起的地表热交换系数增强。地面太阳辐射吸收的增强是由增强的地表向下太阳辐射和强烈的融化引起的积雪减少驱动的,这是由于WRF4km中使用的Noah-MP雪相关参数的缺陷引起的。融化过程中积雪的大量减少降低了地表反照率,从而触发了正反照率反馈,从而进一步加速了融化。我们的分析揭示了WRF和Noah-MP中可能存在的缺陷(例如,冠层过程和雪反照率),并阐明了模型改进的未来方向。

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