Boundary Layer and Surface Verification of the High-Resolution Rapid Refresh, Version 3
Boundary Layer and Surface Verification of the High-Resolution Rapid Refresh, Version 3
复制标题
高分辨率快速刷新版本 3 的边界层和表面验证
DOI：
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发表时间：
2020
影响因子：
2.9
通讯作者：
A. Gallagher
中科院分区：
文献类型：
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作者：
R. Fovell;A. Gallagher
虽然数值天气预报模式在预报技巧方面取得了相当大的进展，但对行星边界层的关注较少。这项研究利用高分辨率快速刷新（HRRR）预测本地水平，1-s无线电探空仪数据，并（主要是机场）在美国接壤的表面观测。我们构建的时间和空间平均复合材料的风速和潜在的温度在最低的1公里选定的几个月，以确定在这一关键层的预测和观测的系统误差。我们发现近地表温度和风速的预测是熟练的，虽然风的偏差与观测到的速度和温度偏差呈负相关，揭示了一个强大的关系与站海拔。在地面以上250米，低于无线电探空仪的风数据显然受到污染的处理，偏差是小的风速和潜在的温度在分析时间（其中包括探空仪数据），但成为实质性的24小时预报。风偏置是积极的，通过层为0000和1200 UTC，和早晨的潜在温度分布的特点是过于陡峭的直减率，持续跨季节和（再次）夸大在海拔较高的网站。虽然这些系统性误差的来源或原因尚未完全了解，但本分析强调了模型可能改进的领域，以及需要持续和可访问的数据档案，使此类分析成为可能。
While numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.