In this study we derive the environmental lapse rate (ELR) from vertical profiles of temperature in the lower troposphere, applying it to downscale air temperature of the new European Centre For Medium‐Range Weather Forecasts (ECMWF) reanalysis ERA5, which replaces ERA‐Interim (ERAI). We focus over the western U.S. region, a data‐rich area with observations of daily maximum and minimum temperature (Global Historical Climatology Network) and snow depth and soil temperature. Observations indicate an ELR of −4.5 K·km⁻¹ in the region, lower than the commonly used −6.5 K·km⁻¹. ERA5 ELR agrees with the observational estimates, with some overestimation in winter and limitations in the diurnal variability. The elevation correction of ERA5 temperature using different ELR showed the benefits of deriving ELR fields from ERA5 vertical profiles, when compared with a constant ELR. Simulations with the ECMWF land surface model, at 9‐km resolution, driven by ERA5 using different ELR corrections showed the added value of the methodology, but the impact of different ELR corrections is limited. However, the validity of the downscaling method in reducing temperature to station altitude suggests that there is sufficient generality for application at kilometer and subkilometer resolutions. By comparing the estimated representativity errors of observations with reanalysis, the improvements from ERAI to ERA5 are mainly visible in the random component of the error. Large systematic biases remain, which require further attention from the modeling and data assimilation, and limit the potential benefits of ELR corrections.

中文翻译：

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高分辨率土地表面缩小的环境流失率：在ERA5中的应用

在本研究中，我们从对流层低层温度的垂直剖面中得出环境衰减率（ELR），并将其应用于新的欧洲中距离天气预报中心（ECMWF）重新分析ERA5的下限气温，该温度替代了ERA-Interim （ERAI）。我们将重点放在美国西部地区，这是一个数据丰富的区域，可观察到每日最高和最低温度（全球历史气候网络）以及积雪深度和土壤温度。观测表明该区域的ELR为-4.5 K·km ^-1，低于常用的-6.5 K·km ^-1。ERA5 ELR与观测值吻合，在冬季过高估计了日变化的局限性。与恒定的ELR相比，使用不同的ELR进行ERA5温度的高程校正显示出从ERA5垂直剖面导出ELR场的优势。由ERA5使用不同ELR校正在9 km分辨率下以ECMWF地表模型进行的仿真显示了该方法的附加值，但不同ELR校正的影响有限。但是，降尺度方法在将温度降低到站点高度方面的有效性表明，有足够的通用性适用于千米和亚千米分辨率。通过将观测值的估计代表性误差与重新分析进行比较，从ERAI到ERA5的改进主要体现在错误的随机分量中。仍然存在较大的系统偏差，这需要建模和数据同化进一步加以注意，并限制了ELR校正的潜在优势。