对美国大陆诺阿模型和现场土壤湿度值及变异性的评估

Peter J. Marinescu, Daniel Abdi, Kyle Hilburn, Isidora Jankov, Liao-Fan Lin
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摘要

美国国家海洋和大气管理局(NOAA)的两个模型对土壤湿度的估算结果与实地观测结果进行了比较。估算结果来自一个高分辨率大气模型和一个地表模型(高分辨率快速刷新模型或 HRRR 模型),以及 NOAA 气候预测中心(CPC)的一个水文模型。与现场观测结果相比,这两种模式都会使干燥地区的土壤更潮湿,潮湿地区的土壤更干燥。这些土壤水分差异出现在大多数土壤深度,但在地表以下较深处(100 厘米)差异更大。土壤水分变异性的比较也作为土壤水分系统的函数进行了评估。在所有土壤水分状态下,两种模式的标准偏差都低于原地观测值。与较湿的土壤相比,HRRR 模式的土壤水分与较干土壤的原位观测值的相关性更好,而这一趋势在 CPC 模式的比较中并不存在。在季节性方面,土壤水分比较因指标、时间和土壤水分状况的不同而不同。因此,需要同时考虑季节性和土壤水分状况,以准确确定模型偏差。NOAA 的这些土壤水分估算值可用于各种预报和社会应用,了解它们的差异可为其应用提供重要的背景信息,并可改进模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of NOAA Modeled and In Situ Soil Moisture Values and Variability Across the Continental United States
Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model (High-Resolution Rapid Refresh or HRRR model) and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for dryer soils as compared to wetter soils – a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements.
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