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Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics

机译:使用同化诊断对SMAP 4级表面和根区土壤水分产品进行全球评估

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摘要

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m−3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) m3 m−3 for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.
机译:主动被动土壤水分(SMAP)任务4级土壤水分(L4_SM)产品提供3小时,9公里的分辨率,对土壤表面水分(0-5 cm)和根区(0-100 cm)的整体估计以及从2015年3月31日到现在的约2.5天延迟的相关陆地表面变量。基于整体的L4_SM算法将SMAP亮度温度(Tb)观测值纳入流域陆地表面模型。这项研究描述了空间分布的L4_SM分析,并评估了观测值减去预测值(O-F)的Tb残留量以及土壤湿度和温度分析的增量。由于同化之前Tb观测的气候变化,该分析基本上是无偏的,O-F Tb残留量的全球平均值约为0.37 K,而土壤水分和温度增量的平均值约为零。但是,OF残差(〜3 K以下)存在适度的区域(绝对)偏差,土壤水分增加(〜0.01 m 3 m -3 以下) ,并且表层土壤温度升高(约1 K以下)。 OF残差的典型瞬时值为〜6 K,表层(根区)土壤水分增量的〜0.01(〜0.003)m 3 m −3 和〜0.6 K为表层土壤温度增量。 O-F诊断表明,该系统的实际误差在沙漠和茂密的植被地区被高估了,而在农业地区和干旱和潮湿气候之间的过渡带中却被低估了。 O-F自相关表明,SMAP观测在北美西部,萨赫勒地区和澳大利亚得到了有效利用,但在许多森林地区和北部高纬度地区却没有得到有效利用。澳大利亚的一个案例研究表明,吸收SMAP观测值可以成功纠正L4_SM降雨强迫中的短期误差。

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