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首页> 外文期刊>Surveys in Geophysics: An International Review Journal of Geophysics and Planetary Sciences >Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS
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Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

机译：通过土地数据同化将卫星观测与水循环变量联系起来：使用NASA GEOS-5 LDAS的示例

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摘要

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.
机译：土地数据同化系统（LDAS）可以将卫星观测到的地面水文状况（包括土壤湿度，积雪和陆地水存储（TWS））合并到地面过程的数值模型中。从理论上讲，这种系统的输出要优于仅基于观测值或单独基于模型的估计，从而增强了我们对陆地水循环关键要素的理解，监测和预测能力。然而，实际上，卫星观测并不直接对应于感兴趣的水循环变量。本文使用基于整体的基于NASA GEOS-5 LDAS的最新研究得出的示例来解决这种看似不匹配的各个方面。这些方面包括（1）将粗尺度观测资料同化为高分辨率的地表模型;（2）将卫星观测资料（例如TWS取回）划分为其水循环组成部分;（3）微波的正演模拟用于基于辐射的土壤湿度和雪吸收的陆地亮度温度;以及（4）选择最相关的观测类型，以分析未观测到的特定水循环变量（例如根区土壤湿度）。解决这些挑战的方法包括精心构建一个观测算子，该算子将从感兴趣的地表模型变量映射到同化观测的空间。

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