Abstract
Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.
Highlights
Soil moisture (SM) is an important variable in coupled climate models and numerical weather prediction systems due to its impacts on land–atmosphere water, energy and carbon exchanges [1,2,3]
Active microwave radars are typically impacted by surface roughness and vegetation structure [11], and passive microwave-radiometers-based SM estimations are generally at tens of kilometers resolutions due to the limitations of satellite antenna technology [12,13,14]
The goal of this study is to identify the needs of finer spatial resolution SM data in the sequential SM data assimilation system, and in turn to investigate the potential application of higher spatial resolution SM observations in the operational models
Summary
Soil moisture (SM) is an important variable in coupled climate models and numerical weather prediction systems due to its impacts on land–atmosphere water, energy and carbon exchanges [1,2,3]. Optical and thermal infrared satellite SM sensing started in 1970, and several approaches were developed to exploit the relationships between surface reflectance and the SM [4,5,6]. These empirical relationships-based SM observations are significantly impacted by the soil spectral characteristics, and could not be obtained on cloudy days [7]. Active microwave radars are typically impacted by surface roughness and vegetation structure [11], and passive microwave-radiometers-based SM estimations are generally at tens of kilometers resolutions due to the limitations of satellite antenna technology [12,13,14]
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