Abstract
The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.
Highlights
Soil moisture plays an important role in the catchment scale water cycle and land-atmosphere interactions [1,2,3]
The horizontal spatial correlation characterizations of microwave brightness temperature data were fitted using the geostatistical semivariogram and incorporated into local ensemble transform Kalman filter (LETKF) analysis to solve the problem of spatial availability of observations by means of observation localization (OL)
The selection of local correlated brightness temperature observations in OL considered the observations located in a local region surrounding the model grid cell to be assimilated, and depended on the observational spatial correlated characteristics, which was modeled using the geostatistical semivariogram fitting methods
Summary
Soil moisture plays an important role in the catchment scale water cycle and land-atmosphere interactions [1,2,3]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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