Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization

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.

Figure 1. The elevation (left) and MODIS plant functional type (right) of Rur Catchment.

Figure 2. The data assimilation flow chart.

Figure 3. Mean RMSE values of covered area soil moisture for open loop run (CLM) and 6 assimilation strategies of 1_Obs, 5_Obs, 9_Obs, 16_Obs, 25_Obs and 36_Obs for depths of 5 cm, 10 cm, 20 cm, 30 cm, and 50 cm.

Figure 4. Mean RMSE values of uncovered area soil moisture for open loop run (CLM) and 6 assimilation strategies of 1_Obs, 5_Obs, 9_Obs, 16_Obs, 25_Obs and 36_Obs for depths of 5 cm, 10 cm, 20 cm, 30 cm, and 50 cm.

Figure 5. Mean RMSE values of whole catchment soil moisture for open loop run (CLM) and 6 assimilation strategies of 1_Obs, 5_Obs, 9_Obs, 16_Obs, 25_Obs and 36_Obs for depths of 5 cm, 10 cm, 20 cm, 30 cm, and 50 cm.

Figure 6. The basin scale soil moisture RMSE values for reference run (Truth), open loop run (CLM), and the assimilation strategy of 9_Obs at depths of 10 cm, 30 cm, and 50 cm.

Figure 7. The basin scale average soil moisture for reference run (Truth), open loop run (CLM), and the assimilation strategy of 9_Obs at depths of 10 cm, 30 cm, and 50 cm.