With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.
The potential for using the ensemble square root filter data assimilation technique to estimate soil moisture profiles, surface heat fluxes, and the state of the planetary boundary layer （PBL） is explored. An observing system simulation experiment is designed to mimic the assimilation of near-surface soil moisture observations （θo ） and in-situ measurements of 2-m temperature （To ）, 2-m specific humidity （Qo ）, and 10-m horizontal winds [Vo =（Uo , Vo ）]. The background forecasts are generated by a one-dimensional coupled land surface-boundary layer model （CLS-BLM） with soil, surface-layer and PBL parameterization schemes similar to those used in the Weather Research and Forecasting （WRF） model. Soil moisture, surface heat fluxes, and the state of the PBL evolve on different characteristic timescales, so the minimum assimilation time intervals required for skillful estimates of each target component are different. Correct estimates of the soil moisture profile are obtained effectively when a 6-h update time interval is used, while skillful estimates of surface fluxes and the PBL state require more frequent updates. The CLS-BLM requires a shorter assimilation time interval to correctly estimate the soil moisture profile than previously indicated by experiments using an off-line land surface model （LSM）. Results from assimilating different subsets of observations show that θo makes a larger contribution to soil moisture estimates, while To , θo , and Vo are more important for estimates of surface heat fluxes and the PBL state. It is therefore necessary to combine these variables to accurately estimate the states of both the land surface and the PBL. Experimentation with different prescribed observational errors shows that the assimilation system is more sensitive to increases in observational errors than to reductions in observational errors.
