A new merging scheme (referred to as HL‐OI) was developed to combine daily precipitation data from high‐resolution gauge (HRG) observations, The Climate Prediction Center morphing technique (CMORPH) satellite estimates, and National Centers for Environmental Prediction (NCEP) numerical simulations over China to perform reliable high‐resolution daily precipitation analyses. The scheme is designed using a three‐step strategy of removing systemic biases, reducing random errors, quantitatively estimating error variances, and combining useful information from each data source. First, a cumulative distribution function matching procedure is adopted to reduce biases and provide unbiased background fields for the following merging processes. Second, the developed error estimation algorithm is implemented to quantify both the background and observation errors from the background departures. Third, the bias‐corrected NCEP and CMORPH data are combined with the HRG data using the optimal interpolation (OI) objective analysis technique. The magnitudes and spatial structures of both observation errors and background errors can be estimated successfully. Results of cross‐validation experiments show that the HL‐OI scheme effectively removes most of systemic biases and random errors in the background fields compared to the independent gauge observations and is robust even with imperfect background fields. The HL‐OI merging scheme significantly improves the temporal variations, agreements between the spatial patterns, frequency, and locations of daily precipitation occurrences. When information from gauge observations, satellite estimates, and model simulations are combined simultaneously, the merged multisource analyses perform better than dual‐source analyses. These results indicate that each independent information source of daily precipitation contributes to improving the quality of the final merged analyses under the framework of HL‐OI scheme.

The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

The coupled data assimilation (CDA) system consisting of ocean, sea‐ice, and atmosphere data assimilation components with the Beijing Climate Center (BCC) Climate System Model has been developed to provide reliable analyses of the atmosphere, ocean, and sea‐ice states. It incorporates ocean temperature/salinity profiles, sea surface temperature, sea level height, and sea‐ice concentration observations at a daily frequency, and atmosphere reanalysis at a 6‐hourly frequency. Results show that the system is capable of realistically reproducing the climatology and variability of ocean, sea‐ice, and atmosphere. The performances in analyzing ocean component are comparable to those of well‐known ocean reanalyses that were once used to initialize the BCC model for climate predictions. A series of experiments with and without sea‐ice observations in the CDA framework are designed to explore the role of sea‐ice data assimilation (DA). The addition of sea‐ice DA exerts very small influence to the analysis of upper ocean temperature over the Arctic area, but leads to an evident reduction of temperature error in the upper 1000 m of ocean south of 60°S. Particularly, only the inclusion of sea‐ice DA can make the ocean/ocean‐atmosphere DA effective in providing skillful analysis in the high‐latitude Southern Ocean. Furthermore, we find that only on the basis of ocean DA, can the addition of sea‐ice concentration assimilation improve the analysis of tropical tropospheric atmosphere, along with a better analysis of mid‐ and high‐latitude stratospheric atmosphere. These results address the importance of coordination of sea‐ice observations and ocean observations in CDA.