The authors wish to make the following corrections to this paper [...]

Firstly, the annual variation of sandstorm and strong sandstorm weather process in China from 2000 to 2012 is analyzed according to the"Sand-Dust Weather Yearbook" (2012). Secondly, based on the ERA-Interim Reanalysis from ECMWF and MISR data from the Terra satellite, we investigate the correlation between different dust weather process and land meteorological elements. Finally, the temporal and spatial distribution features of the aerosol optical depth (AOD) in the Taklamakan Desert is studied. And we compare the Taklamakan Desert AOD with nationwide AOD. The results show that: (1) the frequency of sandstorm and strong sandstorm has shown a downward trend and the occurrence of sandstorm decreases more in recent years. (2) In the Taklamakan Desert, the number of sandstorm is positively correlated with the surface temperature, meanwhile, negatively related to the surface relative humidity. (3) In all seasons, the average of AOD in Taklamakan Desert is higher than that of the whole country, and there are obvious differences among the four seasons.

The background error variance in variational data assimilation can significantly affect a model’s initial field. Around extreme weather events, the variance of the unbalanced control variables have contributed highly to the total variance. This study investigates the effect of flow-dependent unbalanced variance on tropical cyclone (TC) forecasts using the ensemble of data assimilation (EDA) method. The analysis of TC Saudel (October 2020) shows that flow-dependent unbalanced variances can better represent the uncertainty in the background error, which is investigated in terms of magnitude and distribution. The vertical distribution of the temperature-explained variance ratio also shows that the contribution of the vorticity-balanced variance around Saudel is lower than the global average (in the troposphere). Single-observation experiments reveal that the structured flow-dependent errors of unbalanced control variables can also introduce corresponding structural information in analysis increments. As expected, the experiments in which the variances of all variables are flow-dependent in the one-month TC forecast performed better overall. Compared with the reference, these forecasts reduce the average absolute track and intensity errors by approximately 31% and 9%, respectively. The results demonstrate that EDA-based unbalanced variances can indeed improve the mean forecast skills of TC tracks and intensities despite instability at some lead times by improving the forecast of the circulation situation and providing a more appropriate balance relationship between variables.

The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which has been suggested to be effective. In this paper, an adaptive scheme for Schur product covariance localization is proposed, which is easy and efficient to implement in the ensemble data assimilation frameworks. A Gaussian-shaped taper function is selected as the localization taper function for the Schur product in the adaptive localization scheme, and the localization radius is obtained adaptively through a certain criterion of correlations with the background ensembles. An idealized Lorenz96 model with an ensemble Kalman filter is firstly examined, showing that the adaptive localization scheme helps to significantly reduce the spurious correlations in the small ensemble with low computational cost and provides accurate covariances that are similar to those derived from a much larger ensemble. The investigations of adaptive localization radius reveal that the optimal radius is model-parameter-dependent, vertical-level-dependent and nearly flow-dependent with weather scenarios in a realistic model; for example, the radius of model parameter zonal wind is generally larger than that of temperature. The adaptivity of the localization scheme is also illustrated in the ensemble framework and shows that the adaptive scheme has a positive effect on the assimilated analysis as the well-tuned localization.

Abstract. The Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM's capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented in both the finite-volume and spectral-element dynamical cores and an online ocean data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation” constraints by ADA and ODA. The weakly coupled data assimilation (CDA) in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air–sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air–sea interface. A 3-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. The success of the online CESM-ECDA system is the first step to implementing a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.