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Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.

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Human adenoviruses (HAdVs), especially HAdV-B3, -E4 and -B7, are associated with Acute Respiratory Disease in Chinese children, and occasionally in adults. In order to establish and document the profiles of the respiratory adenovirus pathogen among children in Guangzhou, Southern China, a rapid, simple and practical method for identification and typing of respiratory adenoviruses was developed and evaluated. One pair of universal PCR primers was designed according to the conserved region of the hexon gene, which can detect not only HAdV-B3, -E4 and -B7, but also HAdV-B14, -F40 and -F41, with a specific 300bp PCR product. Three pairs of type-specific PCR primers were also designed according to the hypervariable regions of the hexon gene to type HAdV-B3, -E4 and -B7 by three independent PCR reactions, making it easy to optimize the PCR conditions. By using this method, one hundred throat swab specimens collected during Oct 2010 to Dec 2011 and suspected of being positive for adenoviral infection were identified and typed for adenoviruses. Of these samples, fifty-five were adenovirus-positive. The most common HAdV type was HAdV-B3, identified in 92.7% of samples, which is not only consistent with the data reported in 2004-2006, but also consistent with the recent report in Hangzhou, eastern China, indicating that HAdV-B3 has been circulating in Guangzhou, and maybe in eastern China, for many years. The method for the respiratory adenovirus identification and typing we developed is rapid, simple and practical, which has a potential in the real-time surveillance of circulating adenovirus strains and also to provide etiological evidence for the adenovirus-relative disease control and prevention in China.

Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the B matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the B matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the B matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate B matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses.A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-meansquare errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure.

Abstract. Imperfect dynamical core is an important source of model biases that adversely impact on the model simulation and predictability of a coupled system. With a simple pycnocline prediction model, in this study, we show the mitigation of model biases through parameter optimization when the assimilation model consists of a "biased" timedifferencing. Here, the "biased" time-differencing is defined by a different time-differencing scheme from the "truth" model that is used to produce "observations", which generates different mean values, climatology and variability of the assimilation model from the "truth" model. A series of assimilation experiments is performed to explore the impact of parameter optimization on model bias mitigation and climate estimation, as well as the role of different media parameter estimations. While the stochastic "physics" implemented by perturbing parameters can enhance the ensemble spread significantly and improve the representation of the model ensemble, signal-enhanced parameter estimation is able to mitigate the model biases on mean values and climatology, thus further improving the accuracy of estimated climate states, especially for the low-frequency signals. In addition, in a multiple timescale coupled system, parameters pertinent to low-frequency components have more impact on climate signals. Results also suggest that deep ocean observations may be indispensable for improving the accuracy of climate estimation, especially for low-frequency signals.