Information-based data selection for ensemble data assimilation

Ensemble-based data assimilation is rapidly proving itself as a computationally-efficient and skilful assimilation method for numerical weather prediction, which can provide a viable alternative to more established variational assimilation techniques. However, a fundamental shortcoming of ensemble techniques is that the resulting analysis increments can only span a limited subspace of the state space, whose dimension is less than the ensemble size. This limits the amount of observational information that can effectively constrain the analysis. In this paper, a data selection strategy that aims to assimilate only the observational components that matter most and that can be used with both stochastic and deterministic ensemble filters is presented. This avoids unnecessary computations, reduces round-off errors and minimizes the risk of importing observation bias in the analysis. When an ensemble-based assimilation technique is used to assimilate high-density observations, the data-selection procedure allows the use of larger localization domains that may lead to a more balanced analysis. Results from the use of this data selection technique with a two-dimensional linear and a nonlinear advection model using both in situ and remote sounding observations are discussed

A review of operational methods of variational and ensemble-variational data assimilation

Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so various ways have been developed to combine these methods, especially with the aims of improving the background error covariance matrices and of improving efficiency. The field has become confusing, even to many specialists, and so there is now a need to summarise the methods in order to show how they work, how they are related, what benefits they bring, why they have been developed, how they perform, and what improvements are pending. This paper starts with a reminder of basic variational and ensemble techniques and shows how they can be combined to give the emerging ensemble-variational (EnVar) and hybrid methods. A key part of the paper includes details of how localisation is commonly represented. There has been a particular push to develop four-dimensional methods that are free of linearised forecast models. This paper attempts to provide derivations of the formulations of most popular schemes. These are otherwise scattered throughout the literature or absent. It builds on the nomenclature used to distinguish between methods, and discusses further possible developments to the methods, including the representation of model error

A method for retrieving clouds with satellite infrared radiances using the particle filter

Ensemble-based techniques have been widely utilized in estimating uncertainties in various problems of interest in geophysical applications. A new cloud retrieval method is proposed based on the particle filter (PF) by using ensembles of cloud information in the framework of Gridpoint Statistical Interpolation (GSI) system. The PF cloud retrieval method is compared with the Multivariate Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of infrared radiances on multi-sensors onboard both geostationary and polar satellites, respectively. It is found that the retrieved cloud masks with both methods are consistent with other independent cloud products. MMR is prone to producing ambiguous small-fraction clouds, while PF detects clearer cloud signals, yielding closer heights of cloud top and cloud base to other references. More collections of small-fraction particles are able to effectively estimate the semi-transparent high clouds. It is found that radiances with high spectral resolutions contribute to quantitative cloud top and cloud base retrievals. In addition, a different way of resolving the filtering problem over each model grid is tested to better aggregate the weights with all available sensors considered, which is proven to be less constrained by the ordering of sensors. Compared to the MMR method, the PF method is overall more computationally efficient, and the cost of the model grid-based PF method scales more directly with the number of computing nodes

Variational Analysis of Hydrometeors with Satellite Radiance Observations: A Simulated Study

Abstract This study focuses on the assimilation of the simulated radiances using CRTM under cloud and precipitation conditions, both in an incremental 3DVAR and a non-incremental 1DVAR framework. In 3DVAR, a total water control variable and a warm-rain physics scheme are combined to allow the assimilation of cloud and precipitation affected radiances. Cloud and rain information can be to some extent extracted with this scheme by assimilating the simulated SSMIS radiances over a convective case. In 1DVAR, the control variables are the individual hydrometeors instead of the total water as in 3DVAR. No physical constraint is applied and a quasi-Newton algorithm is used for non-linear minimization. The 1DVAR retrieved hydrometeor profiles from the simulated radiances have a good fit with the "truth" when having a carefully specified observation and background error covariance