A regionally enhanced global (REG) data assimilation (DA) method is proposed. The technique blends high-resolution model information from a single or multiple limited-area model domains with global model and observational information to create a regionally enhanced analysis of the global atmospheric state. This single analysis provides initial conditions for both the global and limited-area model forecasts. The potential benefits of the approach for operational data assimilation are (i) reduced development cost, (ii) reduced overall computational cost, (iii) improved limited-area forecast performance from the use of global information about the atmospheric flow, and (iv) improved global forecast performance from the use of more accurate model information in the limited-area domains. The method is tested by an implementation on the U.S. Navy’s four-dimensional variational global data assimilation system and global and limited-area numerical weather prediction models. The results of the monthlong forecast experiments suggest that the REG DA approach has the potential to deliver the desired benefits.

This paper employs local linear, spatial spectral, and Lorenz curve-based diagnostics to investigate the dynamics of uncertainty in global numerical weather forecasts in the NH extratropics. The diagnostics are applied to ensembles in the THORPEX Interactive Grand Global Ensemble (TIGGE). The initial growth of uncertainty is found to be the fastest at the synoptic scales (zonal wavenumbers 7-9) most sensitive to baroclinic instability. At later forecast times, the saturation of uncertainties at the synoptic scales and the longer sustainable growth of uncertainty at the large scales lead to a gradual shift of the wavenumber of the dominant uncertainty toward zonal wavenumber 5. At the subsynoptic scales, errors saturate as predicted by Lorenz's classic theory. While the ensembles capture the general characteristics of the uncertainty dynamics efficiently, there are locations where the predicted magnitude and structure of uncertainty have considerable time-mean errors. In addition, the magnitude of systematic errors in the prediction of the uncertainty increases with increasing forecast time. These growing systematic errors are dominated by errors in the prediction of low-frequency changes in the large-scale flow.

This study investigates the efficiency of the major operational global ensemble forecast systems of the world in capturing the spatiotemporal evolution of the forecast uncertainty. Using data from 2015, it updates the results of an earlier study based on data from 2012. It also tests, for the first time on operational ensemble data, two quantitative relationships to aid in the interpretation of the raw ensemble forecasts. One of these relationships provides a flow-dependent prediction of the reliability of the ensemble in capturing the uncertain forecast features, while the other predicts the 95th percentile value of the magnitude of the forecast error. It is found that, except for the system of the MetOffice, themain characteristics of the ensemble forecast systems have changed little between 2012 and 2015. The performance of the UKMOensemble improved in predicting the overall magnitude of the uncertainty, but its ability to predict the dominant uncertain forecast features was degraded. A common serious limitation of the ensemble systems remains that they all have major difficulties with predicting the large-scale atmospheric flow in the long (longer than 10 days) forecast range. These difficulties are due to the inability of the ensemble members to maintain large-scale waves in the forecasts, which presents a stumbling block in the way of extending the skill of numerical weather forecasts to the subseasonal range. The two tested predictive relationships were found to provide highly accurate predictions of the flow-dependent reliability of the ensemble predictions and the 95th percentile value of the magnitude of the forecast error for the operational ensemble forecast systems.