The modifications to the data assimilation component of the Regional Deterministic Prediction System (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area four-dimensional variational data assimilation (4DVar) algorithm for the limited-area analysis and the associated three-dimensional variational data assimilation (3DVar) scheme for the synchronous global driver analysis by the four-dimensional ensemble–variational data assimilation (4DEnVar) scheme presented in the first part of this study. It is shown that a 4DEnVar scheme using global background-error covariances can provide RDPS forecasts that are slightly improved compared to the previous operational approach, particularly during the first 24 h of the forecasts and in the summertime convective regime. Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in the first part of this study in the RDPS intermittent cycling strategy. The computational savings brought by the 4DEnVar approach are also discussed.

A new stratospheric chemical-dynamical data assimilation system was developed, based upon an ensemble Kalman filter coupled with a Chemistry-Climate Model [i.e., the intermediate-complexity general circulation model Fast Stratospheric Ozone Chemistry (IGCM-FASTOC)], with the aim to explore the potential of chemical-dynamical coupling in stratospheric data assimilation. The system is introduced here in a context of a perfect-model, Observing System Simulation Experiment. The system is found to be sensitive to localization parameters, and in the case of temperature (ozone), assimilation yields its best performance with horizontal and vertical decorrelation lengths of 14 000 km (5600 km) and 70 km (14 km). With these localization parameters, the observation space background-error covariance matrix is underinflated by only 5.9% (overinflated by 2.1%) and the observation-error covariance matrix by only 1.6% (0.5%), which makes artificial inflation unnecessary. Using optimal localization parameters, the skills of the system in constraining the ensemble-average analysis error with respect to the true state is tested when assimilating synthetic Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) retrievals of temperature alone and ozone alone. It is found that in most cases background-error covariances produced from ensemble statistics are able to usefully propagate information from the observed variable to other ones. Chemical-dynamical covariances, and in particular ozone-wind covariances, are essential in constraining the dynamical fields when assimilating ozone only, as the radiation in the stratosphere is too slow to transfer ozone analysis increments to the temperature field over the 24-h forecast window. Conversely, when assimilating temperature, the chemicaldynamical covariances are also found to help constrain the ozone field, though to a much lower extent. The uncertainty in forecast/analysis, as defined by the variability in the ensemble, is large compared to the analysis error, which likely indicates some amount of noise in the covariance terms, while also reducing the risk of filter divergence.

This study discusses the construction of a high-resolution ensemble Kalman filter system (the HREnKF) developed for the Marine Environmental Observation Prediction and Response (MEOPAR) network. The HREnKF runs at a horizontal resolution of 2.5 km and is intended to provide forecasts at lead times up to 12 h. This system was adapted from the global EnKF system in operation at Environment and Climate Change Canada. As a first development step, only the most necessary modifications have been implemented. The changes include an hourly cycling frequency, smaller localization radii, and the explicit representation of microphysical processes. To assess its performance and orient future developments, the HREnKF was continuously cycled for a period of 12 days. Verification against surface observations reveals that the skill of the forecasts initialized from the HREnKF is comparable to that of control forecasts also integrated at a resolution of 2.5 km. A key component of this study is the comparison of correlation estimated from ensembles at resolutions of 2.5, 15, and 50 km. At 2.5 km, correlation lengths are smaller than those found at 15 and 50 km. These short correlation lengths demand a high observational density, which is not available over the west coast domain where the HREnKF was tested. The spatial and temporal variability of the correlations is also assessed for the HREnKF system. It is found that correlation patterns are complex and do not generally decrease monotonically away from the reference point around which they are estimated. This result is important as it indicates that separation distance may not be the ideal parameter to use as a basis for localization strategies.

A B S T R A C TA new stratospheric ensemble Kalman smoother (EnKS) system is introduced, and the potential of assimilating posterior stratospheric observations to better constrain the whole model state at analysis time is investigated. A set of idealised perfect-model Observation System Simulation Experiments (OSSE) assimilating synthetic limb-sounding temperature or ozone retrievals are performed with a chemistryÁclimate model. The impact during the analysis step is characterised in terms of the root mean square error reduction between the forecast state and the analysis state. The performances of (1) a fixed-lag EnKS assimilating observations spread over 48 hours and (2) an ensemble Kalman Filter (EnKF) assimilating a denser network of observations are compared with a reference EnKF. The ozone assimilation with EnKS shows a significant additional reduction of analysis error of the order of 10% for dynamical and chemical variables in the extratropical upper troposphere lower stratosphere (UTLS) and Polar Vortex regions when compared to the reference EnKF. This reduction has similar magnitude to the one achieved by the denser-network EnKF assimilation. Similarly, the temperature assimilation with EnKS significantly decreases the error in the UTLS for the wind variables like the denser-network EnKF assimilation. However, the temperature assimilation with EnKS has little or no significant impact on the temperature and ozone analyses, whereas the denser-network EnKF shows improvement with respect to the reference EnKF. The different analysis impacts from the assimilation of current and posterior ozone observations indicate the capacity of time-lagged background-error covariances to represent temporal interactions up to 48 hours between variables during the ensemble data assimilation analysis step, and the possibility to use posterior observations whenever additional current observations are unavailable. The possible application of the EnKS for reanalyses is highlighted.