The Met Office has developed and demonstrated an hourly cycling 1.5 km resolution NWP‐based nowcast system (0–6 h forecasts) using four‐dimensional variational data assimilation (4D‐Var). This was known as the Nowcasting Demonstration Project (NDP), and was principally for prediction of convective storms for flood forecasting. The NDP was run in real time from March 2012 to April 2013 to cover the period of the London Olympics 2012. The system was run on a domain covering southern England and Wales nested in the UK variable resolution model (UKV). The UKV used a UK‐wide 1.5 km domain with 3 hourly cycling three‐dimensional variational data assimilation (3D‐Var) and produced 36 h forecasts every 6 h. The NDP 4D‐Var included standard observations, Doppler radar radial winds, humidity derived from a 3D cloud cover analysis and geostationary satellite upper‐tropospheric water vapour radiances not contaminated by cloud. This was used in combination with latent heat nudging of radar‐derived precipitation rates. Example case‐studies compare the NDP precipitation forecasts to both the operational extrapolation/merged nowcast system and the UKV forecasts. Objective comparison of fraction skill score for the period June to August 2012 shows that the NDP skill was greater than the latest UKV forecasts, available to forecasters at the same time as the NDP, for the whole 6 h forecast period. The skill of the NDP was greater than the operational extrapolation/merged nowcast beyond T + 2 h.
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.
Scale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble–variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.
An experimental convection-permitting ensemble prediction system (EPS) has recently been developed at the Met Office where the analysis uncertainty is estimated by means of an ensemble transform Kalman filter (ETKF). In this paper, the author reports on a case study where mismatches between the analysis perturbations and the perturbations coming from the lateral boundaries lead to the generation of significant spurious perturbations in the ensemble forecasts of the surface pressure. To alleviate ensemble perturbation mismatches originating from the ensemble technique, he tests a so-called scale-selective ETKF where a revised transform matrix is applied only to the small-scale component of the high-resolution forecasts, while the large-scale component of the analysis perturbations is taken from the driving EPS. Results show that the new approach successfully removes the spurious perturbations in the surface pressure fields and also provides some benefits in the precipitation forecasts for the case studied. An examination of ensemble-derived forecast error covariances reveals that ensemble perturbation mismatches at the lateral boundaries tend to decrease the degree of balance between the mass field and the rotational wind field and to produce more compact horizontal and vertical correlations. Finally, the limitations of the scale-selective approach and future directions are discussed.
This study reports on the progress toward operational weather radar data assimilation in Canada. As a first step, the latent heat nudging (LHN) technique has been tested for a period of 1 month. It is the first time that LHN is used across the North American continent, a domain significantly larger than that of other LHN studies. Other novel aspects of this study include the use of a quality index associated with individual reflectivity measurements and a discussion on matching the effective resolution of the modeled precipitation for a reduction of the representation errors. Various verification scores indicate that LHN has a positive influence on instantaneous precipitation rates for lead times up to 3 h. In comparison, the nowcasting of precipitation rates by a simple Lagrangian extrapolation method yields improvements that last up to approximately 4 h. Verifications against aircraft measurements indicate small but statistically significant improvements throughout the troposphere for lead times up to 24 h.
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