Ensemble data assimilation with the CNMCA regional forecasting system

Abstract

The Ensemble Kalman Filter (EnKF) is likely to become a viable alternative to variational methods for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using the CNMCA regional numerical weather prediction (NWP) forecasting system and compare them to the currently operational variational-based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analyses and forecasts of consistently superior skill in the root mean square error metric than CNMCA operational 3D-Var.One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. To combat this problem a number of different parametrizations of the model error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of adaptive multiplicative and additive background covariance inflations has been found to give adequate results and to be capable of avoiding filter divergence in extended assimilation trials. The additive component of the covariance inflation has been implemented through the use of scaled forecast differences.Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found, and the forecast and analysis ensembles appear to stay remarkably close to the assumed probability distribution functions.

Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

“…Comparing Figure 4d with Figures 4b and 4c shows that the combination of these two methods can more effectively reduce MD than using only a single method in some areas, for example in the southwestern Pacific Ocean. The advantage of the combination has also been confirmed by other studies [ Bonavita et al , 2010]. The impacts of the two bias correction methods used in Exp5 and Exp6 are shown in Figures 4e and 4f.…”

Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

“…Ensemble Kalman filters (EnKFs; Evensen 2003) are now widely used for data assimilation and initialization of numerical weather prediction (NWP) models for different applications ranging from the global (e.g., Houtekamer and Mitchell 2005;Szunyogh et al 2008;Miyoshi et al 2010) to the regional (e.g., Torn and Hakim 2008;Bonavita et al 2010) and convective scales (e.g., Zhang et al 2004;Aksoy et al 2010).…”

A composite state method for ensemble data assimilation with multiple limited-area models

“…A local ETKF has been successfully implemented to generate ensemble perturbations for an operational global-regional model pair in the UK Met Office's MOGREPS system (Bowler and Mylne, 2009). The LETKF has also been tested, with positive results, using the regional models of the Italian and German weather services (Bonavita et al, 2010;Reich et al, 2011;Lange and Craig, 2014).…”