Use of Canadian Quick covariances in the Met Office data assimilation system

In this paper, we describe the use of Canadian Quick (CQ) covariances in the Met Office assimilation system. These covariances have two particular advantages over other methods (such as the National Meteorological Center (NMC) method). First, they are calculated from a single long forecast run, rather than a series of short forecasts, and thus are much quicker to produce. Second, in cases where the vertical range of the assimilation system increases, they can be used immediately. Here, we compare the performance of CQ and NMC covariances in a troposphere/stratosphere configuration of the Met Office assimilation system. The forecast model used has 50 levels from the surface to 63 km. In general, the performance of the two covariances is similar. However, it is clear that a consequence of the bootstrapping approach that was used to develop the NMC covariances is noisy patterns in the error covariances which adversely affects mean errors, particularly in the winter middle and high latitudes above the 10 hPa level. In addition, the NMC covariances show evidence of gravity waves, which appear to have been generated spuriously by the 3D-Var assimilation due to lack of dynamical balance in the 3D-Var analyses. Such signals are absent in the trials where CQ covariances are used. The CQ method was also used to generate covariances for expanded versions of the model which spans the surface to around 80–84 km. Trial results are generally in good agreement with Earth Observing System Microwave Limb Sounder (EOS MLS) correlative measurements. Through this work, the CQ approach is proving to be a very effective tool for developing and testing new models which will be used to provide operational weather forecasts at the Met Office. The results show that the CQ approach is a quick and effective alternative to the NMC method, and can produce similar or sometimes better results. It is a particularly useful tool in the development of new assimilation systems. © Crown Copyright 2008 Reproduced with the permission of Her Majesty's St ationery Office. Published by J ohn Wiley & Sons, Ltd

[1]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[2]  Anthony Hollingsworth,et al.  The statistical structure of short-range forecast errors as determined from radiosonde data , 1986 .

[3]  David W. Tarasick,et al.  Data assimilation with the Canadian middle atmosphere model , 2001 .

[4]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[5]  Theodore G. Shepherd,et al.  Some challenges of middle atmosphere data assimilation , 2005 .

[6]  R. Daley Atmospheric Data Analysis , 1991 .

[7]  N. B. Ingleby,et al.  The statistical structure of forecast errors and its representation in The Met. Office Global 3‐D Variational Data Assimilation Scheme , 2001 .

[8]  Andrew C. Lorenc,et al.  The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var , 2003 .

[9]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[10]  Lars Peter Riishojgaard,et al.  A direct way of specifying flow‐dependent background error correlations for meteorological analysis systems , 1998 .

[11]  Henk Eskes,et al.  The Assimilation of Envisat data (ASSET) project , 2006 .

[12]  A. Lorenc,et al.  The Met Office global four‐dimensional variational data assimilation scheme , 2007 .

[13]  Lance E. Christensen,et al.  Early validation analyses of atmospheric profiles from EOS MLS on the aura Satellite , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  A. Staniforth,et al.  A new dynamical core for the Met Office's global and regional modelling of the atmosphere , 2005 .

[15]  A. Douglass,et al.  Thermodynamic balance of three-dimensional stratospheric winds derived from a data assimilation procedure , 1993 .

[16]  Nedjeljka Žagar,et al.  Balanced tropical data assimilation based on a study of equatorial waves in ECMWF short‐range forecast errors , 2005 .

[17]  Peter H. Siegel,et al.  The Earth observing system microwave limb sounder (EOS MLS) on the aura Satellite , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  N. B. Ingleby,et al.  The Met. Office global three‐dimensional variational data assimilation scheme , 2000 .

[19]  A. Lorenc A Global Three-Dimensional Multivariate Statistical Interpolation Scheme , 1981 .