Reanalysis without Radiosondes Using Ensemble Data Assimilation

Whitaker, Jeffrey S.; Compo, Gilbert P.; Xue Wel; Hamill, Thomas M.
May 2004
Monthly Weather Review;May2004, Vol. 132 Issue 5, p1190
Academic Journal
Studies using idealized ensemble data assimilation systems have shown that flow-dependent background-error covariances are most beneficial when the observing network is sparse. The computational cost of recently proposed ensemble data assimilation algorithms is directly proportional to the number of observations being assimilated. Therefore, ensemble-based data assimilation should both be more computationally feasible and provide the greatest benefit over current operational schemes in situations when observations are sparse. re-analysis before the radiosonde era (pre-1931) is just such a situation. The feasibility of reanalysis before radiosondes using an ensemble square root filter (EnSRF) is examined. Real surface pressure observations for 2001 are used, subsampled to resemble the density of observations we estimate to be available for 1915. Analysis errors are defined relative to a three-dimensional variational data assimilation (3DVAR) analysis using several orders of magnitude more observations, both at the surface and aloft. We find that EnSRF is computationally tractable and considerably more accurate than other candidate analysis schemes that use static background-error covariance estimates. We conclude that a Northern Hemisphere reanalysis of the middle and lower troposphere during the first half of the twentieth century is feasible using only surface pressure observations. Expected Northern Hemisphere analysis errors at 500 hPa for the 1915 observation network are similar to current 2.5-day forecast errors.


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