Extended Abstract (872K)J2.1
Reanalysis before radiosondes using ensemble data assimilation
Jeffrey S. Whitaker, NOAA-CIRES/CDC, Boulder, CO; and G. P. Compo, X. Wei, and T. M. Hamill
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. Reanalysis before the radiosonde era (pre-1948) 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, sub-sampled to resemble the density of observations we estimate to be available for 1915. Analysis errors are defined relative to a three-dimensional variational (3DVar) analysis using several orders of magnitude more observations, both at the surface and aloft. We find that the EnSRF is computationally tractable and considerably more accurate than other candidate analysis schemes which 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 20th 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.
Joint Session 2, Data Assimilation and observational network design: Part II (Joint between the Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean and the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction) (ROOM 6A)
Monday, 12 January 2004, 1:30 PM-2:30 PM, Room 6A
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