Predicting the influence of observations on medium-range forecasts of atmospheric flow

Kathryn J. Sellwood, Sharanya J. Majumdar, Brian E. Mapes, Istvan Szunyogh

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


In recent years, the Ensemble Transform Kalman Filter (ETKF) has been demonstrated to be useful for identifying a priori dynamically important locations for the placement of supplementary dropwindsonde observations aimed at improving short-range (1-3 day) forecasts of high-impact winter weather. In this paper, the ability of this strategy to predict the influence (or 'signal') of assimilating observations into the NCEP Global Forecast System for forecasts of 200 hPa wind up to 6 days is evaluated. Using a 50-member ECMWF ensemble, the ETKF was found to exhibit significantly higher skill than a seasonal 'climatology' of the important locations in predicting both (1) the spatial structure of signals within the storm track on a case-by-case basis; and (2) the variance of these signals over a 2-month period, within objectively chosen verification regions on synoptic scales. The verification region was selected by extracting the zonal envelope of the Rossby wave packet associated with the propagating ETKF signal variance. It is recommended that larger verification regions be used for longer lead times, due to the eastward expansion of the wave packet. The capability of the ETKF to predict signal variance out to 6 days was found to be dependent on the flow regime. The ETKF was most capable when the background flow was predominantly zonal, and least capable in instances where the observations were placed upstream of a blocking high over the north-eastern Pacific. Therefore, the ETKF is sometimes (but not always) able to predict when there exists significant potential for a particular group of observations to improve medium-range forecasts.

Original languageEnglish (US)
Pages (from-to)2011-2027
Number of pages17
JournalQuarterly Journal of the Royal Meteorological Society
Issue number637
StatePublished - 2008


  • Medium-range predictability
  • Signal variance

ASJC Scopus subject areas

  • Atmospheric Science


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