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Keywords:

  • targeted observations;
  • data assimilation

Abstract

Adding targeted observations to the array of observations routinely available to numerical weather prediction is generally found to give small impact, although studies have often focused on a very limited number of cases in field experiments. It is expected that observation targeting in sensitive areas may have more impact on forecasts ‘busts’. A forecast ‘bust’ in a given verification region is defined as a doubling of the mean monthly error for the day-two forecast. This does not occur very often in the operational ECMWF system. The primary purpose of this study is to produce three reference data-assimilation experiments: the first assimilation experiment uses all available observations (as they are used in the operational ECMWF analysis system). The second assimilation experiment systematically excludes all observations in the North Pacific oceanic region. The third assimilation experiment systematically excludes all observations in the North Atlantic oceanic region. These three experiments are then used to evaluate the potential of targeting strategies which are described in two separate papers (parts II and III of this study).

Results, based on a summer and a winter season, indicate that on average:

  • (1)
    Oceanic data are important to reduce day-2 forecast errors over the downstream land areas, the downstream impact of observations over the Pacific Ocean being larger than the downstream impact of observations over the Atlantic Ocean.
  • (2)
    The removal of observations taken over the Pacific Ocean has generally a small impact on medium-range forecasts verified over Europe, but results strongly depend on the data-assimilation system used to assimilate the observations.
  • (3)
    Forecasts started from 3D-Var analyses are worse than forecasts started from 4D-Var analyses and, given the current observing system, the relative impact of ocean observations is higher if a 3D-Var system is used instead of a 4D-Var system. In other words, 4D-Var is better capable of propagating information from data-rich to data-void areas, and is therefore less vulnerable to data gaps in a system which otherwise includes a large number of satellite observations. Copyright © 2007 Royal Meteorological Society