Particle filters for the geosciences
Particle filters for the geosciences
This chapter, compares results from the Canadian global ensemble Kalman filter (EnKF) with observations. This inevitably leads to discrepancies between the observed real atmosphere and its modelled equivalent. These discrepancies originate from system error. In a system simulation experiment, an attempt is made to obtain a coherent picture of the error evolution of a system. Errors can be due to things as different as an inappropriate closure assumption in a forecast model and inaccurate observations of surface pressure. This chapter, first describes Monte- Carlo methods in general to arrive at a definition of “‘system error”.‘. This is followed by an elimination procedure. First, medium-range ensemble forecasts are used to quantify the understanding of weaknesses of the forecast model. Subsequently, consideration turns to the data-assimilation context to see what additional error sources must be present. The chapter ends with some speculation on the types of errors that should be included.
Keywords: ensemble Kalman filter, EnKF, system error, system simulation, Monte-Carlo
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