4.6 Understanding the Temporal Variability in Long Time Series of Forecast Ensemble Variance

Tuesday, 9 January 2018: 9:45 AM
Room 19AB (ACC) (Austin, Texas)
Justin McLay, NRL, Monterey, CA; and E. A. Satterfield

Gaining flow-dependent estimates of the forecast uncertainty and the forecast probability distribution is the main motivation for ensemble forecasting systems (EFSs), yet the nature of the flow dependence and the processes that modulate this dependence are not well understood. Here, the structural characteristics of long time series of EFS forecast variance from the THORPEX Interactive Grand Global Ensemble (TIGGE) data archive are examined. Two features are emphasized, namely that the time series appears to have certain quasi-periodic components on intra-seasonal and shorter time scales and that the times series also exhibits notable volatility accentuated by large swings in the variance. To further understand these features, the time series of variance is regressed against standard flow indices such as the Arctic Oscillation (AO) index, the zonal index, and the Madden-Julien Oscillation (MJO) index. Additionally, case studies are undertaken of some notable events in the time series.
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