A new method for the characterization and verification of local spatial predictability for convective‐scale ensembles. (18th May 2016)

• Record Type: Journal Article
• Authors: Dey, Seonaid R. A.; Roberts, Nigel M.; Plant, Robert S.; Migliorini, Stefano
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 142:Number 698(2016)
• Page Start: 1982
• Page End: 1996
• Publication Date: 2016-05-18
• Subjects: convective‐scale -- ensemble -- forecasting -- neighbourhood -- verification -- spatial -- spread–skill; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.2792
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : The use of kilometre‐scale ensembles in operational weather forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood‐based method is presented for evaluating and characterizing the local predictability variations from convective‐scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S A ) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs ( S ij A ( mm ¯ ) ) with that between members and radar observations ( S ij A ( mo ¯ ) ), this approach allows the location‐dependent spatial spread–skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealized experiment. To demonstrate the methods in an operational context, S ij A ( mm ¯ ) and S ij A ( mo ¯ ) are calculated for six convective cases run with the Met Office UK Ensemble Prediction System (MOGREPS‐UK). S ij A ( mm ¯ ) highlights predictability differences between cases, which can be linked to physical processes. Maps of S ij A ( mm ¯ ) are found to summarize the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and model interpretation. Comparison of S ij A ( mm ¯ ) and S ij A ( mo ¯ ) demonstrates the case‐by‐case and temporal variability of the spatial spread–skill, which can again be linked to physical processes.