Characteristics of occasional poor European forecasts

1. Characteristics of occasional poor European forecasts Mark Rodwell PDP Reading University 18 June 2012

2. ‘Busts’ in European Z500 scores at D+6 Forecast start date

3. Bust frequency: trends and annual cycle Bust definition: European D+6 RMSE > 60m, ACC < 0.40. Based on all ECMWF 12UTC forecasts, 1989-2011

4. Composite work

5. Composite verifying conditions during a bust Z500 anomaly Composite of all 584 busts in ERA Interim forecast prior to the introduction of EDA on 24 June 2010 Bold colours indicate statistical significance at 5% level

6. Composite initial conditions preceding a bust There exists a dominant flow regime that lead to forecast busts • Use anomalies in boxes to create a Trough/CAPE initial condition composite: • Projection of Z500 onto trough >3 and • Projection of CAPE onto CAPE pattern > 1

7. EPS Z500 for trough/CAPE composite • All 84 events 10 November 2010 – 20 March 2012 (0 or 12UTC) – generally in spring • Background is ‘reliable’ • Increased uncertainty in trough/CAPE situations explains part of increased error • Insufficient spread and/or common model errors in all ensemble members?

8. PV budget distinguishes adiabatic from diabatic Budget will indicate whether the trough and its evolution are consistent with purely adiabatic processes or whether diabatic/frictional effects are important. When applied to analyses, results will be partially model-independent.

9. PV330 budget for trough/CAPE composite One way in which diabatic processes can affect the large-scale flow is via PV modification in the jet-stream Act to slow the propagation of the wave All 95 events 25 June 2010 – 20 March 2012 (0 or 12UTC)

10. Case study work • Forecast start: 0 UTC 10 April 2011

11. 10 April. Z500, CAPE and v850 anomalies This single event shows agreement with composite

12. v330K anomaly in analysis 2011 Bust 1 Rossby waves that lead to busts tend to slow-down and intensify as they cross the US Rockies Bust 2 Anomaly from sample-mean

13. Scores for deterministic and ensemble forecasts 10 April EPS shows increased spread (i.e. uncertainty) associated with 10 April bust.

14. 10 April. Deterministic and best ensemble member Z500 D+6 Deterministic error D+6 Best ensemble member error m Best ensemble member initial perturbation Do we have the observations to theoretically constrain this perturbation structure? m Initial perturbation of best member was progressively confined while maintaining small European error. D+6 error using this confined perturbation is shown

15. 10 April. 12hr observed and forecast precipitation Can under-predict (e.g. 24 April) as well Potential for errors to have impact on busts Common model errors in all EPS members? Explanation for under-spread? Also observations often not available in MCSs due to cloud contamination, aircraft diversion etc.

16. Summary • Busts associated with difficulty to predict blocking onset • Trough/CAPE/Convection associated with many busts • Increased (flow-dependent) uncertainty, but not enough to explain error • Flow-dependent background errors too small? • MCS errors common to all EPS members? • (Assuming flow-dependent results are robust) • Do we have theobservations to constrain perturbation structures?

17. 10 April. Increments at observation locations Observations often not available in MCSs due to cloud contamination, aircraft diversion etc.

18. 10 April. T500 tendency/increment budget Increments (i.e. corrections) to MCSc too small relative to analysed evolution?

19. 10 April: Swapping initial conditions Z500 error at D+6 Initial conditions are key. Thanks to Peter Bauer, Paul Earnshaw, Daniel Klocke

20. DJF 2012: Ground-based Radar FG Dep AN Dep Mean RMS Increments tend to reduce departures for recently introduced radar data. May help reduce busts? Quantity: ln(RR+1). RR in mmhr-1