1. Arpae Hydro-Meteo-Climate Service, Bologna, Italy
Predictability of severe weather phenomena with a convection-permitting ensemble
Chiara Marsigli, Andrea Montani, Tiziana Paccagnella
Arpae Hydro-Meteo-Climate Service, Bologna, Italy
With thanks also to Virgina Poli, Thomas Gastaldo and Maria Stefania Tesini

2. Outline Aim of this work The COSMO-IT-EPS ensemble
Evaluation of the impact of the perturbations
=> focus on the precipitation forecast
Conclusions

3. Aim of this work
Assess the role of the different perturbations applied to the ensemble:
IC perturbations (LETKF)
Model perturbations: SPPT, parameter perturbation
Study the effect of combining different model perturbations
Show which components of the forecast uncertainty are represented in the ensemble spread

4. COSMO-IT-EPS 2.2 km 65 v.l. LETKF (Arpae) EnKF DA (COMET)
COSMO-ME-EPS (COMET)
10 km
45 v.l.
ICs
BCs
10 km
40 v.l.
parameterized convection
COSMO-IT-EPS (Arpae)
2.2 km
65 v.l.
explicit
convection

5. Impact of model physics perturbation on autumn precipitation
Model perturbations:
Exp1: no model perturbation (CTRL)
Exp2: SPPT
Exp3: SPPT + Parameter Perturbation
October 2015

6. 6h precipitation - verification over boxes of 0.2 x 0.2 deg
average > 5mm
maximum > 5mm

7. Evaluation of the ensemble spread
Compute dFSS (FSS between all pairs of ensemble members)
Compute SAL between all pairs of ensemble members

8. Evaluation of ensemble spread using the SAL metric
What is the perturbation influencing?
Precipitation intensity
Precipitation structure
Localisation of the precipitation
Use a spatial verification measure: SAL (Wernli et al 2008)
3 independent components:
Structure
Amplitude
Location
Used here not for verification but for evaluating the similarity between fields, only forecasts

9. SAL-spread metric
10/10/15
no physics pert
SPPT
SPPT + PP

10. Impact of perturbed ICs from LETKF on thunderstorm prediction summer 2016

11. 20 June 2016 20 June 2016 ITEPS only physics
Red areas: radar estimate
Probability maps
+7h
+8h 1 10 30 50 70 90 %

12. 20 June 2016 ITEPS physics + IC +1h +2h +3h +4h +5h +6h +7h +8h
Red areas: radar estimate
Probability maps
+7h
+8h 1 10 30 50 70 90 %

13. 25 June 2016 ITEPS only physics
Red areas: radar estimate
Probability maps
+5h
+6h 1 10 30 50 70 90 %

14. 25 June 2016 ITEPS physics + IC +1h +2h +3h +4h +5h +6h
Red areas: radar estimate
Probability maps
+5h
+6h 1 10 30 50 70 90 %

15. 2 July 2016 – ITEPS only physics
Red areas: radar estimate
Probability maps
+10h
+11h 1 10 30 50 70 90 %

16. 2 July 2016 – ITEPS physics + IC
Red areas: radar estimate
Probability maps
+10h
+11h 1 10 30 50 70 90 %

17. radar estimate of hourly precipitation
20 June 2016
radar estimate of hourly precipitation

18. forecasted hourly precipitation – physics only - member 1

19. forecasted hourly precipitation – physics + IC - member 1

20. radar estimate of hourly precipitation
20 June 2016
radar estimate of hourly precipitation

21. forecasted hourly precipitation – physics only - member 1

22. forecasted hourly precipitation – physics only - member 2

23. Concluding remarks
The skill of the precipitation forecast is improved, with a better discrimination ability, by the model-perturbed ensembles
Adding parameter perturbation has no significant impact on the skill over the entire period
Spread in precipitation localisation and structure is highlighted by the SAL-spread metric
Perturbed initial conditions have a positive impact during the first hours of the forecast
Operational use: spatial and temporal aggregation dependent on the predictability of the phenomena

25. forecasted hourly precipitation – physics only - member 3

26. forecasted hourly precipitation – physics + IC - member 2

27. forecasted hourly precipitation – physics + IC - member 3