Revisiting predictability of the strongest storms that have hit France over the past 32 years.

Transcription

1 Revisiting predictability of the strongest storms that have hit France over the past 32 years. Marie Boisserie L. Descamps, P. Arbogast GMAP/RECYF 20 August 2014

2 Introduction Improving early detection of extreme weather events is of crucial importance for national meteorological services. In the past 20 years, Ensemble Forecasting Systems (EFS) has allowed progress in predicting severe events : probability maps, quantiles, etc... severe events are unfrequent ==> predictability poorly documented. 2

3 Introduction An extreme weather event : located at the extremes of the historical distribution EFS Probability density model climatology wind speed (m/s) 3

4 Introduction Forecast indexes for extreme weather events : 1 density function model climatology F(p) EFI = 2 π 1 0 p F(p) p(1 p) SOT + (p=0.9)= Q f (p) Q c (1) Q c (p) Q c (1) Cumulative 0 EFS wind speed (m/s) Zsoter E Recent developments in extreme weather forecasting. ECMWF Newsletter 107 :

5 Introduction Forecast indexes for extreme weather events : 1 density function model climatology F(p) EFI = 2 π 1 0 p F(p) p(1 p) SOT + (p=0.9)= Q f (p) Q c (1) Q c (p) Q c (1) Cumulative 0 EFS wind speed (m/s) Zsoter E Recent developments in extreme weather forecasting. ECMWF Newsletter 107 :

6 Introduction Forecast indexes for extreme weather events : density function 1 p=0.9 model climatology EFI = 2 π 1 0 p F(p) p(1 p) SOT + (p=0.9)= Q f (p) Q c (1) Q c (p) Q c (1) Cumulative 0 EFS Q c (p) Q f (p) Q c (1) wind speed (m/s) Zsoter E Recent developments in extreme weather forecasting. ECMWF Newsletter 107 :

7 Introduction Forecast indexes for extreme weather events : density function Cumulative 1 p=0.9 0 model climatology EFS Q c (p) Q f (p) Q c (1) wind speed (m/s) EFI = 2 π 1 0 p F(p) p(1 p) SOT + (p=0.9)= Q f (p) Q c (1) Q c (p) Q c (1) -0.5<SOT<0 : 2 tails close SOT>0 : EFS tail beyond the climatology 7

8 Objectives Calculate EFI and SOT of the most severe wind events that have hit France over the past 32 years. Model climatology Ensemble Forecasting System (EFS) Determine an optimal EFI and SOT threshold to retrospectively issue warnings while limiting the number of false alarms. Skill scores 8

9 Objectives Calculate EFI and SOT of the most severe wind events that have hit France over the past 32 years. Model climatology Ensemble Forecasting System (EFS) Determine an optimal EFI and SOT threshold to retrospectively issue warnings while limiting the number of false alarms. Skill scores 9

10 Methodology : data Parameter = 24h wind gust SYNOPTIC observations 60 windstorm cases (most severe) STORM NAMES DATE Great storm of /10/1987 Lothar 26/12/1999 Martin 27-28/12/1999 Klaus 24-25/01/2009 Xynthia 27-29/02/2010 Joachim 16-18/12/

11 Methodology : data Classification from the highest to the smallest value compared to a 32-year climatology (32x30=960 data) m/s 24h observed wind gust speed for the 1 st rank (the highest value) for January. 11

12 Methodology : model climatology Model climatology = long set of a reforecast dataset Development and evaluation of the Météo- France ensemble reforecast dataset (Boisserie et al. 2014, submitted) - My Poster at 3pm Blended analysis approach : atmospheric initial state and boundary conditions from ECMWF ERA-interim land surface initial state from SURFEX simulations. 32-year hindcasts (from 1981 to 2012) every 4 days at 1800 UTC up to 4.5 days. 10-member ensemble (different physical packages). 12

13 Methodology : EFS description We need an EFS that can be initialized from dates that go back 32 years Yet, the operational EFS of Météo-France (PEARP) is archived for 5 years only Therefore, we have developed an EFS as consistent as possible with PEARP 13

14 Methodology : EFS description PEARP Labadie s Poster at 3pm EFS Control analysis ARPEGE analysis blended analysis : - ERA-interim/SURFEX Initialization singular vectors singular vectors perturbation + EDA Model error multiphysics same representation Size Resolution T538 ( 15km over France) 65 vertical levels same 14

15 Methodology : EFS skill evaluation EFS characteristics : Reliability : measure of the accuracy of the EFS probabilities Reduced Centered Random Variable (RCRV) : RCRV mean > bias RCRV standard deviation > spread Resolution : measure of the ability to distinguish different forecasted probability categories Brier score Study period : 1 october to 15 november 2011 Comparison with PEARP skill 15

16 Results : 1. EFS skill Scores at day 2 (54-78h lead time) : 24h 10m WIND GUST PEARP EFS WIND SPEED PEARP EFS RCRV mean RVCR mean RCRV std RCRV std Brier score Brier score events>5m.s 1 events>5m.s 1 Brier score Brier score events>10m.s 1 events>10m.s 1 Similar results at day 3 ( lead time) 16

17 Results : 1. EFS skill Scores at day 2 (54-78h lead time) : 24h 10m WIND GUST PEARP EFS WIND SPEED PEARP EFS RCRV mean RVCR mean RCRV std RCRV std Brier score Brier score events>5m.s 1 events>5m.s 1 Brier score Brier score events>10m.s 1 events>10m.s 1 Similar results at day 3 ( lead time) 16

18 Results : 1. EFS skill 850-hpa air temperature 500-hpa geopotential height s td s td PEARP EFS R C RV 7 6 R C RV temperature<273.15k geopotential height<55000m Brier score Forecast lead times (hours) Brier score Forecast lead times (hours) 17

19 Results : 1. EFS skill CONCLUSION : The 21-member EFS have similar reliability and global skill as PEARP. It could be used to evaluate the predictability of the study windstorms. 18

20 Results : 2. EFI and SOT 24h windgust forecast at day 3 Great Storm of 1987 (15-16 October 00-00UTC) EFI 100 SOT< SOT>

21 Results : 2. EFI and SOT 24h windgust forecast at day 3 Martin (27-28 December 1999, 00-00UTC) EFI 100 SOT<

22 Results : 2. EFI and SOT 24h windgust forecast at day 2 Klaus (26-27 January 2009, 00-00UTC) EFI SOT<0 SOT>

23 Objectives Calculate EFI and SOT of the most severe wind events that have hit France over the past 32 years. Model climatology Ensemble Forecasting System (EFS) Determine an optimal EFI ans SOT threshold to retrospectively issue warnings while limiting the number of false alarms. Skill scores 22

24 Objectives Calculate EFI and SOT of the most severe wind events that have hit France over the past 32 years. Model climatology Ensemble Forecasting System (EFS) Determine an optimal EFI and SOT threshold to retrospectively issue warnings while limiting the number of false alarms. Skill scores 23

25 Results : 2. EFI and SOT Between EFI and 99% EFS quantile what is the best option to detect severes storms? 0 R=0.58 observed wind gust rank % EFS quantile EFI is a more appropriate tool 24

26 Results : 2. EFI and SOT Between EFI and 99% EFS quantile what is the best option to detect severes storms? 0 R= R=0.71 observed wind gust rank observed wind gust rank % EFS quantile EFI EFI is a more appropriate tool 25

27 Results : 2. EFI and SOT Between EFI and 99% EFS quantile what is the best option to detect severes storms? 0 R= R=0.71 observed wind gust rank observed wind gust rank % EFS quantile EFI EFI is a more appropriate tool 26

28 Results - 3. Skill scores How to measure each forecast index skill? observed non observed rank <= 10 rank> 10 forecasted index >= 0.6 a b non forecasted index < 0.6 c d 1 Heidke Skill Score (HSS) score measures the fraction of correct forecasts after eliminating those forecasts due purely to random chance. 2 False Alarms (FA) and Hit Rates (HR) 27

29 Results - 3. Skill scores How to measure each forecast index skill? observed non observed rank <= 10 rank> 10 forecasted index >= 0.6 a b non forecasted index < 0.6 c d 1 Heidke Skill Score (HSS) score measures the fraction of correct forecasts after eliminating those forecasts due purely to random chance. 2 False Alarms (FA) and Hit Rates (HR) 27

30 Results - 3. scores EFI skill at day 2 rank=1 most severe rank<=3 once every 10 years rank<=5 once every 7 years HSS EFI 28

31 Results - 3. scores EFI skill at day 2 rank=1 most severe rank<=3 once every 10 years rank<=5 once every 7 years HSS EFI

32 Results - 3. scores EFI skill at day 2 rank=1 HSS HR FA (optimal EFI) most severe % 11% rank<=3 once every 10 years % 15% rank<=5 once every 7 years % 18% 30

33 Results - 3. scores SOT skill at day rank=1 most severe rank<=3 once every 10 years rank<=5 once every 7 years HSS SOT 31

34 Results - 3. scores SOT skill at day rank=1 most severe rank<=3 once every 10 years rank<=5 once every 7 years HSS SOT

35 Results - 3. scores SOT skill at day 2 rank=1 optimal SOT HR FA most severe % 8% rank<=3 once every 10 years % 20% rank<=5 once every 7 years % 18% 33

36 Conclusion EFI is a more appropriate tool than the 21-member EFS quantile For very highly ranked storms (ranks=1,<=3,<=5), optimal EFI values are between 0.7 and 0.8. Optimal SOT values are between -0.5 and A paper is about to be submitted for publication 34

37 Future work Similar study on different parameters : minimum temperature maximum temperature precipitation We envision to produce operationally maps of EFI and SOT every day Using the Météo-France ensemble reforecast dataset, we plan on calibrating PEARP. 35

38 EXTRA SLIDES! 36

39 Results- 2. EFI and SOT 24h windgust forecast at day 3 Great Storm of 1987 (15-16 October 00-00UTC) EFI 100 SOT< SOT>

40 Results- 2. EFI and SOT 24h windgust forecast at day 3 Martin (27 December 1999, 00-00UTC) EFI 100 SOT<

41 Results- 2. EFI and SOT 24h windgust forecast at day 2 Klaus (26 January 2009, 00-00UTC) EFI SOT<0 SOT>

42 Results - 1. EFS skill Scores at 78 forecast range : 24h 10m WIND GUST PEARP EFS WIND SPEED PEARP EFS RVCR mean RVCR mean RVCR std RVCR std Brier score Brier score events>5m/s events>5m/s Brier score Brier score events>10m/s events>10m/s 40