predictability of a large-scale flow conducive to extreme precipitation over western alps*

Predictability of a large-scale flow Predictability of a large-scale flow conducive to extreme precipitation over conducive to extreme precipitation over western Alps*western Alps*

Federico GrazziniFederico GrazziniARPA – Servizio IdroMeteorologico Emilia-Romagna, Bologna, ItalyARPA – Servizio IdroMeteorologico Emilia-Romagna, Bologna, Italy

*work done at ECMWF, Shinfield Park, Reading (UK)*work done at ECMWF, Shinfield Park, Reading (UK)

Grazzini F., 2007: Predictability of a large-scale flow conducive to extreme precipitation over the western Alps, Meteorol. Atmos. Phys., 95, 123-138

Thanks to ONR Global for financial support in the framework of Thanks to ONR Global for financial support in the framework of VSP programVSP program

2 F. Grazzini, F. Grazzini, NCEP – Camp Springs (MD)NCEP – Camp Springs (MD), 1, 199 Feb 2008 Feb 2008

MotivationsMotivations

The quality of numerical medium-range forecast has improved considerably since its beginning.

However this remarkable achievement has to be considered true for average conditions since it is calculated over many days/seasons with very different flow patterns and atmospheric states.

Has the skill increased also in specific high impact weather conditions ?

A study has been carried out to examine the skill of ECMWF global forecasting system in predicting a specific flow configuration that is believed to be associated with extreme precipitation events over the Alpine region. We were not investigating the precipitation itself.


Location of areas exposed to heavy and prolonged precipitation Location of areas exposed to heavy and prolonged precipitation eventsevents


Widespread demages during the main flood of October 2000Widespread demages during the main flood of October 2000

Maximum discharge up to 12.000 m3/s40 casualities32.000 have been evacuatedDemages in the order of billion of Euro


Po river catchment's areaPo river catchment's area ( 10 ( 1055 Km Km2 2 ))


13.0

6.1

-24.2

-15.1

-1.8

-10

-10

-10

-10

-10

10

10

30°N

30°N

40°N

L

528

528

528

540

540

540

552

552

552

552

552

564

564

REFERENCEREFERENCE Anomaly from ERA40 Anomaly from ERA40

50°N

60°N

70°N

80°N

60°W

60°W

40°W

20°W 0° 20°E

40°E

60°E

60°E

80°E

80°E

564

564

576

576

576

576

30°N

30°N

40°N

50°N

60°N

70°N

80°N

60°W

60°W

40°W

20°W 0° 20°E

40°E

60°E

60°E

80°E

80°E

Definition of the reference pattern in 500 hPa Z (SSF)Definition of the reference pattern in 500 hPa Z (SSF)

The reference pattern has been defined as a composite of 6 major EAP events, it is consistent with others patternsin literature based on objective precipitation clustering or averaging over EAP cases(see for example Martius et al., 2006, Int. Journal of Climatology)


0

10

20

30

40

50

60

70

80

n.

even

ts

1 2 3 4 5 6 7 8 9 10 11 12

Months

Monthly frequency of southerly flow regime over Europe (periods: 1958-2003)

In the period 1958-2003 312 days have been classified as SSFIn the period 1958-2003 312 days have been classified as SSF

Selection based on ACC and RMS criteria of 500 hPa Z


RMSE over Europe in Spring and Autumn days onlyRMSE over Europe in Spring and Autumn days only

68 cases

70 cases


ERA40 reforecast suggests that SSF events are indeed more predictableERA40 reforecast suggests that SSF events are indeed more predictable

Grey curves represents the average skill in individual years (180 days Spring+Autumn) of ERA40 reforecast, 43 years from 1958-2001. Black curve is the average skill during SSF days only during the whole periods (223). During SSF days RMSE is lower.


Trend of (D+4/D+6) RMSE over Europe in Spring+AutumnTrend of (D+4/D+6) RMSE over Europe in Spring+Autumn

3 years running mean


An example : 1 December 2003An example : 1 December 2003


Seasonal dependence of the predictive skill of SSFSeasonal dependence of the predictive skill of SSF

SpringSpring AutumnAutumn


D-6Spring

Autumn

Lag composite of 250 hPa v-component and envelopeLag composite of 250 hPa v-component and envelope**

SSF cases between 1980-2001 (ERA40)SSF cases between 1980-2001 (ERA40)Spring: April/May, 65 casesSpring: April/May, 65 casesAutumn: October/November, 45 casesAutumn: October/November, 45 cases

6

6

6

6

7

7

7

710

12.89.2

8.98.4

7.1

4.1

3.6

1.9

-12.6

-11.2

-9.9-7.3

-6.8

-6.5

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

7

8.5

10

11.5

11.53

8

8

9

915.2

11.4

10.3 10.2

7.6 7.1

-3.0-13.1

-11.9

-11.8

-10.4

-9.0-7.1

-0.1

12

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

9

11

12.85

Propagation of wave packets leading to SSF conditions Propagation of wave packets leading to SSF conditions

**As defined in Zimin, Szunyogh et. al., Mon. Wea. Rev, May 2003As defined in Zimin, Szunyogh et. al., Mon. Wea. Rev, May 2003


D-5

9

9

13

17.2

10.4

10.08.0

7.1

5.3

3.0

2.3

-15.2

-13.3

-8.9

-7.2

-7.0

-6.1

-3.9

-0.220°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

7

9

11

13

14.98

8

8

9

9

12.3

12.111.6 10.8

8.46.2

5.1

-16.3 -13.3-12.8

-8.6

-5.7

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

9

11

12.61

Spring

Autumn



D-4

6

6

6

6

6

611

16.6

12.09.0

6.6

5.95.6

4.6

3.6

-16.1

-15.1

-8.7

-8.7-7.7

-7.3-3.5

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

8.5

11

13.5

15.42

8

8

89

9

91313

16.114.6

12.0

11.6

8.0

6.5

6.23.4

3.3

3.1

-17.2-14.0

-11.1

-9.6

-6.1 20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

9

11

13

13.98

Spring

Autumn



D-3

6

6

6

6

6

11

11

14.7

14.5

12.2

7.6

6.8

6.2

5.6 5.6

-19.8

-13.2

-10.9

-10.8

-7.3

-6.3 20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

16.57

6

8.5

11

13.5

16

13

1319.9

14.1

13.8

11.6

5.0

2.0

-3.5

-19.6-18.9

-13.9

-11.8

-8.4

-3.820°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

10

13

16

19

19.06

Spring

Autumn



D-2

6

6

6

6

6

6

6

69

9

917

19.515.5

9.5

7.9

6.4

6.4

6.0

4.9

-25.0

-14.5

-12.4

-11.2

-6.120°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

9

13

17

21

21.80

8

8

8

8

8

9

9

9

171718.8 18.7

13.4

11.8

5.4

3.42.1

-25.4

-16.4-13.0

-5.9-4.0

-2.3

0.720°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

9

13

17

21

22.45

Spring

Autumn



D-1

11

11

1121

2126.114.3

12.4

7.9

6.2

6.2

5.4

-30.2

-16.9

-14.7

-8.5

-6.020°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

11

16

21

25.33

8

8

8

8

11

11

1121

25.820.5

13.5

13.5

6.2

2.1

-29.8

-15.1 -13.7

-6.0

-5.8

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

11

16

21

26

26.35

Spring

Autumn



D-0

6

6

66

6

6

6

9

9 9

17

28.4

13.7

10.9

7.9

7.67.1

-26.7

-15.1

-15.1

-6.1

-4.9

-1.2

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

9

13

17

21

24.38

8

88

8

88

8

9

9

9

9

9

917

17

26.815.8

15.5

13.7

7.6

2.8 1.8

-25.9

-18.7-10.6

-9.7

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

9

13

17

21

23.61

Spring

Autumn



6

6

6

6

6

6

77

7

713

13 13

22.3

10.2

7.8

7.56.9

4.53.7

-20.4

-13.4

-11.4

-8.4

-7.5-5.6

-5.2

-0.2

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

7

10

13

16

18.72

13

19.915.3

14.0

12.1

7.0

4.72.7 1.9

-19.7

-19.5-14.4

-11.0

-6.62.5

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

10

13

16

17.71

Spring

Autumn

D+1



6

6

611

1115.7

14.87.3

6.8

6.4

5.55.1

3.9

3.4

2.5

-18.7

-13.6

-10.9

-8.0

-6.7

-6.5

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

6

8.5

11

13.5

15.08

8

8

88

8.5

8.58.5

14.7

13.2

11.2

10.9

10.5

4.7

3.9

3.9

2.8

-16.3

-15.3

-9.9

-9.6

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

8

8.5

9.75

10.99

Spring

Autumn

D+2Similar to North Atlantic Jet waveguideSimilar to North Atlantic Jet waveguide**

Similar to North African – Asian Jet waveguideSimilar to North African – Asian Jet waveguide* * or circumglobal waveguide or circumglobal waveguide** **

**As defined in Hoskins and Ambrizzi., J. Atmos. Sci., 50 1993 As defined in Hoskins and Ambrizzi., J. Atmos. Sci., 50 1993 ****As defined in Branstator, J. of Climate, 2001 As defined in Branstator, J. of Climate, 2001



20

20

20

2020

20

20

20

20

20

30

30

30

40

-12-12-12

000

0 12

12

1224

20°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

20

25

30

35

40

41.68

20

20

20

20

20

2020

3030

30 30

30

40

40

-12-12-12

0

00

12

121220°N20°N

40°N40°N

60°N60°N

120°E

120°E

160°E

160°E

160°W

160°W

120°W

120°W

80°W

80°W

40°W

40°W

0°

0°

40°E

40°E

80°E

80°E

20

25

30

35

40

45

49.81

Seasonal change in the Jet streamSeasonal change in the Jet stream

Spring

Autumn


The predictive skill of SSF conditions has increased during the years, especially in The predictive skill of SSF conditions has increased during the years, especially in the medium-range where the improvement has been greater than normal conditions.the medium-range where the improvement has been greater than normal conditions.

SummarySummary

(68-60)= +8% (77-65)= +12%

(81-95)= -14m (73-99)= -26m

15/18 30

D+6

ACC

RMS

ALL SSF

Error reduction over Europe from 80’ to 90’

Gain (hours)


The predictive skill of SSF events is higher than average conditions. We argue that this The predictive skill of SSF events is higher than average conditions. We argue that this could be explained by the prolonged linear growth of the wave packet induced by a could be explained by the prolonged linear growth of the wave packet induced by a strong wave guiding effects of the jet. Wave breaking and others highly non linear strong wave guiding effects of the jet. Wave breaking and others highly non linear processes may act to reduce predictability in the decaying stage. Which is the processes may act to reduce predictability in the decaying stage. Which is the predictability limit predictability limit ofof these events these events ? ? Will it be possible to predict them beyond 10 days ?Will it be possible to predict them beyond 10 days ?

Biggest improvement in the autumn cases. The different propagation of wave packets in Biggest improvement in the autumn cases. The different propagation of wave packets in the two seasons may induces different responses to model changes and availability of the two seasons may induces different responses to model changes and availability of observation. Spring events, for example, propagate from N-America where known observation. Spring events, for example, propagate from N-America where known difficulties in correctly simulating the interaction between deep convection and large-difficulties in correctly simulating the interaction between deep convection and large-scale flow might have delayed improvements in the forecast quality. Increase and better scale flow might have delayed improvements in the forecast quality. Increase and better usage of satellite data may have had greater impact in correctly defining the initial usage of satellite data may have had greater impact in correctly defining the initial conditions in data sparse regions, like the Pacific Ocean, where autumn wave packet conditions in data sparse regions, like the Pacific Ocean, where autumn wave packet seems to originate.seems to originate.

SummarySummary


Thanks for your attentionThanks for your attention

predictability of a large-scale flow conducive to extreme precipitation over western alps*

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