Probabilistic forecast skill of extreme Probabilistic forecast skill of extreme

weather in weeks 1weather in weeks 1--4 in the United4 in the United

States d ring interStates d ring interStates during winterStates during winter

Charles JonesCharles Jones11 Jon GottschalkJon GottschalkCharles JonesCharles Jones11, Jon Gottschalk, , Jon Gottschalk, Leila CarvalhoLeila Carvalho11, Wayne Higgins, Wayne Higgins

11University of CaliforniaUniversity of California

Santa BarbaraSanta Barbara

CTB Project: Probabilistic Forecasts of Extreme Events and Weather Hazards over the United States

Goal: develop experimental probabilistico Goal: develop experimental probabilisticforecasts of extreme events and risk ofweather related hazards

o Extremes: heavy precipitation, lowtemperature and high wind speeds

o Season: wintero Lead times: Weeks 1-4 – emphasis on

k 2 3weeks 2-3

2

Extreme precipitation events

Work in progress

Developing an experimental probabilistic forecast modelCalibration issuesPreliminary validations yCurrent work

3

Extreme precipitation events

Winter: 1 Nov – 31 Mar

Extreme: daily amountExtreme: daily amount greater than 90th

percentile of monthly pdf

Observations: CPC daily griddedObservations: CPC daily gridded precipitation 1981-2008

4

Extreme precipitation: persistencepersistence

On average: ~1-2 days

Extremes of the extremes:Extremes of the extremes:Maximum persistence (1981-2008)

5

A prototype probabilistic forecast model of extreme events

NCEP CFS ReforecastsHindcasts performed for each winter (1981-2008)Basic calibrations:

1. remove mean model biasTake season out, use remaining winters to estimate biasBias computed for each month (Nov-Mar) and lead time 1-28 days y

2. Percentile adjustmentPdf comparison: obs and CFS 30-yr free runsDone for each month separately (Nov-Mar)p y ( )

6

Mean model bias (averaged Nov-Mar; weeks 1-4)

Week-1 Week-2

Week-3 Week-47

Percentile adjustment

Model

Observations

P90th

P90thR =

P90

P90th=CFS percentile in 30-yr free run

8

A prototype probabilistic forecast model of extreme events

Hi d t

1 Nov 31 Mar

Hindcasts: each winter (81-08)daily (1 Nov-31 Mar)

W k 1 W k 2 W k 3 W k 4

T0: Forecast release day

Week-1 Week-2 Week-3 Week-4

Δ T

Consider all CFS members with ICs Δ T days prior to T0 T0: Forecast release dayΔ Ty

Δ T : eight weeks – motivation e.g., MJO influence on extremes

Two forecast products:

1) probability of extreme on each day in weeks 1-4 (timing important)

2) b bilit f X ≥ t i W k k (ti i t i t t)2) probability of X ≥ x extremes in Week-k (timing not important)

Pi= n / N n=members predicting extremes; N=total number members; ~14 25 9N=total number members; ~14-25

Validation

Brier skill score (BSS)

Ob i Ok 0 ( ) 1 ( )Observation: Ok = 0 (no event); 1 (yes event)Forecast: yk = probability of X ≥ x extremes in week-kValidation in each winter season separatelyValidation in each winter season separately

P(X ≥ 2 extremes in a week)

10

BSS: two or more extremes in a week – winter 2000

Week-1 Week-2ee ee

Week-3 Week-4

11

BSS: two or more extremes in a week – winter 2002

Week-1 Week-2

Week-3 Week-4

12

Percentage of winters BSS ≥ 5%

Week-1 Week-2ee

Week-3 Week-4

13

Maximum BSS two or more extremes in a week

Week-1 Week-2Week 1 Week-2

Week-3 Week-4

14

Significant interannual variations in forecast skill

Verification X ≥ 2 events in a weekVerification X ≥ 2 events in a week

Percentage of region with BSS ≥ 5%with BSS ≥ 5%

15

Percentage of region with BSS ≥ 5%

Verification X ≥ 2 events in a weekVerification X ≥ 2 events in a week

16

Percentage of region with BSS ≥ 5%

Verification X ≥ 2 events in a weekVerification X ≥ 2 events in a week

17

Percentage of region with BSS ≥ 5%

V ifi ti X ≥ 2 t i kVerification X ≥ 2 events in a week

18

SummaryProbabilistic forecast model of extreme events based on CFSreforecasts:

Potentially usefulThere is skill, sometimes much better than climatologyReliability is low (poor resolution; overconfident)Need to develop additional calibrationsp

Current WorkFurther understand interannual variations in skill

Modulation by the MJO:yJones et al. (2004), J. Climate, 17, 4575-4589.

Modulation by ENSODevelop additional probabilistic models: combination of CFSp pand others (e.g., statistical MJO)Implementation in real-time

19