assessing the skill of an all season statistical forecast model of the madden julian oscillation l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation PowerPoint Presentation
Download Presentation
Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation

Loading in 2 Seconds...

play fullscreen
1 / 20

Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation - PowerPoint PPT Presentation


  • 147 Views
  • Uploaded on

Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation. Xian-an Jiang, Duane E. Waliser Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA Matthew C. Wheeler Bureau of Meteorology Research Centre, Melbourne, Australia

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation' - erma


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
assessing the skill of an all season statistical forecast model of the madden julian oscillation

Assessing the skill of an all-season statistical forecast model of the Madden-Julian Oscillation

Xian-an Jiang, Duane E. Waliser

Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA

Matthew C. Wheeler

Bureau of Meteorology Research Centre, Melbourne,Australia

Charles Jones

Institute for Computational Earth System Science, UCSB, CA

Myong-In Lee, and Siegfried D. Schubert

Global Model and Assimilation Office, NASA/GSFC, MD

motivation
Motivation:

Why Subseasonal Variability / Madden-Julian Oscillation?

  • Provide a more realistic benchmark for numerical forecast models;
  • Provide assessment of a statistical model for the real-time forecast of the MJO;
  • …seenext page

Weather

(~days)

Interannual

(~seasons)

Subseasonal (~weeks)

Why empirical model?

Dynamical models: skill only up to lead times of about 7-10days.

(e.g., Waliser et al.,1999, 2004; Hendon et al.,2000; Jones et al, 2000;Seo et al, 2005; andothers.)

Empirical models:15 days (rainfall/OLR), 20-25days for dynamical fields.

(e.g., von Storch and Xu,1990; Waliser et al.,1999; Lo and Hendon, 2000;Jones et al., 2004; Wheeler and Weickmann, 2001; Mo, 2001; and others)

Why current study (real-time scheme; Wheeler and Hendon 2004)?

?

Real-time application

Time-filtering

slide3

Motivation (cont’d)

Hybrid Forecast: Empirical + Dynamical

  • Provide a near-term means to improve extended-range predictions;
  • Motivate the need to improve intrinsic capability in MJO prediction in the dynamical models.
slide4

Model construction (I)

MJO Index

EOF 1 (11%)

Combined EOF (OLR,u850, u200)

(15oS-15oN ; Wheeler and Hendon 2004)

NCEP-DOE2 Reanalysis

Daily, 1983-2004

  • Annual cycle removed;
  • Interannual variability (ENSO) removed:
    • Regression pattern of each variable against the PC of SST rotated EOF1 (ENSO);
    • 120-day mean of previous 120 days.

EOF 2 (10%)

slide5

Model construction (II)

Time series of Combined EOF

PC1

PC2

Combined Amplitude

Strong MJO

Weak MJO

slide6

Model construction (III)

Lagged regression Model

Time at forecast point: t0

Variable A at particular grid point at forecast lead ndt: A(x,y,p,t0+ndt)

A(x,y,p,t0+ndt) = reg1(x,y,p,ndt) × PC1(t0) + reg2(x,y,p,ndt) × PC2(t0)

reg1(x,y,p,ndt) andreg2(x,y,p,ndt):

  • Based on historical observations
  • Seasonal dependent

KEY: How to get PC1(t0) and PC2(t0) (the real-time MJO index)?

slide7

Real-time forecast flow chart

Remove

Project to SST EOF1

SST’

SST

PCsst

annual cycle

Regression pattern to PCsst based on historical dataset

Real-time Observations at t0

IAV of OLR,

u850,u200

Remove IAV

OLR,

u850,u200

OLR’*,

u850’*,u200’*

OLR’,

u850’,u200’

Remove

annual cycle

Remove previous 120-day mean

Project to CEOF 1 & 2

reg1(x,y,p,ndt),reg2(x,y,p,ndt)

Lag Regression Forecast Model

PC1(t0), PC2(t0)

Lag Regression pattern for each variable against PC1 & 2 based on historical dataset

A(x,y,p,t0+ndt)

A: u, v, t, q & OLR, SLP.

slide8

Cross-validation:

1

1983

1984 1985 1986 …... …... …... 2002 2003 2004

Construct lag-regression coefficients

Forecast/validation

1983

1984

1985 1986 …... …... …... 2002 2003 2004

2

……..

…..

21

1983 1984 1985 …... 2001 2002

2003

2004

22

1983 1984 1985 …... 2001 2002 2003

2004

Forecast starting date: 1st, 6th, 11th, 16th, 21st, and 26th of each month.

Summer: Forecasts with the starting date during Jun, Jul, Aug.

Winter: Nov, Dec and Jan.

slide9

Predictive Skills

Pattern correlation (Forecast vs EOF filtered Observations)

(30oS-30oN, global longitudes)

OLR

0.36

0.19

slide12

Correlation (vs. unfiltered OBS)

Winter

850mb u-wind

OLR

5d

10d

15d

20d

slide13

Correlation (vs. unfiltered OBS)

200mb u-wind

Winter

5d

10d

15d

20d

slide14

Sensitivity Tests (I)

Advantage by using seasonal-dependent regression coefficients

slide15

Sensitivity Tests (II)

Including more PCs in the regression model

Summer

Winter

2PCs

5PCs

slide16

Sensitivity Tests (III)

More lagged information in the regression model

slide17

Sensitivity Tests (V)

Impact of MJO status at the initial forecast time

Total: 396

Strong MJO (172)

Weak MJO (115)

OLR

slide18

Correlation vs unfiltered OBS at 20d

Winter

Strong MJO

Ctrl_exp

OLR

u850

u200

slide19

Hindcast ’03~’04 Winter

OLR (15oS-15oN)

Obs

5d

10d

15d

Jun/04

May/04

Apr/04

Mar/04

Feb/04

Jan/04

Dec/03

Nov/03

conclusions
Conclusions
  • The current real-time forecast model exhibits useful extended-range skill for real-time MJO forecast with correlation skill of 0.3 (0.5) between predicted and observed unfiltered (EOF-filtered) fields at a lead time of 15d.
  • The predictive skill is increased significantly when there are strong MJO signals at the initial forecast time.
  • Predictive skill for the upper-tropospheric winds is relatively higher than for the low-level winds and convection signals.

Bureau of Meteorology Research Centre, Australia

http://www.bom.gov.au/bmrc/clfor/cfstaff/matw/maproom/rmm