evolution of mjo in ecmwf and gfs precipitation forecasts john janowiak 1, peter bauer 2, p. arkin...

Evolution of MJO in ECMWF and GFS Precipitation Forecasts

John Janowiak1, Peter Bauer2 , P. Arkin1, J. Gottschalck3

1 Cooperative Institute for Climate and Satellites (CICS)

Earth Systems Science Interdisciplinary Center (ESSIC) University of Maryland, College Park, Maryland, USA

2 ECMWF Reading, U. K.

3 Climate Prediction Center Camp Springs, Maryland, USA

34th Climate Diagnostics and Prediction Workshop, Monterey,CA Oct 29, 2009

“Satellites”

Outline• Motivation

• CMORPH Background (“observations”)

• Case Study of MJO as represented in precip. field from:- CMORPH- ECMWF forecasts (1-10 day)- GFS forecasts (1-15 day)

• Conjecture … and a Forecast

Janowiak: MWR, 1990

Models circa 1989:

Some MJO behavior in dynamic fields … but not reflected in precipitation

… so, let’s reexamine using today’s models

“observed” (GPI)

Modelfcsts

Note: 12-36h forecasts

Outline• Motivation

• CMORPH Background (“observations”)

• Case Studies of MJO as represented in precip. field from:

- CMORPH

- ECMWF forecasts (1-10 day)

- GFS forecasts (1-15 day)

• Conjecture and … a Forecast

CMORPH*NOAA/CPC “Morphing” technique

Provides quantitative estimates of precip @ 0.07o x 0.07o lat/lon / ½ hr( ~ 8 km @ equator)

Uses IR or model winds to propagate & ‘morph’ precip. identified by passive microwave

Dec 2002 – present; extending back to ~1998

* Joyce et al. (J. Hydromet 2004)

“morphing”: spatial/temporal interpolation

RADAR CMORPH

Hourly Precipitation Loops: 15Z 8Jun2008 – 06Z9Jun2008

0.25o lat/lon 0.07o lat/lon

mm/hr

CMORPH

Yields confidence that satellite estimates are useful over water

Note: estimates are theoreticallybetter over water than land

Outline• Motivation

• CMORPH Background (“observations”)

• Case Studies of MJO as represented in precip. field from:

- CMORPH

- ECMWF forecasts (1–10 day)

- GFS forecasts (1-15 day)

• Conjecture and … a Forecast

Case Study:

Mod-Stg MJO

Nov 2007 – Feb 2008

(CPC: Jon Gottschalck)

CMORPH

Anomaly from Period Mean

15N-15S

Precipitation from Indian Ocean across the Pacific to Greenwich

Seasonal mean removed

MJO signatures clearly evident

Diagonal lines subjectively drawn to identify axis of MJO (and intervening dry periods) & eastward progression of features

T IME

Anomaly from Period Mean

15N-15S

Case Study:

Mod-Stg MJO

Nov 2007 – Feb 2008

CMORPH

Arrows identify westward moving elements within MJO envelope (Nakazawa, 1988)

~10days

~10 days

Difference from Nov 2007 – Feb 2008 Period Mean

Dec 4-15, 2007

Dec 16 – Jan 3

Jan 5-20, 2008

Dec 4-15, 2007

Dec 16 – Jan 3

Jan 5-20, 2008

Difference from Nov 2007 – Feb 2008 Period Mean

Excellent

W

W

Dec 4-15, 2007

Dec 16 – Jan 3

Jan 5-20, 2008

Difference from Nov 2007 – Feb 2008 Period Mean

W

Excellent

Dec 4-15, 2007

Dec 16 – Jan 3

Jan 5-20, 2008

Difference from Nov 2007 – Feb 2008 Period Mean

W

Excellent

Difference from Nov 2007 – Feb 2008 Period Mean

A

B

C

Dec 4-15

CMORPHGFS 10 dyECMWF 10 dy

(5 dy smoothed)

- Models clearly show MJO signal- But late compared to obs- More spread out in time

Difference from Nov 2007 – Feb 2008 Period Mean

A

B

C

Dec 16-Jan 3

CMORPHGFS 10 dyECMWF 10 dy

(5 dy smoothed)

Difference from Nov 2007 – Feb 2008 Period Mean

A

B

C

Jan 5-20

(5 dy smoothed)CMORPHGFS 10 dyECMWF 10 dy

6

0

1

2

345

7

These show pattern correlations over the region between forecasts and observations for different lags (the different colored lines) and for different forecast lead time (the horizontal axis)

The green line labeled “1” represents the correlation between forecasts initialized one day later than the observations they are compared to, etc.

“Persistence” Corr: 0.51

Model beats persistence:3-4 days

Conjecture and … a Forecast …

• Model forecasts of MJO precip. evolution can be helped by ocean-atmosphere coupling

• Plans: perform same analyses on CFSRR ‘hindcasts’

“obs”“observed” Modelfcsts

“yesterday” (1989)

CMORPHGFS 10 dyECMWF 10 dy

“today” (2007)

CMORPHGFS 10 dyECMWF 10 dy

“Tomorrow” (within a decade or so)

Thank you … johnj@essic.umd.edu

EXTRA

`

~10 days

Jan-May 2005 (weak-mod)

Same, except for global Tropics

6

0

1

2

345

7

These show pattern correlations over the region between forecasts and observations for different lags (the different colored lines) and for different forecast lead time (the horizontal axis)

The green line labeled “1” represents the correlation between forecasts initialized one day later than the observations they are compared to

“Interesting if true” – we are working to figure out what this might mean

Conclusions• Both the GFS and (particularly) ECMWF exhibit realistic MJO

precipitation patterns and variability

– At longer leads, both models lose details and lag behind the observations

– Perhaps the initialization is imperfect in some fashion – or these results make a case for more effective precipitation initialization?

• These advances (relative to ~1990) suggest that useful skill in predicting MJO-related precipitation may be close to being attained