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 … [email protected]
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
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