1. An Evaluation of Aspects of Tropical Precipitation Forecasts from the ECMWF & NCEP Model Using CMORPH John Janowiak 1, M.R.P. Sapiano 1, P. A. Arkin 1, F.J. Turk 2 1 Cooperative Institute for Climate and Satellites (CICS) Earth Systems Science Interdisciplinary Center (ESSIC) University of Maryland, College Park, Maryland, USA 2 Jet Propulsion Laboratory Pasadena, California, USA

2. Outline What is CMORPH? The Diurnal Cycle in GFS Precipitation MJO-related convective precipitation from: - CMORPH (observations) - ECMWF forecasts (5-, 10-day) - GFS forecasts (5-, 10-, 15-day) Conclusions

3. CMORPH* NOAA/CPC “Morphing” technique Provides quantitative estimates of precipitation for 0.07 o x 0.07 o lat/lon / ½ hr ( ~ 8 km @ equator) Uses geostationary IR to propagate & ‘morph’ (interpolate/smooth in time and space) precipitation estimated from passive microwave observations Dec 2002 – present; extending back to ~1998 * See Joyce et al. (J. Hydromet 2004) RADARCMORPH Hourly Precipitation Loops: 15Z 8Jun2008 – 06Z9Jun2008 0.25 o lat/lon 0.07 o lat/lon

4. mm/hr CMORPH CMORPH looks quite realistic over water, maybe less so over land Probably related to fact that microwave estimates are better over water than land

5. Outline What is CMORPH? The Diurnal Cycle in GFS Precipitation MJO-related convective precipitation from: - CMORPH (observations) - ECMWF forecasts (5-, 10-day) - GFS forecasts (5-, 10-, 15-day) Conclusions

6. 09 12 15 18 21 00 03 06 LST CMORPH GFS 1 day fcst

7. 09 12 15 18 21 00 03 06 LST CMORPH GFS 1 day fcst

8. 09 12 15 18 21 00 03 06 LST CMORPH GFS 1 day fcst

9. 09 12 15 18 21 00 03 06 LST CMORPH GFS 1 day fcst

10. 07 10 13 16 19 22 01 04 LST CMORPH GFS 1 day fcst

11. 12 15 18 21 00 03 06 09 LST CMORPH

12. Outline What is CMORPH? The Diurnal Cycle in GFS Precipitation MJO-related convective precipitation from: - CMORPH (observations) - ECMWF forecasts (5-, 10-day) - GFS forecasts (5-, 10-day) Conclusions

13. Case Study: Moderate-Strong MJO Nov 2007 – Feb 2008 CMORPH 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 15N-15S

14. These lines identify westward moving elements within MJO envelope 15N-15S Case Study: Mod-Stg MJO Nov 2007 – Feb 2008 CMORPH

15. Line are same as previous slides; on model plots, lines represent observed features

16. ~10days

17. Dec 4-15, 2007 Dec 16 – Jan 3 Jan 5-20, 2008 Difference from Nov 2007 – Feb 2008 Period Mean

18. Dec 4-15, 2007 Dec 16 – Jan 3 Jan 5-20, 2008 Difference from Nov 2007 – Feb 2008 Period Mean

19. A B C Dec 4-15 CMORPH GFS 10 dy ECMWF 10 dy (5 dy smoothed)

20. Difference from Nov 2007 – Feb 2008 Period Mean A B C Dec 16-Jan 3 CMORPH GFS 10 dy ECMWF 10 dy (5 dy smoothed)

21. Difference from Nov 2007 – Feb 2008 Period Mean A B C Jan 5-20 (5 dy smoothed)CMORPH GFS 10 dy ECMWF 10 dy

22. 0 1 2 3 4 5 6 712 0 89 10 11 13 1414 15 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

23. Conclusions GFS precipitation forecasts over the tropical oceans do exhibit a diurnal cycle –But the peak occurs earlier than observations –And the amplitude decreases with forecast lead, at least in the central Pacific Both GFS and ECMWF exhibit a reasonably realistic MJO precipitation pattern and variability –At longer leads, both models lose details and seem to lag behind the observations –Possible that the initialization is imperfect and some spin-up is required to attain a more realistic precipitation field? Results, particularly for ECMWF, indicate that useful skill in predicting MJO-related precipitation is close to being attained