Development of Multi-model High Resolution Seasonal Forecasting System: An Application to SE US

1. Development of Multi-model High Resolution Seasonal Forecasting System: An Application to SE US Young-Kwon LimCollaborators: Climate modeling group (Drs. L. Stefanova, D.W. Shin, S. Cocke, and T. E. LaRow) at FSU/COAPS, Dr. G. Baigorria (Univ. of Florida), Dr. K. H. Seo (Pusan Nat’l Univ., Korea), Dr. S. Schubert (NASA/GSFC), Dr. H. Juang (NOAA)Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA

2. Current high-resolution seasonal forecast in FSU/COAPS • One dynamical model (FSU/COAPS RSM) and one statistical downscaling model at 20km resolution (Cocke et al. 2007; Lim et al. 2007, 2009). • Real-time seasonal forecasts (up to 6 month ahead) are updated four times a year through CMO web (http://coaps.fsu.edu/cmo).

3. Realtime high-resolution forecast by FSU/COAPS (example: Nov. 2009, 20km resolution) Rainfallmean T mean Rainfallanomaly T ano.

4. Capability of the current FSU/COAPS downscaling system, and Motivation • Corr. skill of the current FSU/COAPS downscaling system: 1) Winter rainfall: Corr. > 0.5 (FL,GA) (Cocke et al. 2007) 2) Crop growing seasons (spring and summer) : Sfc. air T. (Lim et al. 2007): Corr.=0.3~0.8, Rainfall: Improvement of correlation over the large scale CFS. Statistical significance problem (Lim et al. 2009). • Skill (correlation and categorical predictability) tends to be model dependent (e.g., summer rainfall: higher skill over inland by FSU model, while higher skill over Florida peninsula by NCEP model) • Question: Can we improve the skill over the entire SE US with statistical signifcance via MM downscaling system?

5. Error variance and Seasonal Anomaly Correlation (current downscaling system) (Lim et al. 2009) Corr. (0.~0.2) • Downscaled seasonal forecast with an improvement of Corr. • Reduction in Relative error variance (REV) (≈ 2  0.6~1.4) REV > 6.0 REV < 1 Down. from FSU model REV Corr. Corr. (0.4~0.6)

6. Categorical predictability (HSS) for the frequency of rainfall extremes (Lim et al. 2009) 1 std. + climatology Downscaling Rescaling (OA) from the CFS -0.2 ~ 0.1 • Downscaling: Florida and S. Georgia : > 0.1, Alabama and C. Georgia : -0.1 ~ 0.2, • Rescaling: -0.2 ~ 0.2 0.1~0.5 Difference (Down. - Rescaling) ≥0.1

7. Dynamical and statistical models involved in the multi-model downscaling study • Dynamical models 1.FSU/COAPS NRSM 2. RSM (NCEP, ECPC) 3. RegCM3 (ICTP) • Statistical models 1. CRT (CSEOF + Regression + Time series generation) 2. NLCCA (Neural network based CCA) (Hsieh et al. 2006) 3. Geo-spatial weather generator (Baigorria et al. 2007)

8. Difference between FSU/COAPS downscaling works and other downscaling projects

9. Procedures • Downscaling large-scale reanalysis using dynamical models for model validation (bias, reliable distribution) • Downscaling large-scale retrospective forecasts • High-resolution seasonal forecasts on real-time basis • Probabilistic forecasts and application of the MME for the improved deterministic forecasts • Expansion to high-resolution climate change projection

10. Preliminary result: RCM response to downscaling (2.5˚ → 20km) 2m T. (JJA/2004) 2m T. (JJA/2005)

11. Preliminary result: RCM response to downscaling (2.5˚ → 20km) Prcp. (JJA/2004) Prcp. (JJA/2005)

12. Preliminary result: Statistical downscaling models 2m T. (JJA/2004) 2m T. (JJA/2005)

13. Preliminary result: Statistical downscaling models Prcp. (JJA/2004) Prcp. (JJA/2005)

14. Summary • Multi-model high-resolution seasonal forecasting system study at FSU/COAPS was begun in September. • Three dynamical and three statistical models have been involved in this study. • We aim at the spatial resolution as fine as 20km for the southeastern US (FL, GA, AL, NC, and SC). • Improvement of the skill over our existing downscaling system (one dynamical and one statistical model) is expected.