1. Young-Kwon Lim, D.W. Shin, S. Cocke, T. E. LaRow, J. J. O’Brien, and E. P. Chassignet Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA Statistical Downscaling of the NCEP CFS Retrospective forecasts (precipitation) over the SE US

2. Background and Motivation  Global NCEP/CFS : 1) Retrospective forecasts longer than 20 year period (1981-2006), 2) Widely used in many studies, 3) the low seasonal predictive skill (e.g., precipitation for growing season) in certain areas.  Question: Can we successfully downscale the CFS data which have 2.5 degree resolution and the low skill over several regions?

3. Why downscaling over the SE USA?  Extremely high temperature and heavy rainfall with severe storms during summer, resulting in potential property damage and injuries.  The largest areas of agricultural farms in the nation.  An accurate forecast with higher spatial resolution is essential to adapt management, increase profits, reduce production risks, and mitigate damages.

4. Regional climate simulation in FSU/COAPS  FSU/COAPS Global Spectral Model (FSU/COAPS GSM) has been downscaled to the 20km grid resolution by FSU/COAPS nested regional spectral model (FSU/COAPS NRSM) over the southeast US.  Dynamical Downscaling  Statistical downscaling model has been also developed. (CSEOF, multiple regression, and stochastic PC generation are used.)

5. Training Predictor : model output Predictand : observation & Regressed eigenfunctions of CFS runs used 0.2°  0.2° (~20km res.)2.5°  2.5° (~250km res.) Eigenfunctions of the Obs. over training period and the Generated PC used Prediction period Withholding different year for Cross-validation

6. Data (Obs. & CFS) and period  Variables : Daily precipitation  Period : 1987 ~ 2005 (Spring (MAM) ~ Summer (JJA) each year (daily))  Observed data source : National Weather Service Cooperative Observing Program surface data over the southeast US : ~20km×20km  Large-scale data to be downscaled : NCEP/CFS retrospecitve forecasts : 2.5°×2.5°, 10 members with lagged initial conditions. Seasonal integrations starting from February each year.

7. Results  2-d seasonal mean field (CFS, Downscaled data, and Observation)  Time series over ~20 years (Interannual variation) for three states (Tallahassee, Jacksonville, Orlando, Miami, Atlanta, Tifton, Birmingham, and Montgomery)  Error variance and correlations  Categorical Predictability for above/below seasonal climatology  Extremes: Frequency of heavy rainfall events per season  Extremes: Frequency of dry spells per season  Application of downscaled data: agricultural model  Realtime forecast (2008 winter)

8. Biased NCEP/CFS fields (comparison with Obs.) CFS Obs.  Overestimation (largest: Georgia) MAMJJA  East > West  Florida is not the wettest region in summer. Problems?

9. Seasonal mean field (before and since 2000) NCEP/CFS Downscaling Observation  Little change in rainfall amount  Similar regional distribution  Rainfall increase  Reduction in bias

10. Black : Observation Red : Downscaling Blue : CFS  Observed variation is better captured by downscaling.  Several poor captures are found (e.g., before 1990, and 94~97).  CFS overestimates the observed variation.  Anomaly time series : CFS data show smaller amplitude variation. Interannual variation at coarse scale (all area averaged seasonal anomaly)

11. Black : Observation Blue : Downscaling  Better capture of observed variation since 1999.  Several poor captures in the early period (e.g., before 1990, and 1994). Interannual variation at regional scale (seasonal anomaly time series) Florida Pan. Southern Florida Central Florida NE Florida Northern Alabama Southern Georgia Northern Georgia Southern Alabama

12. Error variance and Seasonal Anomaly Correlation  Localized seasonal forecast with a slight increase in Corr.  Reduction in Relative error variance (REV) (≈ 2  0.6~1.4) REVCorr. Corr. (0.3~0.4) Corr. (0.4~0.6) REV > 2.0 REV < 1

13. Categorical predictability (HSS) for Seasonal anomaly Downscaling Rescaling (OA) from the CFS with bias- correction CFS  Downscaling: Positive on most grid points (0~0.5)  Skill in overall: Downscaling > CFS and Rescaling (OA) 0.2~0.45 0.1~0.2 0.0~0.1

14. Black : Observation Red : Downscaling Blue : Rescaling from the CFS  Observed variation is captured reasonably by downscaling.  Several poor captures are found in early period (before 1995).  Rescaling overestimates the observed variation. Extremes (Frequency of daily heavy rainfall events) Threshold : exceeds 1 std. + climatology

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

16. Black : Observation Red : Downscaling Blue : Rescaling from the CFS  Downscaled data are closer to the observation.  Rescaled data have serious underestimation problem with little amplitude fluctuation. Extremes (Frequency of Subseasonal dry spells) Threshold : a week average < 0.1mm/day

17. Categorical predictability (HSS) for the frequency of dry spells HSS (Downscaling)  Downscaling: Better prediction in Georgia and Alabama than Florida : -0.1 ~ 0.4,  Rescaling: no skill in terms of HSS. Threshold : a week < 0.1mm/day 0.0~0.4

18. Application example: Downscaled atmospheric data to the crop model Maize Yields Precipitation Tifton (GA) Crop Yields and Precipitation Red (CFS) Black (Observed) Green (Bias-corrected downscaled CFS)‏

19. Application example: Realtime seasonal forecasts (2008 winter) CFS Downscaling

20. Concluding remarks  Precipitation for growing season from NCEP/CFS (~2.5° res.) run have been downscaled to local scale of ~20km for the SE US.  Downscaling simulates the regional-scale seasonal precipitation with reduction in wet biases.  Correlation, categorical predictability for seasonal anomaly has been improved from the coarsely resolved NCEP/CFS.  Heavy rainfall events: In overall, downscaling better produces the interannual frequency variation than bias-corrected rescaling.  Subseasonal dry spells: Rescaled data show significant underestimation with much smaller amplitude variation than observation.  Application to crop model and realtime forecast.

21. Statistical downscaling procedure (1) 1. Cyclostationary EOF analysis for the model output and the observation : CSEOF (Kim and North 1997) : analysis technique for extracting the spatio-temporal evolution of physical modes (e.g., seasonal cycle, ENSO, ISOs, etc.) and their long-term amplitude variations. P(r,t)=∑ n S n (t) B n (r,t) B n (r,t) : time-dependent eigenfunctions, S n (t) : PC time series. In this study, CSEOF is conducted on both observation and FSUGSM runs over the training period.

22. Statistical downscaling procedure (2) 2. Multiple regression between the model output and the observation : CSFOF PC time series of the first significant modes of a predictor variable (FSUGSM data) are regressed onto a certain PC time series of the target variable (observation) in the training period. PCT n (t)=∑ i α ni ·PCP i (t)+ε(t) i=1,2,…10 PCT n (t): target PC time series, α ni : regression coefficient PCP i (t): predictor PC time series Relationship between model output and the observation is extracted from CSEOF and multiple regression.

23. Result of multiple regression PC time series Eigenfunction (from Observation) Regressed Eigenfunction (model) Both are physically consistent. (training period) ? forecast period

24. Result of multiple regression Eigenfunction (from Observation) Regressed Eigenfunction (model)

25. Statistical downscaling procedure (3) 3. Generating CSEOF PC of the model data over the forecast period from the regressed fields in the training : CSFOF PC time series of the model data are generated for the prediction period. Modeled data and the regressed eigenfunctions identified from training are used. PC n (t)=∑ g P(g,t)·B n + (g,t) PC n (t): the nth mode PC time series for the prediction period g : large-scale grid point B n + (g,t) : regressed CSEOF eigenfunctions P(g,t): global model anomaly over the prediction period

26. Statistical downscaling procedure (4) 4. Downscaled data construction from the eigenfunctions of the observation and the generated CSEOF PC time series : D(s,t)=∑ n PC n (t)·B n o (s,t) PC n (t) : generated PC time series from the previous step B n o (s,t): CSEOF eigenfunctions of the observation (training period) D(s,t) : downscaled output 5. Generating downscaled output for the entire period (9yrs) by cross-validation framework

27. Black : Observation Red : Downscaling Blue : Rescaling from the CFS  Observed variation is captured by downscaling to a certain extent.  Several peaks are not captured well (e.g., 1998 in Florida).  Rescaled data with bias- correction oscillates near zero (significant underestimation). Extremes (Frequency of Subseasonal dry spells (anomaly)) Threshold : a week average < 0.1mm/day