New NWS Western Region Local Climate Products

1. New NWS Western Region Local Climate Products 1Marina Timofeyeva, 2Andrea Bair and 3David Unger 1 UCAR/NWS/NOAA 2 WR HQ/NWS/NOAA 3 CPC/NCEP/NWS/NOAA Contributors: Bob Livezey, Shripad Deo, Heather Hauser, Holly Hartmann, Eugene Petrescu, Michael Staudenmaier

2. OUTLINE • Need for Local Climate Products • Challenges in Local Climate Product Development • Methods and Data • Product Design • Operational Organization • Next Steps

3. Need For Local Climate Products • CPC products and Local Climate

4. Figures courtesy of Klaus Wolter, CDC Need For Local Climate Products • Localized Climate Impacts are of public interest

5. Challenges in Local Climate Product Development

6. POF (%) Forecasted Temperature (°F) Methods and Data • Modified CPC Translation of CD Seasonal Temperature POE Observed T

7. Methods and Data • Modification included: • Regression coefficients estimate: use of straight regression coefficients versus ones inflated by correlation; • Forecasting methodology: station mean and variance are estimated from CD forecasted mean and variance and use of normal distribution for POE ordinates versus use of inflated correlation coefficients and CD POE temperature ordinates; • Local Product design is customer friendlier

8. Methods and Data • Data: NCDC provided an experimental “homogenized and serially complete data” set with: • Monthly/daily value internal consistency check • Bias adjusted to a midnight to midnight observation schedule • Spatial QC • Artificial change point detected and adjusted • Estimated missing or discarded data

9. Climatological Spread ρ (CD fcst/obs corr) Spread of Station Forecast Confident Prediction ri – Station/CD Correlation Methods and Data

10. Methods and Data

11. 1941,1958,1966, 1973,1983,1987, 1988,1992,1995, 1998 1941-2000 Eastern North Dakota Temperature (°F) Eastern North Dakota Temperature (°F) Methods and Data • CPC Composite Analysis extended by Risk Analysis and CPC forecasting method

12. Methods and Data • Extension includes Risk Analysis identifying statistically significant signal

13. Methods and Data • Making forecast using Composite Analysis NINO 3.4 INITIAL TIME 5 2004 PROJECTION FRACTION Lead Mo BELOW NORMAL ABOVE JJA 0.5 0.076 0.371 0.552 ………………………………………… DJF 6.5 0.053 0.388 0.559 JFM 7.5 0.080 0.393 0.527 FORECAST USING CURRENT CPC Nino 3.4: Example – ElNino with 7.5 month lead (forecast for JFM 2005):

14. Product Design • Translated POE: Customer “wants a number”

15. CRPSS Product Design • Verification with cross-validation

16. Product Design • Verification with cross-validation Observed Frequency Probability

17. Product Design • CD verification indicates space & time differences in forecast performance

18. Bordered Probability bars are statistically significant Product Design • Composite Based: Customer “wants a number” Probability, % Temperature, °F

19. Betatakin Wupatki Seligman Flagstaff Winslow Petrified Forest Prescott Childs Payson McNary Product Design Analysis of WFO Flagstaff Composites for Tmean JFM

20. Precip, JFM Tmean, JAS Tmean, JAS Product Design • Verification

21. Product Design • Verification

22. WR HQ CPC/CSD WR WFO Station Forecast; Verification Methodology; Software; CD Forecast Prognostic Discussion; Product Delivery; Customer Feedback Operational Organization • 87 site in NWS WR area will be introduced in 01/05

23. Next Steps • Product Documentation • Experimental Phase • Customer Feedback • Product Adjustment • Product Introduction in NWS operations