New NWS Western Region Local Climate Products 1 Marina Timofeyeva, 2 Andrea Bair and 3 David 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

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

Need For Local Climate Products CPC products and Local Climate

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

Challenges in Local Climate Product Development

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

Methods and Data Modification included: versus –Regression coefficients estimate: use of straight regression coefficients versus ones inflated by correlation; versus –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

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

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

Methods and Data

CPC Composite Analysis extended by Risk Analysis and CPC forecasting method ,1958,1966, 1973,1983,1987, 1988,1992,1995, 1998 Eastern North Dakota Temperature (°F)

Methods and Data Extension includes Risk Analysis identifying statistically significant signal

Methods and Data Making forecast using Composite Analysis FORECAST USING CURRENT CPC Nino 3.4: Nino3.4 Term WarmNeutralCold Above 67%33%11% Near 13%53%28% Below 20%14%61% Example – ElNino with 7.5 month lead (forecast for JFM 2005): NINO 3.4 INITIAL TIME PROJECTION FRACTION Lead Mo BELOW NORMAL ABOVE JJA ………………………………………… DJF JFM

Product Design Translated POE: Customer “wants a number”

Product Design Verification with cross-validation CRPSS

Product Design Verification with cross-validation Probability Observed Frequency

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

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

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

Product Design Verification Tmean, JAS Precip, JFM

Product Design Verification

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

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