1. 1 Progress on Science Activities: Climate Forecast Products Team Probabilistic forecasts of Extreme Events and Weather Hazards in the US (PI: Charles Jones (UCSB); NCEP Co-PI: Jon Gottschalck (CTB)) Funding pendingFunding pending Activities: Develop sub-monthly to monthly probabilistic forecast models of extreme events (precipitation, temperature, and wind) trained and validated using CFS and GR-2 Low-frequency modes (ENSO, MJO, AO) are to be incorporated in probabilistic forecast models System-wide Advancement of User-Centric Climate Forecast Products (PI: Holly Hartmann (UAZ); NCEP Co-PI: Ed O’Lenic (CPC)) Funding pendingFunding pending Activities: Improve user understanding, access, utility of existing products; predict more variables (wind, humidity, heat index, wind chill, burn index, …); extreme events; new sector-oriented products, based on NWS Field and RISA input.

2. 2 3.2.5 New drought monitoring indices3.2.5 New drought monitoring indices Activities:Activities: Monitoring based on the RCDAS on-going, updated weekly and monthly 1. Monitoring based on the NLDAS systems: (a) ensemble means are more stable and reliable than individual analyses, (b) NLDAS depends on model and forcing and they differ in variability and interrelationships among variables. (c) Utilize the 10-yr NLDAS systems (Noah, VIC and Mosaic) to form ensemble and to set up prototype web page for now Switch to 27-year 4 NLDAS ensemble for monitoring 2. Week1 and week2 hydrological conditions: We are in the testing stage for error-corrected ensemble hydrological variables. Science Plans: Climate Forecast Products Team, cont’d

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4. 4 σ Classic regression uses the ensemble mean, which ignores the fact that this “near normal” forecast comes from averaging warm and cold realizations. Consolidation: Assimilating Forecast Information

5. 5 Ensemble regression (consolidation) always outperforms the ensemble mean through calibration and use of information from the spread and skill of the members Temperature (F) σzσz Correlation with obs: (con) R z =.93, (ens mean) R fm =.87, (ave of corr. of members) R f =.30

6. 6 NAEFS Official Observations North American Ensemble Forecast System (NAEFS)

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8. 8 U.S. Average, ½-Month Lead, Seasonal Mean Temperature, Percent improvement over climatology (heidke skill score, non-area-weighted) ~20% improvement through consolidation

9. 9 U.S. Average, ½-Month Lead, Seasonal Total Precipitation, Percent improvement over climatology (heidke skill score, area-weighted) 195% improvement through consolidation

10. 10 Possible New Products, 2007-08 Prototype forecasts for weeks 3, 4 using consolidation of forecasts from CFS, LIM and other available models with skill histories.Prototype forecasts for weeks 3, 4 using consolidation of forecasts from CFS, LIM and other available models with skill histories. New ability to assess week 2, 3, 4 extreme event hazards for use in U.S. Hazards and Global Hazards AssessmentsNew ability to assess week 2, 3, 4 extreme event hazards for use in U.S. Hazards and Global Hazards Assessments Prototype interactive cost/loss assessment toolPrototype interactive cost/loss assessment tool Experimental probabilistic seasonal drought outlookExperimental probabilistic seasonal drought outlook