1 Progress on Science Progress on Science Activities: Activities: Climate Forecast Products Team Climate Forecast Products Team Probabilistic forecasts of Extreme Events and Weather Hazards in Probabilistic forecasts of Extreme Events and Weather Hazards in the US (PI: Charles Jones (UCSB); NCEP Co-PI: Jon the US (PI: Charles Jones (UCSB); NCEP Co-PI: Jon Gottschalck (CTB)) Gottschalck (CTB)) • Funding pending Funding 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 System-wide Advancement of User-Centric Climate Forecast Products (PI: Holly Hartmann (UAZ); NCEP Co-PI: Ed O’Lenic (CPC)) (PI: Holly Hartmann (UAZ); NCEP Co-PI: Ed O’Lenic (CPC)) • Funding pending Funding 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.
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Progress on Science Activities:Progress on Science Activities:Climate Forecast Products TeamClimate Forecast Products Team
Probabilistic forecasts of Extreme Events and Weather Hazards in the US Probabilistic forecasts of Extreme Events and Weather Hazards in the US (PI: Charles Jones (UCSB); NCEP Co-PI: Jon Gottschalck (CTB))(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 System-wide Advancement of User-Centric Climate Forecast Products (PI: Holly Hartmann (UAZ); NCEP Co-PI: Ed O’Lenic (CPC))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.
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• 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.
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
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Ensemble regression (consolidation) always outperforms the Ensemble regression (consolidation) always outperforms the ensemble mean through calibration and use of information from ensemble mean through calibration and use of information from the spread and skill of the membersthe spread and skill of the members
Temperature (F)
σz
Correlation with obs: (con) RCorrelation with obs: (con) Rzz=.93, (ens mean) R=.93, (ens mean) Rfmfm=.87, (ave of corr. of members) R=.87, (ave of corr. of members) Rff=.30=.30
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NAEFS Official
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North AmericanEnsemble
Forecast System(NAEFS)
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U.S. Average, ½-Month Lead, Seasonal Mean Temperature, U.S. Average, ½-Month Lead, Seasonal Mean Temperature, Percent improvement over climatology (heidke skill score, Percent improvement over climatology (heidke skill score,
non-area-weighted)non-area-weighted)~20% improvement through consolidation~20% improvement through consolidation
1/2 Month-Lead 3-Month Mean Temperature Forecast % Improvement over Climatology
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U.S. Average, ½-Month Lead, Seasonal Total Precipitation, U.S. Average, ½-Month Lead, Seasonal Total Precipitation, Percent improvement over climatology (heidke skill score, Percent improvement over climatology (heidke skill score,
area-weighted)area-weighted)195% improvement through consolidation195% improvement through consolidation
1/2-Month-Lead 3-Month Total Precipitation Forecast, % Improvement Over Climatology
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Possible New Products, 2007-08Possible New Products, 2007-08
• Prototype forecasts for weeks 3, 4 using consolidation of forecasts Prototype forecasts for weeks 3, 4 using consolidation of forecasts from CFS, LIM and other available models with skill histories.from CFS, LIM and other available models with skill histories.
• New ability to assess week 2, 3, 4 extreme event hazards for use in New ability to assess week 2, 3, 4 extreme event hazards for use in U.S. Hazards and Global Hazards AssessmentsU.S. Hazards and Global Hazards Assessments
