1. Meteorology 485 Long Range Forecasting Friday, February 13, 2004

2. Long Range Guidance CDC - Climate Diagnostic Center (Boulder) CDC - Climate Diagnostic Center (Boulder) Blended model - both dynamic and statistical Blended model - both dynamic and statistical DYNAMIC: Four AGCM - NCEP MRF9; GFDL-R30; NCAR-CCM3 and IRI-ECHAM3 using forced SST’s from 1950-99 climatology DYNAMIC: Four AGCM - NCEP MRF9; GFDL-R30; NCAR-CCM3 and IRI-ECHAM3 using forced SST’s from 1950-99 climatology STATISTICAL: Multi-variate regression model trained on the relationship between tropical SST’s and US Seasonal T,P training period 1950-94. STATISTICAL: Multi-variate regression model trained on the relationship between tropical SST’s and US Seasonal T,P training period 1950-94. Tested from 1995-2003 for skill scores based on hindcasts Tested from 1995-2003 for skill scores based on hindcasts http://www.cdc.noaa.gov/seasonalfcsts/ http://www.cdc.noaa.gov/seasonalfcsts/

3. Models Climate Diagnostic Center Climate Diagnostic Center Precipitation Temperature Precipitation Temperature

4. Long Range Guidance Statistical Models Statistical Models CA=Constructed Analog/ Climate Prediction Center CA=Constructed Analog/ Climate Prediction Center Huug van den Dool Huug van den Dool A linear combination of past observed anomaly patterns such that the combination is as close as desired to the initial state. A linear combination of past observed anomaly patterns such that the combination is as close as desired to the initial state. A forecast is obtained by persisting the weights assigned to each year in the historical record and linearly combining the states following the initial time in the historical years. A forecast is obtained by persisting the weights assigned to each year in the historical record and linearly combining the states following the initial time in the historical years. See: ftp://ftpprd.ncep.noaa.gov/pub/cpc/wd51hd/sst/200312/cahgt_anom.1.gif

5. Models CA Outlook for Feb-Mar-Apr, 2004 CA Outlook for Feb-Mar-Apr, 2004

6. Constructed Analog February 2004 Departures (to date) February 2004 Departures (to date) http://www.cpc.ncep.noaa.gov/soilmst/index_jh.html

7. Constructed Analog February 2004 Departures: Analog-Mapper February 2004 Departures: Analog-Mapper http://www.cdc.noaa.gov/USclimate/USclimdivs.html

8. Constructed Analog CA based on Feb 1-11 Anomalies for March,2004 CA based on Feb 1-11 Anomalies for March,2004 http://www.cdc.noaa.gov/USclimate/USclimdivs.html

9. Constructed Analog Checks and Balances Checks and Balances Do the Temp and Precip fields make sense? Do the Temp and Precip fields make sense? Are the fields dominated by 1-2 highly anomalous years? Are the fields dominated by 1-2 highly anomalous years? Do other techniques produce similar analog years? Do other techniques produce similar analog years? 500mb flow pattern analogs (NA or NH) 500mb flow pattern analogs (NA or NH) Can the historical contributions be weighted? Can the historical contributions be weighted? Can GFS 10 day surface anomaly fields be used to extend and adjust the constructed analog Can GFS 10 day surface anomaly fields be used to extend and adjust the constructed analog Is an ‘ensemble’ of analogs useful, or even a lag- averaged analog Is an ‘ensemble’ of analogs useful, or even a lag- averaged analog What is the reliability of downscaling? What is the reliability of downscaling? Can this be automated? Can this be automated?

10. Constructed Analog Downscaled - example for western Pennsylvania Downscaled - example for western Pennsylvania

11. Constructed Analog Downscaled - example for a western U.S. city Downscaled - example for a western U.S. city

12. Long Range Guidance Statistical Models Statistical Models Markov – Climate Prediction Center Markov – Climate Prediction Center A future variable is determined by the present variable, but is independent of the way in which the present state arose from its predecessors. A future variable is determined by the present variable, but is independent of the way in which the present state arose from its predecessors. Examples: Examples: Weather conditions deduced from seaweed (ie, wet seaweed means it is raining, dry means sunny, damp is indeterminate) Weather conditions deduced from seaweed (ie, wet seaweed means it is raining, dry means sunny, damp is indeterminate) State of the weather not restricted to state of seaweed, but most recent past conditions combined with current state can yield a credible future state... State of the weather not restricted to state of seaweed, but most recent past conditions combined with current state can yield a credible future state... See: ftp://ftpprd.ncep.noaa.gov/pub/cpc/wd52yx/web/MKmodel_ellfb_clim71-00/fig1.gif

13. Models Markov Outlook for SST Markov Outlook for SST

14. Long Range Guidance Soil Moisture - Constructed Analog System Soil Moisture - Constructed Analog System Database too limited (not enough actual observations) -must generate estimated soil moisture using simple hydrologic model (Huang, et al 1996) dW/dT = Precip - Evaporation - Runoff + Transpiration Database too limited (not enough actual observations) -must generate estimated soil moisture using simple hydrologic model (Huang, et al 1996) dW/dT = Precip - Evaporation - Runoff + Transpiration Challenge: P varies by 2 or 3 times annual E Challenge: P varies by 2 or 3 times annual E Data calculated for 344 Climate Divisions since 1932 Data calculated for 344 Climate Divisions since 1932 Analog constructed based on soil moisture anomalies with each year contributing a weight for the best fit Analog constructed based on soil moisture anomalies with each year contributing a weight for the best fit Projection based on same weights, but for next month or two or season. Projection based on same weights, but for next month or two or season. Most effective tool in April-July when dW/dT changes Most effective tool in April-July when dW/dT changes http://www.cpc.ncep.noaa.gov/soilmst/index_jh.html http://www.cpc.ncep.noaa.gov/soilmst/index_jh.html

15. Models Soil Moisture Soil Moisture

16. Next Few Weeks Commercial Long Range Forecast Commercial Long Range Forecast Accuweather, WSI, Dynamicpredictables, WxRisk, Wx Data Accuweather, WSI, Dynamicpredictables, WxRisk, Wx Data International Forecast Centers International Forecast Centers UKMET, ECMWF, CMC, Brazil, South Africa, Australia, Japan and Korea UKMET, ECMWF, CMC, Brazil, South Africa, Australia, Japan and Korea