1 / 6

Mesoscale model ensemble for MDSS

Mesoscale model ensemble for MDSS. Paul Schultz NOAA Forecast Systems Laboratory March 2001. Purpose of ensemble modeling . Ensemble of model forecasts has been shown to be a better forecast than any single ensemble member (1+1=3!)

leanna
Download Presentation

Mesoscale model ensemble for MDSS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mesoscale model ensemble for MDSS Paul Schultz NOAA Forecast Systems Laboratory March 2001

  2. Purpose of ensemble modeling • Ensemble of model forecasts has been shown to be a better forecast than any single ensemble member (1+1=3!) • Demonstrated on global scale (NCEP), “large” regional scale (SAMEX), not yet at “local” scale, but no reason to believe it won’t work • Ensemble members can be used to create probabilities for key predictands • e.g., t < 32ºF, V>25kt, etc.

  3. The dilemma -- the research opportunity • Are computer resources best spent on ensemble modeling, or a single (or few) at maximum resolution? • MDSS project can shed some light. • FSL’s jet computer lets us do both.

  4. Configuration • Four 48-hr forecasts per day • 12-km grid • Two high-resolution MM5 nests • 4-km grid • centered on SLC, DEN • Four 12-hr forecasts per day • Benchmarking spreadsheet Status:

  5. This is the domain for the nine ensemble members;the fine-mesh nests are outlined in red

  6. Ensemble products • State variables: t, , rh, etc. • Categorical probability of precipitation (PoP), probability of precipitation type (PoPT) • Probabilities of key forecast variables

More Related