Model Forecast Output

WxChallenge Stuff

Submit Forecast
Forecast Results
Apparently @WxChallenge is on Twitter
Notes on Current Stations (see also spring 2017fall 2016)
Stuff I want to look up

NCEP Model Output

Brian Tang’s Page (NAM, GFS)
Nice Visualizations of Outputs:
   Tropical Tidbits
   Pivotal Weather
    NCEP Model Guidance
    Penn State e-Wall
    University of Wyoming NAM, GFS, and RAP
*Watch out for models predicting huge areas of “light rain,” this sometimes means you should expect scattered showers instead (GFS especially tends to spread precipitation out over large regions). For Tropical Tidbits and Pivotal Weather, click anywhere to see a sounding.

Numerical MOShere’s what the symbols are and here are the symbols for extended forecast.
Can also get MOS outputs from a long time ago here.
NCEP Model Diagnostic Discussions – see here for model biases
Vince Agard’s MOS verification page

Brian Tang’s Boston Page


Other Model Output

USL Forecast
NWS Forecast and Forecast Discussion
NCAR Ensemble Forecasts:
    NCAR/WRF-ARW Ensemble Models
NOAA ESRL High-Resolution Rapid Refresh Output:
    About HRRR
    Real-Time HRRR Maps – Philippe Papin
    Experimental HRRR Ensemble
    Tropical Tidbits
NOAA Storm Prediction Center Short-Range Ensemble Forecasts:
    About SREF
    SREF Plumes – use dProg/dt “the trend is your friend”
    SREF Precipitation Viewer

Other People’s Useful Pages

Penn State e-Wall 
MIT Page
MIT Maps
Vince Agard’s Page
Philippe Papin’s Page

Notes on Weather Models

North American Mesoscale (NAM) Forecast System is just for North America and is higher resolution than the Global Forecast System (GFS).  NAM is best within 48 hours or so; GFS model goes out two weeks or so, but the first week is much better resolved, second week is just for trends.  GFS is spectral model, whereas NAM is run on a grid.  GFS has slightly more vertical resolution (NAM is sigma vertical scale, GFS transitions from sigma to isobars).  GFS uses longer timesteps than NAM.  GFS uses slightly more complex convection scheme (Arakawa) than NAM (Betts/Miller).  But parametrized convective schemes are generally bad for precipitation.  Spectral models (such as GFS) better for dry forecasts?  GFS tends to spread precipitation over a large area.

Uncoupled Surface Layer (USL): doesn’t simulate anything about the atmosphere, but rather uses other models’ atmosphere output for its boundary conditions.  Works with a thin surface boundary layer without turbulence.  However, is trained against a lot of previous data to optimize output based on the diurnal cycle, soil temperature, etc., so generally better for temperature than for precipitation-related things.  Precipitation is based on some kind of averaging of what other models say (RAP, SREF, …).  Probably better for stations where convective showers aren’t the dominant mode of precipitation (e.g. Key West).  2016, creator of USL said it should be best for Harrisburg and Reno.  USL has pretty much no resolution through the boundary layer and uses other models.