Weather Research and Forecasting (WRF) Model




(January 17, 2014)


The HiRes CWSU WRF (ver 3.1) has been upgraded to run four times a day at 00, 06, 12 and 18UTC.   Recent upgrades to UAlbany’s RIT systems have allowed the model to complete within an hour of initiation time.  During April and May this version of the WRF will be upgraded to the latest STRC version with significant upgrades to model layers, domain and physics.  If you notice any WRF problems let me know via my NOAA email (below).   


Warren Snyder – Science & Operations Officer, WFO Albany, NY



The WEATHER RESEARCH and FORECASTING (WRF) model displayed here are experimental local high resolution mesoscale models, run in real time in collaboration with UAlbany.   The models are run at the Research IT (RIT) high performance computing center at UAlbany, and transferred to WFO Albany, New York as part of the CSTAR projects.  They are used experimentally and to support the National Weather Service Forecast Office at Albany, New York and Center Weather Service Units at Nashua, New Hampshire and Oberlin, Ohio.   This model is also a member of the Great Lakes Ensemble.


The Northeastern US WRF domain covers much of the United States from the Mississippi Valley east, as well as adjacent areas of Canada.  This domain is not displayed or posted to the web, but is the outer domain for the HiRes WRF.   It is meant as a big picture overview, and provides hourly output to the inner domains.  At this scale Kain-Fristch Convective parameterization is used.  

The HiRes WRF is at 5km resolution as is nested in the Northeast US WRF domain.  It covers both the CWSU Oberlin and Nashua service areas as well as much of the area from the Great Lakes to New England.  Convection is explicit; therefore the Convective Precipitation Graphics are blank.  This version of the WRF is an ARW core, or MM6 based core.  It is a non-hydrostatic model.


The WRF uses a variety of state of the art physics and dynamics packages. It is particularly adept at forecasting local effects, due to mountains, coastlines and lakes, winds, as well as localized heavy precipitation.   It also is useful in identifying areas where thunderstorms are likely to form first.  


This project is managed by Science and Operations Officer Warren R. Snyder and Information Technology Officer Vasil T. Koleci.  Address any modeling questions to .  Problems with the webpage should be addressed to .