CC Hennon ATMS 350 UNC Asheville Model Output Statistics Transforming model output into useful forecast parameters

CC Hennon ATMS 350 UNC Asheville Forecast Output United States (FOUS) Raw model output (e.g. from NGM, NAM, GFS) Only includes such parameters as –Mean relative humidity in certain layers –Vertical velocity at 700 mb – mb thickness –Temperature at a few model layers Not incredibly useful for surface forecasting applications

CC Hennon ATMS 350 UNC Asheville Station ID Day of month UTC time of model cycle Fcst. valid time 6-hr Accum Precip 0.01” Mean RH in lowest layer Mean RH up to 500 mb Mean RH ( mb) Lifted Index SLP (coded) WDIR (lowest model layer) WSPD (kt) in lowest layer Vert. Veloc. (‘-’ is down) mb thickness (dm) Temp. (C) of lowest layer Temp. (C) of layer 3 Temp(C) of layer 5

CC Hennon ATMS 350 UNC Asheville Model Output Statistics (MOS) Production of surface variables not created by dynamical models Improvement of other variables that are created by dynamical models Developed at the Meteorological Development Lab (MDL)

CC Hennon ATMS 350 UNC Asheville How MOS Works Relates model output variables to common forecast variables (e.g. surface temperature, dew point, precipitation) through statistical techniques Analyze past correlations between model outputs and forecast variables –‘Analog’ method of forecasting MOS is produced from NGM, NAM, and GFS models

CC Hennon ATMS 350 UNC Asheville ** See page in text for decoding information

CC Hennon ATMS 350 UNC Asheville Interpreting some MOS output Probability of Precipitation (P06, P12) –Precipitation chance (%) for a point –’40%’ means it will precipitate 4/10 times at that point in the given situation Probability of Snow (POS) –Conditional probability –If precipitation occurs, this is the chance (%) that it will be snow –Actual chance of snow is the product of P06/P012 and POS

CC Hennon ATMS 350 UNC Asheville

Things to consider when using MOS output for forecasting Not proficient at depicting local and mesoscale events Beware of rare events (since MOS is statistical) MOS better 1.5 and 4.5 months into the season –Uses seasonal equations tuned to be best at those times Extended forecasts less skillful –Trends toward climatology MOS usually too warm for shallow cold air events –common east of Appalachians

CC Hennon ATMS 350 UNC Asheville MOS Seasonal Equations ElementGFS MAVNAM METNGM FWCGFS MEX Temperature 4/1 - 9/30 10/1 - 3/31 3/1 - 5/31 6/1 - 8/31 9/1 - 11/30 12/1 - 2/29 4/1 - 9/30 10/1 - 3/31 Ptype9/1 - 5/31 9/16 - 5/15 -- CONUS 9/1 - 5/31 -- AK 9/1 - 5/31 Thunderstorms 10/16 - 3/15 3/16 - 6/30 7/1 - 10/15 4/1 - 9/30 10/1 - 3/31 10/16 - 3/15 3/16 - 6/30 7/1 - 10/15 All other Elements 4/1 - 9/30 10/1 - 5/31 4/1 - 9/30 10/1 - 3/31

CC Hennon ATMS 350 UNC Asheville

MAV – GFS MOS MET – ETA MOS FWC – NGM MOS MEX – GFSX MOS (Extended)

CC Hennon ATMS 350 UNC Asheville MOS Links Changes/updates – FAQ – Definition of MOS elements/acronyms – MOS performance (WRF vs. GFS vs. NAM) – MDL –