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

CC Hennon ATMS 350 UNC Asheville

Model Output Statistics

Transforming model output into useful forecast parameters

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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– 1000-500 mb thickness– Temperature at a few model layers

• Not incredibly useful for surface forecasting applications

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Station ID

Day of monthUTC time ofmodel cycle

Fcst. valid time

6-hr AccumPrecip0.01”

MeanRH inlowestlayer

MeanRH upto 500mb

MeanRH (500-200 mb)

LiftedIndex

SLP(coded) WDIR

(lowestmodellayer)

WSPD(kt) inlowestlayer

Vert.Veloc.(‘-’ isdown)

1000-500 mbthickness(dm)

Temp.(C) of lowestlayer

Temp.(C) of layer 3

Temp(C)of layer 5

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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)

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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

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** See page 21-22 in text for decoding information

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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

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

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MOS Seasonal EquationsElement GFS MAV NAM MET NGM FWC GFS MEX

Temperature4/1 - 9/3010/1 - 3/31

4/1 - 9/3010/1 - 3/31

3/1 - 5/316/1 - 8/319/1 - 11/3012/1 - 2/29

4/1 - 9/3010/1 - 3/31

Ptype 9/1 - 5/31 9/1 - 5/319/16 - 5/15 -- CONUS9/1 - 5/31 -- AK

9/1 - 5/31

Thunderstorms10/16 - 3/153/16 - 6/307/1 - 10/15

4/1 - 9/3010/1 - 3/31

10/16 - 3/153/16 - 6/307/1 - 10/15

10/16 - 3/153/16 - 6/307/1 - 10/15

All other Elements4/1 - 9/3010/1 - 5/31

4/1 - 9/3010/1 - 3/31

4/1 - 9/3010/1 - 3/31

4/1 - 9/3010/1 - 3/31

CC Hennon ATMS 350 UNC Asheville

CC Hennon ATMS 350 UNC Asheville

MAV – GFS MOSMET – ETA MOSFWC – NGM MOSMEX – GFSX MOS (Extended)

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MOS Links

• Changes/updates– http://www.nws.noaa.gov/mdl/synop/changes.php

• FAQ– http://www.nws.noaa.gov/mdl/synop/faq.php

• Definition of MOS elements/acronyms– http://www.nws.noaa.gov/mdl/synop/avnacronym.htm

• MOS performance (WRF vs. GFS vs. NAM)– http://www.nws.noaa.gov/mdl/synop/wrfmoseval.htm

• MDL– http://www.nws.noaa.gov/tdl/