MOS What does acronym stand for ? –MODEL OUTPUT STATISTICS What is the difference between the GFS and GFS MOS ?

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Presentation transcript:

MOS What does acronym stand for ? –MODEL OUTPUT STATISTICS What is the difference between the GFS and GFS MOS ?

GFS MOS KUNV GFS MOS GUIDANCE 2/26/ UTC DT /FEB 26/FEB 27 /FEB 28 /FEB 29 HR N/X TMP DPT CLD OV OV OV OV OV OV OV OV OV OV OV OV OV SC BK BK BK SC CL SC OV WDR WSP P P Q Q T06 1/ 0 2/ 1 0/ 1 0/ 0 0/ 0 0/ 0 0/ 0 0/ 0 0/ 3 0/ 0 T12 3/ 1 0/ 1 0/ 0 0/ 0 0/ 3 POZ POS TYP S R S S S S S S S S S S S S S S S S S S S SNW CIG VIS OBV BR BR BR BR N N N N N N N N N N N N N N N N N

GFS MODEL Station: UNV Lat: Lon: Elev: 378 Closest grid pt: 29.6 km. Initialization Time: UTC HOUR VALID PMSL THCK 6HRPCN 2m_TMP 850TMP 850REL 700REL 10m_WD 850WND / /003 20/ / /005 20/ / /005 26/ / /014 34/ / /014 33/ / /014 31/ / /013 31/ / /011 31/ / /010 30/ / /013 30/ / /008 30/ / /003 28/ / /007 24/023

MOS AVN = Dynamical Model –Seven fundamental equations !

Seven Fundamental Equations: –Temperature equation (dT/dt=) ADVECTION/DIABATIC/ADIABATIC –Three equations of motion (dV/dt=) HORIZONTAL MOTIONS: PGF/COR/FR VERTICAL MOTIONS –Hydrostatic Equation (dp/dz= -  g) –Continuity equation (du/dx + dv/dy + dw/dz=0) –Water vapor equation (dq/dt=)

MOS AVN = Dynamical Model –Seven fundamental equations ! AVN MOS = Statistical Model –No seven fundamental equations ! –Equations are statistical, not dynamical !

MOS Why even have MOS ? –Predicts unique parameters Visibility Cloud ceilings –Predicts better than dynamic models (averaged over all cases) Surface weather-> temp., dew pt., winds

MOS How does MOS make its predictions? –Uses technique of association –Objectively relates (associates) model output to observed weather using statistical technique of linear regression Glahn and Lowry (1972)

MOS Two steps to MOS ………. –Equation Development –Equation Application

MOS Equation Development ->> –PREDICTOR Variable that is doing the PREDICTING –PREDICTAND Variable that is getting PREDICTED (Multiple) Linear Regression –Relates PREDICTOR to PREDICTAND

MOS: Equation Development Y1 = mx1 + b1

MOS: Temperature Predictors –Model low level temps (i.e. 850mb/2m) –Model relative humidity Accounts for clouds –Model wind direction /speed –Climatology –Previous day’s min (max) Single site development Glahn and Lowry (1972)

MOS: Precipitation Predictors –Model mean relative humidity (i.e mb layer average) –Precipitation output of model –Model vertical velocity (i.e. 700, 500, 850mb) –Model low level wind direction (i.e. 10m) Regional development

MOS: Wind Predictors –Low-level wind direction/speed output of model (i.e. 10m, 850mb wind) Single site development

MOS Characteristics Requires large sample size –Several years of model output –Increases statistical significance

MOS Partially removes systematic model errors (i.e. biases) –If model has a cool bias at 850mb, MOS will account for/remove model bias Works best when models are not tweaked (i.e. no change to physics)

MOS: Equation Application NAM

GFS MOS KUNV GFS MOS GUIDANCE 2/26/ UTC DT /FEB 26/FEB 27 /FEB 28 /FEB 29 HR N/X TMP DPT CLD OV OV OV OV OV OV OV OV OV OV OV OV OV SC BK BK BK SC CL SC OV WDR WSP P P Q Q T06 1/ 0 2/ 1 0/ 1 0/ 0 0/ 0 0/ 0 0/ 0 0/ 0 0/ 3 0/ 0 T12 3/ 1 0/ 1 0/ 0 0/ 0 0/ 3 POZ POS TYP S R S S S S S S S S S S S S S S S S S S S SNW CIG VIS OBV BR BR BR BR N N N N N N N N N N N N N N N N N

GFS MODEL Station: UNV Lat: Lon: Elev: 378 Closest grid pt: 29.6 km. Initialization Time: UTC HOUR VALID PMSL THCK 6HRPCN 2m_TMP 850TMP 850REL 700REL 10m_WD 850WND / /003 20/ / /005 20/ / /005 26/ / /014 34/ / /014 33/ / /014 31/ / /013 31/ / /011 31/ / /010 30/ / /013 30/ / /008 30/ / /003 28/ / /007 24/023

MOS ERRORS: Who’s at fault? Dynamic model (gfs model) –Garbage In = Garbage Out Statistical model (gfs mos) –Imperfect statistical relationships (i.e. lines of best fit are not line of perfect fit!) Forecasting MOS error (utilizing association method)

HOW TO BEAT MOS Know how it works MOS tends to do well: –Weather near climatology (equations lean toward modal case) MOS tends to do poor: –Weather departs from climatology ( the “outliers” of the scatter plot) –Bad model data used as input (GI=GO)

HOW TO BEAT MOS Which city is more likely to have the bigger bust in the following situation? –Clear skies, light winds, snow cover ST. LOUIS vs. INTERNATIONAL FALLS