Model Output Statistics - MDL - Virtual Lab

Model Output Statistics - MDL - Virtual Lab
Model Output Statistics - MDL
What is MOS?
Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast by relating model outputs to observational or additional model data. MOS was defined by
Glahn and Lowry
(pdf) in 1972 as the following:
Model Output Statistics is an objective weather forecasting technique which consists of determining a statistical relationship between a predictand and variables forecast by a numerical model at some projection time(s). It is, in effect, the determination of the "weather related" statistics of a numerical model.
The predictors MDL currently uses are:
NWP Model Forecasts
Prior Observations
Geoclimatic Data
Predictor points can come from either point observations or from data that has been calculated on / interpolated to points on a grid. The MOS products which are more heavily based on true observation points are referred to as station-based mos, while MOS generated based on gridded data is called gridded MOS (GMOS).
The models used for MOS data also can be changed. MDL is currently producing MOS based on the GFS, Ensemble GFS, and NAM models.
The statistical method used by MDL is multiple linear regression (with forward selection). Other techniques are possible, such as: Polynomial or logistic regression; or neural networks. (For more information on these and other statistical techniques, see: Wilks, 2006: Statistical Methods in the Atmospheric Sciences).
Why MOS?  Advantages & Limitations
What MOS Does
Objectively interprets NWP Model based on historical sample
Predicts events forced by synoptic-scale systems
Corrects for certain systematic NWP model biases
Mimics conceptual forecast models
Quantifies uncertainty in NWP model forecasts
Accounts for deterioration NWP model skill with increasing forecast projection
Accounts for some local effects
Incorporates climatic considerations
What MOS Does NOT Do
Account for forecaster excitement factor
Predict events forced by mesoscale features
Correct for systematic NWP model errors related to map type or synoptic situation
Correct for certain deficiencies in NWP model physics, analysis schemes, or parameterizations
Account for changes to NWP model components
Account for EVERY local effect
Account for unusual climatic conditions
Advantages of MOS
Mathematically simple, yet powerful
Non-linearity can be modeled by using NWP variables and transformations
Probability forecasts possible from a single run of NWP model
Removal of some systematic model bias
Limitations of MOS
Need historical record of observations at forecast points (quality matters!)
Short-term sample periods cannot be used
Changing NWP models means re-configuring equations - with a new sample set
Station-based GFS MOS
Station-based NAM MOS
MOS
MOS Products
GFS MOS
Short Range GFS MOS
Extended Range GFS MOS
Coop GFS MOS
Marine GFS MOS
River Basin GFS MOS
NAM MOS
NAM MOS Guidance
Marine NAM MOS
NAM MOS Documentation
MOS Text Bulletins
MOS Product Cards
MAV Card
MEX Card
MET Card
Marine Card
MOS Documentation
Change Log
MOS Technical Notices
Stations
Weather Elements
SHEF Information
MOS FAQ
MOS Verification
GFS MOS Parallel Evaluation 2010
GFS MOS Refresh Evaluation 2009
GFS MOS Parallel Evaluation 2007
NAM MOS Parallel Evaluation 2011
NAM MOS Prototype Evaluation 2008
Score Definitions
Station Info
Verif Station List
Verif Precip Station List
Additional MOS Resources
LAMP
Contact MOS
For further information about the current state of MDL's MOS initiatives, please contact
Mark.Antolik@noaa.gov.