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An Overview of Numerical Weather Prediction Models


Title: Numerical Weather Prediction Models (How to Utilize These Tools More Effectively) Last modified by: Ryan Wade Created Date: 7/25/1999 7:17:34 PM – PowerPoint PPT presentation

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Title: An Overview of Numerical Weather Prediction Models

An Overview of Numerical Weather Prediction
  • What is a Numerical Forecast Model
  • Models The Good, Bad, and Ugly
  • Increasing awareness and understanding of models
    and how they work
  • The Importance of Using Forecast Models only as a
  • Model Behavior and Verification Actual studies
    of model performance

Where is Hurricane Georges Going?
Model Forecasts
Where is Hurricane Georges Going?
Model Forecasts
Georges Went Here Instead!
Lesson from this Exercise
  • Numerical model forecasts are not a panacea.
  • Experienced and alert forecasters/analysts may
    add significant value to numerical weather
  • Forecasters must have a basic understanding of
    model interpretation, biases and limitations in
    different forecasting situations before they can
    accurately deviate from model forecasts.
  • Model interpretation needs to be rooted a solid
    knowledge of meteorological theory and model

Operational Model Overviews
  • Models are either regional or global
  • Regional Models
  • Model Owner
  • LFM (NCEP) (Phased out)
  • NGM (NCEP) (Phased out)
  • ETA (NCEP) (Replaced by NAM, NAM-WRF)
  • RUC (NCAR/NCEP) (Replaced by RAP)
  • MM5 (PSU / Air Force, etc.)
  • GFDL (NCEP and GFDL Lab)
  • COAMPS (Navy)
  • WRF (NCEP)

Operational Model Overviews
  • Global Models
  • Model Owner
  • GFS (Formerly the
  • NOGAPS (Navy)
  • GEM (Canada)
  • ECMWF (European Union)
  • UKMET (United Kingdom)

What is the Fundamental Difference Between Grid
Point and Spectral Models?
  • Grid point and spectral models are based on the
    same set of primitive equations. However, each
    type formulates and solves the equations
    differently. The differences in the basic
    mathematical formulations contribute to different
    characteristic errors in model guidance. The
    differences in the basic mathematical
    formulations lead to different methods for
    representing data. Grid point models represent
    data at discrete, fixed grid points, whereas
    spectral models use continuous wave functions.
    Different types and amounts of errors are
    introduced into the analyses and forecasts due to
    these differences in data representation. The
    characteristics of each model type along with the
    physical and dynamic approximations in the
    equations influence the type and scale of
    features that a model may be able to resolve.

What is a Numerical Forecast Model?
(i.e., First Guess)
Model Components
  • Numerical Models consist of multiple parts.
  • The actual forecasting part of the model is
    only one part of a very complicated procedure of
  • data collection and assimilation
  • Forecasting
  • Post Processing
  • Distribution to users

Caution! Caution! Caution!
There are many sources of possible error in
an NWP forecast!!!
Sources of Model Error
Data Assimilation
  • Data assimilation is the process through which
    real world observations enter the model's
    forecast cycles
  • Provide a safeguard against model error growth
  • Contribute to the initial conditions for the next
    model run

  • The goal of the DA system is to provide a reality
    check for the short-term model forecast used to
    start up the current forecast cycle.

Observation Increment
  • How does DA make observations comparable to the
    first guess?
  • How can observations made at different times,
    with different patterns of coverage and sampling
    characteristics, all be combined into one
  • Why does good data sometimes get rejected in the
  • How can observations, providing a reality check,
    be combined with crucial structure information
    in the model short-range forecast?

  • Using Observation Increments to Make the Analysis
  • Problems from the Start
  • Heart of Analysis How Much to Weight
    Observations Versus the Forecast
  • Balancing Observations Versus the Forecast
  • Assumptions Affecting the Analysis
  • Why Some Data Types Are Not as Useful as Others
  • Including the Data Rejection Process in the
    Analysis Stage ("Nonlinear QC")
  • Tuning

Operational Tips
  • Judging Analysis Quality
  • Compensating for a Bad Analysis History of Data
  • Compensating for a Bad Analysis Bad First Guess
  • Compensating for a Bad Analysis Good Data
  • Compensating for a Bad Analysis Analysis
  • Assumptions Violated
  • Compensating for a Bad Analysis Tuning

Observational Data Coverage
Surface Observations
Observational Data Coverage
Surface Observations
Observational Data Coverage
Weather Buoys
Observational Data Coverage
Conus Rawinsonde Network
Observational Data Coverage
Oceanic Rawinsonde Network
Observational Data Coverage
Satellite-Derived Winds
Errors in Data and Quality Control
  • Instrument Errors
  • Representativeness Errors
  • vertical, horizontal, and temporal
  • Converting remotely-sensed data into high-quality
    observations which can be appropriately
    integrated with other data.
  • Satellite data must be constantly calibrated by
    rawinsonde data for constantly changing
    atmospheric environments. (What happens in
    regions void of rawinsondes?)

Errors in Data and Quality Control
Bogus Lows
Errors in Objective Analysis
Errors in Objective Analysis
Errors in Data Assimilation
Errors in Data Assimilation
Model Initialization Problems
  • Model first guess can sometimes overwhelm actual
  • First guess may result in observations being
  • In this case, Hurricane Earl incorrectly analyzed
    several hundred miles too far SW of its actual
  • (From COMET)

Poor Analysis Huge Forecast Errors
  • Initial placement of a few hundred miles
    translated to a nearly 1,000 mile error by 48-h
  • Such errors can disrupt the larger-scale pattern
  • (From COMET)

Ways to Check the Quality of Model Analyses
  • Use observations, local area workcharts, radar,
    and satellite to check the quality of the model
  • Compare different model analyses to each other.
  • Look for dynamic consistency between model levels
  • Read the Model Diagnostic Discussion at HPC
  • http//
  • http//

Atmospheric Variables Not Routinely Measured
  • Longwave and Shortwave Radiation
  • Cloud Water and Ice Content
  • Vertical Motion
  • Surface roughness of the ocean (i.e., waves)
  • Turbulence
  • Many more..

The Power of the First Guess
  • The first guess (an earlier 6 or 12 hour
    forecast) is the models initial impression of
    the atmospheres current condition.
  • The first guess is innocent until proven guilty.
  • The first guess is most easily modified in
    data-rich areas (i.e., CONUS)
  • The first guess is least easily modified in data
    poor areas (i.e. oceans, etc)

Ways to Check the First Guess Influence on the
  • Overlay observations on the model analysis
  • Compare hand-analyzed workcharts and alternate
    local computer-generated analyses to the model
  • Remember that your local computer-generated
    analyses (without a gridded background field) are
    totally blind except for the observations you
    feed them. Use with caution!

Ways to Check the First Guess Influence on the
  • Compare different model analyses to each other.

Ways to Check the First Guess Influence on the
  • Compare model analyses to satellite, radar, and
    other real-time information.
  • These are important functions all forecasters
    should routinely perform. It is one of the most
    powerful ways a forecaster can add value to the
    model guidance!- especially for short-range

Errors in the Model
  • Equations of Motion are Incomplete
  • Massive simplifications (i.e., parameterizations)
    are necessary to make the model work.
  • Errors in the Numerical Approximation
  • Horizontal and Vertical Resolution
  • Time integration procedure
  • Boundary Conditions
  • Horizontal and Vertical boundaries

Horizontal Resolution
  • The horizontal resolution of an NWP model is
    related to the spacing between grid points for
    grid point models or the number of waves that can
    be resolved for spectral models.
  • 'Resolution' is defined here in terms of the grid
    spacing or wave number and represents the average
    area depicted by each grid point in a grid point
    model or the number of waves used in a spectral
  • Note that the smallest features that can be
    accurately represented by a model are many times
    larger than the grid 'resolution.' In fact,
    phenomena with dimensions on the same scale as
    the grid spacing are unlikely to be depicted or
    predicted within a model.

Horizontal Resolution
From COMET http//
rameset.htm (Introduction)
Horizontal Resolution (Grid)
From COMET http//
rameset.htm (Grid Spacing)
Horizontal Resolution (Grid)
  • It is important to know the amount of area
    between grid points, since atmospheric processes
    and events occurring over areas near to or
    smaller than this size will not be included in
    the model.

From COMET http//
rameset.htm (Grid Spacing, p2)
Horizontal Resolution (Grid)
  • Grid point models can incorporate data at all
    resolutions, but can introduce errors by doing
    so. It takes about five to seven grid points to
    get reasonable approximations of most weather
    features. Still more points per wave feature are
    often necessary to get a good forecast.

Horizontal Resolution (LFM)
Horizontal Resolution (ETA)
Horizontal Resolution (Spectral)
  • In spectral models, the horizontal resolution is
    designated by a "T" number (for example, T80),
    which indicates the number of waves used to
    represent the data. The "T" stands for triangular
    truncation, which indicates the particular set of
    waves used by a spectral model.
  • Spectral models represent data precisely out to a
    maximum number of waves, but omit all the more
    detailed information contained in smaller waves.
    The wavelength of the smallest wave in a spectral
    model is represented as
  • minimum wavelength 360 degrees
  • N
  • where N is the total number of waves (the "T"

From COMET http//
rameset.htm (Wave No., p1)
Conversion of Spectral to Grid Resolution
  • Because spectral and grid point models preserve
    information in different ways, no precise
    equivalent grid spacing can be given for a
    spectral model resolution. However, we can
    approximate the grid spacing to obtain equivalent
    accuracy to a spectral model with a fixed number
    of waves using a very simple approach. First, we
    assume that three grid points are sufficient to
    capture the information contained in each of a
    series of continuous waves. The approximate grid
    spacing with the same accuracy as a spectral
    model can then be represented as

From COMET http//
rameset.htm (Wave No., p2)
Conversion of Spectral to Grid Resolution
  • For a T80 model, this results in a maximum grid
    spacing for equivalent accuracy of about

From COMET http//
rameset.htm (Wave No., p2)
Discussion of Horizontal Resolution between
Grid-Point and Spectral Models
  • The dynamics of spectral models retain far better
    wave representation than grid point models.
  • However, the spectral model physics is calculated
    on a grid, with about three (better number is
    five) times as many grid lengths as number of
    waves used to represent the data.
  • Since it takes five to seven grid points to
    represent 'wavy' data well and even more for
    features that include discontinuities, the
    resolution of the physics is poorer than the
    above formulation indicates and degrades the
    quality of the spectral model forecast.

From COMET http//
rameset.htm (Wave No., p2)
Horizontal Resolution Summary (Grid vs Spectral
  • In summary, spectral models do a fine job with
    'dry' waves in the free atmosphere, but have
    coarser representation of the physics, including
    surface properties.
  • The resulting overall forecast quality is
    somewhere between these two extremes and varies
    on a case-by-case basis. The more physics that is
    involved in the evolution of the forecast, the
    less the advantage in spectral model forecasts
    compared to comparable resolution grid point

From COMET http//
rameset.htm (Wave No., p2)
  • Two factors limit model representation of
  • The horizontal resolution of the model
  • The horizontal resolution of the terrain dataset
  • If the terrain dataset is coarse, it cannot
    provide details about the topography to
    high-resolution models. If the model cannot
    resolve terrain features, terrain details
    provided in the dataset will be averaged out. In
    most cases, some terrain smoothing is desirable,
    in part because airflow over complex terrain
    otherwise generates small-scale noise that can
    mask the larger-scale signal.

From COMET http//
rameset.htm (Terrain, p.3)
(No Transcript)
(No Transcript)
NGM Topography (200 m intervals)
Vertical Resolution Comparisons
Vertical Resolution (Early ETA)
Vertical Resolution (Meso ETA)
Terrain-following Sigma Coordinates
Domain and Boundary Conditions
  • Model domain refers to a model's area of
    coverage. Limited-area models (LAMs) have
    horizontal (lateral) and top and bottom
    (vertical) boundaries, whereas global models,
    which by nature cover the entire earth, have only
    vertical boundaries. For limited-area models,
    larger-domain models supply the data for the
    lateral boundary conditions.

Errors in the Model
  • Terrain
  • Physical Processes
  • Precipitation
  • Stratiform
  • Convective
  • Radiation (short- and long-wave)
  • Surface energy balance
  • Boundary Layer
  • Boundary Conditions

Errors in the Model
  • Parameterizations
  • NWP models cannot resolve weather features and/or
    processes that occur within a single model grid
  • These sub-grid scale features and processes
    must be parameterized in the model
  • Go to the following web pages

Errors in the Model
  • Post processing of information
  • Forecast fields are interpolated, smoothed, and
    manipulated. The forecast panels you see may not
    be at the detail that the model produced.
  • Many products (vorticity, divergence, relative
    humidity, sea level pressure, etc...) are derived
    indirectly from model forecasts of winds,
    temperature, moisture, and surface pressure).

Errors in the Model
  • Interpret forecast products carefully! Learn
    about what is actually being displayed.
  • Examples
  • Surface Winds
  • FOUS vs MOS
  • Meteograms

Example of Model Forecast Error
  • Extremely cold, dense arctic air was forcing a
    shallow arctic frontal layer southward through
    the Plains.
  • The poor vertical resolution of the NGM could not
    resolve the correct placement of the shallow
  • The somewhat better vertical resolution of the
    ETA provided a more accurate forecast.
  • But both models had sizeable forecast errors.

Anticipate Model Derived Fields!
  • Example Anticipate Vertical Motion Fields
    before looking at model cloud cover and precip
  • Temperature Advection
  • Warm Advection (ascent)
  • Cold Advection (descent)
  • Vorticity Advection
  • PVA (lifting)
  • NVA (sinking)
  • Kinematic Wind Characteristics
  • Apply the terms of the ageostrophic wind equation
  • Also look for upper tropospheric confluence
    (possible convergence) or diffluence (possible

Anticipate Model Derived Fields!
  • Kinematic Wind Characteristics (continued)
  • Surface Convergence (lifting)
  • Surface Divergence (sinking)
  • Topography
  • Upslope/Downslope
  • Coastal seabreeze/landbreeze
  • Thermodynamic Stability/Instability
  • Vertical temperature moisture structure
  • Fog
  • Convection vs stratiform
  • Lake-effect precip