The Forecast Process

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					452 NWP
      Major Steps in the Forecast
               Process
•   Data Collection
•   Quality Control
•   Data Assimilation
•   Model Integration
•   Post Processing of Model Forecasts
•   Human Interpretation (sometimes)
•   Product and graphics generation
                 Data Collection
• Weather is observed throughout the world and the
  data is distributed in real time.
• Many types of data and networks, including:
   –   Surface observations from many sources
   –   Radiosondes and radar profilers
   –   Fixed and drifting buoys
   –   Ship observations
   –   Aircraft observations
   –   Satellite soundings
   –   Cloud and water vapor track winds
   –   Radar and satellite imagery
Observation and Data Collection
                Quality Control
• Automated algorithms and manual intervention to
  detect, correct, and remove errors in observed
  data.
• Examples:
   – Range check
   – Buddy check
   – Comparison to first guess fields from previous model
     run
   – Hydrostatic and vertical consistency checks for
     soundings.
• A very important issue for a forecaster--sometimes
  good data is rejected and vice versa.
3 March 1999: Forecast a snowstorm   Eta 48 hr SLP Forecast valid 00 UTC 3
… got a windstorm instead            March 1999
Pacific Analysis
At 4 PM
18 November
2003




 Bad Observation
        Objective Analysis/Data
             Assimilation
• Numerical weather models
  are generally solved on a
  three-dimensional grid
• Observations are scattered in
  three dimensions
• Need to interpolate
  observations to grid points
  and to insure that the various
  fields are consistent and
  physically plausible (e.g.,
  most of the atmosphere in
  hydrostatic and gradient wind
  balance).
  Objective Analysis/Data Assimilation

• Often starts with a “first guess”, often the gridded
  forecast from an earlier run (frequently a run
  starting 6 hr earlier)
• This first guess is then modified by the
  observations.
• Adjustments are made to insure proper balance.
• Objective Analysis/Data Assimilation produces
  what is known as the model initialization, the
  starting point of the numerical simulation.
  Model Integration: Numerical
      Weather Prediction
• The initialization is used as the starting
  point for the atmospheric simulation.
• Numerical models consist of the basic
  dynamical equations (“primitive equations”)
  and physical parameterizations.
          “Primitive” Equations
•   3 Equations of Motion: Newton’s Second Law
•   First Law of Thermodynamics
•   Conservation of mass
•   Perfect Gas Law
•   Conservation of water
    With sufficient data for initialization and a
    mean to integrate these equations, numerical
    weather prediction is possible.
    Example: Newton’s Second Law: F = ma
One Form
     Physics Parameterizations
• We need physics parameterizations to
  include key physical processes.
• Examples include radiation, cumulus
  convection, cloud microphysics, boundary
  layer physics, etc.
• Why? Primitive equations with lack the
  necessary physics or lack sufficient
  resolution to resolve key processes.
           Parameterization
• Example: Cumulus Parameterization
• Most numerical models (grid spacing of 12-
  km is the best available operationally)
  cannot resolve convection (scales of a few
  km or less).
• In parameterization, represent the effects of
  sub-grid scale cumulus on the larger scales.
  Numerical Weather Prediction
• A numerical model includes the primitive
  equations, physics parameterization, and a way to
  solve the equations (usually using finite
  differences on a grid)
• Make use of powerful computers
• Keep in mind that a model with a horizontal grid
  spacing is barely simulating phenomenon with a
  scale four times the grid spacing. So a 12-km
  model barely is getting 50 km scale features
  correct.
  Numerical Weather Prediction
• Most modeling systems are run four times a day
  (00, 06, 12, 18 UTC), although some run twice a
  day (00 and 12 UTC)
• The main numerical modeling centers in the U.S.
  are:
   – Environmental Modeling Center (EMC) at the National
     Centers for Environmental Prediction (NCEP)--part of
     the NWS. Located near Washington, DC.
   – Fleet Numerical Meteorology and Oceanography
     Center (FNMOC)-Monterey, CA
   – Air Force Weather Agency (AFWA)-Offutt AFB,
     Nebraska
           Major U.S. Models
• Global Forecast System Model (GFS). Uses
  spectral representation rather than grids in the
  horizontal. Global, resolution equivalent to 35 km
  grid model. Run out to 384 hr, four times per day.
• Weather Research and Forecasting Model
  (WRF). Two versions: WRF-NMM and WRF-
  ARW(different ways of representing the
  dynamics). WRF is a new mesoscale modeling
  system system that is used by the NWS and the
  university/research community. AFWA is also
  moving to WRF. The NWS runs WRF-NMM.
  WRF-NMM is run at 12-km grid spacing, four
  times a day to 84h.
          Major U.S. Models
• MM5 (Penn. State/NCAR Mesoscale Model
  Version 5). Has been the dominant model in the
  research community. Run here at the UW (36, 12
  and 4 km resolution).
• COAMPS (Navy). The Navy mesoscale
  model..similar to MM5
• There are many others--you will hear more about
  this in 452.
• Forecasters often have 6-10 different models to
  look at. Such diversity can provide valuable
  information.
Major International NWP Centers
• ECMWF: European Center for Medium-
  Range Weather Forecasting. The Gold
  standard. Their global model is considered
  the best.
• UK Met Office: An excellent global model
  similar to GFS
• Canadian Meteorological Center
• Other lesser centers
           Accessing NWP Models
• The department web site (go to weather loops or
  weather discussion) provides easy access to many
  model forecasts.
• The NCEP web site is good place to start for NWS
  models.         http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/



• The Department Regional Prediction Page gets to the
  department regional modeling output.
  http://www.atmos.washington.edu/mm5rt/
             A Palette of Models
• Forecasters thus have a palette of model forecasts.
• They vary by:
   –   Region simulated
   –   Resolution
   –   Model Physics
   –   Data used in the assimilation/initialization process
• The diversity of models can be a very useful tool
  to a forecaster.
             Post-Processing
• Numerical model output sometimes has systematic
  biases (e.g., too warm or too cold in certain
  situations). Why not remove it?
• Numerical models may not have the resolution of
  physics to deal with certain problems (e.g., low
  level fog in a valley). Some information be
  derived from historical model performance.
• The solution: post-processing of model forecasts.
                     MOS
• In the 1960s and 1970s, the NWS developed and
  began using statistical post-processing of model
  output…known as Model Output Statistics…MOS
• Based on linear regression: Y=a0 + a1X1 + a2X2+
  a3X3 + …
• MOS is available for many parameters and greatly
  improves the quality of most model predictions.
             Post-Processing
• There are other types of post-processing.
• Here at the UW we have developed a way of
  removing systematic bias.
• Others have used “neural nets” as an approach.
• Another approach is to combine several models,
  weighing them by previous performance (called
  Bayesian Model Averaging).
         Ensemble Forecasting
• All of the model forecasts I have talked about
  reflect a deterministic approach.
• This means that we do the best job we can for a
  single forecast and do not consider uncertainties in
  the model, initial conditions, or the very nature of
  the atmosphere. These uncertainties are often very
  significant.
• Traditionally, this has been the way forecasting
  has been done, but that is changing now.
         A More Fundamental Issue
• The work of Lorenz (1963, 965, 1968)
  demonstrated that the atmosphere is a
  chaotic system, in which small
  differences in the initialization…well
  within observational error… can have
  large impacts on the forecasts,
  particularly for longer forecasts.
• Similarly, uncertainty in model physics
  can result in large forecast
  differences..and errors.
• Not unlike a pinball game….
• Often referred to as the “butterfly
  effect”
    Probabilistic-Ensemble NWP
• Instead of running one forecast, run a collection
  (ensemble) of forecasts, each starting from a
  different initial state or with different physics.
• The variations in the resulting forecasts could be
  used to estimate the uncertainty of the prediction.
            Ensemble Prediction
•Can use ensembles to provide a new generation
of products that give the probabilities that some
weather feature will occur.

•Can also predict forecast skill!

  •It appears that when forecasts are similar, forecast
  skill is higher.

  •When forecasts differ greatly, forecast skill is less.
           Ensemble Prediction
• During the past decade the size and sophistication of the
  NCEP and ECMWF ensemble systems have grown
  considerably, with the medium-range, global ensemble
  system becoming an integral tool for many forecasters.
• Also during this period, NCEP has constructed a higher
  resolution, short-range ensemble system (SREF) that uses
  breeding to create initial condition variations.
            The Thanksgiving Forecast 2001                              Verification

             42h forecast (valid Thu 10AM)
                   SLP and winds
          - Reveals high uncertainty in storm track and intensity
1: cent   - Indicates low probability of Puget Sound wind event




2: eta      5: ngps                      8: eta*                    11: ngps*




3: ukmo     6: cmcg                      9: ukmo*                   12: cmcg*




4: tcwb     7: avn                       10: tcwb*                  13: avn*
        Human Interpretation
• Once all the numerical simulations and
  post-processing are done, humans still play
  an important role:
  – Evaluating the model output
  – Making adjustments if needed
  – Attempting to consider features the model can’t
    handle
  – Communicating to the public and other users.
         Product Generation
• Some completely objective and automated.
• Others depend on human intervention
• Example: the National Weather Service
  IFPS system
Interactive Forecast Preparation System (IFPS) and
    National Digital Forecast Database (NDFD)
The Forecast Process
            The Forecast Process
• Step 1: What is climatology for the
  location in question?
  What are the record and average maxima and minima? You
  always need very good reasons to equal or break records.


• Step 2: Acquaint yourself with the
  weather evolution of the past several days.
  How has the circulation evolved? Why did
  past forecasts go wrong or right?
• Step 3: The Forecast Funnel.
  Start with the synoptic scale and then downscale
  to the meso and local scales. Major steps:
  I. Synoptic Model Evaluation
  Which synoptic models have been the most skillful during
  the past season and last few days?
  Has there been a trend in model solutions?
  Have they been stable?
  Are all the model solutions on the same page? If so, you
  can more confidence in your forecast.
  Evaluate synoptic ensemble forecasts. Are there large or
  small spread of the solutions?
  Which model appears to most skillful today based on
  initializations and short-term (6-12h forecasts)?
  Satellite imagery and surface data are crucial for this
  latter step
II. Decide on the synoptic evolution you believe to be most
   probable. Attempt to compensate for apparent flaws in the best
   model.
III: Downscaling to the mesoscale. What mesoscale evolution will
   accompany the most probable synoptic evolution?
   This done in a variety of ways:
   a. Subjective rules and experience: e.g., the PSCZ occurs when
   the winds on the WA coast are from the W to NW? Onshore push
   occurs when HQM-SEA gets to 3.5 mb. Knowledge of these rules
   is a major component of forecast experience.
   Typical diurnal wind fields in the summer.
   b. High resolution mesoscale modeling: e.g., MM5.
   Clearly becoming more and more important
   c. Model Output Statistics (MOS, for some fields)
IV. Downscaling to the microscale for point
forecasts.
Subjective approach using knowledge of terrain and
other local characteristics.

For subjective forecasts remember the DT approach:
It is nearly impossible to forecast a parameter value
from first principles--so consider what has changed.
STEP 4. The Homestretch
• Combine the most probable synoptic, mesoscale, and
  microscale evolution in your mind to produce a predicted
  scenario
• Attempt to qualify the uncertainty in the forecast. Synoptic
  and mesoscale (SREF) ensemble systems are becoming
  increasingly important for this task.
• Ask yourself: am a missing something? Am I being
  objective? Overcompensating for a previous error? Check
  forecast discussions from other forecasters to insure you
  are not missing something.
Today’s Forecast

				
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posted:9/16/2012
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