The Traditional Forecast Process

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The Traditional Forecast Process
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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?

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• 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

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• 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, WRF.
• Clearly becoming more and more important
• c. Model Output Statistics (MOS, for some
  fields)

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• 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 increasingy 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.

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Psychology of Weather Prediction

• The psychological element is crucial. Must
  strive to be mentally neutral about forecasts.
• Think like Mr. Spock(or Data)
• In some ways, meteorologists are the last people
  you want to be making forecasts, because we love
  interesting weather and tend to forecast it too
  frequently.
• Sometimes forecasters with great technical
  knowledge have poor performance because of
  psychological reasons!

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Psychology of Weather Prediction

• When many things are happening at once,
  meteorologists often focus on one of them to the
  detriment of others.
• Humans like conceptual models and often hold on
  to them even when reality is at odds.
• Humans are deterministic animals and often push
  uncertainty informaton away when we shouldnt.

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Major Psychological Elements

• LOVE
• Meteorologists love interesting weather and tend
  to overforecast it
• OVERCOMPENSATION
• We tend to excessively compensate for previous
  error. This can produce a classic sinusoidal
  error evolution.
• MACHO
• There is a tendency to go for extreme or
  improbable situations. If you hit, it is like
  meteorological cocaine high!
• INSECURE
• Going with MOS or NWS forecast or fearing to
  deviate from them substantially.

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The Bottom Line

• Forecasting is very important and critically
  affects peoples lives. It requires professional
  detachment.

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The Future Role of Human Forecasters

• Humans will still play crucial roles
• Short-term forecasts where our imagety
  interpretation abilities are critical.
• For communicating and interacting with growing
  user communities.
• For producing watches and warnings
• For watching over automated systems
• To continue local research.