The Traditional 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, WRF.
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 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.
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!
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
shouldn’t.
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.
The Bottom Line
• Forecasting is very important and critically
affects people’s lives. It requires
professional detachment.
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.