The Traditional Forecast Process

1. The Traditional Forecast Process

2. 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?

3. 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

4. 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)

5. 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.

6. 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!

7. 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.

8. 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.

9. The Bottom Line • Forecasting is very important and critically affects people’s lives. It requires professional detachment.

10. 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.