Weather Forecast Assessment & Verification

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  • 0:02 Weather Forecast Verification
  • 0:49 Defining a Good Forecast
  • 2:13 Eyeball Verification
  • 3:15 Contingency Table Verification
  • 5:52 Lesson Summary
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Lesson Transcript
Instructor: Christopher Muscato

Chris has a master's degree in history and teaches at the University of Northern Colorado.

Weather forecasting is a difficult science, and like all sciences we want to test the accuracy and reliability of our techniques. So, we need to verify our results. In this lesson we will examine basic methods of forecast verification.

Weather Forecast Verification

Being a meteorologist is a stressful job. You make a weather forecast, a prediction about future atmospheric conditions, and people take that forecast pretty seriously. This means that people can get pretty upset if you get it wrong. But what do people expect? After all, this is science, not fortune telling.

Well, as a science, meteorology is based on the creation and testing of hypotheses. The forecast is a hypothesis, and we want these to be correct. And, frankly, so does everyone else. This means we need to verify forecasts, or compare the predictions against actual, observable truths. By verifying, meteorologists continually improve their forecasting techniques, making this world a little safer for everyone.

Defining a Good Forecast

The point of verification is to create more accurate forecasts. So, let's start by defining the elements of a good forecast. A good forecast has three things. First is consistency, or the degree to which the forecast reflects the knowledge and judgment of the forecaster. Second, it must have high quality, or the degree to which the forecast corresponds to the actual weather. The third element is value, or the degree to which the forecast is useful and helpful.

Verification is primarily focused on evaluating the quality of a forecast, or whether or not it was accurate. If you said it was going to rain, did it? Now, there are dozens of different forecasting techniques, each one needing to be verified in a unique way, but we can start looking at this by breaking down forecasts into two categories.

A qualitative forecast describes observable weather without strict measurements. It will be sunny today. There is a chance of showers later. Those are qualitative forecasts. A quantitative forecast predicts numerically describable weather. It will rain two inches today. That's quantitative. It's based on a numerical quantity. Got it? Okay, now it's time to start verifying.

Eyeball Verification

Just as we have qualitative and quantitative forecasts, our verification methods can be described the same way. We'll start with one of the most common methods, called eyeball verification. Basically, you look at a forecast and compare it to actual weather. It's cheap. It's easy. It's generally enough to get an idea if forecasts are mostly correct or are way off.

Eyeball verification is an example of qualitative verification, basically defined by the question: Does it look correct? Qualitative verification is really the only way to evaluate qualitative forecasts, and it can be somewhat subjective. To compensate for this, most forecasters will look over charts and graphs that provide more structured details about weather forecasts versus actual weather patterns. This ensures that they are evaluating accurate data. But this is still a qualitative, not quantitative, method of verification.

Contingency Table Verification

Now, sometimes you do want a more quantitative verification, which answers the question of: How numerically accurate was the forecast? The most basic, and one of the most common, quantitative methods is the use of a contingency table, or a chart of correct and incorrect forecasts. This method can only verify dichotomous forecasts, or those with accuracy that can be answered with yes and no.

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