METplus Practical Session Guide (Version 5.0) | Basic Verification Statistics Review > Attributes of Forecast Quality

Attributes of Forecast Quality

Forecast quality attributes are the basic characteristics of forecast quality that are of importance to a user and can be assessed through verification. Different forecast evaluation approaches will measure different attributes of the quality of the forecasts. Some verification statistics can be decomposed into several attributes, providing more nuance to the quality information. It can be commonplace in operational and research settings to find and settle on one or two of these complex statistics that can provide meaningful guidance for adjustments to the model being evaluated. For example, if a given verification statistic shows that a model has a high bias and low reliability, that can seem to provide a researcher with all they need to know to make the next iteration of the model perform better, having no need for any other statistical input. However, this is an example of the law of the instrument: “If the only tool you have is a hammer, you tend to see every problem as a nail”. More complete, meaningful verification requires examining forecast performance from multiple perspectives and applying a variety of statistical approaches that measure a variety of verification attributes.

In most cases, one or two forecast verification attributes will not provide enough information to understand the quality of a forecast. In the previous example where one verification statistic showed a model had high bias and low reliability, it could have been a situation where a second verification measure would have shown that the accuracy and resolution of the model were good, and making the adjustments to the next model iteration to correct bias and reliability would degrade accuracy and resolution. To fully grasp how well a particular forecast is performing, it is important to select the right combination of statistics that give you the “full picture” of the forecasts’ performance, which may consist of a more complete set of attributes, measuring the overall quality of your set of forecasts.

The following forecast attribute list is taken from Wilks (2019) and summarized for your convenience. Note how statistics showing one of these attributes on their own will not tell you exactly how “good” a forecast is, but combined with statistics showcasing other attributes you can have a better understanding of the utility of the forecast.

  • Accuracy – The level of difference (or agreement) between the individual values of a forecast dataset and the individual values of the observation dataset. This should not be confused with the informal usage of “accurate”, which is often used by the general population to describe a forecast that has high quality.
  • Skill – The accuracy of a forecast relative to a reference forecast. The reference forecast can be a single or group of forecasts that are compared against, with common choices being climatological values, persistence forecasts (forecasts that do not change over time), and older numerical model versions.
  • Bias – The similarity between the mean forecast and mean observation. Note that this differs slightly from the accuracy attribute, which measures the individual value’s similarity.
  • Reliability – The agreement between conditional forecast values and the distribution of the observation values resulting from that condition. Another way to think of reliability is as a measure of all of the observational value distributions that could happen given a forecast value. 
  • Resolution – In a similar thought as reliability, resolution is the measure of the forecast’s ability to resolve different observational distributions given a change in the forecast value. Simply put, if value X is forecast, what level of difference is there in the resulting observation distributions than a forecast of value Y.
  • Discrimination – A simpler definition could be considered the inverse of resolution: discrimination is the measure of a forecast’s distribution given a change in the observation value. For example, if a forecast is just as likely to predict a tornado regardless of the actual observation of a tornado occurring, that forecast would have a low discrimination ability for tornadoes.
  • Sharpness – This property pertains only to the forecast with no consideration of its observational pair. If the forecast does not deviate from a consistent  (e.g., climatological) distribution, and instead sticks close to a “climatological value”, it exhibits low sharpness. If the forecast has the ability to produce values different from climatology that change the distribution, then it demonstrates sharpness.