Enhancing Weather Information with Probability Forecasts
This information statement discusses the state of science in probabilistic forecasting. All-weather forecasts should include information that quantifies their uncertainty. Although there are exceptions, most weather predictions do not include such information. This probabilistic information could be widely distributed to provide economic and social benefits. Users would be better able to make informed decisions if they explicitly account for uncertainty in forecasts.
For many weather parameters, producing weather forecasts in probabilistic format will require improvement in or implementation of techniques for quantifying uncertainty. Ensemble forecasting is one example.
Forecasters must be trained in how to use probabilistic information to make their final forecasts, as well as the different requirements of people who use probability forecasts. Users will also need information about how to interpret and utilize probabilistic forecast information. This is essential if you want to communicate uncertainty in weather forecasts. Use a probability calculator for easy calculation. And you can easily find the probability calculator easily.
Weather forecasts have improved dramatically over the past few decades at all times and at all scales. This is due to improved forecast models and satellite data that are more precise and more detailed than ever.
Various sectors of the global economy, including agriculture, energy, and water supply, have been increasingly reliant on weather forecasts and are now more adept at integrating these better forecasts into their short- and long-term business planning and decision-making. Weather forecasts are by nature uncertain. Most users don’t have easy access to this information, and often the information is not communicated effectively to them.
A probability forecast is a numerical expression that expresses uncertainty about the event or quantity being forecast. All elements (temperatures, wind, precipitation, and so forth) should be included in a probability forecast. A weather forecast should include information that quantifies the uncertainty inherent in each element.
According to surveys, users want information on uncertainty and confidence in weather forecasts. Because users are able to make informed decisions about forecast uncertainty, they can be widely disseminated and effectively communicate this information. This will likely lead to significant economic and social benefits.
Although there have been many advances in the development of probabilistic forecasting methods, only a fraction of elements of weather, hydrologic and climate forecasts can be expressed probabilistically.
For decades, forecasts of the likelihood of precipitation occurring have been widely accepted. However, they are not always correctly interpreted. The NWS2 in the United States has recently issued probability forecasts for a range of weather phenomena. These include daily outlooks on tornado hazards and wind speed fields in tropical storms, as well as weekly and seasonal outlooks for temperature, and precipitation.
Explanation of Probabilities
Even though we all encounter probabilities in our daily lives, it can be hard to grasp the concept of probability. There is no one way to communicate probabilities effectively or interpret them in a consistent manner. There are many ways to produce probability forecasts. A probability forecast for a weather event is a forecaster’s estimate of the likelihood of that event occurring. These forecasts can be generated directly from the NWP models, or statistically from the output of these models.
You can express uncertainty in other ways than just probabilistic terms like odds and frequencies. Social scientists have repeatedly shown that uncertainty can be expressed in qualitative terms such as “likely” without creating unnecessary ambiguity. One user may interpret the same term as indicating a higher probability than another.
Production of Probability Predictions
Improved methods for quantifying uncertainty will be required to produce probabilistic weather forecasts. Any forecast product can have uncertainty attached to evaluating past performance. These four main approaches to producing probability forecasts are situation-dependent.
Based on a combination of NWP model predictions, the probabilities of an event are calculated.
Post-processing statistically of NWP outputs from one model run or ensemble-based NWP.
These techniques are observationally-based and use historical climate and weather data to generate statistical relationships between current predictors (predictands), and future unknown observations (predictands).
Interpretation of NWP forecasts or other information is subjective (i.e. human).