Weather is something everybody has to deal with everyday, therefore people rely on weather forecasts to plan their day or their week. Weather information can be found from a variety of sources including internet, television, radio and newspapers. But how are forecasters actually trying to convey their forecasts with the user? The exact methodology may differ a bit between the private sector and the government, but the desired end result is the same...to prepare the public appropriately for what type of weather is expected.
For hazardous weather situations, the National Weather Service issues headlines (watches, warnings, and advisories) to alert the public about a particular threat, and the severity of that event, in an effort to enhance the local and national economy, and to save lives and property. Most people are quite familiar with these headlines, but they may not be as familiar by what the terms 'a chance of rain', or 'A 60 percent chance of rain' actually mean. Hopefully, this explanation will answer that question and will also shed some light on what forecasters are thinking about while prepping a forecast.
Forecasts are generated by meteorologists who rely on current observations, model data that projects these observations into the future, and their forecast knowledge or experience about similar situations. Each meteorologist has their own routine, but the overall process follows a general pattern. The first step in preparing a forecast is to diagnose the current state of the atmosphere. This includes looking at satellite, radar, observed sounding data, and station observations. Then, the forecaster can begin looking at model data and the initial conditions used in that model. If the model depicts the current state of the atmosphere well, then it is more encouraging to trust that model over one that may depict the current atmosphere poorly.
After all the model data has been studied, the forecaster critiques the data. Oftentimes, models have well known biases that forecasters can easily correct such as a wet bias, warm bias, etc. However, other times these biases compound and branch out into additional issues, making them much more difficult to correct. This leads to the forecaster's confidence about a forecast. Forecasts may be associated with a high confidence level (certain something will play out as expected) and others may have a very low confidence level (many different solutions are equally possible). It is the job of the forecaster to ingest all of this data, consider the confidence level of the forecast, try to correct model biases, rely on past experiences with similar situations, and make decisions accordingly.
So what does 'A 60 percent chance of rain' mean? Technically speaking, a 60 percent chance of rain means it will rain on 60 percent of the days like that day (and measure at least 0.01 inch). In addition, it also means it will NOT rain on 40 percent of the days like that day. However, the 60 percent chance of rain does not imply it will rain 60% of the time on that day, it does not mean it will rain over 60% of the area, nor does it mean 60% of meteorologists think it will rain.
How do meteorologists decide to use 60, rather than 30 or 70? No, we don't memorize the details of every storm that has ever affected Wisconsin, but we do use 'model output statistics' or MOS as a guide. MOS is a technique used to objectively interpret numerical model output and produce site-specific guidance. In other words, it calculates forecasted values of temperature, wind, probability of precipitation (PoP), sky cover, and precipitation type, among other elements, based on what happened from similar events, daily climatology, and the model's forecast. Meteorologists take this data and tweak it based on their confidence level of an event happening or not happening.
All model guidance is just that, guidance. It is the responsibility of the forecaster to take that information, make it better, and to appropriately communicate the forecast to the users.
National Weather Service Milwaukee/Sullivan