Reliable weather forecast made easy.
We all use weather apps today to get an idea for the weather in the near future. But how reliable are such programs, what are their limits? And why do the apps’ predictions differ from each other?
The classic weather forecast on TV
Every evening on television, the weather is forecasted for the coming days during the most expensive broadcasting time (around 8 p.m.). That happens in less than one minute, in Germany over the whole country, sometimes even with parts of the northern Alps. Of course the forecast is roughly shown here.
In order to get a feeling for the probability of the forecast, however, it requires a general understanding of the general weather situation (current weather), the desired location (precision), the local topography (very interesting in the mountains) and the knowledge of how a forecast is made (calculation of the weather models). It is also important to know that there are commercial and non-commercial forecasts (more about this below).
Various weather models
Basically, the current general weather situation is the basis for any forecast. It is confirmed by weather radar. However, weather radars can show us only the past and present weather situation (actual value). Weather stations provide measured values for computer-generated weather models. These weather models include data from past (real) weather, previous (virtual) calculations and the current (real) measured values of the weather stations. In addition, differing (virtual) calculations are weighted accordingly (different input variables). The results are published several times a day.
There are numerous weather models from different institutes. Four of the most important models are GFS/NOAA (Global Forecast System with a resolution of 22 km), ECMWF (European Centre for Medium-Range Weather Forecasts with a resolution of 9 km), ICON/DWD (German Meteorological Service with a resolution of 7 km) and NEMS/Meteoblue (with a resolution of 4 km).
» Often forecasts are very accurate, sometimes less accurate and sometimes completely wrong. Deviations between different models indicate uncertainties in the weather forecast. It is important to understand that these deviations are not visible in a single weather model – you need access to different models!
The truth about commercial weather apps
Everybody knows the following situation: While planning a tour, you get the following statement: “My weather app tells me that there should be nice weather at the destination tomorrow”. Another app may come to a different forecast. How comes?
Each weather app has access to a certain weather model and displays its calculation in a prepared form. And because different weather models work with different data bases and input variables, different interpretations are created at the end. This information is accordingly summarized and displayed as weather symbols (in the best case meteograms) which represent the forecast for a certain period of time.
» Today serious forecasts can be calculated up to a maximum of three days in advance, but depending on the weather conditions only for the next six hours.
What does a rain probability of 63% mean?
It’s simple: it means that it will rain in 63 out of 100 days with such a weather situation. But how does this exact number come about? Computers read out the data of the weather stations and calculate the weather situation as well as when and where exactly it has rained in the past. In this case, 63 out of 100 days with identical weather situations have experienced rainfall within 24 hours in the past, but on the 37 remaining days it stayed dry!
An example: a probability of rainfall of 63% is predicted for the Harz (a low mountain range in the middle of Germany). Where is the probability for a rain shower higher – on the Brocken (with 1141 m highest mountain in the Harz) or in Goslar (a town 15 km north of the Brocken in the Harz) on a height of 255 m?
Because of the topography it is very likely that it will rain on the mountain, while it remains dry in Goslar with the greatest probability.
Incidentally, the probability of rain does not provide any information about the amount of precipitation, the period of time (shower or continuous rain) or whether it rains, hails or snows.
» Numbers are easy to sell in commercial weather apps.
Wet bias (rain distortion)
“Wet bias” refers the process of meteorologists to specify rain probabilities (usually low probabilities of precipitation) in the weather forecast to compensate the interpretation of users (a 5% probability is then predicted as a 20% probability).
» Here we’re in the field of weather psychology.
Different forecasting qualities
» Weather reports (examples: chamonix-meteo.com, meteoschweiz.ch, dwd.de, alpenverein.de):
Daily interpretation of the general weather situation (actual value) and weather models (set value) of professional meteorologists offer a high quality and reliability of the forecast.
The interpretation of the weather models can also be done by yourself with the necessary experience and knowledge of the area.
» Meteograms (examples: meteoblue.com, yr.no, wetterzentrale.de):
A meteogram can be used to show trends and temporal progressions. This makes it possible to clearly display many forecasting information on a small space/display.
» Weather icons (examples: wetter.com, wetteronline.de):
Simple weather icons in a weather app represent the lowest quality level of forecasting with the greatest inaccuracy and most uncertainties.
The limits of prediction
Tracks of thunderstorms lead to small-scale or temporal shifts. Thin layer clouds are not detected and distort sunny or foggy conditions. The topography on mountains/valleys with its differences in altitude and temperature is another major challenge for weather forecasts.