Weather forecasting is a complex process that requires sophisticated tools to collect, analyze and interpret weather data in real time. These tools provide accurate and reliable forecasts to individuals, businesses and governments.
Weather data comes from a variety of sources, including ground weather stations, weather radar, radiosondes and satellites.
Weather stations measure parameters such as temperature, air pressure, wind speed and direction, humidity and precipitation. According to the World Meteorological Organization (WMO) in 2020, before the pandemic, there were more than 16,000 ground-based weather stations around the world to feed weather models. Unfortunately this number has been decreasing for the last 3 years.
Weather radars are used to detect rain, while satellites provide real-time weather images and allow for the monitoring of large-scale weather systems. For example, in metropolitan France, Météo-France has 31 weather radars on the territory, of which part of the data is assimilated for regional models.
Radiosondes, also called weather balloons, are instruments used to measure temperature, atmospheric pressure, humidity and wind speed in the atmosphere. This data is used to improve medium- and long-term weather forecasts. According to the U.S. National Weather Service, approximately 92 radiosondes are launched each day in the United States to collect atmospheric data.
The weather service also uses sensor data from aircraft climbing and descending around airports to obtain vertical profiles of temperature, humidity and wind. These data have replaced radiosondes in many areas for economic reasons. Thus, during the civil aviation shutdown in 2020, the quality of weather forecasts was strongly degraded because of the absence of these data in model input !
Finally, weather satellites, of which there will be 20 according to the WMO in 2020, provide real-time weather images and allow the monitoring of large-scale weather systems. These satellites are used to collect data such as surface temperature, cloud cover, wind speed and direction, precipitation, ocean temperature, etc.
Once the data is collected, it is processed by sophisticated numerical weather prediction models. These models use complex mathematical equations, such as the Navier-Stokes equations, to predict the behavior of the atmosphere. The models take into account many factors such as temperature, atmospheric pressure, wind speed and direction, precipitation, and humidity. Short-term weather forecasts, from a few hours to a few days, are usually based on these numerical models. Numerical models require high computing power to solve the complex mathematical equations. For example, the European weather model, called the ECMWF, uses more than 7,000 computer processors and generates weather forecasts every day, 4 times a day.
For longer-term forecasts, weather models are more complex and require much more computing power. Climate models take into account a wide range of factors, including ocean temperatures, atmospheric circulation, topography, and greenhouse gas concentrations. In addition, long-term climate predictions must also take into account natural phenomena such as the North Atlantic Oscillation, El Niño and La Niña. These phenomena can have a significant impact on the global climate and must be taken into account in the long-term forecast. Climate models can provide seasonal or annual forecasts to help governments, businesses, and communities make informed decisions for the future.
Weather forecasts are communicated to individuals, businesses, and governments through various channels, such as television weather reports, websites, mobile applications, and SMS alerts.
In conclusion, the sophisticated tools used for weather forecasting are essential for providing accurate and reliable information about upcoming weather conditions. However, it is important to understand that these tools have limitations and weaknesses that can affect forecast accuracy.