"To ensure that clients receive only the highest quality weather observation and forecast data, DTN has developed a detailed methodology that consists of Five components"
Chief Meteorology Officer
When it comes to weather forecasting, many people rely on free weather apps and common sense. But predicting the weather is serious business. Everyone from shipping companies, oil rigs, energy distributors, air transport and even the automotive industry: they all depend on accurate and reliable weather forecasts to make informed business decisions.
Instead of checking a free weather app, these companies turn to professional meteorologists trained in weather forecasting. What do they know that you don’t? We’ve asked the weather experts to share their experience and insights on what makes a highly accurate weather forecast, to create How It’s Made.
To start, we’ll outline the Five Categories that. We’ll discuss them one by one in this article.
You can tell a lot about the weather outside by looking out your window. It’s kind of what meteorologists do too! However, instead of using only their own eyes, they employ thousands of weather stations and other sensors all around the world to find out what’s going on. There are two main types of observation networks:
- Physical locations: For example, weather stations (on land) and buoys (on water) but also modern IoT techniques to capture data e.g. from car sensors
- Remote observations: Radars detecting precipitation, lightning sensors triangulating thunderstorms and satellites to observe clouds (and much more)
Physical locations measure conditions at their exact location, whereas remote observations measure conditions in a radius around a certain point. Weather experts draw on a mix of both to gather high-quality data.
2.) Meteorological and oceanographic (MetOcean) models
Experts use weather and oceanographic models to forecast weather conditions, waves and currents in the coming hours, days and weeks. These models are often complex, as they’re built on the laws of physics, chemistry and fluid motion, and a coordinate system that divides the Earth into a 3D grid.
Atmospheric motion, pressure, temperature and humidity are calculated per grid cell, and the interactions with neighboring cells are used to predict future atmospheric properties. To make it even more complicated, each weather model comes with its own characteristics performing better in certain weather conditions and worse in others, meaning there’s no such thing as the ultimate weather model providing always the best predictions. This is why weather experts use a combination of models to optimize accuracy.
3.) Statistical post-processing
By combining several weather models and conducting statistical analyses on them, meteorologists can create an optimized forecasting system. Such a forecasting system is the foundation for predicting future weather conditions and can be adapted to specific requirements. Within DTN, we work with four main flavors, which can be applied to different use-cases:
- Model Output Statistics (MOS) – the most generic and well-known forecasting system using actual observations of the past to find the best combination of weather models for a particular location
- Scalable Downscaling (ScaDo) – a forecasting system to predict for locations away from weather stations, which can also anticipate differences in altitude (think of mountain ranges) and land-use (think of urban heat island)
- Nautical MeteoBase (NMB)– a forecasting system to predict marine weather and the state of the sea
- Road and route models
- Road surface model A combined physical/statistical model designed to calculate forecasts for road surface temperatures and conditions at specific locations
- Route based forecast model – A model designed to calculate forecasts for whole road networks (or routes)
4.) Quality control & data management
Weather data comes in many different forms, such as observation, radar and lightning data, satellite information and data derived from models. All of these different data needs to be structured and organised, so they can be analyzed and transformed into valuable information. Therefore, weather experts work with high-available technology that provides them and their customers with near real-time information. As data volumes grow and technology gets better every day, there should always be data specialists looking for new technological solutions to handle future data volumes in a fast and reliable way.
Securing quality over all elements of the forecasting process can be an challenge when it comes to something as variable as the weather, meaning there’s a strong need for quality control. For example, incoming observation data has to be checked on accuracy, completeness and irregularities. Forecasts have to be checked in a continuous way so that models can be further improved and the level of accuracy towards customers can be increased secured and increased.
5.) Meteorology and forecasting expertise
To truly “know” the weather, you first need both weather forecasters and data specialists. Second, you need a services team that deals with customer feedback and draw up reports. Third, you need a research team that innovates new scientific and technical methods, develops customer-specific solutions, and defines algorithms to verify forecasting quality. This research team is also responsible for knowledge management, for example by monitoring weather model performance and keeping track of new developments and improvements.
Forecast accuracy = high maintenance?
In terms of accuracy, there are clear differences between freely available sources and high-quality weather forecasting. This improvement is due to the combination of data sources, weather models, forecasting systems, specialists and technologies, which weather experts rely on to improve the accuracy of their forecasting.
You might say accuracy inevitably comes with high maintenance, which is why companies choose to work with specialist weather experts. But utilizing this accuracy is what sets apart the good from the great. Average companies can make decisions using average data, but leading companies use the highest accurate weather data in their decision making processes.