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How can CPP data help weather services?

How can CPP data help weather services?

Ground truth

The last time, we introduced you to the fundamental building blocks of a weather model, and demonstrated how important it was to have a high-resolution, fine-grained weather model in order to be able to project underlying topography. 

Another important aspect is that the quality of a weather model is highly dependent on what often gets referred to as “ground truth” . 

Ground truth describes actual measurements and observations of weather stations that are used for initialization of the weather model, so it “knows” where to start from. Besides ground weather stations, other measurement methods like weather balloon soundings, radars or satellites are used to complement the initialization data set.

The more measured data the weather model has from its point of initialization, the better the forecast will be as there will be less inaccuracy to begin with. This is especially true for the first forecast hours of very high resolution models because they are able to reflect small-scale features. If the starting point of the model is already a poor guess, any forecast resulting from it will be likely even worse than that.

The density of weather observations across the world is very diverse with some countries having hundreds of weather stations and countries with regions that are almost unknown regarding measured meteorological parameters. 

Cross-CPP can help with that, as it aims to provide a platform where service providers like us can buy weather data from sensors, that origin from cars, buildings or other technology. These sensors need a different handling than regular weather station data and must undergo a special plausibility check, that we develop within the project. However, because of their density, their data will still help with the model initialization process, especially in regions and areas where “ground truth” is currently rare.

Individualized weather forecasts

Another aspect of access to different data sources is that we are able to enhance our services for the data provider themselves. For example, imagine a building owners who want to automate the operation of window blinds or optimize energy usage (heating, cooling, etc.): these owners would benefit greatly from a tailored weather forecast for their buildings, that takes the special local meteorological characteristics into account and adjusts for them. Smart buildings usually own a weather station located on the roof of a building, whose data we can use to refine our forecast for that specific building after having collected at least ~1 year of measurements from this station. 

Weather sensors and weather data can be derived from various sources and transformed into a variety of use cases. In our next blog, we will show you some other products we are working on like weather-based navigation and a car sensor derived precipitation map! 

Thanks for reading and stay with us 🙂 

Your Meteologix Team and Cross-CPP consortium partners