How We Build the World's Best Weather Forecasts

Here is how we guarantee the most reliable forecasts

Continued development of ever more accurate forecast methods and data sources ensures Foreca's position as one of the most accurate and responsible weather service providers in the world. We don’t just pass on raw data; we actively own and ensure the quality of our forecasts using a rigorous scientific approach.

Here’s how that process works in practice:

1. Using the world's best global model (ECMWF) as our foundation.
2. Enriching this baseline with other global supercomputer models, local models, AI-models, observation data, and topography.
3. Selecting the best model for each location and situation using AI and machine learning.
4. Adjusting the selected model with real-time observations and nowcasting methods.
5. Continuously verifying our performance and training our machine learning models to improve accuracy
6. Delivering the ready-to-use data through an API optimized for consumer-facing services

1. Using the world's best global model (ECMWF) as our foundation

To build the most accurate weather forecast possible, you must start with the absolute best foundation. Weather forecasting operates on a massive scale, requiring supercomputers to calculate the baseline state of the Earth's atmosphere. For this crucial first step, we rely on the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF).

ECMWF is widely acknowledged as the most accurate traditional global numerical weather prediction (NWP) model in the world. It provides a highly reliable, comprehensive view of global weather patterns. However, while it is the premium gold standard for global models, relying solely on a single raw model is only the starting line for Foreca.

This chart compares Foreca’s short-term (yellow) and long-term (blue) forecasts with the actual weather observed later. The red and green lines show the accuracy of the ECMWF model as a benchmark. You can see that Foreca’s forecasts become significantly more accurate after applying our post-processing methods described below.

2. Enriching this baseline with other global supercomputer models, local models, AI-models, observation data, and topography

Because no single model is flawless in every corner of the globe or in every specific weather scenario, we significantly expand our meteorological data pool. We enrich our ECMWF foundation by integrating data from other leading global supercomputer models (such as GFS), highly detailed local models, and the latest generation of advanced AI-based weather models that are revolutionizing the industry.

Beyond predictive models, we feed our processing engine a massive volume of curated, real-time observation data gathered from official weather stations, radars, and satellites worldwide. We also factor in detailed topographical data, as local terrain – like mountains, coastlines, and valleys – drastically impacts microclimates and local weather behavior. This creates a uniquely rich and diverse meteorological database.

3. Selecting the best model for each location and situation using AI and machine learning

With such a vast array of models and data available, the challenge becomes knowing which source to trust at any given moment. This is where our proprietary data science and machine learning expertise steps in. We do not just blindly average the models together; our forecasting engine intelligently analyzes the current atmospheric conditions and geographic location in real-time.

Based on historical performance and statistical significance, our AI automatically selects the specific model – whether traditional NWP or AI-driven – that is proven to be the most accurate for that exact coordinate and specific weather event. This dynamic, intelligent selection process ensures that your users are always getting the optimal forecast, rather than a one-size-fits-all guess.

4. Adjusting the selected model with real-time observations and nowcasting methods

Even the most intelligently selected models can have systematic blind spots or fail to predict sudden, highly localized weather shifts. To bridge the gap between scientific modeling and actual reality, we apply rapid, real-time corrections to the chosen models using live local observations.

We utilize advanced nowcasting methods to handle the immediate future. If a sudden storm develops or the temperature drops faster than the original models predicted, our system catches it and adjusts the output instantly. This ensures the forecast responds exceptionally fast to real-time atmospheric changes, providing your end-users with a reliable, up-to-the-minute weather experience.

5. Continuously verifying our performance and training our machine learning models to improve accuracy

Our premium forecast quality is not static; it is constantly evolving. Continuous forecast verification is the secret to our consistent outperformance of competing service providers. We non-stop monitor and compare our outgoing forecasts against the newest real-world observations to see exactly how we performed.

This rigorous, automated quality assurance process serves a vital purpose: it continuously trains our machine-learning algorithms. By learning from every single forecast and actual weather event, our engine automatically identifies systematic biases and removes errors. As new data becomes available, or we identify new patterns and trends, our algorithms fine-tune themselves, resulting in more precise forecasts over time. Thanks to this feedback loop, and the possibility add, test, and curate new data sources, our forecasting accuracy improves day by day.

6. Delivering the ready-to-use data through an API optimized for consumer-facing services

TWe handle all the complex data aggregation, machine learning, and meteorological heavy lifting so your development team does not have to. The end result of this meticulous scientific process is highly accurate, fully refined, and completely ready-to-use global weather data.

We make this premium intelligence available through our fast, developer-friendly Weather API. Designed specifically to support consumer-facing services, our API scales effortlessly and is easy to implement. You can trust that you are powering your applications with the highest quality data on the market, leaving you free to focus on driving engagement and building a profitable business.

Our Data Sources

High-quality forecasts start with high-quality raw materials. We aggregate an curate scattered weather and environmental data globally, from each country and each provider.

Foreca seeks to use all the data sources fit for a particular forecasting point/territory. The choice of sources strongly depends on data reliability, stability of data flow from sources and various other factors which are part of a complex data selection algorithm Foreca employs. In total, Foreca gathers and fuses 50+ different data sources world-wide.

A non-exhaustive sample of incoming weather data providers:

• European Centre for Medium-Range Weather Forecasts ECMWF
• Europe’s meteorological satellite agency EUMETSAT
• Copernicus Climate Change Service C3S
• National Oceanic and Atmospheric Administration NOAA (United States)
• Met Office (United Kingdom)
• Deutscher Wetterdienst DWD (Germany)
• Météo-France (France)
• Zentralanstalt für Meteorologie und Geodynamik ZAMG (Austria)
• Finnish Meteorological Institute FMI (Finland)
• Japan Meteorological Business Support Center JMBSC (Japan)
• Koninklijk Nederlands Meteorologisch Instituut KNMI (Netherlands)
• Korea Meteorological Administration KMA (Korea)
• Agencia Estatal de Meteorología AEMET (Spain)
• Sveriges meteorologiska och hydrologiska institut SMHI (Sweden)
• Central Weather Administration CWA (Taiwan)

Foreca has integrated over a dozen different weather models into its system. To put it simply, Foreca seeks to provide the best possible forecasting output for a forecasting territory, whether an in-house model output or some 3rd party solution, including government services. The choice was made after thorough comparisons of data sources and constant sensing of latest offerings available in the field of meteorology. The data selection process is constant, and Foreca continuously adds new data sources, sometimes dropping old ones, if they no longer perform up to expectations.

It is also possible to integrate sensor data from cars or private stations into the Foreca prediction system. Though Foreca has the capacity and successful history of assimilating data from private stations, they are usually part of customised services and the standard global offering does not include them. Adding private weather stations to the service is subject to separate commercial and technical discussions.