What is ECMWF weather forecast model and how it works

What is ECMWF weather forecast model and how it works

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The ECMWF is one of two major global weather forecast models in the world, on a par with the GFS, which originates from the US, yet the first one is considered the most reliable among all models. In other words, if right now you're looking at the weather forecast for the world or your city or region, it's probably from one of these two models.

In this post, you will learn about ECMWF, its working principle and the main features, and where to find the weather forecast by this model.

What is ECMWF weather forecast model?

The ECMWF (European Centre for Medium-Range Weather Forecasts) is a European global forecast seamless model. It is widely regarded as the best and most reliable model currently in existence.

It uses a concept called 4D, which is assimilation that allows the model to be constantly updated as new satellite or other input data becomes available.

It is a well-known fact that the ECMWF was the only model that accurately predicted where hurricane Sandy 2012 was moving.

The official website of the model is ecmwf.int.

What are the main features of ECMWF weather model and how it works?

The ECMWF model has various resolutions up to tens of km. The resolution is the distance between two points of the weather model grid. Bigger resolutions of 50 to 10 km in size are usually deployed in relatively flat terrains, while mountain ranges require the nodes to be a lot closer to each other, usually 5, 2, or 1 km.

The forecast depth of ECMWF weather model is 10 days. The depth of the weather forecast is the number of hours or days for which the forecast is made. As a rule, the lower the depth, the more accurate the forecast.

The ECMWF forecast step is 3 hours. The forecast step is for how long you can see a forecast in the application or on a website. There are two main step types: 1 h and 3 h.

The ECMWF updates frequency is 2 times a day. The update frequency of the forecast is the regular time interval after which new forecast data is received from the supercomputers. There are models like HRRR with 24 times a day updates, but in general, the frequency of the updates of weather models is 1, 2, or rather 4 times a day.

Let's summ up the main features of ECMWF model:

  • Resolution: various (up to tens of km)
  • Forecast depth: 10 days
  • Step: 3 hours
  • Updates frequency: 2 times/day

Where to get the weather forecast from the ECMWF model in Windy.app?

The ECMWF weather forecast model is also the main model in Windy.app among 10+ other models. In the app, the model has a 14 km resolution. To get a weather forecast:

1. Open your favorite or the nearest spot to your current location in the app from the Home screen or Wind and precipitation map. To see the nearest spots, allow the app to see your location in Settings.

 

 

2. Choose ECMWF weather model in the slider under the Wind rose:

 

3. Read the weather forecast from the ECMWF for your particular sport or outdoor activity. In Windy.app you can get the following weather parameters:

  • Wind speed
  • Wind gusts
  • Wind direction (both cardinal and degrees)
  • Air temperature
  • Clouds
  • Precipitation: rain, snow, hail and other

 

In addition, you can have the Rain and show chart with the data from ECMWF:

 

4. Choose or delete one of these weather parameters using the Customization feature. That how you will make your own custom weather profile in Windy.app:

 

 

5. Compare weather data from ECMWF model to other models in Windy.app using special Pro feature Compare mode.

 

6. Compare ECMWF weather forecast accuracy with other weather models thanks to special Windy.app feature based on data directly from weather stations. The weather stations are always located on the same spot's screen:

 

7. Return to Wind and precipitation map to see the bigger wind picture in the region from the ECMWF model.

 

Read Guide to all weather forecast models used in Windy.app and the general Guide to weather forecast models all around the world.

Cover photo: Joshua-peacock / Unsplash

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