7.2 Getting Weather Data

Overview

In our trajectory computation, we set our wind values to zero for each sector, but left room in the functionality to include wind as an input. In our project specs we mentioned that we want to use the 'Real World Weather' option in X-Plane. In order to match the flight plan to the X-Plane environment, we have three options:

1. parse out weather data that X-Plane is using

2. create my own METAR and upperwinds weather files in X-Plane 11 format, and load them into the simulator (X-Plane allows this) 

3. get weather from an outside party and see if it matches 

I decided to try option 3, because I think it will require the least amount of work. We aren't looking for perfection; we just want a decent match between the forecast weather and what we see in the simulator.  I should tell you, however, that X-Plane 11 documentation warns against doing this:

But we are going to try it anyway because I want to see what happens. 

The Open-Meteo Weather API 

Globally, there are several institutions which run sophisticated weather forecasting systems. Examples include the NOAA  and the UK Met Office. These institutions generate tons of detailed weather data. The access methods to this data, as well as the structure and format of it, can be very complex. To make life easier, 3rd party services exist which offer simplified interfaces to the weather data. 

After doing some shopping, I found a service provider called Open-Meteo which offers what I am looking for. From their website:

Per the website:

• there's a free tier that offers up to 10,000 calls per day

• upper level wind forecasts are available 

• forecasts are updated hourly 

• there is an easy-to-use Python library to interface with the service 

Coding

We are going to code a very simple process as an experiment. If the results look promising we can always come back and make improvements. After installing the open-meteo python library, we take the following steps. 

Compute Centroids of Each Sector

First, calculate the centroid (centre coordinates) of each sector:

Here is the printout:

Prepare API Call

Next, prepare the call to the API. Note in the call we are requesting data for 7 points, i.e. the centroid of each sector:

Process the Response

There are 7 responses to process, one for each sector. We will process them one by one. The reasoning behind each step in the process is explained in the code comments. 

Here is the weather dataframe for sector 0; we've highlighted the first row, which corresponds to the forecast wind valid about 30 minutes from the time of the request:

And here is the summarized result for all sectors:

This list is in the format the the trajectory compute is expecting, so it can be used as-is without further modification. 

Remarks

1. Note that in the API call, we did not request the winds at 30,000 ft MSL; the altitude was approximated by asking for the weather forecast at 300 hPa. If you squint closely at the following diagram of the International Standard Atmosphere, you will see that 300 hPa corresponds to a standard altitude of about 30,000 ft MSL (about 10km). This figure is close enough for our weather experiment. 

2. It would definitely be possible to use the entire planned flight path (lat, lon, alt) in our call to the weather API, instead of just requesting data for the sector centroid coordinates; however, as with Remark 1, I just wanted to keep it simple to get started.