Density Altitude in FlightSpan

Beginning with 8.6.7, FlightSpan obtains local pressure and temperature information from an online source (

Here’s how it works:

1. When you create your route for the day (or pull it down from a flight schedule), FlightSpan will collect the current pressure and temperature for all locations in your route as well as the next 24 hours worth of hourly forecast for temperature and pressure for those locations.’s API allows us to simply send them the coordinates for a location and their numerical weather prediction model provides data for that location even if the location doesn’t have a weather reporting station (see footnote below if you’re interested.) Bottom line is that you should get a reasonable estimate of pressure and temp even at locations that don’t have weather reporting.

2. When you load the Performance tab (for either takeoff or landing), the app will again check to see if the iPad is online, and if so, attempt to collect the current data plus the next 24 hours—if we successfully access current data, we’ll feed that into the appropriate fields on the performance tab—if not, we’ll use the applicable hourly forecast temp and pressure that we stored in step 1.

3. If you create your route, or add locations to an existing route, when you are out of internet coverage, FlightSpan will first look for and use valid weather data from the point closest to you that was stored in FlightSpan in step 1. FlightSpan will extrapolate pressure and temperature from the point found in our data and the point you are adding based on the difference in altitude between the two points. If FlightSpan can’t find valid data stored locally for a location close to you, then it will revert to a formula that will produce a conservative DA (generally close for operations in the tropics and roughly equivalent to a hot summer day in the US).

Remember: pilots need to verify all current conditions (surface, wind, oat and pressure altitude) to get the most accurate takeoff and landing roll estimates.

Openweather NWP model

Openweather NWP (numerical weather prediction) allows us to calculate weather data for any location. We use proprietary convolutional neural network that collects and processes wide range of data sources to cover any location and consider the local nuances of climate. ML technology allows us to reach resolution about 500 m and very high accuracy between 90% and 100% with inaccuracy about 1%. Amongst data sources we feed to the NWP are 82,000 weather stations spread globally; national meteorological agencies (NOAA, Environment Canada, Met Office, etc.), radars, weather satellites.