Meteo-Particle model for wind and temperature field construction using Mode-S data
This is the Python (version 3) library for wind field estimation based on the Meteo-Particle particle model. The wind and temperature is computed from ADS-B and Mode-S data using pyModeS library.
- You must install
pyModeSlibrary for ADS-B and Mode-S decoding.
- You also need the following common scientific libraries:
- You may install optional
geomaglibrary, to support the correction of magnetic declination in BDS60 heading.
For a fresh install, run following commands:
$ pip install git+https://github.com/junzis/pyModeS $ pip install numpy pandas matplotlib geomag
Examples of using the model with recorded data and real-time streaming are given in
To quickly test the model out of the box, try:
$ python run-recoded.py
or if you have access to a ModeSBeast raw stream on TCP port:
$ python run-realtime.py --server xx.xx.xx.xx --port xxxxx
Configurable model parameters (with defaults) are:
AREA_XY = (-300, 300) # Area - xy, km AREA_Z = (0, 12) # Altitude - km GRID_BOND_XY = 20 # neighborhood xy, +/- km GRID_BOND_Z = 0.5 # neighborhood z, +/- km TEMP_Z_BUFFER = 0.2 # neighborhood z (temp), +/- km N_AC_PTCS = 300 # particles per aircraft N_MIN_PTC_TO_COMPUTE = 10 # number of particles to compute CONF_BOUND = (0.0, 1.0) # confident normalization AGING_SIGMA = 180.0 # Particle aging parameter, seconds PTC_DIST_STRENGTH_SIGMA = 30.0 # Weighting parameter - distance, km PTC_WALK_XY_SIGMA = 5.0 # Particle random walk - xy, km PTC_WALK_Z_SIGMA = 0.1 # Particle random walk - z, km PTC_VW_VARY_SIGMA = 0.0002 # Particle initialization wind variation, km/s PTC_TEMP_VARY_SIGMA = 0.1 # Particle initialization temp variation, K ACCEPT_PROB_FACTOR = 3 # Measurement acceptance probability factor PTC_WALK_K = 10 # Particle random walk factor