https://github.com/junzis/meteo-particle-model
Weather (wind and temperature) field reconstruction based on meteo-particle model using ADS-B and Mode-S data
https://github.com/junzis/meteo-particle-model
ads-b adsb meterology mode-s modes wind
Last synced: over 1 year ago
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Weather (wind and temperature) field reconstruction based on meteo-particle model using ADS-B and Mode-S data
- Host: GitHub
- URL: https://github.com/junzis/meteo-particle-model
- Owner: junzis
- License: gpl-3.0
- Created: 2017-10-04T13:45:07.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2021-07-05T14:42:57.000Z (almost 5 years ago)
- Last Synced: 2025-03-18T05:11:44.736Z (over 1 year ago)
- Topics: ads-b, adsb, meterology, mode-s, modes, wind
- Language: Python
- Homepage:
- Size: 71.2 MB
- Stars: 33
- Watchers: 7
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Meteo-Particle model for wind and temperature field construction using Mode-S data
This repository is a Python library for wind field estimation based on the Meteo-Particle particle model. The wind and temperature are computed from ADS-B and Mode-S data using the pyModeS library.
This tool was developed together with [a research paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205029) by Junzi Sun and his colleagues at TU Delft CNA/ATM research group. If you find it useful for your research, please consider citing it as:
```
@article{sun2018mp,
title={Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model},
author={Sun, Junzi and V{\^u}, Huy and Ellerbroek, Joost and Hoekstra, Jacco M},
journal={PloS one},
volume={13},
number={10},
pages={e0205029},
year={2018},
}
```
---
## Dependent libraries
1. You need to install [`pyModeS`](https://github.com/junzis/pyModeS) library for ADS-B and Mode-S decoding.
2. You also need to install the following common scientific libraries: `numpy`, `pandas`, and `matplotlib`.
3. You may install optional `geomag` library, to support the correction of magnetic declination in BDS60 heading.
For a fresh install, run following commands:
```shell
$ pip install git+https://github.com/junzis/pyModeS
$ pip install numpy pandas matplotlib geomag
```
## Code examples
Examples of using the model with recorded data and real-time streaming are given in `run-recoded.py` and `run-realtime.py` file.
To quickly test the model out of the box, try:
```shell
$ python run-recoded.py
```
or if you have access to a ModeSBeast raw stream on TCP port:
```shell
$ python run-realtime.py --server xx.xx.xx.xx --port xxxxx
```
Configurable model parameters (with defaults) are:
```python
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
```
---
## Plots
One minute simulation:
Wind field from the sample dataset (snapshot at 01/01/2018 09:02 UTC)
Temperature field from the sample dataset (snapshot at 01/01/2018 09:02 UTC)
