# PLidar_Auto_Analysis

**Repository Path**: mualidar/PLidar_Auto_Analysis

## Basic Information

- **Project Name**: PLidar_Auto_Analysis
- **Description**: MATLAB scripts to analyze Polarization lidar data automatically.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 3
- **Forks**: 0
- **Created**: 2020-03-19
- **Last Updated**: 2025-12-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Automatic PLidar Analyzer

This repository provides the MATLAB scripts to calibrate the [MUA Polarization lidar system][1] and retrieve profiles without cloud contamination in an automatic way.

## Requirements

- MATLAB (>= 2016b)
- [JDBC][2] (Java Database connector)

> JDBC is required to manipulate the SQLite database. Find detailed information [here][3], regarding how to setup the connector for MATLAB.

## Download

```bash
git clone https://gitee.com/mualidar/PLidar_Auto_Analysis.git --recurse-submodules
cd PLidar_Auto_Analysis
```

## Run

### Add paths

```matlab
cd /path/to/PLidar_Auto_Analysis
setupToolbox;
```

### Edit configuration file

Before executing the program, the [`settings.yml`](./config/settings.yml) needs to be edited. If you are not a expert user, you only need to change the configurations about the paths. See below:

```yaml
lidar_data_folder: './PLidar_Auto_Analysis/data/PLidar'
GDAS1_folder: './PLidar_Auto_Analysis/data/GDAS1'
output_folder: './PLidar_Auto_Analysis/results/profiles'
log_folder: './PLidar_Auto_Analysis/log'
lcDBFile: './PLidar_Auto_Analysis/results/lc.db'
```

And the temporal range that you're interested in, see below:

```yaml
start_time: '2019-11-24 00:00:00'   # year month day hour minute second
stop_time: '2019-11-24 23:00:00'   # year month day hour minute second
```

### Start processing

```matlab
PLidar_auto_analysis('/path/to/config_file')   % calibrate the lidar system and output cloud free profiles
PLidar_target_classification('/path/to/config_file')   % detect aerosol/cloud bits
```

## Products

### lidar calibration constants

After running the scripts as above, the lidar calibration constants will be calculated and saved to SQLite database as you configured in [`settings.yml`](./lib/config/global_config_auto_analysis.yml).

<p align='center'>
<img src='./img/lidar_calibration.png', width=300, height=300, lat='lc'>
<br>
<b>table of lidar calibration constants</b>

### aerosol profiles

The scripts can detect continuous profiles without cloud contamination and calculate the backscatter coefficient and particle depolarization ratio profiles with [Fernald][4] method, in which the reference height is determined automatically by [Rayleigh fit][5] algorithm. The retrieved results will be saved to [HDF5][6] file, as listed below:

<p align='center'>
<img src='./img/products.png', width=500, height=300, lat='aerosol_profile'>
<br>
<b>Aerosol profile retrieved with Fernald method</b>

### target classification

The [target classification algorithm](docs/intro_target_classification.md) can be used to determine 7 targets for both aerosol and clouds. See an example as below.

<p align='center'>
<img src='./img/target_classification.png', width=550, height=280, lat='tc'>
<br>
<b>Target classification plot</b>

The results will be saved to [HDF5][6] files and can be used for further analysis.

<p align='center'>
<img src='./img/target_classification_products.png', width=500, height=180, lat='tc_prod'>
<br>
<b>Target classification products</b>

## Related Publications

1. Yin, Z., Yi, F., Liu, F., He, Y., Zhang, Y., Yu, C., & Zhang, Y. (2021). Long-term variations of aerosol optical properties over Wuhan with polarization lidar. Atmospheric Environment, 259, 118508. doi:https://doi.org/10.1016/j.atmosenv.2021.118508
2. Jing, D., He, Y., Yin, Z., Liu, F., & Yi, F. (2024). Long-term characteristics of dust aerosols over central China from 2010 to 2020 observed with polarization lidar. Atmospheric Research, 297, 107129. doi:https://doi.org/10.1016/j.atmosres.2023.107129
3. He, Y., Jing, D., Liu, F., and Yin, Z.: Declining trend of dust aerosols over central China observed with polarization lidar during 2010-2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2273, https://doi.org/10.5194/egusphere-egu24-2273, 2024.
4. He, Y., Jing, D., Yin, Z., Ohneiser, K., and Yi, F.: Long-term (2010–2021) lidar observations of stratospheric aerosols at Wuhan, China, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1611, 2024.

## Contact

Zhenping Yin <zp.yin@whu.edu.cn>

[1]: http://dx.doi.org/10.1002/2015JD023248
[2]: https://bitbucket.org/xerial/sqlite-jdbc/downloads/
[3]: https://ww2.mathworks.cn/help/database/ug/sqlite.html
[4]: https://www.osapublishing.org/ao/abstract.cfm?uri=ao-23-5-652
[5]: https://www.atmos-chem-phys.net/16/5111/2016/
[6]: https://support.hdfgroup.org/HDF5/