ZJU_AERO (Accurate and Efficient Radar Operator)

Supports state-of-the-art simulations of dual-polarimetric radar observable varibales.

• Developers and contributors

Features

1. Non-spherical Hydrometeor Particle Shapes: Including spheroid, hexagonal column, superformula snowflake with different shape parameters... Nonspherical and inhomogeneous particle optical data base are computed with invariant-imbedding T-matrix.
2. Orientation Perference: Solid particle orientation perference data stems from snow camera observation (MASC)
3. New Mass-Diameter Scheme: Adapted for non-spherical shape hydrometeor database.
4. Beam Broadening: Implemented by beam-sampling method.
5. Beam Bending: Use online beam propogation method, which accounts for atmosphere refraction index derived from moisture and temperature.
6. (Partial) Terrain Block: Terrian Blocking Effect can be simulated with ZJU_AERO by beam-sampling and propogation algorithm.
7. Path Attenuation: ZJU_AERO can simulate the attenuation effect in the path of radar beam.
8. Spaceborne and Ground-Based Radar In One Operator: Users can apply ZJU-AERO for spaceborne radar (like GPM-DPR) simulation and ground based radar simulation.

References:

• Bi, Lei, and Ping Yang. "Accurate simulation of the optical properties of atmospheric ice crystals with the invariant imbedding T-matrix method." Journal of Quantitative Spectroscopy and Radiative Transfer 138 (2014): 17-35.
• Garrett, Timothy J., et al. "Orientations and aspect ratios of falling snow." Geophysical Research Letters 42.11 (2015): 4617-4622.

Install From Release Package

Currently ZJU_AERO only supports installing from release package. The more user-friendly method of conda package installation will be available soon.

1. Install GCC

First check you have GCC installed on your machine. It is mandatory and should be used together with SWIG to build C-extentions for ZJU_AERO.

    $gcc -v

If not, try install it with:

    $sudo apt-get install gcc

2. Install SWIG

SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages, like python. For more information on this topic, please view http://www.swig.org/.
ZJU_AERO builds C extensions with SWIG, to speed up the radar operator simulations. So check the SWIG version on your machine.

    $swig -version

Compilation tests have been performed for swig==3.x.x / 4.x.x. So if you do not have swig, or the swig on your machine is obsolete, install it with:

    $sudo apt-get install swig

3. Create Conda Environment For ZJU_AERO

We recommend Anaconda or Miniconda for environment management of ZJU_AERO:

So if you do not have anaconda or miniconda installed on your machine, install it with:

    $wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    $chmod +x Miniconda3-latest-Linux-x86_64.sh
    $./Miniconda3-latest-Linux-x86_64.sh

With Anaconda or Miniconda install, it is recommended to create a new conda environment when using ZJU_AERO or even other packages.
To create a new environment based on the environment.yml:

    $conda env create -f environment.yml

Here we list the dependencies in environment.yml. You can check it by yourself:

• numpy
• scipy
• xarray
• netcdf4-python
• h5netcdf
• h5py
• pandas
• pyproj
• matplotlib
• pyyaml
• multiprocess
• future
• basemap

To plot the simulation results, you need to install a radar ploting package, Currently ZJU_AERO supports two packages:

• arm_pyart, please see https://github.com/ARM-DOE/pyart;
• pycwr, please see https://github.com/YvZheng/pycwr;

4. Install The ZJU_AERO Packge

Install from downloaded release package ZJU_AERO_x.y.z.tar.gz with:

    $tar -zxvf ZJU_AERO_<x.y.z>.tar.gz
    $cd ZJU_AERO_<x.y.z>
    $python setup.py install

Quick Start

To get started using ZJU_AERO, you need to first get your LUT and model file right in place

• Hydrometeor Back Scattering Look-up Table
  For test cases, we place the lookup-table under the following directory:
  You can specify the db_name in option_files: microphysics - folder_lut

    ZJU_AERO_<x.y.z>/pathos/lut/<db_name>/lut_SZ_<H>_<Freq>_<Freq>_<mp_scheme>_Level<A/B/C>.nc

• NWP Model Output Including Hydrometeor 3D Fields
  ZJU_AERO supports two kinds of NWP model output: GRAPES and WRF
  You can specify the model_name in option_files: nwp - name
  Take WRF as example, we place the wrfout file for test cases under directory:

    ZJU_AERO_<x.y.z>/pathos/WRF/thompson/wrfout_xxxx.nc

• Example1: PPI Scan Simulation

  • Test Script: example/ppi.py
  • User Option File: example/option_files/example.yml
  • WRF Model File: pathos/WRF/thompson/wrfout_d02_2021-08-08_00_00_00
  • LUT for Grauel, Cloud Ice, Snow and Rain:
    • pathos/lut/tm_masc_release/lut_SZ_G_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_I_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_S_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_R_9_41_1mom_LevelB.nc

  $ cd example
  $ python ppi.py
  

  • Plotted PPI Scan Reflectivity-ZH

  • Plotted PPI Scan Differential Reflectivity-ZDR

  • Plotted PPI Scan Radial Velocity-VR

• Example2: RHI Scan Simulation

  • Test Script: example/rhi.py
  • User Option File: example/option_files/example.yml
  • WRF Model File: pathos/WRF/thompson/wrfout_d02_2021-08-08_00_00_00
  • LUT for Grauel, Cloud Ice, Snow and Rain:
    • pathos/lut/tm_masc_release/lut_SZ_G_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_I_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_S_9_41_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_R_9_41_1mom_LevelB.nc

  $ cd example
  $ python rhi.py
  

  • Plotted RHI Scan Reflectivity-ZH

  • Plotted RHI Scan Differential Reflectivity-ZDR

  • Plotted RHI Scan Radial Velocity-VR

• Example3: Spaceborne Scan Simulation

  • Test Script: example/spaceborne.py
  • User Option File: example/option_files/example_spaceborne.yml
  • WRF Model File: pathos/GRAPES/typhoon_haishen_20200905/modelvar202009050000900.nc
  • LUT for Grauel, Cloud Ice, Snow and Rain:
    • pathos/lut/tm_masc_release/lut_SZ_G_13_6_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_I_13_6_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_S_13_6_1mom_LevelB.nc
    • pathos/lut/tm_masc_release/lut_SZ_R_13_6_1mom_LevelB.nc

  $ cd example
  $ python spaceborne.py
  

  • Simulated Spaceborne Scan Reflectivity-ZH at 0km altitude

  • Observed Spaceborne Scan Reflectivity-ZH at 8km altitude

  • Simulated Spaceborne Scan Reflectivity-ZH at 0km altitude

  • Observed Spaceborne Scan Reflectivity-ZH at 8km altitude

For more Details and examples, please read User_Guide

Project development plan

• Melted Ice simulation
• Thompson Microphysics constants
• Online Documentation
• Upload ZJU_AERO as a conda package