RaProM is a novel MRR processing methodology, with enhanced spectra processing and Doppler dealiasing, that produces as output data a number of fields which include equivalent reflectivity (Ze), Doppler fall speed and derived parameters such as spectral width, skewness, and kurtosis, plus a simplified precipitation type classification (drizzle, rain, mixed, snow, and hail), and additional variables depending on the precipitation type. Note: the scripts works for MRR-2.
More information at: Garcia-Benadí et al (2020) https://doi.org/10.3390/rs12244113
The main script is called RaProM.py and it is avalaible in python 2.7. and 3.8. The following libraries are necessary::
numpy , version 1.14.5 or later
miepython, version 1.3.0 or later
netCDF4, version 1.2.7 or later
The script works with the MRR raw archives.
If you use this script for your publication, please cite as:
Garcia-Benadi, A.; Bech, J.; Gonzalez, S.; Udina, M.; Codina, B.; Georgis, J.-F. Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology. Remote Sens. 2020, 12, 4113.DOI: 10.3390/rs12244113
The script produces the following outputs from MRR raw data:
W: fall speed with aliasing correction (m s-1)
spectral width: spectral width of the dealiased velocity distribution (m s-1)
skewness: skewness of the dealiased velocity distribution
kurtosis: kurtosis of the dealiased velocity distribution
PIA: Path Integrated Attenuation
Type: Hydrometeor type (unknown[20], rain [10], drizzle [5], snow [-10], mixed [-15] and hail [-20])
LWC: Liquid water content (g m-3)
RR: Rain rate (mm h-1)
SR: Snow rate (mm h-1)
Z: Reflectivity considering only liquid drops (dBZ)
Ze: Equivalent Reflectivity (dBZ)
N(D): Drop Size Distribution (log10(m-3 mm-1))
SNR: Signal noise relation from signal without deliasing (dB)
Noise: Noise from spectra reflectivity (m-1)
Nw: Intercept of the gamma distribution normalized to the liquid water content (log10(m-3 mm-1))
Dm: Mean mass-wighted raindrop diameter (mm)
BBbottom: Bright Band bottom height (m) (above MRR level)
BBtop: Bright Band top height (m) (above MRR level)
TyPrecipi: Rainfall type where the value 5 is convective, 0 is transition and -5 is stratiform"
The script can be executed from a command line at the system prompt (see MS-Windows example):
at the directory where RaProM_XX.py has been copied, where XX is 27 or 38 in function of your python version:
python RaProM_XX.py
The script asks the directory where the raw files to be processed are located (it will process all the MRR raw files of the folder selected), for example:
c:\mrrdata\test\
NOTE: the path must end with \ in Windows or a / in Linux
The script asks for the integration time (in seconds, usually 60)
The script indicates the number of raw files in the folder and starts the process.
The result is stored in a netcdf file with the same name but finished "-processed"
If you have any question, please contact with Albert at albert.garcia@meteo.ub.edu or albert.garcia-benadi@upc.edu