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. MRR stands for Micro Rain Radar, a Doppler vertically pointing radar manufactured by Metek GmbH. A description of the RaProM processing and examples is available at
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: http://dx.doi.or/10.3390/rs12244113
Note1: the scripts are designed to work with MRR-2.
Note2: a different version similar to RaProM, called RaProM-Pro, is available for MRR-Pro files. More information on RaProM-Pro is available at:
Garcia-Benadí A, Bech J, Gonzalez S, Udina M, Codina B. A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations. Remote Sens. 2021, 13, 4323. https://doi.org/10.3390/rs13214323
An additional Python script CorrecRawFile is also included. If you know that your raw file has writing errors (temporal inconsistencies), please execute this file before RaProM. You can execute this file from the command window or idle.
More information at: Garcia-Benadí et al (2020) https://doi.org/10.3390/rs12244113
The main script is called RaProM.py and it is available in python 2.7. and 3.8. The following libraries are necessary:
numpy , version 1.14.5 or later until 1.19.
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 only for liquid type
PIA_all: Path Integrated Attenuation without the hydrometeor type consideration
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)
Za: Attenuated 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 dealiasing (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-weighted 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
Notice that PIA and PIA_all have 1 height bin more, because the first element is at 0 m height a.g.l. imposed by the manufacturer
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 possible command line argument available is: -hxxxx.
-hxxxx: forces the antenna height is at xxx meters above sea level (for example -h100.8 would mean the antenna is at 100.8 m above sea level). This parameter is important to determine the hydrometeor terminal speed in function of height.
For example, to set that the antenna height is at 100.8 m above sea level, the syntax is:
python RaProM_XX.py -h100.8
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 1: the path must end with \ in Windows or a / in Linux
NOTE 2: Be careful to not have spaces and special characters in your file path
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 so, your RAW files may be corrupted. There is a new script for this called CorrecRawFiles-py_XX.py . This script analyses every line in the original RAW file and fixes it. If errors are found, a new file with the same name but finished as -corrected will be created. To execute the script follow the same steps described above.
If you have any question, please contact with Albert at albert.garcia@meteo.ub.edu or albert.garcia-benadi@upc.edu