mrrpropy processing workflow

Data flow

Load a raw MRR-PRO NetCDF file with MRRProData.from_file().
Run or load the RaProMPro product.
Inspect processed fields such as Ze, Za, DBPIA, Dm, Nw, LWC and RR.
Use the plotting and analysis methods on the same object.

Processed outputs commonly used in analysis

Ze: equivalent reflectivity corrected by PIA for drizzle/rain.
Zea: equivalent attenuated reflectivity.
DBPIA: path-integrated attenuation in dB.
Dm, Nw, LWC, RR: microphysical variables used downstream by the analysis API.

Representative workflow

processed = mrr.process_raprompro(
    save=False,
    save_spe_3d=False,
    save_dsd_3d=False,
)

fig, ax = mrr.quicklook(variable="Ze", source="raprompro", vmin=0, vmax=40)

Microphysics workflow

The default layer-trend workflow is non-parametric: Kendall's tau captures the direction and monotonic consistency of the vertical change, while the Theil-Sen slope captures its robust physical magnitude.

from datetime import datetime

analysis = mrr.rain_process_analyze(
    period=(datetime(2025, 3, 8, 12, 0, 0), datetime(2025, 3, 8, 12, 10, 0)),
    layer=(1000.0, 2000.0),
    k=11,
    ze_th=-5.0,
    trend_method="kendall_theilsen",
    tau_zero_tol=0.05,
    min_points_trend=10,
)

classified = mrr.classify_rain_process(
    analysis=analysis,
    min_tau_strength=0.10,
)

The downstream RGB mapping and classification consume canonical trend_* variables, so the method can still be swapped to trend_method="ols" for legacy or diagnostic comparison.

Scientific reference and provenance

The package exposes the RaProMPro processing chain through a higher-level Python API, but the repository still preserves the original scientific implementation. Source-code attribution in those retained modules points to Albert García Benedí as the original author of that implementation.

The main MRR-PRO reference for this processing chain is: García-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 Sensing. 2021;13(21):4323. https://doi.org/10.3390/rs13214323.

The repository implementation is specific to MRR-PRO data. A related code base for MRR-2 data is distributed separately as RaProM.py; see: García-Benadí A, Bech J, Gonzalez S, Udina M, Codina B, Georgis JF. Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology. Remote Sensing. 2020;12(24):4113. https://doi.org/10.3390/rs12244113.

Original code repository: https://github.com/AlbertGBena/RaProM-Pro.

Open references page