Products

Products#

DISDRODB transforms raw disdrometer data into standardized products through a sequential processing chain, from sensor output to physically meaningful microphysical and radar-derived quantities.

Each product has a well-defined scope, quality-control procedures, and output format. This uniform structure across all stations enables reproducible analysis and consistent downstream processing.

The processing chain is fully customizable. See the Products Configuration for more details.

DISDRODB L0A Product#

The DISDRODB L0A product converts heterogeneous raw disdrometer files into a standardized tabular dataset.

Purpose

Transform raw text files into cleaned, DISDRODB-compliant data.

Input

  • Raw disdrometer text files

  • Optional issue files defining problematic time periods

Description

A station-specific reader function (specified in metadata) processes raw files to produce a tabular dataset where each row is a measurement timestep and each column is a logged variable following DISDRODB naming conventions.

Array variables (particle spectra, velocity-diameter distributions) are stored as delimited strings, later reshaped into multidimensional arrays in L0B.

Quality Control

  • Removes rows with missing or duplicated timestamps

  • Excludes periods flagged in issue files

  • Converts corrupted numeric entries to NaN

  • Enforces data types and valid ranges

  • Logs all detected issues

Output

  • L0A dataset in Apache Parquet format

  • Variables depend on sensor type; raw spectrum always included

  • Detailed processing logs

DISDRODB L0B Product#

The DISDRODB L0B product converts tabular L0A data into the netCDF4 data model.

Purpose

Provide a self-describing dataset with explicit physical dimensions and standardized metadata.

Input

  • L0A dataset

Description

The L0B processing:

  • Parses string-encoded arrays into numerical arrays

  • Constructs an xarray.Dataset with dimensions: time, diameter_bin_center, and velocity_bin_center (when available)

  • Adds bin centers and bounds for diameter and velocity

  • Attaches station geolocation (longitude, latitude, altitude)

Metadata

  • Climate and Forecast (CF) compliant variable attributes

  • Attribute Convention for Data Discovery (ACDD) global attributes

  • Optimized NetCDF encodings to minimize disk usage

Output

  • NetCDF4 files suitable for scientific analysis

  • Variables depend on sensor type; raw spectrum always included

DISDRODB L0C Product#

The DISDRODB L0C product ensures temporal consistency and consolidates L0B files into fixed-period outputs (daily by default; configurable as weekly or monthly).

Purpose

Create time-consistent datasets with fixed measurement intervals, unique timesteps, and standardized file grouping.

Description

The L0C processing:

  • Removes duplicated timesteps from file concatenation

  • Discards measurements with inconsistent intervals

  • Separates data into distinct datasets if multiple measurement intervals exist

  • Corrects small timestamp drifts to exact interval boundaries

  • Stores the verified measurement interval as a coordinate

Quality Control

  • Computes qc_time to assess temporal continuity

  • Logs irregular sampling patterns and intermittent measurements

Output

  • Time-consistent L0C datasets grouped by fixed periods

  • Variables depend on sensor type; raw spectrum always included

For configuration options, see DISDRODB L0C Product Configuration.

DISDRODB L1 Product#

The DISDRODB L1 product aggregates observations at multiple temporal resolutions and performs hydrometeor classification. Starting from the DISDRODB L1 product, all stations have the same variables and data structure. The DISDRODB L1 product serves as a common foundation for existing and future DISDRODB L2 products.

Temporal Resampling#

Purpose

Aggregate particle spectra and auxiliary variables to user-defined temporal resolutions.

Features

  • Fixed-interval and rolling-window aggregation

  • Typical resolutions: 1, 5, and 10 minutes

  • Rolling windows reduce data loss and increase sample density

Quality Control

  • qc_resampling reports the fraction of missing data within each window

Hydrometeor Classification#

Purpose

Identify the dominant hydrometeor type and precipitation phase at each timestep.

Description

  • Operates on the diameter-velocity particle spectrum

  • Applies sensor-specific noise filtering

  • Uses physically based size-velocity masks

  • Adjusts fall-velocity relationships for air density (altitude)

  • Optionally refines classification using temperature

Output

  • Hydrometeor and precipitation-type labels

  • Classification-related quality-control flags

For configuration options, see DISDRODB L1 Product Configuration.

DISDRODB L2E Product (Empirical)#

The DISDRODB L2E product derives microphysical parameters and radar observables directly from observed particle spectra. Currently, L2E provides geophysical quantities for rainfall observations only. The default DISDRODB L2E configuration process only timesteps with precipitation, resulting in temporally discontinuous data.

Purpose

Compute integral drop size distribution (DSD) parameters and simulate polarimetric radar variables for rainfall.

Description

  • Selects liquid precipitation timesteps

  • Filters particles by diameter and fall-velocity

  • Estimates drop number concentration

  • Computes DSD moments and rainfall variables

  • Simulates polarimetric radar observables via T-matrix

Customization

  • User-defined thresholds on minimum particle counts, populated bins, and rain rate

  • User-defined spectrum filtering criteria

For configuration options, see DISDRODBL2E Product Configuration.

DISDRODB L2M Product (Modelled)#

The DISDRODB L2M product fits parametric DSD models to observed drop number concentrations from L2E and derives microphysical and radar variables from the fitted distributions. The default DISDRODB L2E configuration process only timesteps with precipitation, resulting in temporally discontinuous data.

Purpose

Support microphysical studies, radar retrieval development, and model evaluation.

Description

  • Fits multiple parametric DSD models (lognormal, exponential, gamma, generalized gamma, and normalized variants)

  • Supports grid search, maximum likelihood, and method-of-moments estimation

  • Computes goodness-of-fit diagnostics

  • Derives integral DSD parameters and radar observables from modeled DSDs

Applications

  • Evaluation of bulk microphysics parameterizations

  • Development and validation of radar-based DSD retrievals

For configuration options, see DISDRODB L2M Product Configuration and L2M Models Configuration.

Polarimetric Radar Variables#

Polarimetric radar variables are simulated using electromagnetic scattering calculations based on the T-matrix method. pytmatrix must be installed to enable radar simulations (see pytmatrix installation).

For configuration options, see DISDRODB Radar Configuration Options.

Features

  • Compatible with L2E (empirical) and L2M (modeled) products

  • Simulates reflectivity, attenuation, phase, and polarimetric variables

  • Flexible configuration of radar and microphysical assumptions

  • Parallelized execution with caching for efficiency