Sensor Configurations

Sensor Configurations#

DISDRODB tailors the processing of disdrometer measurements based on instrument type and characteristics.

Currently Supported Sensors:

DISDRODB can currently process data from:

  • Distromet RD-80 (RD80)

  • OTT Parsivel (PARSIVEL)

  • OTT Parsivel2 (PARSIVEL2)

  • Thies Laser Precipitation Monitor (LPM)

  • Campbell Present Weather Sensor 100 (PWS100)

  • Eigenbrot Optical Disdrometer 470 (ODM470),

  • Biral Visibility and Present Weather Sensors (SWS250).

List Available Sensor Configurations

To view all available sensor configurations, use:

import disdrodb

disdrodb.available_sensor_names()

Configuration File Structure

Sensor configurations are stored in the disdrodb.l0.configs directory. Each sensor has its own subdirectory named after the sensor_name.

Required Configuration Files

For each sensor, the following configuration YAML files are required:

📁 disdrodb/
├── 📁 l0 : Contains the software to produce DISDRODB L0 products
├── 📁 configs : Contains specifications for various disdrometer types
├── 📁 <sensor_name> : e.g., PARSIVEL, PARSIVEL2, LPM, RD80
├── 📜 bins_diameter.yml : Diameter bin specifications
├── 📜 bins_velocity.yml : Velocity bin specifications
├── 📜 raw_data_format.yml : Variables logged by the sensor
├── 📜 l0a_encodings.yml : Variable encodings for L0A product
├── 📜 l0b_encodings.yml : Variable encodings for L0B product
├── 📜 l0b_cf_attrs.yml : CF attributes for L0B product variables

Adding a New Sensor Configuration To add a new sensor configuration:

  1. Copy the YAML files from an existing sensor directory

  2. Adapt them to your sensor’s specifications

  3. Validate the configuration using the code below:

from disdrodb.l0.check_configs import check_sensor_configs

sensor_name = "PARSIVEL"  # Replace with your sensor_name
check_sensor_configs(sensor_name)

Configuration File Details

The following sections provide detailed information about each configuration YAML file.

Sensor Diameter Bins#

The bins_diameter.yml file specifies drop diameter bins.

Required Fields:

For each bin, define:

  • center: Bin center value

  • width: Bin width

  • bounds: Lower and upper bin boundaries

Sensor Velocity Bins#

The bins_velocity.yml file specifies drop fall velocity bins.

Required Fields:

For each bin, define:

  • center: Bin center value

  • width: Bin width

  • bounds: Lower and upper bin boundaries

Note: If the sensor (e.g., an impact disdrometer) does not measure fall velocity, leave this file empty.

Sensor Logged Variables#

The raw_data_format.yml file contains numeric information about each sensor variable.

Required Fields:

For each variable, specify:

  • n_digits: Number of digits logged (including sign if present, excluding decimal point)

  • n_characters: Total characters (digits, decimal point, and sign)

  • n_decimals: Number of decimal digits (right of the decimal point)

  • n_naturals: Number of natural digits (left of the decimal point)

  • data_range: Valid data range for the variable

  • nan_flags: Values indicating missing or invalid data

  • field_number: Field number in the sensor documentation

Note: Use null for character variables or when a value cannot be specified.

Usage During L0 Processing:

During DISDRODB L0 processing:

  • If data_range is specified, values outside this range are set to NaN

  • If nan_flags are specified, these values are converted to NaN

Header Inference:

The n_digits, n_characters, n_decimals, and n_naturals information is used to infer raw file headers when they are unknown. See the infer_column_names function usage in the reader_preparation.ipynb Jupyter Notebook.

Multi-Dimensional Variables:

For variables with values that depend on more than just time (e.g., precipitation spectra), add two additional keys:

  • n_values: Total number of variable values in the array

  • dimension_order: Order for reshaping the flattened array into a multi-dimensional matrix

Examples:

The n_values key corresponds to the total number of variable values in the array.

Example 1: OTT PARSIVEL precipitation spectrum

  • 32 diameter bins × 32 velocity bins = 1024 total values

  • n_values = 1024

Dimension Order:

The dimension_order controls how the flattened precipitation spectrum array is reshaped into a 2D matrix.

Example 1: OTT PARSIVEL

The OTT PARSIVEL logs the precipitation spectrum by providing drop counts for each diameter bin within velocity bin 1, then velocity bin 2, and so on.

  • Flattened array: [v1d1 ... v1d32, v2d1, ..., v2d32, ...]

  • dimension_order = ["velocity_bin_center", "diameter_bin_center"]

Example 2: Thies LPM

The Thies LPM logs the precipitation spectrum by providing drop counts for each velocity bin within diameter bin 1, then diameter bin 2, and so on.

  • Flattened array: [v1d1 ... v20d1, v1d2, ..., v20d2, ...]

  • dimension_order = ["diameter_bin_center", "velocity_bin_center"]

DISDRODB L0B Variable Attributes#

The l0b_cf_attrs.yml file specifies Climate and Forecast (CF) convention attributes for L0B NetCDF variables.

Important Notes:

  • Variables listed here must be a subset of those defined in raw_data_format.yml

  • Only variables listed here are referenced in other l0*.yml configuration files

Required Fields:

For each variable, provide:

  • long_name: Descriptive name following CF conventions

  • units: Units of measurement following CF conventions

  • description: Detailed description of the variable

Resources:

For more information, consult the Climate and Forecast Conventions guidelines:

DISDRODB L0A Encodings#

The l0a_encodings.yml file specifies which variables are saved in the L0A Apache Parquet format and defines each variable’s data type.

Automatically Included Variables:

The following variables are always included in L0A products:

  • time: Timestamp column (in UTC)

  • latitude and longitude: Geolocation columns (if the disdrometer station is mobile)

DISDRODB L0B Encodings#

The l0b_encodings.yml file specifies encodings for variables saved in the L0B NetCDF4 format.

Required Specifications:

For each variable, you must specify:

  • Compression: Compression method and level

  • Data type: NetCDF data type (e.g., float32, int16)

  • _FillValue: Fill value used for NaN-to-integer conversion

  • Chunking: Chunk size across time (and diameter/velocity) dimensions

These specifications are used to define the NetCDF4 encodings for each variable, optimizing storage and access performance.