Products Configuration

Products Configuration#

DISDRODB provides a flexible configuration system that allows full customization of the processing chain for generating its products. Each DISDRODB product (L0C, L1, L2E, L2M) can be configured globally or customized for specific sensors and temporal resolutions.

Configuration Directory Structure#

DISDRODB products configuration files are organized in a hierarchical directory structure:

products_configs_dir/
├── L0C/
│   ├── global.yaml              # Global L0C configuration
│   └── <sensor_name>/           # Optional sensor-specific configurations
│       └── global.yaml
├── L1/
│   ├── global.yaml              # Global L1 configuration
│   └── <sensor_name>/           # Optional sensor-specific configurations
│       ├── global.yaml
│       └── <temporal_res>.yaml  # Optional custom temporal resolution configurations
├── L2E/
│   ├── global.yaml              # Global L2E configuration
    └── <temporal_res>.yaml      # Global custom temporal resolution configuration
│   └── <sensor_name>/           # Optional sensor-specific configurations
│       ├── global.yaml
│       └── <temporal_res>.yaml  # Optional custom temporal resolution configurations
└── L2M/
    ├── global.yaml              # Global L2M configuration
    └── <temporal_res>.yaml      # Global custom temporal resolution configuration
    ├── <sensor_name>/           # Optional sensor-specific configs
    │   ├── global.yaml
    │   └── <temporal_res>.yaml  # Optional custom temporal resolution configurations
    └── MODELS/                  # L2M model configurations
        ├── model1.yaml
        ├── model2.yaml
        └── ...

Configuration Priority

Products options are loaded with the following priority (from lowest to highest):

  1. Global product configuration (<product>/global.yaml)

  2. Sensor-specific global configuration (<product>/<sensor_name>/global.yaml)

  3. Temporal resolution configuration (<product>/<temporal_res>.yaml)

  4. Temporal resolution sensor-specific configuration (<product>/<sensor_name>/<temporal_res>.yaml)

Higher priority configurations override lower priority ones for matching keys.

You can quickly access your products configuration directory from the terminal:

disdrodb_open_products_options

Setting Up Custom Products Configurations#

DISDRODB comes with predefined product configurations in the disdrodb/etc/products/ directory. To customize the processing chain for your data:

1. Copy the default products configurations

from disdrodb.configs import copy_default_products_configs

products_configs_dir = "/path/to/your/custom/configs"
copy_default_products_configs(products_configs_dir)

2. Set the products configuration directory

import disdrodb

products_configs_dir = "/path/to/your/custom/configs"
disdrodb.define_configs(products_configs_dir=products_configs_dir)

3. Open the products configuration directory

You can quickly access your configuration directory from the terminal:

disdrodb_open_products_options

4. Validate products options

After modifying configurations, validate them:

disdrodb_check_products_options

This command checks:

  • Directory structure validity

  • Presence of required files

  • YAML syntax correctness

  • Configuration option values

  • Temporal resolution consistency across products

  • L2M model configuration validity

Configuration Options#

Archive Options#

Archive options control how processed data is organized into output files.

Strategy: time_block

Groups data into fixed time periods (e.g., daily, weekly, monthly files):

archive_options:
  strategy: time_block
  strategy_options:
    freq: day  # Options: day, week, month, year
  folder_partitioning: year/month

Strategy: event

Groups data by precipitation events:

archive_options:
  strategy: event
  strategy_options:
    variable: n_particles
    detection_threshold: 2            # Minimum number of particles to detect precipitation
    neighbor_min_size: 1              # Minimum neighbors to form event
    neighbor_time_interval: 1min      # Time window for neighbor search
    event_max_time_gap: 30min         # Maximum gap within event
    event_min_duration: 1min          # Minimum event duration
    event_min_size: 10                # Minimum event size (timesteps)
  folder_partitioning: year/month

Currently, only the time_block strategy is supported for DISDRODB L0C and L1 products.

Folder Partitioning

Controls the subdirectory structure for output files. Options include:

  • year: YYYY/

  • year/month: YYYY/MM/

  • year/month/day: YYYY/MM/DD/

  • year/week: YYYY/WW/

Note

Memory Considerations

DISDRODB L0C and L1 products default to daily time blocks, allowing safe processing of large datasets on machines with limited memory.

DISDRODB L2E and L2M products default to monthly files since they typically compute rainfall parameters only for rainy timesteps, resulting in smaller data volumes.

Optimization Tips:

  • On machines with sufficient memory, increase time block frequency (weekly or monthly) to reduce output file count

  • If experiencing out-of-memory errors, switch to daily archiving

  • Adjust DASK_NUM_WORKERS environment variable to control parallel processing workers

Radar Simulation Options#

Available for DISDRODB L2E and L2M products when pytmatrix is installed. For details, see Polarimetric Radar Variables Simulations.

radar_enabled: true
radar_options:
  frequency: [5.6, 9.7, X]           # GHz or band names (S, C, X, Ku, Ka, W)
  num_points: 50                     # T-matrix integration points
  diameter_max: 8.0                  # Maximum diameter (mm)
  canting_angle_std: 10              # Canting angle std dev (degrees)
  axis_ratio_model: Thurai2007       # Axis ratio models
  permittivity_model: Turner2016   # Permittivity models
  water_temperature: 10              # Water temperature (°C)
  elevation_angle: 0                 # Elevation angle (degrees)

All radar options accept single values or lists for batch processing. When specified as lists, each option becomes a dimension in the output DISDRODB L2E and L2M datasets, allowing multi-frequency or multi-parameter radar simulations.

L0C Product Configuration#

The L0C configuration controls time consistency and file consolidation. For product description, see DISDRODB L0C Product.

Available Options

archive_options:
  strategy: time_block               # Only time_block supported for L0C
  strategy_options:
    freq: day                        # Consolidation period
  folder_partitioning: year/month    # Output folder structure

Key Notes

  • Only time_block archiving strategy is currently supported

  • Daily consolidation (freq: day) ensures time-consistent L0B records

  • No temporal resolution options available for L0C

Example Configuration

1archive_options:
2  strategy: time_block
3  strategy_options:
4    freq: day
5  folder_partitioning: "year"

L1 Product Configuration#

The L1 configuration defines temporal resampling and aggregation settings. For product description, see DISDRODB L1 Product.

Available Options

temporal_resolutions:              # List of resolutions to generate
  - 30S
  - 1MIN
  - 5MIN
  - ROLL10MIN                      # ROLL suffix for rolling windows

archive_options:
  strategy: time_block             # Only time_block currently supported for L1
  strategy_options:
    freq: day
  folder_partitioning: year/month

Temporal Resolution Syntax

  • <N>S: Fixed N-second intervals (e.g., 30S, 60S)

  • <N>MIN: Fixed N-minute intervals (e.g., 1MIN, 5MIN, 10MIN)

  • ROLL<N>MIN: Rolling N-minute windows (e.g., ROLL5MIN, ROLL10MIN)

Rolling windows use sliding windows for aggregation, reducing “data loss” compared to fixed-interval resampling.

Key Notes

  • All temporal resolutions are generated in a single processing run

  • Rolling windows reduce data “loss” during resampling

  • DISDRODB L1 processing chain only supports time_block strategy currently

Example Configuration

1temporal_resolutions: ["1MIN", "5MIN", "10MIN", "ROLL1MIN"]
2archive_options:
3  strategy: time_block
4  strategy_options:
5    freq: day
6  folder_partitioning: "year"

L2E Product Configuration#

The L2E configuration controls filtering drop spectra. For product description, see DISDRODB L2E Product.

Available Options

temporal_resolutions:
  - 1MIN
  - 5MIN

archive_options:
  strategy: time_block                  # time_block or event
  strategy_options:
    # ... (see Archive Options above)
  folder_partitioning: year/month

product_options:
  # Computation flags
  compute_spectra: false           # Compute mass spectra, rainfall spectra
  compute_percentage_contribution: false

  # Quality control thresholds
  minimum_ndrops: 10               # Minimum drops required
  minimum_nbins: 3                 # Minimum populated bins
  minimum_rain_rate: 0.1           # Minimum rain rate (mm/h)

  # Spectrum filtering
  fall_velocity_model: Beard1976   # Fall velocity model
  minimum_diameter: 0.0            # Diameter filter (mm)
  maximum_diameter: 10.0
  minimum_velocity: 0.0            # Velocity filter (m/s)
  maximum_velocity: 12.0

  # Drop filtering options
  keep_mixed_precipitation: false  # Keep mixed phase
  above_velocity_fraction: null    # Fraction threshold above terminal velocity
  above_velocity_tolerance: 0.5    # Tolerance above terminal velocity (m/s)
  below_velocity_fraction: null    # Fraction threshold below terminal velocity
  below_velocity_tolerance: 0.5    # Tolerance below terminal velocity (m/s)
  maintain_drops_smaller_than: 1.0 # Keep drops < threshold (mm) if maintain_smallest_drops true
  maintain_drops_slower_than: 1.0  # Keep drops < threshold (m/s) if maintain_smallest_drops true
  maintain_smallest_drops: true    # Always keep smallest drops
  remove_splashing_drops: true     # Remove splashing drops

radar_enabled: true
radar_options:
  # ... (see Radar Simulation Options above)

Key Notes

  • Currently processes only liquid precipitation

  • Both time_block and event archiving strategies supported

  • Raw spectra filtering to remove unrealistic rain drops based on diameter-velocity criteria

  • Radar simulations require pytmatrix installation

For implementation details, see disdrodb.generate_l2e().

Example Configuration

 1temporal_resolutions: ["1MIN", "5MIN", "10MIN", "ROLL1MIN"]
 2archive_options:
 3  strategy: time_block
 4  strategy_options:
 5    freq: month
 6  folder_partitioning: ""
 7product_options:
 8  compute_spectra: False
 9  compute_percentage_contribution: False
10  minimum_ndrops: 1
11  minimum_nbins: 1
12  minimum_rain_rate: 0.01
13  fall_velocity_model: "Beard1976"
14  minimum_diameter: 0
15  maximum_diameter: 10
16  minimum_velocity: 0
17  maximum_velocity: 12
18  keep_mixed_precipitation: False
19  above_velocity_fraction: null
20  above_velocity_tolerance: 3
21  below_velocity_fraction: null
22  below_velocity_tolerance: 3
23  maintain_drops_smaller_than: 1
24  maintain_drops_slower_than: 2.5
25  maintain_smallest_drops: True
26  remove_splashing_drops: True
27radar_enabled: False
28radar_options:
29  frequency: ["S", "C", "X", "Ku", "K", "Ka", "W"]
30  num_points: 1024
31  diameter_max: 10
32  canting_angle_std: 7
33  axis_ratio_model: Thurai2007
34  permittivity_model: Turner2016
35  water_temperature: 10
36  elevation_angle: 0

L2M Product Configuration#

The L2M configuration controls parametric DSD model fitting. For product description, see DISDRODB L2M Product.

Available Options

models:                            # List of models to fit
  - GAMMA_GS_ND_SSE
  - GAMMA_ML

temporal_resolutions:
  - 1MIN
  - 5MIN

archive_options:
  strategy: event                  # time_block or event
  strategy_options:
    # ... (see Archive Options above)
  folder_partitioning: year/month

product_options:
  fall_velocity_model: Beard1976   # Fall velocity model
  diameter_min: 0.1                # Diameter range for model fitting (mm)
  diameter_max: 8.0
  diameter_spacing: 0.1            # Grid spacing for numerical integration (mm)
  gof_metrics: true                # Compute goodness-of-fit metrics (R2, RMSE, etc.)
  minimum_ndrops: 10               # Minimum drops required for fitting
  minimum_nbins: 3                 # Minimum populated bins for fitting
  minimum_rain_rate: 0.1           # Minimum rain rate threshold (mm/h)

radar_enabled: true
radar_options:
  # ... (see Radar Simulation Options above)

For implementation details, see disdrodb.generate_l2m().

Example DISDRODB L2M default configuration

 1temporal_resolutions: ["1MIN", "5MIN", "10MIN"]
 2models:
 3  [
 4    "GAMMA_ML",
 5    "GAMMA_GS_ND_SSE",
 6    "NGAMMA_GS_LOG_ND_SSE",
 7    "NGAMMA_GS_ND_SSE",
 8    "LOGNORMAL_ML",
 9    "LOGNORMAL_GS_ND_SSE",
10  ]
11archive_options:
12  strategy: time_block
13  strategy_options:
14    freq: month
15  folder_partitioning: ""
16product_options:
17  fall_velocity_model: "Beard1976"
18  diameter_min: 0
19  diameter_max: 10
20  diameter_spacing: 0.05
21  gof_metrics: True
22  minimum_ndrops: 1
23  minimum_nbins: 5
24  minimum_rain_rate: 0.01
25radar_enabled: False
26radar_options:
27  frequency: ["S", "C", "X", "Ku", "K", "Ka", "W"]
28  num_points: 1024
29  diameter_max: 10
30  canting_angle_std: 7
31  axis_ratio_model: Thurai2007
32  permittivity_model: "Turner2016"
33  water_temperature: 10
34  elevation_angle: 0

L2M Model Configuration#

Each model specified in the models list of the L2M/global.yaml file must have a corresponding YAML file in the L2M/MODELS/ directory.

DISDRODB L2M supports fitting multiple parametric drop size distribution (DSD) models using different optimization methods. Models are fit independently, allowing comparison of different parameterizations.

Available PSD Models

Available Optimization Methods

  • GS: Grid search minimizing specified loss functions over parameter space. Supports single or multi-objective optimization with various error metrics. See get_gs_parameters for details.

  • ML: Maximum likelihood estimation using scipy optimizers. See get_gs_parameters for details.

  • MOM: Method of moments. Analytically solves for parameters using moment relationships. See get_mom_parameters for details.

Grid Search (GS) Example

Grid search explores the parameter space to find values that minimize the specified loss function(s).

# MODELS/GAMMA_GS_ND_SSE.yaml
psd_model: "GammaPSD"
optimization: "GS"
optimization_settings:
  objectives:
    - target: "N(D)"              # Target variable: N(D), H(x), Z, R, LWC, or M<p>
      transformation: "identity"  # Transform: identity, log, sqrt
      censoring: "none"           # Censoring: none, left, right, both
      loss: "SSE"                 # Error metric: SSE, SAE, MAE, MSE, RMSE, KLDiv, WD, etc.

Multiple objectives can be combined for multi-objective optimization, allowing simultaneous fitting to both distribution shape and integral properties:

optimization_settings:
  objectives:
    - target: "N(D)"              # Fit drop size distribution shape
      transformation: "identity"
      censoring: "none"
      loss: "SSE"
      loss_weight: 0.8            # Higher weight prioritizes distribution fit
    - target: "Z"                 # Also match radar reflectivity
      transformation: "identity"
      loss: "AE"                  # Absolute error for integral target
      loss_weight: 0.2            # Lower weight constrains integral property

Maximum Likelihood (ML) Example

Maximum likelihood estimation finds parameters that maximize the probability of observing the measured drop counts.

# MODELS/GAMMA_ML.yaml
psd_model: "GammaPSD"
optimization: "ML"
optimization_settings:
  init_method: "None"              # Parameter initialization: None or "MOM"
  probability_method: "cdf"        # Probability calculation: cdf or pdf
  likelihood: "multinomial"        # Likelihood function: multinomial or poisson
  truncated_likelihood: True       # Account for sensor bin truncation
  optimizer: "Nelder-Mead"         # Scipy optimizer: Nelder-Mead, Powell, BFGS, etc.

Method of Moments (MOM) Example

Method of moments uses analytical relationships between distribution parameters and integral moments for fast parameter estimation.

# MODELS/GAMMA_MOM.yaml
psd_model: "GammaPSD"
optimization: "MOM"
optimization_settings:
  mom_method: "M246"                # Moment combination (varies by model)
                                    # GammaPSD: M234, M246, M346, M456
                                    # LognormalPSD: M346
                                    # ExponentialPSD: M234

Key Notes

  • Multiple models can be fit simultaneously for comparison

  • Both time_block and event archiving strategies supported

  • Goodness-of-fit metrics (R2, RMSE, bias) help assess model performance

  • Model configurations are validated at startup to catch errors early

  • Different optimization methods may yield different results; compare multiple approaches

Sensor-Specific Configurations#

Sensor-specific configurations override global settings for particular sensor types. This is useful when different sensors require different processing parameters.

Creating sensor-specific configurations

Create a subdirectory named after the sensor within the product directory:

L2E/
├── global.yaml          # Applied to all sensors
└── PARSIVEL/            # Specific to OTT Parsivel
    ├── global.yaml      # Override L2E defaults for this sensor
    └── 1MIN.yaml        # Override 1min temporal resolution settings

Example Use Cases

  • Different diameter/velocity filtering for different sensor types

  • Sensor-specific quality control thresholds

  • Different archive strategies per sensor

  • Custom radar simulation parameters

Temporal Resolution-Specific Configurations#

For DISDRODB L1, L2E, and L2M products, you can customize settings for individual temporal resolutions. This allows different processing parameters at different time scales.

Creating temporal resolution configurations

Create a YAML file named after the temporal resolution:

L2E/
├── global.yaml          # Applied to all temporal resolutions
├── 1MIN.yaml            # Override settings for 1-minute resolution
└── 5MIN.yaml            # Override settings for 5-minute resolution

This allow you to set different quality control thresholds, filtering options, or archive strategies for different temporal resolutions:

# L2E/1MIN.yaml
product_options:
  minimum_ndrops: 5      # More lenient for 1-minute data
  minimum_nbins: 2

# L2E/5MIN.yaml
product_options:
  minimum_ndrops: 20     # Stricter for 5-minute data
  minimum_nbins: 5

You only need to specify options you want to override. Non-specified options inherit from the global configuration !

Configuration Validation#

DISDRODB validates all configuration files to ensure consistency and correctness.

Validation components

  1. Directory structure

    • Required product directories exist (L0C/, L1/, L2E/, L2M/)

    • global.yaml file present in each product directory

    • L2M/MODELS/ directory exists with model YAML files

    • Sensor subdirectory names match valid DISDRODB sensor names

  2. Configuration options

    • Temporal resolutions use valid syntax

    • Archive strategy and options are compatible

    • Folder partitioning uses valid patterns

    • Numeric thresholds are within valid ranges

    • Radar simulation parameters are valid

  3. Model configurations

    • PSD model names are valid

    • Optimization methods are supported

    • Optimization settings match the chosen method

    • Required parameters are present

  4. Temporal resolution consistency

    • L1 temporal resolutions include all L2E and L2M resolutions

    • L2E temporal resolutions include all L2M resolutions

Running validation

disdrodb_check_products_options

Common validation errors

  • Missing global.yaml in product directory

  • Invalid sensor name in subdirectory

  • Temporal resolution not listed in L1 but used in L2E/L2M

  • Model file missing for model listed in L2M configuration

  • Invalid PSD model or optimization method in model configuration

Best Practices#

  1. Start with the defaults product configuration

    Copy default configurations and modify incrementally rather than creating from scratch.

  2. Validate frequently

    Run validation after each significant change to catch errors early.

  3. Use sensor-specific configuration sparingly

    Only create sensor-specific configurations when truly necessary. Too many customizations make maintenance difficult.

  4. Document customizations

    Add comments in YAML files explaining why specific values were chosen, especially when deviating from defaults.

  5. Test with representative data

    After configuration changes, test with a small representative dataset before processing large archives.

  6. Temporal resolution hierarchy

    Ensure L1 includes all temporal resolutions needed by L2E and L2M to avoid runtime errors.

  7. Event strategy tuning

    When using event-based archiving, tune parameters iteratively using test data to achieve desired event identification.