README.md

metadata

license: cc-by-4.0
language:
  - en
tags:
  - scikit-learn
  - xgboost
  - random-forest
  - regression
  - weather-radar
  - quantitative-precipitation-estimation
  - dual-polarization-radar
  - geoscience
  - taiwan

Tree-Based Radar Quantitative Precipitation Estimation Models

Overview

This archive contains trained Random Forest (RF) and Extreme Gradient Boosting (XGB) models used to evaluate how radar predictor representation affects quantitative precipitation estimation (QPE). Models are provided for 10-minute and 1-hour rainfall estimation and for Experiments A-G described below.

The RF models were trained with scikit-learn, and the XGB models were trained with XGBoost. The restricted radar, rain-gauge, training, and validation data are not included in this archive.

File Naming

Model filenames follow this pattern:

{model}_{timescale}_Exp{experiment}[_{KDP_variant}].joblib

• model: RF or XGB
• timescale: 10min or 1h
• experiment: A through G
• KDP_variant: present only for Experiments F and G

Examples:

• RF_10min_ExpA.joblib: 10-minute RF model for Experiment A
• XGB_1h_ExpE.joblib: 1-hour XGB model for Experiment E
• RF_10min_ExpF_3d.joblib: 10-minute RF model using the formal three-dimensional KDP representation in Experiment F
• XGB_1h_ExpG_3d_plus_max.joblib: 1-hour XGB model using the vertical KDP profile together with maximum KDP in Experiment G

Experiments

Experiment	Predictor representation
A	Composite reflectivity: two-dimensional maximum dBZ
B	Experiment A plus latitude, longitude, and elevation
C	Vertical reflectivity profiles sampled from CAPPI data
D	Experiment C plus maximum dBZ, 18-dBZ echo top, 45-dBZ echo top, and vertically integrated liquid (VIL)
E	Experiment D plus latitude, longitude, and elevation
F	Experiment D plus a KDP representation
G	Experiment E plus a KDP representation

The formal Experiments F and G use the vertical KDP profile (3d). Additional F/G files are sensitivity experiments that use alternative KDP representations:

Suffix	KDP representation
3d	Vertical KDP profile
low	Low-level KDP representation
max	Maximum KDP representation
3d_plus_max	Vertical KDP profile plus maximum KDP

The reflectivity profile contains 21 CAPPI levels from 1.0 to 17.0 km. The KDP profile contains 34 levels from 0.5 to 17.0 km. Radar predictors use seven temporal lags from t-60 to t0, matched to the nearest radar observation within 5 minutes.

The formal A-G representations contain 7, 10, 147, 175, 178, 413, and 416 predictors, respectively. Sensitivity variants of F and G may have different predictor counts.

Prediction Targets

• 10min: rainfall accumulated over 10 minutes and expressed as an hourly-equivalent intensity in mm h^-1. It is not a 1-hour accumulation.
• 1h: hourly rainfall intensity in mm h^-1.

Model predictions therefore use units of mm h^-1 for both time scales.

Joblib Contents

Each joblib file contains a dictionary with these entries:

Key	Meaning
model	Trained RF or XGB model
features	Ordered feature names required by the model
feature_importances	Stored feature-importance table
importance_type	impurity for RF or gain for XGB
model_type	RF or XGB
target	10min or 1h
experiment	Experiment identifier
kdp_shape	KDP representation, where applicable
backend	Model implementation recorded during export
versions	Relevant package versions recorded during export

Always prepare input columns in the exact order stored in features. A model should not be applied to data with a different preprocessing procedure, feature definition, vertical level, temporal lag, unit, or column order.

Loading and Prediction

The following example loads an artifact and produces predictions from a pandas DataFrame named frame:

import joblib
import numpy as np
import xgboost as xgb

artifact = joblib.load("RF_10min_ExpA.joblib")
model = artifact["model"]
feature_names = artifact["features"]

X = frame.loc[:, feature_names].to_numpy(dtype=np.float32)

if artifact["model_type"] == "XGB":
    predictions = model.predict(xgb.DMatrix(X, feature_names=feature_names))
else:
    predictions = model.predict(X)

The required Python packages include joblib, numpy, scikit-learn, and xgboost. Consult artifact["versions"] for the package versions recorded for an individual model. Matching those versions as closely as possible is recommended for reproducible loading and prediction.

Feature Importance

Feature importance is available in artifact["feature_importances"]:

• RF importance is the scikit-learn impurity-based importance.
• XGB importance is the XGBoost gain importance.

These definitions measure different quantities. Importance values should be interpreted within the context of each model and should not be treated as directly equivalent between RF and XGB. Correlated radar predictors can also share or redistribute importance.

Validation Design

The models were developed using observations from 2020-2023 at 252 stations in the training region. Spatial validation used 30 held-out stations from the same period, and temporal validation used 2024 observations from the training-region stations. Validation data and results are not part of this model-only archive.

Data Availability and Limitations

The source radar products, including three-dimensional CAPPI reflectivity were provided by the Central Weather Administration and the National Science and Technology Center for Disaster Reduction. Redistribution restrictions prevent their inclusion. Rain-gauge observations and derived training or validation samples are also not included here.

Reusing these models requires independently obtained data with matching variables and identical preprocessing. The archive alone is not sufficient to reconstruct the restricted input dataset or reproduce model training from raw observations.

Security Notice

Joblib files use Python's pickle-based serialization. Load these files only from a trusted source, because loading an untrusted pickle or joblib file can execute arbitrary code.