Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method
Bibliographic record
Abstract
Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".