Development and Testing of a Low-Cost Solar-powered Disdrometer for Rainfall Characterization
Bibliographic record
Abstract
This research explores the development, construction, and validation of a cost-effective, solar-powered disdrometer designed to enhance the study of rainfall parameters. Disdrometers are essential tools for quantifying rainfall characteristics, such as drop size distribution and intensity, which are critical for understanding precipitation microphysics and improving weather radar and satellite rainfall estimation. However, the high cost of commercial disdrometers limits their accessibility, particularly in resource-constrained regions. To address this challenge, a low-cost disdrometer was developed using widely available and affordable components, without compromising performance. The device integrates a piezoelectric sensor for raindrop detection, an amplification system, and a microcontroller for data processing. The system is powered by solar energy, further reducing operational costs and enabling remote deployment. Field tests conducted in a tropical region demonstrated that the disdrometer reliably captures rainfall parameters comparable to those obtained by commercial systems, making it a valuable resource for atmospheric research, hydrology, and meteorology, particularly in resource-limited settings. The study concludes that this solar-powered disdrometer offers a feasible, efficient, and sustainable solution for rainfall characterization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".