Bibliographic record
Abstract
In this study the two problems of rainfall estimation and forecasting using data from weather radars and rain-gauges are studied. A GIS multi-component interface is developed for the analysis of weather radar precipitation data. This interface performs different operations, such as loading and redelivering radar and satellite data, projecting geographical features into the radar coordinate system, and overlaying data from multi-sensor into a common coordinate system. Additional features include accumulating radar rainfall depths, radar comparison with rain-gauge data, animating storm evolution on top of geographical features, and tracking and forecasting rainfall fields. Accurate measurements of rainfall duration, timing, location, and intensity are important for different water resources applications. Weather radars can provide valuable information on the space-time variations of rainfall. However, there are uncertainties in the radar measurements of precipitation. Thus rain-gauges are used to calibrate Z-R relations, which are used to convert from radar reflectivity Z to rainfall rate R. Sampling errors cause differences between rainfall estimated by radar and that estimated by rain-gauges. These errors constitute a limitation for use of radar data for high resolution applications such as urban applications. A methodology is developed to address and correct the effects of these errors. The results prove that correction for these errors reduces the variation between the two sensors. In addition, given that the radar is properly calibrated, correction for sampling errors can provide temporally detailed radar rainfall fields that can be used for high resolution hydrological applications. The comparisons between two Canadian radars (King City and Exeter) show that there is good agreement between rainfall fields estimated by the two radars. The comparisons between radar rainfall intensities estimated by the two radars and the corresponding rain-gauge intensities show that the classical Z-R equation used by the National Canadian Radar Network is biased and can lead to serious underestimation of rainfall. An optimum Z-R relation is calibrated using surface rain-gauge data to be used for unbiased rainfall estimation by the two radars. A new radar-based model is developed for quantitative short-term forecasting of rainfall fields. The new model is called the AARS (Automated Adaptive Rainfall Simulator). The AARS model employs an optimization strategy for performing the cross-correlation analysis that reduces the run time significantly and makes the technique attractive for real-time applications. In addition, the model tracks and forecasts the changes in rainfall intensities in space and time and produces forecasted rainfall fields for the specified lead time. The AARS model employs the adaptive exponential smoothing algorithm for real-time parameters estimation. Performance comparisons between the AARS model and the Canadian short-term prediction model SHARP (Short-Term Automated Radar Prediction) show that the AARS is superior in terms of tracking run time and slightly better in terms of accuracy for forecasting lead times up to 30 minutes. The application of the AARS model for rainfall forecasting in Hamilton-Wentworth Region shows promising results for forecasting lead times less than 60 minutes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.226 | 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 teacher head, 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".