Impact of meteorological predictions on real‐time spring flow forecasting
Bibliographic record
Abstract
Abstract Meteorological predictions, such as precipitation and temperature, are commonly used to improve real‐time hydrologic forecasting, despite their inherent uncertainty and their absence in the model calibration stage. In this study, we quantify the effect of meteorological prediction errors on the accuracy of daily spring reservoir inflow forecasts using weather predictions in both the model calibration and testing phases. Different modelling experiments are compared using an operational conceptual model and nonlinear empirical models to assess the effects of using daily numerical weather predictions as opposed to the use of historical observations. It is found that, even with large prediction errors, meteorological forecasts can provide significant improvement of spring flow forecast for up to 7 days lead time, particularly for low flows. Spring flow prediction errors associated with the type of hydrological model used are significantly larger than those related to the meteorological predictions, particularly for 1 to 4 days ahead forecasts. The experimental results also indicate that multiple model‐based forecasting using an iterative prediction approach appears to be the most effective method for an adequate use of weather predictions. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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) | 0.003 | 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".