Snow water equivalent time‐series forecasting in Ontario, Canada, in link to large atmospheric circulations
Bibliographic record
Abstract
Abstract The present study applies different time‐series models for forecasting daily and monthly snow water equivalent (SWE) data in Ontario, Canada, during 1987–2011. For daily time series, which showed a significant negative trend, four categories of the autoregressive moving‐average (ARMA) and ARMA model with exogenous variables (ARMAX) were applied. The North Atlantic Oscillation, Southern Oscillation Index and Pacific/North American Pattern, as large‐scale atmospheric anomalies, as well as temperature time series are considered as exogenous variables for ARMAX models. According to the multicriteria performance evaluation, a time‐trend ARMAX model demonstrated the best performance for modelling and forecasting daily SWE. Two models, seasonal autoregressive integrated moving average (SARIMA) and SARIMA with exogenous variables (SARIMAX), were also fitted to the monthly SWE time series. The results revealed that the SARIMAX model showed a better performance than the SARIMA model according to multicriteria evaluation. The three nonparametric tests, Wilcoxon, Levene and Kolmogorov–Smirnov for forecasting evaluation demonstrated that the selected time‐series models had enough reliability for short‐term SWE forecasting in Ontario. The results of this study also demonstrate the importance of incorporating both trend and appropriate exogenous variables for SWE time‐series modelling and forecasting. Copyright © 2014 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 | 0.000 |
| 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) | 0.005 | 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".