Seasonal Precipitation forecasting with large scale climate predictors: A hybrid  wavelet multiresolution -NARX scheme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Much of northern Tunisia regularly experiences extremes of drought and flooding, with high rainfall variability. Development of reliable and accurate seasonal rainfall forecasts can provide valuable information to help mitigate some of the outcome of floods and enhance water management, particularly for agriculture. Ensemble monthly rainfall forecasts are carried out over horizons ranging from 1 to 6 months using a hybrid wavelet neural network model. The hybrid model called MWD-NARX based on a non-linear autoregressive network with exogenous inputs (NARX) coupled to multiresolution wavelet decomposition (MWD) is developed in this work. First, The MWD is used to decompose the data into different components on various time scale. Then to predict each precipitation decomposition the NARX ensemble model is employed. For an operational forecasting, the forecasts obtained from the decompositions are summed to represent the true precipitation forecast value. The outcomes of MWD-NARX are compared with Artificial Neural Networks (ANN). The seasonal forecasts of average precipitation by sub-basins of the Medjerda river basin are carried out. Large scale climate teleconnection indicators of ENSO, PDO, NAO and Mediterranean Oscillation were used as inputs to the model. The results indicate that exogenous inputs like climatic indices clearly improves the accuracy of forecasts in terms of the coefficient R<sup>2</sup> on 82% of SBVs compared to a model that uses only climate indices as inputs with 1 month delay time. It increases then the forecast lead-time up to 6 months. The same conclusion is made when compared to an ANN. The correlation coefficient between observed and forecasted monthly precipitation is ranging from 0.5 to 0.8. It was also found that the MWD-NARX underestimates the extremes. The spatial variability of the quality of the forecasts depends mainly on the local effect of precipitation more than on the quality of the hydrological data observed on the forecasts. It can be concluded that exogenous inputs like climate indices can add some additional information to enhance monthly precipitation forecasts at longer lead-times. The forecasting model coupled to data pre-processing method made it possible to produce very satisfactory forecasts of non-stationary data by extracting significant modes of variability.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it