Multimodel Prediction of Monsoon Rain Using Dynamical Model Selection
Bibliographic record
Abstract
In this paper, dynamical-model-selection-based multimodel ensemble (DMS-MME) technique is developed for skill improvement of monsoon rain prediction in the medium range (i.e., 24-120 h ahead). The data set consists of 24-120 h daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.). The DMS-MME forecasts are constructed during the monsoon months (JJAS) for the years 2008-13 over the Indian mainland. For the training and verification purposes, India Meteorological Department rainfall is used. The forecast skill of the DMS-MME model has been compared with the performance of individual models and regression-based MME model. Further, to remove the nonnormality of rainfall distribution, square-root and logarithmic transfer functions are used for normalizing the precipitation data. The impact of these transfer functions on the forecast skill of the DMS-MME model has been assessed. The forecast skill of the proposed model is evaluated using the standard statistical measures. DMS-MME forecasts carries higher skill in terms of verification scores compared with the MME forecasts up to 120 h. It has been found that using the DMS-MME approach with a square-root transfer function (SDMS-MME) gives the best results. SDMS-MME outperforms the operational models and the regression-based MME at all forecast steps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".