Evaluation of ensemble precipitation forecasts from NWP models in Indian River basins and agro-climatic zones
Bibliographic record
Abstract
This study evaluates the deterministic and ensemble quantitative precipitation forecasts (QPFs) obtained from four Numerical Weather Prediction (NWPs) models over the Indian region during the monsoon period (June to September) for the years 2011 to 2020. We considered 18 river basins and 14 Agro climatic zones to compare the skill of the forecasts with the observation data. From The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) archives, we obtained QPFs from Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Agency (KMA), and National Centres for Environmental Prediction (NCEP) with 1 to 5 day lead time at a spatial resolution of 0.50. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) data for the same time period is used as observation data. Deterministic (RMSE, NSE, and CC) and dichotomous (POD and FAR) assessment have been performed to evaluate the skill of the QPF(s). Our result shows that overall the performance of ECMWF ensembles mean is better than the other NWPs model, as the NSE and CC value is more close to 1. The river basins in the southern part of the country (Godavari, Krishna and Cauveri River Basins) have the higher error (RMSE more than 100 and NSE close to 0) compared to Brahmputra, Ganga, and Barak River basins. The errors are less in those agro-climatic zones which has high elevation where the rainfall is less. The detailed result of the ongoing research will be presented at the conference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".