Assessing improvement in the fifth‐generation <scp>ECMWF</scp> atmospheric reanalysis precipitation over East Africa
Bibliographic record
Abstract
Abstract This study aims to assess the performance of European Center for Medium‐Range Weather Forecasting (ECMWF) Reanalysis fifth generation (ERA5) in terms of the progress made over its predecessor, ERA‐Interim, over East Africa (EA) in reproducing observed rainfall. The observed rainfall used as reference rainfall are Global Precipitation Climatology Center (GPCC) and Climate Research Unit Time Series (CRU‐TS). The performance of ERA5 to its predecessor is evaluated using root‐mean‐square error (RMSE), correlation, and bias. The reductions of wet bias from ERA‐Interim to ERA5 are 16.81–6.94% from June to September (JJAS), 31.99–19.33% from September to December (SOND), and 29.24–17.69% from December to February (DJF) over most of EA relative to CRU‐TS. Similar reductions in the wet bias relative to GPCC are also noted. Spatially, notable reductions are observed over western Ethiopia, Sudan Republic, South Sudan, and Uganda. The decreasing trends revealed in the bias of seasonal ERA5 rainfall relative to CRU‐TS and GPCC are attributed to an increase in the number and quality of assimilated observations in ERA5 over its predecessors. RMSE of ERA5 rainfall relative to the two reference rainfall and correlations with the two reference rainfall are consistent with spatial and temporal features captured in the bias. Specifically, the correlation of the monthly time series of ERA5 ranges from 0.90 with CRU‐TS over equatorial EA (EEA) to 0.98 with GPCC over southern EA (SEA) in contrast to that of ERA‐Interim which ranges from 0.84 with CRU‐TS over EEA to 0.93 with CRU‐TS over SEA. However, the wet bias in ERA5 along the Great Rift Valley remained the same as in ERA‐Interim whereas spatial correlation of ERA5 with the two reference rainfall is relatively weaker than that of ERA‐Interim.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".