Evaluating Weather Research and Forecasting Model in Simulating March-May Rainfall in Tanzania: Implications of Selecting Parameterization Schemes
Bibliographic record
Abstract
The current study evaluates the performance of the Weather Research and Forecasting (WRF) model in simulating seasonal rains of March-May (MAM) in Tanzania, based on the implication of selecting parameterization scheme combinations. The model was configured into two domains with horizontal resolutions of 36 km and 12 km and the initial and lateral boundary conditions were provided by the Climate Forecast System Version 2 at 00 UTC. However, only the inner domain of 12 km was used for analysis, which has a fine horizontal resolution that accounts for small scale features such as terrains. Twelve simulations have been performed using four Cumulus and three Microphysics schemes to determine the best scheme combination for MAM seasonal rainfall. The model outputs were compared with the Climate Hazards Group InfraRed Precipitation with Station and gauged rainfall data. The performance of the model in simulating MAM seasonal rainfall was analyzed using standard statistical measures and ranking transformation analysis. The results indicated that Grell-Freitas (GFE) and Betts-Miller-Janjic (BMJ) cumulus schemes when combined with the WRF Double Moment 6 (WDM6) class microphysics scheme performed reasonably better in simulating MAM seasonal rainfall in Tanzania. Moreover, the combination of New Tiedtke (TDK) cumulus and Kessler (KSS) microphysics was found to be the less accurate combination among all. Therefore, in improving operational seasonal prediction in Tanzania and increase the confidence of the forecast, the study recommends that GFE-WDM6 and BMJ-WDM6 scheme combinations should be used for operational forecasting of MAM seasonal rainfall.
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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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".