Significance of Cloud Microphysics and Cumulus Parameterization Schemes in Simulating an Extreme Flood-Producing Precipitation Event in the Central Himalaya
Bibliographic record
Abstract
Between 11 and 14 August 2017, the southern belt of the central Himalaya experienced extreme precipitation, with some stations recording more than 500 mm of accumulated rainfall, which resulted in widespread, devastating flooding. Precipitation was concentrated over the sub-Himalaya, and the established forecasting systems failed to predict the event. In this study, we evaluate the performance of six cloud microphysics schemes in the Weather Research and Forecasting (WRF) model forced with the advanced ERA5 dataset. We also examine the importance of the cumulus scheme in WRF at 3 km horizontal grid spacing in highly convective events like this. Six WRF simulations, each with one of the six different microphysics schemes with the Kain–Fritsch cumulus scheme turned off, all fail to reproduce the spatial variability of accumulated precipitation during this devastating flood-producing precipitation event. In contrast, the simulations exhibit greatly improved performance with the cumulus scheme turned on. In this study, the cumulus scheme helps to initiate convection, after which grid-scale precipitation becomes dominant. Amongst the different simulations, the WRF simulation using the Morrison microphysics scheme with the cumulus turned on displayed the best performance, with the smallest normalized root mean square error (NRMSE) of 0.25 and percentage bias (PBIAS) of −6.99%. The analysis of cloud microphysics using the two best-performing simulations reveals that the event is strongly convective, and it is essential to keep the cumulus scheme on for such convective events and capture all the precipitation characteristics showing that in regions of extreme topography, the cumulus scheme is still necessary even down to the grid spacing of at least 3 km.
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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 | 0.000 | 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".