Comparing the performance of convection-permitting WRF output with reanalysis datasets for glacier energy balance and hydrological modelling in the Central Himalaya
Why this work is in the frame
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Bibliographic record
Abstract
Global warming impacts water resources through rapid glacier retreats in the high-altitude and high-latitude regions, posing an immediate threat to ecosystems and human societies. So, exploring the warming-driven hydro-climatic changes is critical, particularly in the mountainous areas with extreme and complex topography, where geophysical processes function at a very find spatial and temporal scale. The under-representation of such scale-dependent processes in the existing literature has limited our ability to quantify glacier melt rate and changes in stream discharge accurately. In this study, we take Himalaya’s glaciated catchments (the Langtang catchment) as an example study area and employ a cloud-resolving atmospheric model (Weather Research and Forecasting (WRF) model) coupled with a fully distributed hydro-glacial model (WRF-Hydro/Glacier) to investigate how atmospheric processes – that are unique to extreme topographic settings – influence glacial melt in these regions. To establish the robustness of our approach, we also force the WRF-Hydro/Glacier model with the advanced global climate reanalysis datasets, which are widely referenced in the literature. We then evaluate the WRF-Hydro/Glacier output against surface observations, highlighting the superiority of the cloud-resolving WRF output in providing initial conditions to the hydro-glacial model. The representation of cloud processes in the high-resolution atmospheric model, a critical atmospheric mechanism that occurs at fine spatial and temporal scales, is significant in mountainous topography and is crucial in glacier energy balance and streamflow simulation. Therefore, this approach is essential for accurately assessing the impacts of climate change on high-altitude glaciated catchments worldwide.
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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 | 0.001 | 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 | 0.001 | 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 it