Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective
Bibliographic record
Abstract
Snow water equivalent (SWE) is a key variable in climate and hydrology studies. Yet, current SWE products mask out high-topography areas due to the coarse resolution of the satellite sensors used. The snow remote sensing community is hence pushing towards active-microwave approaches for global SWE monitoring. Designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. Retrieval algorithm designs are converging towards forward modeling approaches using an educated first guess on the snowpack structure. Snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. In Canada, automatic weather stations are too sparse, and high-resolution numerical weather prediction systems have a maximal resolution of 2.5 km × 2.5 km, which is too coarse to capture snow spatial variability in a complex topography. In this study, we designed a subgridding framework for the Canadian High Resolution Deterministic Prediction System (HRDPS). The native 2.5 km × 2.5 km resolution forecast was subgridded to a 100 m × 100 m resolution and used as the input for snow modeling over two winters in Glacier National Park, British Columbia, Canada. Air temperature, relative humidity, precipitation, and wind speed were first parameterized regarding elevation using six automatic weather stations. We then used Alpine3D to spatialize atmospheric parameters and radiation input accounting for terrain reflections, and we performed the snow simulations. We evaluated modeled snowpack state variables relevant for microwave remote sensing against simulated profiles generated with automatic weather station data and compared them to simulated profiles driven by raw HRDPS data. The subgridding framework improves the optical grain size bias by 18 % on average and the modeled SWE by 16 % compared to simulations driven with raw HRDPS forecasts. This work could improve the snowpack radar backscattering modeling by up to 7 dB and serves as a basis for SWE retrieval algorithms using forward modeling in a Bayesian framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".