Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over North-Eastern Canada
Bibliographic record
Abstract
Abstract. Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states using passive microwave (PMW) satellite observations without using any surface-based data. In this DA experiment, a particle filter with a Sampled Importance Resampled algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. The Advanced Microwave Scanning Radiometer – 2 (AMSR-2) brightness temperatures (TB) observations were assimilated into a chain of models composed of the Crocus multi-layer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer – Multi-Layers (DMRT-ML)), the vegetation transmissivity model (ω–τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation and the soil, respectively, at the top of the atmosphere. DA experiments were performed over 12 stations where daily continuous SWE measurements were acquired during 4 winters (2012–2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19 and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 71% compared to original SWE simulations, from 23.7 kg m−2 without assimilation to 6.9 kg m−2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.6 % for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50 % of the PMW signal.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".