Assimilation of multiple types of snow observations through a large scale spatialized particle filter
Bibliographic record
Abstract
Particle filtering is interesting for snow data assimilation because of its minimal assumptions. However, implementing a particle filter over a large spatial domain is challenging for many reasons. For instance, the number of required particles rises exponentially as the domain size increases. Another important issue when spatializing a particle filter for snow data assimilation is the creation of spatial discontinuities when resampling the particles at locations where snow observations are available. In this presentation, we will describe how we implemented a spatialized particle filter for snow data assimilation over a large portion of the province of Quebec, Canada (600 000 km2 ). Two different types of snow observations where assimilated with this particle filter: sporadic manual snow surveys, which measure snow water equivalent directly, and continuous automated snow depth observations, which we converted to snow water equivalent using an ensemble of neural networks. We will then explain how a more frequent data assimilation can create unwanted discontinuities and break the spatial structure of the particles, and how we can remediate that by using an adaptation of the Schaake Shuffle reordering method. We will show that this solution significantly reduces the random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. We emphasize that the proposed spatialized particle framework could also eventually accommodate other types of data, such as citizen science data and gamma monitoring data. Overall, the proposed method allows to obtain improved spatial representation of snow water equivalent compared to the previous operational method used by the government of Quebec.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".