Integration of satellite-based passive microwave brightness temperature observations and an ensemble-based land data assimilation framework to improve snow estimation in forested regions
Bibliographic record
Abstract
The utilization of a machine learning algorithm-based (i.e., support vector machine [SVM]) model as the observation operator within a one-dimensional ensemble Kalman filter (EnKF) framework for the purpose of improving snow estimates across regional-scales is explored. The multifrequency, multipolarization framework employs an SVM to predict brightness temperature spectral difference (i.e., ΔTb) between 10.65 GHz, 18.7 GHz and 36.5 GHz as a function of land surface model state information. The EnKF then merges the predictions with observations obtained from the Advanced Microwave Scanning Radiometer (AMSR-E) sensor onboard the Aqua satellite. Case studies are presented for Quebec and Newfoundland, Canada, and several pixels in North America covered with evergreen needle-leaved forest colocated with taiga snow cover type. Model results with and without assimilation were compared against in-situ snow observations as well as state-of-the-art snow products. It is shown that using an atmospheric-forest-decoupling procedure prior to SVM training and prediction activities is useful in enhancing snow characterization. However, without adequate ground-based snow observations, it is still relatively difficult to draw a full conclusion as to the feasibility of the proposed assimilation framework employing the two-step decoupling procedure.
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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) | 0.000 | 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 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".