Estimating Passive Microwave Brightness Temperature Over Snow-Covered Land in North America Using a Land Surface Model and an Artificial Neural Network
Bibliographic record
Abstract
An artificial neural network (ANN) is presented for the purpose of estimating passive microwave (PMW) brightness temperatures over snow covered land in North America. The NASA Catchment Land Surface Model (Catchment) is used to define snowpack properties; the Catchment-based ANN is then trained with PMW measurements acquired by the Advanced Microwave Scanning Radiometer (AMSR-E). A comparison of ANN output against AMSR-E measurements not used during training activities as well as a comparison against independent PMW measurements collected during airborne surveys demonstrates the predictive skill of the ANN. When averaged over the study domain for the 9-year study period, computed statistics (relative to AMSR-E measurements not used during training) for multiple frequencies and polarizations yielded a near-zero bias, a root mean squared error less than 10 K, and a time series anomaly correlation coefficient of approximately 0.5. The ANN demonstrates skill during the accumulation phase when the snowpack is relatively dry as well as during the ablation phase when the snowpack is ripe and relatively wet. Overall, the results suggest the ANN could serve as a computationally efficient measurement operator for data assimilation at the continental scale.
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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".