Snowmelt Detection with Calibrated, Enhanced‐Resolution Brightness Temperatures (CETB) in Colorado Watersheds
Bibliographic record
Abstract
Abstract Understanding the timing of snowmelt is critical for water resources management in snow‐dominated watersheds. Passive microwave remote sensing can be used to estimate snowmelt events through brightness temperature satellite observations. Previous studies were limited to lower resolution (~25 km or coarser) datasets, making it difficult to quantify snowpack variability in heterogeneous, high‐relief areas. This study investigates the use of recently available Calibrated, Enhanced‐Resolution Passive Microwave Daily EASE‐Grid 2.0 Brightness Temperature ESDR (CETB) to estimate snowmelt timing at much higher spatial resolution (~3‐6 km) than has been previously available. This study investigates the effectiveness of the CETB product for snowmelt detection in several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002‐2003), along with data for the Senator Beck Basin from the Center for Snow and Avalanche Studies (CSAS). We compare melt variability with nearby air temperature and stream discharge to show that the new CETB product allows detection of hydrologic processes in mountainous watersheds. We show that the higher resolution CETB product can detect snowmelt in heterogeneous terrain more accurately than the coarser resolution product in terms of the number of winter melt events and seasonal melt onset date. This work lays the foundation for the utilization of higher resolution reprocessed CETB data for snowpack evolution more broadly in a range of environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".