Bibliographic record
Abstract
Reliable precipitation forcing is essential for calculating the water balance, seasonal snowpack, glacier mass balance, streamflow, and other hydrological variables. However, satellite precipitation is often the only forcing available to run hydrological models in data-scarce regions, compromising hydrological calculations when unreliable. The IMERG product estimates precipitation quasi-globally from a combination of passive microwave and infrared satellites, which are intercalibrated based on GPM’s DPR and GMI instruments. Current GPM-DPR radar algorithms have satisfactorily estimated rainfall, but a limited consideration of PSD, attenuation correction, and ground clutter have degraded snowfall estimation, especially in mountain regions. This study aims to improve satellite radar snowfall estimates for this situation. Nearly two years (between 2019 and 2022) of aloft precipitation concentration, surface hydrometeor size, number and fall velocity, and surface precipitation rate from a high elevation site in the Canadian Rockies and collocated GPM-DPR reflectivities were used to develop a new snowfall estimation algorithm. Snowfall estimates using the new algorithm and measured GPM-DPR reflectivities were compared to other GPM-DPR-based products, including CORRA, which is employed to intercalibrate IMERG. Snowfall rates estimated with measured Ka reflectivities, and from CORRA were compared to MRR-2 observations, and had correlation, bias, and RMSE of 0.58 and 0.07, 0.43 and -0.38 mm h-1, and 0.83 and 0.85 mm h-1, respectively. Predictions using measured Ka reflectivity suggest that enhanced satellite radar snowfall estimates can be achieved using a simple measured reflectivity algorithm. These improved snowfall estimates can be adopted to intercalibrate IMERG in cold mountain regions, thereby improving regional precipitation estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".