A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients
Bibliographic record
Abstract
Satellite passive microwave (PMW) remote sensing is widely used for monitoring the snow water equivalent (SWE) in the Northern Hemisphere. Existing operational SWE retrieval methods, especially those without assimilating ground-based snow depth priors, still utilize globally constant coefficients to construct regression-based retrieval algorithms. The current Fengyun-3 (FY-3) series of SWE product algorithms has made improvements in China, where biases have been significantly reduced locally but not in other regions. Within the context of the successful launch of the FY-3F satellites in 2023, we developed a better Northern Hemisphere algorithm for the Microwave Radiation Imager-II (FY-3F/MWRI-II) using pixel-sensitive coefficients regressed on a reference SWE dataset. We utilized the random forest model coupled with the snow emission model (HUT-RF) to obtain a high-accuracy SWE reference dataset. Then, we employed linear regression equations to fit the reference HUT-RF dataset at each pixel to construct the new operational FY-3F algorithms. We innovatively introduced the brightness temperature differences between 18.7 and 89 GHz and the polarization differences at 10.65 GHz in the regression after noting their sensitivity in deep snow estimation. The proposed FY-3F algorithm was extensively validated via four spatially independent datasets. The results demonstrated that the proposed FY-3F algorithm performed well in non-mountainous and sparsely forested areas, e.g., the overall unbiased root mean square error (unRMSE) values were 27.15 mm over Russia and 13.70 mm over China. High uncertainties still occurred in complex terrains and densely forested areas, e.g., the overall unRMSE values were 75.30 mm over Canada and 129.06 mm over western North America. The proposed FY-3F algorithm could improve global snow cover monitoring capabilities and enhance the complete and timely understanding of SWE changes.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".