Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach
Bibliographic record
Abstract
Abstract. The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100 m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within <100 m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from “A Big Earth Data Platform for Three Poles” (https://doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".