Quantifying Seasonal Melt Water Amount and Depth of Infiltration in the Percolation Zone of the Greenland Ice Sheet using Multifrequency Microwave Radiometry
Bibliographic record
Abstract
Greenland ice sheet (GrIS) is one of the leading contributors to sea level rise. Nonetheless, around half of the total surface melt in the percolation zone of the GrIS percolates through the porous space of the firn and refreezes there buffering sea level rise. However, increased melting (and subsequent refreezing) in recent years has not only depleted a part of the available pore space, but it also created impermeable ice layers that hinder vertical percolation intensifying lateral runoff. Therefore, to better understand ice sheet surface mass balance (SMB) and its evolution, and project sea level rise accurately, quantification of seasonal meltwater amount and their percolation depths are critical. Currently, large uncertainties exist among regional climate models in estimating both the meltwater amount and their depth of infiltration across the GrIS. Spaceborne microwave radiometers are very sensitive to ice sheet melting regardless of day-night and weather conditions. Here, we present a multifrequency algorithm to quantify and monitor seasonal meltwater amounts and their depth of infiltration across the percolation zone of the GrIS. We use enhanced-resolution (3.125 km) 1.4 GHz (L-band) brightness temperature (TB) data from NASA Soil Moisture Active Passive (SMAP) mission, and 6.9, 10.7, 18.7, and 37 GHz (C- to Ka-band) TB from the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard GCOM-W1 satellite for 2015 – 2023. Different frequencies show distinct sensitivities to melt water at varying depths. The high frequency signals (18.7 GHz and 37 GHz) are effective in detecting surface melt, while the low frequency signals, especially the L-band signal can track the surface as well as the subsurface melt. Accordingly, the algorithm utilizes the full range of surface-sensitive frequencies to retrieve the depth profile of seasonal meltwater amounts across the GrIS. In situ ice core measurements as well as the meteorological observations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station (AWS) network were used to validate the retrievals. The results demonstrate significant advancements for monitoring daily melting and refreezing of polar ice sheets allowing better understanding of meltwater retention, and thus more realistic projection of global sea level rise. For More, please see this and follow the progress: Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2563 , 2024.
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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 | 0.001 | 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".