Towards Ice Sheet and Ice Shelf Meltwater Profile Retrieval from Copernicus Imaging Microwave Radiometer (CIMR)
Bibliographic record
Abstract
Successful adaptation and mitigation of rising sea level demands improved constraints on emerging ice sheet processes controlling the magnitude and rate of sea level change in a warming climate. Therefore, it is vital to enhance the confidence in quantitative assessments of present-day ice sheet mass balance arising from meltwater generation and refine the explicit treatment of meltwater refreezing in firn densification models to evaluate the time evolution of runoff/retention and surface elevation change. The European Space Agency's Copernicus Imaging Microwave Radiometer (CIMR), scheduled for launch in 2028, responds to this need by measuring the brightness temperature (TB) at 1.4, 6.9, 10.7, 18.7, and 36.5 GHz frequencies multiple times a day over polar regions without gaps at the poles. These measurements can resolve the stratification of the seasonal meltwater from the immediate surface to the deeper firn layers. Furthermore, the 1.4 GHz TB is sensitive to the amount of meltwater.We are developing retrieval algorithms using 1.4 GHz measurements from NASA Soil Moisture Active Passive (SMAP) and ESA Soil Moisture Ocean Salinity (SMOS) satellites and 6.9, 10.7, 18.7, and 36.5 GHz measurements from JAXA Advanced Microwave Scanning Radiometer – EOS (AMSR-E) and AMSR2 instruments on NASA Aqua and JAXA (Global Change Observation Mission – Water) GCOM-W satellites. In the algorithm development, we use ground measurements and coupled energy and mass balance models to test, calibrate, and validate the algorithm. The ground measurements include surface and subsurface measurements from PROMICE/GC-Net (Greenland) and other station and campaign data sets. The model suite includes locally calibrated and forced energy balance models and regionally forced models, such as the Ice Sheet System Model's Glacier Energy and Mass Balance module. The retrieved data product will provide twice-daily parameters such as meltwater amount, melt layer depth, and near-surface snow status (wet/dry) profile. It will also include seasonal parameters such as firn aquifer extent and evolution.The meltwater algorithm for the CIMR mission will eventually encompass two satellites capable of monitoring diurnal melt-freeze cycles and perennial firn aquifers for at least a 15-year mission period. CIMR will measure all frequencies simultaneously, which will eliminate the uncertainties related to different observation times of the current instrument combinations, increase the revisit time of the L-band observations, and improve the spatial resolution of the 6.9, 10.7, 18.9, and 36.5 GHz channels compared to what is currently available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".