Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration
Bibliographic record
Abstract
Abstract In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".