Influence of Radiative Transfer Model-Based Atmospheric Correction and Dynamic Tie Points on Sea Ice Concentration Retrieval From Near-90 GHz Algorithm With FY-3D MWRI Data
Bibliographic record
Abstract
Sea ice concentration (SIC) has been monitored with passive microwave (PM) observations for decades. Various techniques have been developed for its improvement. While techniques such as weather filters are commonly used, the necessity of combing radiative transfer model (RTM)-based atmospheric correction and dynamic tie points (DTP) remains an open question, particularly for near-90 GHz algorithm. This study investigates their respective influence on SIC retrieval using the FY-3D Microwave Radiation Imager (MWRI) data in 2019. The original and atmospherically corrected Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm (ASI and ASI2, respectively) are used in combination with fixed tie points (FTP) and DTP, resulting in four sets of ice concentration retrievals, namely ASI-FTP, ASI-DTP, ASI2-FTP, and ASI2-DTP. They are inter-compared with three PM-based ice concentration products and evaluated with a synthetic aperture radar (SAR)-based ice/water classification product and 20 clear-sky Moderate Resolution Imaging Spectroradiometer (MODIS) images from February to July 2019. The ASI2-based ice concentrations are overall higher and perform better, with the root mean square error (RMSE) and bias reduced by 5.4%–7.4% and 7.2%–8.0%, respectively. In comparison, the use of DTP has varying performances depending on the tie points extraction procedure. Good tie points work similarly to the atmospheric correction in mitigating SIC underestimations. The combined use of both varies substantially with seasons. During summer, it well captures the seasonal variability of tie points and effectively mitigates the atmospheric influence, thus significantly improving the retrievals. This highlights the necessity of combining both techniques for near-90 GHz algorithm, especially for summer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".