A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing
Bibliographic record
Abstract
In this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRTM fine-tuned through three sensitivity experiments with respect to sample-size determination, model separation, and introduction of novel features toward improving the accuracy of the model. In addition to the BT simulation, we evaluated the Jacobians with respect to surface and atmospheric parameters. Atmosphere profiles from the European Centre for Medium-Range Weather Forecasts, sea surface temperature from the Canadian Meteorology Centre, and ATMS sensor data records were used as FCDN_CRTM inputs. In comparison to CRTM, the FCDN_CRTM minus CRTM mean biases were within several hundredths of a Kelvin (K), and the corresponding standard deviations (SDs) were between 0.05 and 0.15 K for all ATMS bands. The accuracies for both mean bias and SD were consistent throughout the evaluation period, which spanned approximately 1 year beyond the period of the FCDN_CRTM training dataset. Furthermore, the model Jacobians generally compared well with CRTM Jacobians in terms of surface temperature, wind speed, air temperature, and (log) water vapor. The performance of the FCDN_CRTM forward and Jacobian model indicate potential for use in data assimilation and physical retrieval systems, such as the NOAA operational Microwave Integrated Retrieval System.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".