Estimation of sea surface temperature using passive microwave satellite imagery
Bibliographic record
Abstract
Sea surface temperature (SST) is routinely estimated from space by infrared radiometers, but only where the ocean surface is cloud free. Thermal radiation at microwave frequencies however is much less likely to be blocked by cloud. Spaceborne passive microwave imagers can, in principle, use this radiation to estimate SST under a much wider range of atmospheric conditions than can infrared radiometers. It has been generally acknowledged that the range of microwave frequencies measured by the SSM/I sensor is not well suited to this purpose. In spite of this, we have devised a model for estimation of SST from SSMA data, using band differences and derived geophysical parameters. We tested the model using data collected in May and June 2001 in a region of the north-west Atlantic Ocean from the central Grand Banks across the Gulf Stream to the northern Sargasso Sea. Temperatures in the region varied from 2/spl deg/C to 23/spl deg/C. The model was calibrated with concurrent AVHRR and SSM/I imagery, and validated against an independent set of AVHRR-SSM/I image pairs. The model reproduced the AVHRR SST values with a mean difference of close to 0 degC, and a standard deviation of /spl plusmn/2.5 degC. The strength of gradients and the direction of isotherms were reproduced very well by the model. These quantities were estimated consistently through cloud, in regions where AVHRR estimates were not possible. The model could not make an estimate of SST however in regions where cloud liquid water was present. Even though the model can provide a reliable estimate of SST from SSM/I in this region in most weather conditions, it has probably a bit too much scatter to be considered as a stand-alone system. SSM/I estimated SST fields however may provide useful input for interpolation systems working to create SST fields from sparse buoy data, or from time sequences of cloud filled AVHRR imagery.
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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 | 0.000 | 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 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".