Satellite estimation of spectral surface UV irradiance: 2. Effects of homogeneous clouds and snow
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Bibliographic record
Abstract
This paper extends the theoretical analysis of the estimation of the surface UV irradiance from satellite ozone and reflectivity data from a clear‐sky case to a cloudy atmosphere and snow‐covered surface. Two methods are compared for the estimation of cloud‐transmission factor C T , the ratio of cloudy to clear‐sky surface irradiance: (1) the Lambert equivalent reflectivity (LER) method and (2) a method based on radiative transfer calculations for a homogeneous (plane parallel) cloud embedded into a molecular atmosphere with ozone absorption. The satellite‐derived C T from the NASA Total Ozone Mapping Spectrometer (TOMS) is compared with ground‐based C T estimations from the Canadian network of Brewer spectrometers for the period 1989–1998. For snow‐free conditions the TOMS derived C T at 324 nm approximately agrees with Brewer data with a correlation coefficient of ∼0.9 and a standard deviation of ∼0.1. The key source of uncertainty is the different size of the TOMS FOV (∼100 km field of view) and the much smaller ground instrument FOV. As expected, the standard deviations of weekly and monthly C T averages were smaller than for daily values. The plane‐parallel cloud method produces a systematic C T bias relative to the Brewer data (+7% at low solar zenith angles to −10% at large solar zenith angles). The TOMS algorithm can properly account for conservatively scattering clouds and snow/ice if the regional snow albedo R S is known from outside data. Since R S varies on a daily basis, using a climatology will result in additional error in the satellite‐estimated C T . The C T error has the same sign as the R S error and increases over highly reflecting surfaces. Finally, clouds polluted with absorbing aerosols transmit less radiation to the ground than conservative clouds for the same satellite reflectance and flatten spectral dependence of C T . Both effects reduce C T compared to that estimated assuming conservative cloud scattering. The error increases if polluted clouds are over snow.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it