Inferring Optical Depth of Broken Clouds above Green Vegetation Using Surface Solar Radiometric Measurements
Bibliographic record
Abstract
A method for inferring cloud optical depth τ is introduced and assessed using simulated surface radiometric measurements produced by a Monte Carlo algorithm acting on fields of broken, single-layer, boundary layer clouds derived from Landsat imagery. The method utilizes a 1D radiative transfer model and time series of zenith radiances and irradiances measured at two wavelengths, λ1 and λ2, from a single site with surface albedos αλ1 < αλ2. Assuming that clouds transport radiation in accordance with 1D theory and have spectrally invariant optical properties, inferred optical depths τ′ are obtained through cloud-base reflectances that are approximated by differencing spectral radiances and estimating upwelling fluxes at cloud base. When initialized with suitable values of αλ1, αλ2, and cloud-base altitude h, this method performs well at all solar zenith angles. Relative mean bias errors for τ′ are typically less than 5% for these cases. Relative variances for τ′ for given values of inherent τ are almost independent of inherent τ and are <50%. Errors due to neglect of net horizontal transport in clouds yield slight, but systematic, overestimates for τ ≲ 5 and underestimates for larger τ. Frequency distributions and power spectra for retrieved and inherent τ are often in excellent agreement. Estimates of τ depend weakly on errors in h, especially when h is overestimated. Also, they are almost insensitive to errors in surface albedo when αλ1 is underestimated and αλ2 overestimated. Reversing the sign of these errors leads to overestimation of τ, particularly large τ. In contrast, the conventional method of using only surface irradiance yields almost entirely invalid results when clouds are broken. Though results are shown only for surfaces resembling green vegetation (i.e., αλ1 ≪ αλ2), the performance of this method depends little on the values of αλ1, and αλ2. Thus, if radiometric data have sufficient signal-to-noise ratios and suitable wavelengths can be found, this method should yield reliable estimates of τ for broken clouds above many surface types.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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 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".