Improving the modelling of short‐wave radiation through the use of a 3D scene construction algorithm
Bibliographic record
Abstract
Active satellite sensors, such as Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat, provide cloud properties that are not available from passive sensors, such as MODerate‐resolution Imaging Spectroradiometer (MODIS). While active sensors provide vertical profiles of clouds, their spatial coverage is limited to their narrow, nadir ground‐track. As a result, estimation of radiation by combining active sensors and broadband instrument has limitations due to their different spatial coverages. This study uses a scene construction algorithm (SCA) and MODIS data to extend two‐dimensional (2D) nadir cloud profiles into the cross‐track direction, and examines how the resulting constructed 3D cloud fields improve simulation of solar radiative transfer. Clouds and the Earth's Radiant Energy System (CERES) radiances are used as references to assess the improvements. While use of constructed 3D cloud fields only slightly impacts mean‐bias errors for instantaneous 20 km CERES footprint‐averaged top‐of‐atmosphere (TOA) radiances, reductions in random errors are about 40%. The largest improvements in TOA radiance simulation are for clouds with small‐scale horizontal inhomogeneity such as stratocumulus and cumulus. In contrast, uniform clouds such as nimbostratus, and deep convective clouds (Dc) show little response to the SCA. The impact of using the SCA on instantaneous surface irradiances is significant for stratocumulus and cumulus, but weak for nimbostratus and Dc. Conversely, SCA significantly influences atmospheric absorption and heating rates for nimbostratus and Dc. Differences in TOA radiances simulated by 1D and 3D transfer models are smaller than differences due to use of only the 2D nadir cross‐sections and the 3D constructed fields. This is because of smoothing of 3D radiative effects when averaged up to CERES footprints. For surface irradiance and atmospheric absorption, however, differences simulated by 1D and 3D transfer models are more comparable to differences that stem from use of 2D and 3D cloud information.
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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 | 0.001 | 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 | 0.001 |
| 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".