Downwelling surface solar irradiance in the tropical Atlantic Ocean: a comparison of re-analyses and satellite-derived data sets to PIRATA measurements
Bibliographic record
Abstract
Abstract. This paper assesses the merits and drawbacks of several data sets of the solar downwelling radiation received at the surface of the tropical Atlantic Ocean where the field of solar radiation is hardly known. The data sets are compared to qualified measurements of hourly irradiance made at five buoys of the PIRATA network for the period 2012–2013. The data sets comprise the re-analyses MERRA 2 and ERA 5 and three satellite-derived data sets: HelioClim 3v5, SARAH 2 and CAMS Radiation Service v2. The re-analyses often report cloud-free conditions while actual conditions are cloudy and reciprocally, actual cloudless conditions as cloudy. The medium and high level clouds exhibit more bias than the low level clouds. The re-analyses poorly correlate with the optical state of the atmosphere derived from the measurements. The actual irradiance field is spatially distorted by re-analyses, especially for MERRA 2. Performances are similar between the three satellite-derived data sets. They correlate well with the optical state of the atmosphere and reproduce well the dynamics of the solar irradiance. The three data sets exhibit overestimation with the lowest biases reached by CAMS Radiation Service v2. The bias of HelioClim 3v5 is fairly similar from one location to the other which means that the actual spatial gradients are well reproduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".