Evaluating Cloud Contamination in Clear-Sky MODIS Terra Daytime Land Surface Temperatures Using Ground-Based Meteorology Station Observations
Bibliographic record
Abstract
Abstract Environment Canada meteorological station hourly sampled air temperatures Tair at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (<0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (>0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–Tair relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".