Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China
Bibliographic record
Abstract
The 3-km resolution MODerate resolution Imaging Spectroradiometer (MODIS) aerosol product has advantages for local-scale aerosol monitoring over land. This study assessed the accuracy and feasibility of the product over East China and investigated the potential for aerosol climatology studies. The long-temporal aerosol optical depth (AOD) of the product from 2002 to 2015 was collected and analyzed. Validation results show good overall accuracy. The correlation coefficient between MODIS AOD and ground measurements of the Aerosol Robotic Network (AERONET) is 0.79, and 63.1% data points are within the expected error range. However, in some areas, the MODIS AODs are highly overestimated because of bias and noise. Seasonal average AOD maps indicate the spatio-temporal distributions of aerosol. In general, seasonal AOD values follow the sequence summer > spring > fall > winter. Higher AODs (>1.0) usually occur over urban areas and cropland whereas lower values coincide with forest, shrub, and grassland. A simple moving average technique was applied to remove noise. Trend slopes were calculated and the significances were tested. Most areas show remarkable increases in AOD values prior to 2010, followed by significant downward trends. Differences in MODIS AOD were calculated between 2002 and 2009 and 2010 and 2015. Despite significant downward trends after 2010, the AODs are still higher than before 2010. The study demonstrates potential application of the 3 km product in aerosol climatology but confirms that it is crucial to first remove noise.
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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 | 0.000 | 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 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".