The Berkeley High Resolution Tropospheric NO_2 product
Bibliographic record
Abstract
We describe upgrades to the Berkeley High Resolution (BEHR) NO2 satellite retrieval product. BEHR v3.0B builds on the NASA version 3 standard Ozone Monitoring Instrument (OMI) tropospheric NO_2 product to provide a high spatial resolution product for a domain covering the continental United States and lower Canada that is consistent with daily variations in the 12km a priori NO_2 profiles. Other improvements to the BEHR v3.0 product include surface reflectance and elevation, and factors affecting the NO_2 a priori profiles such as lightning and anthropogenic emissions.- \n \nWe describe the retrieval algorithm in detail and evaluate the impact of changes to the algorithm between v2.1C and v3.0B on the retrieved NO_2 vertical column densities (VCDs). Not surprisingly, we find that, on average, the changes to the a priori NO_2 profiles and the update to the new NASA slant column densities have the greatest impact on the retrieved VCDs. More significantly, we find that using daily a priori profiles results in greater average VCDs than using monthly profiles in regions and times with significant lightning activity. \n \nThe BEHR product is available as four subproducts on the University of California DASH repository, using monthly a priori profiles at native OMI pixel resolution (https://doi.org/10.6078/D1N086) and regridded to 0.05° × 0.05° (https://doi.org/10.6078/D1RQ3G) and using daily a priori profiles at native OMI (https://doi.org/10.6078/D1WH41) and regridded (https://doi.org/10.6078/D12D5X) resolutions. The subproducts using monthly profiles are currently available from January 2005 to July 2017, and will be expanded to more recent years. The subproducts using daily profiles are currently available for years 2005–2010 and 2012–2014; 2011 and 2015 on will be added as the necessary input data are simulated for those years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.008 |
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".