COMPARING A RANGE OF SIMPLE PLUME RISE MODELS AND MISR AEROSOL HEIGHT MEASUREMENTS
Bibliographic record
Abstract
Abstract. Rapid economic development leads to increasing sources of aerosols from both urban and biomass sources, which in turn have a significant impact on the atmosphere and the environment. There are significant differences however between urban sources, which tend to be emitted at low temperature, and biomass sources, which are co-emitted with a significant amount of heat. In this work, we first analyse the spatial and temporal distribution of aerosol height from 3.5 years of day-by-day global measurements of aerosol plume height from MISR from January 2008 through June of 2011. We next use a simple plume rise model (PRM) based on FRP and various meteorological variables both from MISR and from other data sources. We find that the PRM makes a reasonable reproduction of the MISR measurements in Western Siberia, Alaska, Central Canada, Argentina, and Eastern Europe, although it underestimates the MISR measurements everywhere. We compute the amount of aerosol above the boundary layer as well as its distribution, and find that the PRM can only come close to reproducing this in conditions which are dry and found in extra-tropical regions. In specific we find that there is a slight model improvement when we apply factors to the wind speed. In general, we find the results are optimized when wind speed is adjusted by 20% around the given mean value, and the vertical velocity is adjusted by −20% to +40% of the original value. The best fitting region, Argentina, is obtained with an RMS error (model biased low) of 0.39 km, when the horizontal wind is unadjusted and the vertical wind is adjusted by −20%. We further find that the PRM approach is not applicable over those regions which have the highest magnitude of aerosol emissions, as detected by OMI and MOPITT measurements of NO2 and CO respectively, leading to future plans on how to correct for and improve this approach.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".