Fine and Coarse Dust Effects on Radiative Forcing, Mass Deposition, and Solar Devices over the Middle East
Bibliographic record
Abstract
In desert regions like the Middle East (ME), dust has a profound impact on the environment, climate, air quality, and solar devices. The size of dust particles determines the extent of these effects. Dust deposition (DD) measurements show that coarse dust particles with geometric radius r > 10 μm comprise most of the deposited mass. Still, these particles are not represented in the current models that are tuned to fit the observed aerosol visible optical depth (AOD). As a result, the existing models and reanalysis products underestimate DD and dust emission (DE) almost three times. This is the first study to constrain the dust simulations by both AOD and DD measurements to quantify the effect of coarse and fine dust using the WRF-Chem model. We found that, on average, coarse dust contributes less than 10% to dust shortwave (SW) radiative forcing (RF) at the surface but comprises more than 70% of DE. Annual mean net RF over the Arabian Peninsula and regional seas locally reaches -25 W m-2. Airborne fine dust particles with radii r < 3 μm are mainly responsible for the significant dimming (5-10%) of solar radiation, cooling the surface and hampering solar energy production. However, dust mass deposition is primarily linked to coarse particles, decreasing the efficiency of Photovoltaic panels by 2-5% per day. Therefore, incorporating coarse dust in model simulations and data assimilation would improve the overall description of the dust mass balance and its impact on environmental systems and solar devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".