Simulation of Severe Dust Events over Egypt Using Tuned Dust Schemes in Weather Research Forecast (WRF-Chem)
Bibliographic record
Abstract
Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to simulate selected severe dust storm events over Egypt in terms of the aerosol optical depth (AOD). Two severe events, which occurred on 22 January 2004 and 31 March 2013, are examined. The analysis includes three dust emission schemes: Goddard Chemistry Aerosol Radiation and Transport (GOCART), GOCART with Air Force Weather Agency (GOCART-AFWA), and GOCART with University of Cologne (GOCART-UOC). Each scheme was tested by adjusting coefficients related to the dust flux. The AOD and Single scattering albedo (SSA) from the model were compared against the same parameters derived from the Moderate-resolution Imaging Spectroradiometer (MODIS). The grid spacing for both of the data sets is 10 km. Results from the March 2013 event were also compared against point measurements from an Aerosol Robotic Network (AERONET) station in Cairo. Using WRF with built-in coefficients, all schemes resulted in underestimating AOD. After tuning the coefficients, it was possible to bring the model results closer to the observations from satellite and AERONET. Each severe event required a different tuning, depending on the origin and composition of the dust storm. Sensitivity analysis for each case is performed to identify the scheme that best simulates the given events based on spatial error distribution. A novel comparison of eigenvalue structures for images of both for AOD and SSA from model and MODIS was used. After tuning, the adjusted coefficient GOCART scheme is found to simulate AOD best across the country in both events. However, the results for the 2004 event from GOCART-UOC were closest to MODIS AOD over Cairo (within 5% bias). On the other hand, GOCART-AFWA produced nearest estimate of AOD for the 2013 event when compared to AERONET measurements (within 7% bias). For both of the events, SSA from GOCART and GOCART-AFWA schemes were found to be comparable to MODIS measurements with accuracy that was close to 98%. The accuracy from GOCART-UOC was around 93%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".