Marine And Coastal Fog: Forecasts and Evaluations
Bibliographic record
Abstract
Accurate marine fog forecast is of importance for human activities in the coastal regions and over the ocean. However, it is still a challenge due to lack of observation and unsatisfactory model performance. This dissertation includes three studies on forecasting marine fog. The first study has summarized the performances of three different models during the FATIMA (Fog And Turbulence Interactions in the Marine Atmosphere) field campaign on Sable Island, Nova Scotia, including two WRF (Weather Research and Forecasting) models and a COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) model. It is found that the models can perform differently on fog prediction with similar errors in temperature, dew point, relative humidity, wind speed and wind direction. Additional tests show that the WRF model can be improved by adjusting the horizontal and vertical domains, and a spin-up time is necessary for forecasting fog. The second study has compared the liquid water content and droplet concentration observations from the field campaign to WRF variables in two periods. It is found that the WRF model produces liquid water contents up to 0.6 g m-3 while the observation has up to 0.3 g m-3, partly due to the updraft speed in the microphysics scheme being too high and causing a high activation rate towards droplets. The surface temperature of the island, not included in the GFS (Global Forecast System) data but assigned by WPS (WRF Preprocessing System) is also too high, causing the fog to incorrectly dissipate in daytime. The last study has used XGBoost, a machine learning model to post-process WRF output. XGBoost is trained with the ERA5 (ECMWF Reanalysis v5) data of St. John’s, Newfoundland and Labrador, and Yarmouth, Nova Scotia. XGBoost can predict fog or clear with given 2 m temperature and relative humidity, 10 m U and V winds, land surface pressure, their corresponding values one hour ago, hour, day and month. Tests using forecasts of the features from WRF in 2024 found that XGBoost improved the recall by up to 0.07 without decreasing the precision, compared to using WRF only. This shows the potential to combine the strength of both models.
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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".