Towards Improving Visibility Forecasts in Taiwan: A Statistical Approach
Bibliographic record
Abstract
Re duced vis i bil ity is a ma jor safety con cern at air ports lead ing to flight de lays or di ver sions.The pri mary mo ti va tion for this study is to en hance un der stand ing of vis i bil ity and to build a sim ple, prac ti cal vis i bil ity fore cast ing method across Tai wan in an op er a tional set ting by us ing readily avail able, ground-based observations.This pa per pres ents for the first time a sys tem atic, quan ti ta tive ex am i na tion of the con trols on vis i bil ity over the en tire Tai wan re gion by adopt ing a sta tis ti cal ap proach re lat ing vis i bil ity to var i ous phys i cal vari ables.A mul ti ple lin ear re gres sion is car ried out for the early morn ing hours dur ing the months of November ~ April, when vis i bil ity is es pe cially low.The re gres sion re veals that on the west coast of Tai wan, the con cen tra tion of fine par ti cles (PM 2.5 ) and rel a tive hu mid ity (RH) are most re lated to vis i bil ity, and to a lesser ex tent, coarse par ti cles (PM 10-2.5 ) and windspeed.The sig nif i cantly el e vated PM con cen tra tions would, there fore, cause a marked re duc tion in vis i bil ity on Tai wan's west coast -where most of Tai wan's pop u la tion and anthropogenic PM emis sions are found.Vis i bil ity on the east coast ap pears to be con trolled by some what dif fer ent mech a nisms, with rain fall play ing a larger role.The prob a bil ity of oc cur rence of es pe cially low vis i bil ity (£ 1600 m) was re vealed by lo gis tic re gres sion to be the most sta tis ti cally re lated to RH, and, to a less extent, to PM concentrations.An un cer tainty anal y sis to un der stand cur rent lim i ta tions in pre dict ing vis i bil ity in di cated that 24-hour vis i bil ity for ecasts were dom i nated by a) er rors in fore cast ing RH and b) in ad e qua cies in the adopted sta tis ti cal model, fol lowed by c) er rors in PM fore casts.Hence to min i mize the con sid er able un cer tain ties in vis i bil ity fore casts, which could reach stan dard de vi a tions of sev eral thou sand me ters, fu ture work needs to adopt a more so phis ti cated sta tis ti cal model as well as re duce the con sid er able er rors in pre dict ing RH.Fi nally, the un cer tain ties as so ci ated with PM can be re duced by improving PM emission estimates through an inverse analysis method.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".