MétaCan
Menu
← Back to cohort

Coupled and Stand-alone Regional Climate Modeling of Intensive Storms in Western Canada

2021· preprint· en· W3122297101 on OpenAlexaffabout
Kai Ernn Gan, Vijay Pal Singh, Thian Yew Gan, Chun Chao Kuo, Holger Schüttrumpf

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWeather Research and Forecasting ModelEnvironmental scienceClimatologyDownscalingClimate modelSensible heatPrecipitationDiurnal cycleLatent heatAtmosphere (unit)MM5MeteorologyAtmospheric sciencesClimate changeMesoscale meteorologyGeographyGeology

Abstract

fetched live from OpenAlex

A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer, and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called WRF (Weather Research Forecasting) was coupled with a land surface scheme (Noah) to simulate intensive storms in central Alberta, Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land surface temperature influence latent and sensible heat fluxes, and modulate both thermal and dynamical characteristics of land and lower atmosphere. WRF was set up in a two-way, three-domain nested framework so that the output of the outermost domain (D1) was used to run the second domain (D2) and the output of D2 was used to run the innermost domain (D3). In two-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from Global Climate Models (GCMs) for the baseline period 1980–2005. A quantile-quantile bias correction method and a regional frequency analysis were applied to develop intensity-duration-frequency (IDF) curves from precipitation simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agreed well with observed rain gauge data of Edmonton. The 5th‐generation NCAR mesoscale atmospheric model (MM5) was also set up in a 3-domain, but one-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by SRES climate scenarios of CMIP3 and WRF-Noah driven by RCP climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s, and to the 2080s.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.034
GPT teacher head0.243
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicClimate variability and models→French-language works237,207→