Result: On joint deterministic grid modeling and sub-grid variability conceptual framework for model evaluation

Title:
On joint deterministic grid modeling and sub-grid variability conceptual framework for model evaluation
Authors:
CHING, Jason, HERWEHE, Jerold, SWALL, Jenise
Source:
Special Issue on Model Evaluation: Evaluation of Urban and Regional Eulerian Air Quality ModelsAtmospheric environment (1994). 40(26):4935-4945
Publisher Information:
Oxford: Elsevier Science, 2006.
Publication Year:
2006
Physical Description:
print, 18 ref
Original Material:
INIST-CNRS
Subject Terms:
Environment, Environnement, Climatology, meteorology, Climatologie, météorologie, Pollution, Toxicology, Toxicologie, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Pollution, Pollution atmosphérique, Atmospheric pollution, Méthodes d'analyse, Analysis methods, Donnée observation, Observation data, Dato observación, Evaluation performance, Performance evaluation, Evaluación prestación, Maillage, Grid pattern, Celdarada, Méthode sous maille, Subgrid scale method, Metodo submalla, Performance algorithme, Algorithm performance, Resultado algoritmo, Pollution air, Air pollution, Contaminación aire, Qualité air, Air quality, Calidad aire, Résolution spatiale, Spatial resolution, Resolución espacial, Résultat mesure, Measurement result, Resultado medición, Simulation numérique, Numerical simulation, Simulación numérica, Zone urbaine, Urban area, Zona urbana, CMAQ fine scale modeling, Multiscale air-quality modeling, Neighborhood-scale models, Sub-grid distributions, Sub-grid variability
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Atmospheric Sciences Modeling Division, ARL, NOAA, RTP, NC, United States
ISSN:
1352-2310
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18043218
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Pollution
Accession Number:
edscal.18043218
Database:
PASCAL Archive

Further Information

The general situation (but exemplified in urban areas), where a significant degree of sub-grid variability (SGV) exists in grid models poses problems when comparing grid-based air-quality modeling results with observations. Typically, grid models ignore or parameterize processes and features that are at their sub-grid scale. Also, observations may be obtained in an area where significant spatial variability in the concentration fields exists. Consequently, model results and observations cannot be expected to be equal. To address this issue, we suggest a framework that can provide for qualitative judgments on model performance based on comparing observations to the grid predictions and its SGV distribution. Further, we (a) explore some characteristics of SGV, (b) comment on the contributions to SGV and (c) examine the implications to the modeling results at coarse grid resolution using examples from fine scale grid modeling of the Community Multi-scale Air Quality (CMAQ) modeling system.