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

Ching, Jason; Herwehe, Jerold A.; Swall, Jenise L.

doi:10.1016/j.atmosenv.2006.01.021

Cited by 32 publications

(26 citation statements)

References 11 publications

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“…Andréassian et al, 2004;Ching et al, 2006). There are infinitely many spatially and temporally distributed rainfall fields that yield the same average catchment rainfall.…”

Section: Model Structural Errormentioning

confidence: 99%

There are no hydrological monsters, just models and observations with large uncertainties!

Kuczera

Renard²,

Thyer

et al. 2010

Hydrological Sciences Journal

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There are no hydrological monsters, just models and observations with large uncertainties! Hydrol. Sci. J. 55(6), 980-991.Abstract Catchments that do not behave in the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall-runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model error, about which little is currently known. In addition, hydrological observation systems are far from perfect, with the principal catchment forcing (rainfall) often subject to large sampling errors. When ignored or treated simplistically, these errors develop into monsters that destroy our ability to model certain catchments. In this paper, these monsters are tackled using Bayesian Total Error Analysis, a framework that accounts for user-specified sources of error and yields quantitative insights into how prior knowledge of these uncertainties affects our ability to infer models and use them for predictive purposes. A case study involving a catchment with an apparent water balance anomaly (a hydrological monstrosity!) illustrates these concepts. It is found that, in the absence of additional information, the rainfall-runoff record is insufficient to explain this anomaly -it could be due to a large export of groundwater, systematic overestimation of catchment rainfall of the order of 40%, or a conspiracy of these factors. There is "no free lunch" in hydrology. The rainfall-runoff record on its own is insufficient to decompose the different sources of uncertainty affecting calibration, testing and prediction, and hydrological monstrosities will persist until additional independent knowledge of uncertainties is obtained.Key words Bayesian total error analysis; model structural error; data errors; rainfall-runoff models; ill-posedness Il n'ya pas de monstres hydrologiques, juste des modèles et des observations avec de grandes incertitudes! Résumé Les bassins versants se comportant de manière inattendue aux yeux de l'hydrologue mettent en avant les faiblesses des sciences hydrologiques, en particulier le traitement indûment simplifié des incertitudes affectant les données d'entrée du modèle et le modèle lui-même. Un modèle conceptuel est une hypothèse extrêmement simplifiée sur la nature de la transformation pluie-débit. La variabilité spatiale des phénomènes associée à d'inévitables erreurs de conceptualisation des processus conduisent à une erreur structurelle sur le modèle, qui reste encore aujourd'hui largement incomprise. De plus, les appareils et réseaux de mesures hydrologiques sont loin d'être parfaits. En particulier, la principale donnée d'entrée du modèle (la pluie de bassin) est souvent affectée par des erreurs significatives liées à la faible densité spatiale du réseau pluviométrique. Lorsque ces erreurs sont ignorées ou traitées de manière inadéquate, elles se métamorphosent en M...

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“…Andréassian et al, 2004;Ching et al, 2006). There are infinitely many spatially and temporally distributed rainfall fields that yield the same average catchment rainfall.…”

Section: Model Structural Errormentioning

confidence: 99%

There are no hydrological monsters, just models and observations with large uncertainties!

Kuczera

Renard²,

Thyer

et al. 2010

Hydrological Sciences Journal

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“…Ching et al [53,54] developed an approach where they accounted for sub-grid spatial variability using the results of grid model simulations conducted with a fine spatial resolution and proposed the use of additional models, such as a Gaussian dispersion model, to superimpose sub-grid scale variability on the grid model results. Touma et al [55] discuss the pros and cons of the hybrid approach versus other sub-grid scale approaches, including PinG modeling.…”

Section: Hybrid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

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Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to correctly represent processes that occur at very fine scales, such as the near-source transport and chemistry of emissions from elevated point sources, because of their relatively coarse horizontal resolution. Several different approaches have been used to address this limitation, such as using fine grids, adaptive grids, hybrid modeling, or an embedded sub-grid scale plume model, i.e., plumein-grid (PinG) modeling. In this paper, we first discuss the relative merits of these various approaches used to resolve sub-grid scale effects in grid models, and then focus on PinG modeling which has been very effective in addressing the problems listed above. We start with a history and review of PinG modeling from its initial applications for ozone modeling in the Urban Airshed Model (UAM) in the early 1980s using a relatively simple plume model, to more sophisticated and state-of-the-science plume models, that include a full treatment of gas-phase, aerosol, and cloud chemistry, embedded in contemporary models such as CMAQ, CAMx, and WRF-Chem. We present examples of some typical results from PinG modeling for a variety of applications, discuss the implications of PinG on model predictions of source attribution, and discuss possible future developments and applications for PinG modeling. OPEN ACCESSAtmosphere 2011, 2 390

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“…The problem of comparing grid model predictions with point measurements is well 361 known, namely the within-grid cell variability in emission sources due to different land 362 uses, topography, traffic activities, and other characteristics that typically vary at finer 363 scales. This problem is partially solved, but never eliminated entirely, by using finer 364 scale grid sizes or by applying within-grid model treatments for the major point sources 365 (Ching et al, 2006). This is particularly relevant for NO 2 , which usually shows a 366 heterogeneous spatial distribution at the intraurban level, with relatively large contrast 367 within short distances (Lewné et al, 2004).…”

Section: Results 241 242mentioning

confidence: 99%

Evaluation of the CALIOPE air quality forecasting system for epidemiological research: The example of NO2 in the province of Girona (Spain)

Aguilera

Basagaña

Pay

et al. 2013

Atmospheric Environment

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48 49Background: Air quality models are being increasingly used to estimate long-term 50 individual exposures to air pollution in epidemiological studies. Most of them have been 51 evaluated against measurements from a limited number of monitoring stations, which 52 may not properly reflect the exposure characteristics of the study population. 53Methods: We evaluated the performance of the high-resolution CALIOPE air quality 54 forecasting system over a large sample of passive measurements of NO 2 conducted at 55 635 home outdoor locations of the Girona province (Spain) during several 4-week 56 sampling campaigns over one year. Sampling sites were superposed over the 4 km x 4 57 km CALIOPE grid, and average NO 2 modeled concentrations were derived for all 58 measurements conducted during the same sampling campaign at all the sampling sites 59 located within the same grid cell. In addition, the ratio between measured and modeled 60 concentrations for the whole study period at one fixed monitoring station was used to 61 post-process the modeled values at the home outdoor locations. 62Results: The correlation between measured and modeled concentrations for the entire 63 study area (which includes urban settings, middle-size towns, and rural areas) was 0.78. 64After correcting the modeled concentrations by the measured to modeled ratio at the 65 fixed station, they were very similar to the measured concentrations (27.7 µg/m 3 and 66 29.3 µg/m 3 , respectively).However, the performance of the modeling system depends 67 on the type of subarea and is affected by the sub-grid emission sources. 68 Conclusions: The evaluation over the heterogeneous Girona province showed that 69 CALIOPE is able to reproduce the spatial variability of 4-week NO 2 concentrations at 70 the small regional level, but not at the smaller within-city scale. CALIOPE output data 71 is as a valuable tool to complement study-specific air pollution measurements by 72 incorporating regional spatial variability and long-term temporal variability of 73 background pollution in epidemiological research.

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

Cited by 32 publications

References 11 publications

There are no hydrological monsters, just models and observations with large uncertainties!

There are no hydrological monsters, just models and observations with large uncertainties!

Sub-Grid Scale Plume Modeling

Evaluation of the CALIOPE air quality forecasting system for epidemiological research: The example of NO2 in the province of Girona (Spain)

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