The Near-Real-Time, Global, Four-Dimensional Analysis Experiment During the GATE Period, Part I

Miyakoda, K.; Umscheid, L.; Lee, D. H.; Sirutis, Joseph J.; Lusen, R.; Pratte, Francis

doi:10.1175/1520-0469(1976)033<0561:tnrtgf>2.0.co;2

Cited by 34 publications

(9 citation statements)

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“…In the literature, any modelling approach coupling a model and field measurements in order to improve the accuracy of the prediction of a generic physical system is usually referred to as data assimilation [41][42][43][44][45]. Data assimilation methods are used since several decades in atmospheric sciences [46][47][48][49][50]. More recently, they have also been applied in air quality studies [51][52][53][54][55], mainly adopting chemical transport models [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67], and more rarely with urban dispersion models [68].…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

2018

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Abstract:Developing effective strategies for reducing the atmospheric pollutant concentrations below regulatory threshold levels requires identifying the main origins/sources of air pollution. This can be achieved by implementing so called source apportionment methods in atmospheric dispersion models. This study presents the results of a source apportionment module implemented in the SIRANE urban air-quality model. This module uses the tagged species approach and includes two methods, named SA-NO and SA-NOX, in order to evaluate the sources' contributions to the NO 2 concentrations in air. We also present results of a data assimilation method, named SALS, that uses the source apportionment estimates to improve the accuracy of the SIRANE model results. The source apportionment module and the assimilation method have been tested on a real case study (the urban agglomeration of Lyon, France, for the year 2008) focusing on the NO 2 emissions and concentrations. Results of the source apportionment with the SA-NO and SA-NOX models are similar. Both models show that traffic is the main cause of NO 2 air pollution in the studied area. Results of the SALS data assimilation method highlights its ability in improving the predictions of an urban atmospheric models.

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Section: Introductionmentioning

confidence: 99%

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

2018

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“…The current four-dimensional data assimilation scheme at GFDL is a compromise between the two above dynamic methods (Miyakoda et al, 1976;Miyakoda et al, 1978;Stern and Ploshay, 1983). The current model state at the main synoptic times (0000 GMT or 1200 GMT) is used as first guess fields for 01…”

Section: Dynamic Methodsmentioning

confidence: 99%

“…Originally Euler backwards time stepping was used (Miyakoda et al, 1976); at present an NL Machenhauer NMI is used (Stern and Ploshay, 1983).…”

Section: Dynamic Methodsmentioning

confidence: 99%

Outlook for Improved Numerical Weather Prediction Using Satellite Data with a Special Emphasis on the Hydrological Variables,

Kaplan¹,

Hoffman²,

Isaacs³

et al. 1983

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“…According to the development of new observing systems, many advanced analysis systems * 1987 Meteorological Society of Japan have been developed (e.g., Rutherford, 1976;Miyakoda et al, 1976;Schlatter et al, 1976;Bergman, 1979;Lorenc, 1981;Lyne et al, 1982;Bourke et al, 1982;Baker, 1983). Since the long past, the objective analysis has used not only the observations, but also predicted fields as the guess fields.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Sapce-based Observing Systems in the JMA Global Forecast/Analysis System

Kashiwagi

1987

Journal of the Meteorological Society of Japan

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An Observing System Experiment was carried out using the JMA global forecast/analysis system and the FGGE Main Level II-b data sets. Parallel sets of analyses and 5-day forecasts were produced for the three observing system configurations and the impact of the space based observing system was statistically and synoptically evaluated.Space based data are indispensable for the analyses in low latitude and the southern hemisphere. Even in the northern hemisphere, satellite data improve the analysis over the Pacific region and thereby improve the forecast over North America downstream of that region. The impact on the analysis at a certain time level appears as the sum of the impact of data existing at that time and the impact of data from previous time levels assimilated through the analysis and forecast cycles. On the other hand, satellite data have little impact on the forecast around East Asia because sonde data basically determine the quality of the analysis upstream of that region.

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The Near-Real-Time, Global, Four-Dimensional Analysis Experiment During the GATE Period, Part I

Cited by 34 publications

References 0 publications

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

Outlook for Improved Numerical Weather Prediction Using Satellite Data with a Special Emphasis on the Hydrological Variables,

On the Impact of Sapce-based Observing Systems in the JMA Global Forecast/Analysis System

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