Optimization of the computing environment to improve the speed of the modeling (WRF and CMAQ) calculation of the National Air Quality Forecast System

Myoung, Jisu; Kim, Tae-Hee; Lee, Yonghee; Suh, Insuk; Jang, Limsuk

doi:10.5322/jesi.2018.27.8.723

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The deterministic models depend on other models output such as weather and emission models, which also require extensive computing resources. In Myoung et al [2018], they indicate that national air quality forecasting system runs weather, emission, and air quality models and takes 3 hour 40 minutes even without preprocessing and postprocessing while our models need few minutes for forecasting with trained weights. In South Korea, air quality forecasting is done by every 6 hours and models should run on every prediction.…”

Section: Hyperparameter Optimizationmentioning

confidence: 98%

Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss

Kim,

Lee

2021

Preprint

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Forecasting the particulate matter (PM) concentration in South Korea has become urgently necessary owing to its strong negative impact on human life. In most statistical or machine learning methods, independent and identically distributed data, for example, a Gaussian distribution, are assumed; however, time series such as air pollution and weather data do not meet this assumption. In this study, the maximum correntropy criterion for regression (MCCR) loss is used in an analysis of the statistical characteristics of air pollution and weather data. Rigorous seasonality adjustment of the air pollution and weather data was performed because of their complex seasonality patterns and the heavy-tailed distribution of data even after deseasonalization. The MCCR loss was applied to multiple models including conventional statistical models and state-of-the-art machine learning models. The results show that the MCCR loss is more appropriate than the conventional mean squared error loss for forecasting extreme values.

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Section: Hyperparameter Optimizationmentioning

confidence: 98%

Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss

Kim,

Lee

2021

Preprint

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“…The NIER operates an air quality forecast system using the CMAQ model [21,22] along with meteorological inputs provided by the Weather Research Forecasting (WRF) model ( [23]). An operational forecast system has been introduced [24,25] and the model configurations are summarized in Table 1. CMAQ v4.7 was used to estimate the concentrations of atmospheric chemical species.…”

Section: Description Of the Nier Operational Forecast Modelmentioning

confidence: 99%

Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model

et al. 2022

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Currently, significant efforts are being made to enhance the performance of the National Institute of Environmental Research (NIER) operational model. However, the model performance concerning Aerosol Optical Depth (AOD) estimation remains uninvestigated. In this study, three different estimation methods for AOD were implemented using the NIER operational model and validated with satellite and ground observations. In the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) method, AOD exponentially increases with relative humidity owing to a hygroscopic growth factor. However, alternative methods show better performance, since AOD estimation considers the size dependency of aerosol particles and is not sensitive to high relative humidity, which reduces the high AOD in areas with large cloud fractions. Although some R values are significantly low, especially for a single observational comparison and small numerical domain analysis, one of the alternative estimation methods achieves the best performance for diagnosing AOD in the East Asia region.

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Deep particulate matter forecasting model using correntropy-induced loss

Kim

Lee

2021

J Mech Sci Technol

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Optimization of the computing environment to improve the speed of the modeling (WRF and CMAQ) calculation of the National Air Quality Forecast System

Cited by 3 publications

References 13 publications

Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss

Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss

Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model

Deep particulate matter forecasting model using correntropy-induced loss

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