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您的检索式：作者名="Ismail GULTEPE"
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| 1 | Effects of Additional HONO Sources on Visibility over the North China Plain显示文摘The objective of the present study was to better understand the impacts of the additional sources of nitrous acid(HONO)on visibility, which is an aspect not considered in current air quality models. Simulations of HONO contributions to visibility over the North China Plain(NCP) during August 2007 using the fully coupled Weather Research and Forecasting/Chemistry(WRF/Chem) model were performed, including three additional HONO sources:(1) the reaction of photo-excited nitrogen dioxide(NO*2) with water vapor;(2) the NO2 heterogeneous reaction on aerosol surfaces; and(3) HONO emissions. The model generally reproduced the spatial patterns and diurnal variations of visibility over the NCP well. When the additional HONO sources were included in the simulations, the visibility was occasionally decreased by 20%–30%(3–4 km) in local urban areas of the NCP. Monthly-mean concentrations of NO-3, NH+4, SO2-4and PM2.5were increased by 20%–52%(3–11μg m-3), 10%–38%, 6%–10%, and 6%–11%(9–17 μg m-3), respectively; and in urban areas, monthly-mean accumulationmode number concentrations(AMNC) and surface concentrations of aerosols were enhanced by 15%–20% and 10%–20%,respectively. Overall, the results suggest that increases in concentrations of PM2.5, its hydrophilic components, and AMNC,are key factors for visibility degradation. A proposed conceptual model for the impacts of additional HONO sources on visibility also suggests that visibility estimation should consider the heterogeneous reaction on aerosol surfaces and the enhanced atmospheric oxidation capacity due to additional HONO sources, especially in areas with high mass concentrations of NOxand aerosols. | LI Ying AN Junling Ismail GULTEPE | 2014 | Advances in Atmospheric Sciences2014，31，5： | 3 |
| 2 | Prediction of visibility in the Arctic based on dynamic Bayesian network analysis显示文摘With the accelerated warming of the world,the safety and use of Arctic passages is receiving more attention.Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages.Numerical weather prediction and statistical prediction are two methods for predicting visibility.As microphysical parameterization schemes for visibility are so sophisticated,visibility prediction using numerical weather prediction models includes large uncertainties.With the development of artificial intelligence,statistical prediction methods have received increasing attention.In this study,we constructed a statistical model with a physical basis,to predict visibility in the Arctic based on a dynamic Bayesian network,and tested visibility prediction over a 1°×1°grid area averaged daily.The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6%compared with the inferred visibility from the artificial neural network.However,dynamic Bayesian network can predict visibility for only 3 days.Moreover,with an increase in predicted area and period,the uncertainty of the predicted visibility becomes larger.At the same time,the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data.It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes. | Shijun Zhao Yulong Shan Ismail Gultepe | 2022 | Acta Oceanologica Sinica2022，41，4： | 0 |
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