Validation and Spatiotemporal Distribution of GEOS-5–Based Planetary Boundary Layer Height and Relative Humidity in China

Si, Ying; Li, Shenshen; Chen, Liangfu; Yu, Chao; Wang, Zifeng; Wang, Hongmei

doi:10.1007/s00376-017-6275-3

Cited by 12 publications

(6 citation statements)

References 47 publications

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“…Based on the fact that the availability of the data used in this paper decreases significantly with height (Figure 1c), data continuity at high altitudes is relatively poor. However, the BLH in the region is typically below 2,000 m in early summer (only 2.3% of the BLH exceeded 2,000 m during the observation period according to ERA5), and the extent of our study includes both the boundary layer and the free atmosphere, which are quite different (J. Guo et al., 2016, 2019, 2021; Si et al., 2018). That is, the vertical height levels selected in this study cover the lower troposphere.…”

Section: Methodsmentioning

confidence: 78%

“…However, the BLH in the region is typically below 2,000 m in early summer (only 2.3% of the BLH exceeded 2,000 m during the observation period according to ERA5), and the extent of our study includes both the boundary layer and the free atmosphere, which are quite different (J. Guo et al, 2016Guo et al, , 2019Guo et al, , 2021Si et al, 2018). That is, the vertical height levels selected in this study cover the lower troposphere.…”

Section: Datamentioning

confidence: 93%

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Wind Gust Parameters in the Lower Troposphere Based on Doppler Lidar Data

Zhou

Zhang

et al. 2023

JGR Atmospheres

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Section: Methodsmentioning

confidence: 78%

Section: Datamentioning

confidence: 93%

Wind Gust Parameters in the Lower Troposphere Based on Doppler Lidar Data

Zhou

Zhang

et al. 2023

JGR Atmospheres

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“…This underestimation of RH may affect the convective processes like PBLH. To reduce the uncertainties in the model simulation, the quality of the input meteorological data like PBLH and RH datasets reportedly needs further improvement [84].…”

Section: Evaluation Of Surface Meteorologymentioning

confidence: 99%

“…Therefore, in this study, we illustrate the seasonal BC and CO correlation over Dibrugarh using surface observations, multiple models, and satellite assimilated data (Figure 4). In order to ascertain the atmospheric pollution picture over Dibrugarh, the measured BC and CO data available in 5 min intervals are used for calculating the correlation between them [38,84]. In doing so we get a better correlation between BC and CO than the daily average observed values.…”

Section: Local Sourcesmentioning

confidence: 99%

Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin

et al. 2019

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The temporal distributions of meteorological drivers and air pollutants over Dibrugarh, a location in the upper Brahmaputra basin, are studied using observations, models and reanalysis data. The study aims to assess the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the WRF coupled with Sulfur Transport dEposition Model (WRF-STEM), and Copernicus Atmosphere Monitoring Service (CAMS) model over Dibrugarh for the first time. The meteorological variables and air pollutants viz., black carbon(BC), carbon monoxide(CO), sulphur dioxide(SO2), Ozone(O3), and oxides of Nitrogen(NOx) obtained from WRF-Chem, WRF-STEM and CAMS are evaluated with observations. The source region tagged CO simulated by WRF-STEM delineate the regional contribution of CO. The principal source region of anthropogenic CO over Dibrugarh is North-Eastern India with a 59% contribution followed by that from China (17%), Indo-Gangetic Plains (14%), Bangladesh (6%), other parts of India (3%) and other regions (1%). Further, the BC-CO regression analysis is used to delineate the local emission sources. The BC-CO correlations estimated from models (0.99 for WRF-Chem, 0.96 for WRF-STEM, 0.89 for CAMS), and reanalysis (0.8 for Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) are maximum in pre-monsoon whereas surface observations show highest correlations (0.81) in winter. In pre-monsoon season, 90% of the modeled CO is due to biomass burning over Dibrugarh.

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“…Meteorological conditions have monthly differences in the effects on O 3 pollution. For example, the variation in RH is abrupt in North China, with peak values of approximately 66% in July and the trough values of 45% during April (Si et al 2018). In addition, Sun et al (2019) uses sensitivity tests to demonstrate that meteorological conditions variations lead to significant changes in tropospheric O 3 .…”

Section: Introductionmentioning

confidence: 99%