Evaluation of Five Reanalysis Products With Radiosonde Observations Over the Central Taklimakan Desert During Summer

Huang, Jie; Yin, Jinfang; Wang, Minzhong; He, Qing; Guo, Jianping; Zhang, Jiantao; Liang, Xudong; Xie, Yanxin

doi:10.1029/2021ea001707

Cited by 17 publications

(9 citation statements)

References 40 publications

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“…The PBLH is a crucial meteorological parameter for air quality analysis. Compared with other reanalysis products, such as JRA-55 and MERRA-2, ERA5 has been recognized as the most promising product for PBLH measurements, with an estimated deviation of approximately 130 m, as compared to measurements using radiosondes released during daytime (Bao and Zhang, 2019;Guo et al, 2021;Huang et al, 2021). The atmospheric thermal factors related to temperature, humidity, and atmospheric stratification stability have a considerable impact on heavy pollution (Ning et al, 2018).…”

Section: Meteorological Observations and Reanalysis Datasetmentioning

confidence: 99%

Two Typical Patterns of Regional PM2.5 Transport for Heavy Air Pollution Over Central China: Rapid Transit Transport and Stationary Accumulation Transport

Wang

Bai

Zhi

et al. 2022

Front. Environ. Sci.

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The regional transport of air pollutants has been identified as a critical factor that affects air quality over downwind receptor areas; however, the regional transport patterns for air pollution episodes remain poorly understood. In this study, we identified two distinct patterns of regional transport: rapid transit transport (RTT) and stationary accumulation transport (SAT), both of which induced severe PM2.5 pollution episodes in central China during 2015–2020. The differences and similarities between the two regional transport patterns of air pollution were characterized by their influencing meteorological factors. Similar meteorological conditions trigger the RTT and SAT, with a high-PM2.5 concentration air mass from the upstream regions transported to the Twain-Hu Basin (THB) by the strong northerly winds with a southward advance of cold air mass. The average rate of increase in the PM2.5 concentration of the RTT (12.5 μg/m3/h) is more than that of the SAT (5.7 μg/m3/h). However, meteorological conditions evolved differently in RTT and SAT. For RTT with the fast passage of cold air, the THB is located behind the strong cold front during the later stage, and these events rapidly ended owing to the northerly winds and wet scavenging of precipitation. For SAT with the slow cold air accumulation, the THB is mainly occupied by a weak high-pressure system with low wind speeds, strong descending airflow, and a low atmospheric boundary layer height, trapping the air pollutants accumulated in the THB and resulting in persistent heavy pollution incidents. This study provides new insights into the meteorological mechanism underlying the formation of severe air pollution episodes over a receptor region owing to the regional transport patterns of air pollutants.

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Section: Meteorological Observations and Reanalysis Datasetmentioning

confidence: 99%

Two Typical Patterns of Regional PM2.5 Transport for Heavy Air Pollution Over Central China: Rapid Transit Transport and Stationary Accumulation Transport

Wang

Bai

Zhi

et al. 2022

Front. Environ. Sci.

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“…Compared to its predecessors, ERA5/ERA5‐land incorporated more historical observation records especially the satellite data in the assimilation and modeling system, hence, credibility in many aspects has been greatly improved. Several studies have stated that ERA5 has higher accuracy for temperature, air pressure, geopotential height, water–heat covariations (i.e., joint variation of ST and SM) over North China (Huai et al., 2021; Huang et al., 2021; M. Li et al., 2020; H. Li et al., 2021). ERA5 also outperforms other reanalysis for wind speed, total cloud cover, surface incident solar radiation, precipitation and precipitation extremes in North China, although their performances are not as good as ground‐based observations (Y.…”

Section: Methodsmentioning

confidence: 99%

“…High mountains (e.g., Tianshan and Kunlun Mountains), desert and sparsely vegetated basins (e.g., Tarim and Junggar Basins) make up most of this area (Figure 1a). Such a variety of complex topography and geographical location contribute to its hot and dry climate in most places (Figures 1b and 1c; Figures S1 and S2 in Supporting Information ) and make it one of the key areas for studying the effects of land–atmosphere interactions (Huang et al., 2021; Shi et al., 2007). In this study, we focused on the boreal summer when LAC strength in China is the strongest during a year (Hua et al., 2016; Miralles et al., 2012; X. Zeng et al., 2010).…”

Section: Methodsmentioning

confidence: 99%

“…Compared to its predecessors, ERA5/ERA5-land incorporated more historical observation records especially the satellite data in the assimilation and modeling system, hence, credibility in many aspects has been greatly improved. Several studies have stated that ERA5 has higher accuracy for temperature, air pressure, geopotential height, water-heat covariations (i.e., joint variation of ST and SM) over North China (Huai et al, 2021;Huang et al, 2021;M. Li et al, 2020;H.…”

Section: Datasetsmentioning

confidence: 99%

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Variation of Surface Air Temperature Induced by Enhanced Land–Atmosphere Coupling During 1981–2020 in Xinjiang, Northwest China

et al. 2023

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Land–atmosphere coupling (LAC) is a critical process in the climate system, and has great effects on surface air temperature over the humid–dry transition zones. Using ERA5/ERA5‐land reanalysis datasets, we show that the summer temperature anomalies over Northwest China (NWC), known as one of the most important LAC hotpots in China, are significantly correlated with the LAC strength during 1981–2020. The stronger the coupling strength between air temperature and land surface, the more pronounced decrease in air temperature and extreme hot days over NWC. Among the four pathways of land–air temperature coupling, including soil moisture (SM)/soil temperature–latent heat flux/sensible heat flux–air temperature coupling, SM–sensible heat flux–air temperature pathway corresponds most closely with the temperature changes in summer. By analyzing the possible local and non‐local physical links in this relationship, we show that the wave trains composed of the intensification of Ural ridge and Central–Asia trough stimulate a meridional fluctuation of westerly airflow over the mid–high latitudes of Eurasia. Large moisture, therefore, converges into NWC, leading to excessive precipitation and deficient incident solar radiation reaching the land surface. In such case, wetter and cooler soil states accelerate the cooling of the environment through feedbacks between the lands, land–atmosphere exchanging fluxes and air temperature locally, accompanied by a significant enhancement in LAC strength. Our study provides insight into the physical processes causing variations in summer temperature over a typical LAC hotspot and suggests that the role of land surface–atmosphere interactions needs to be considered when studying temperature extremes under climate change.

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“…In order to numerically estimate the deviations of the reanalysis data from the measurement data, we calculated the mean absolute errors and the standard deviations of air temperature and wind speed. The mean absolute errors and the standard deviations were estimated using the formulas [29]:…”

Section: Era-5 Reanalysis Datamentioning

confidence: 99%

Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory

Shikhovtsev,

Kiselev,

Kovadlo

et al. 2023

Atmosphere

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In the present article, we study the possibilities of machine learning for the estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24″ N, 66∘53′47″ E) using only Era-5 reanalysis data. Seeing is usually associated with the integral of the turbulence strength Cn2(z) over the height z. Based on the seeing measurements accumulated over 13 years, we created ensemble models of multi-layer neural networks under the machine learning framework, including training and validation. For the first time in the world, we have simulated optical turbulence (seeing variations) during night-time with deep neural networks trained on a 13-year database of astronomical seeing. A set of neural networks for simulations of night-time seeing variations was obtained. For these neural networks, the linear correlation coefficient ranges from 0.48 to 0.68. We show that modeled seeing with neural networks is well-described through meteorological parameters, which include wind-speed components, air temperature, humidity, and turbulent surface stresses. One of the fundamental new results is that the structure of small-scale (optical) turbulence over the Maidanak Astronomical Observatory does not depend or depends negligibly on the large-scale vortex component of atmospheric flows.

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Evaluation of Five Reanalysis Products With Radiosonde Observations Over the Central Taklimakan Desert During Summer

Cited by 17 publications

References 40 publications

Two Typical Patterns of Regional PM2.5 Transport for Heavy Air Pollution Over Central China: Rapid Transit Transport and Stationary Accumulation Transport

Two Typical Patterns of Regional PM2.5 Transport for Heavy Air Pollution Over Central China: Rapid Transit Transport and Stationary Accumulation Transport

Variation of Surface Air Temperature Induced by Enhanced Land–Atmosphere Coupling During 1981–2020 in Xinjiang, Northwest China

Estimation of Astronomical Seeing with Neural Networks at the Maidanak Observatory

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