scholarly journals Numerical Simulations of Local Circulation and Cumulus Generation over the Loess Plateau, China

2009 ◽  
Vol 48 (4) ◽  
pp. 849-862 ◽  
Author(s):  
Masanori Nishikawa ◽  
Tetsuya Hiyama ◽  
Kazuhisa Tsuboki ◽  
Yoshihiro Fukushima
Keyword(s):  
Numerical Simulations ◽  
Loess Plateau ◽  
Cumulus Cloud ◽  
The Loess Plateau ◽  
Local Circulation ◽  
Cumulus Clouds ◽  
Thermal Generation ◽  
The Real ◽  
Flat Terrain ◽  
Vertical Scales

Abstract The Loess Plateau of China consists of dissected flat tablelands with steep gullies. To evaluate the effect of topography on local circulation and cumulus generation over the Loess Plateau, numerical simulations of atmospheric boundary layer (ABL) development were conducted using a cloud-resolving nonhydrostatic model. Two types of numerical simulation were carried out under two sets of bottom boundary conditions: real terrain and flat terrain. The differences in ABL development and cumulus generation between the flat- and real-terrain conditions are described and the local circulation structures induced by ABL development are illustrated. More cumulus clouds were generated over the real terrain than over the flat terrain. In the real-terrain case, large amounts of cumulus cloud were generated on the windward slopes and on the edge of the tableland, with updrafts caused by thermal generation and a local circulation developing with horizontal and vertical scales of several kilometers. Cumulus clouds clearly developed at the top of the ABL because the water vapor is nonhomogeneously lifted by the local circulation on windward slopes and on edge of the tableland. Thus, the topography of the Loess Plateau plays an important role in cumulus generation.

scholarly journals Vertical Distributions of Aerosol and Cloud Microphysical Properties and the Aerosol Impact on a Continental Cumulus Cloud Based on Aircraft Measurements From the Loess Plateau of China

2022 ◽  
Vol 9 ◽  
Author(s):  
Zhaoxin Cai ◽  
Zhanqing Li ◽  
Peiren Li ◽  
Junxia Li ◽  
Hongping Sun ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Water Content ◽  
Loess Plateau ◽  
Cloud Droplet ◽  
Vertical Profiles ◽  
Aircraft Measurements ◽  
Cumulus Cloud ◽  
The Loess Plateau ◽  
Cumulus Clouds ◽  
Microphysical Properties

Based on aircraft measurements of aerosols and continental cumulus clouds made over the Loess Plateau of China (Xinzhou, Shanxi Province) on 30 July 2020, this study focuses on the vertical profiles of microphysical properties of aerosols and cumulus clouds, and use them to study aerosol-cloud interactions. During the study period, the boundary layer was stable with a height ∼1,500 m above sea level. Aerosols in the boundary layer mainly came from local emissions, while aerosols above this layer were mostly dust aerosols transported over long distances. Vertical profiles of aerosols and cloud condensation nuclei were obtained, and aerosol activation ratios at different supersaturation (SS) levels ranged from 0.16 to 0.32 at 0.2% SS and 0.70 to 0.85 at 0.8% SS. A thick cumulus cloud in the development stage was observed from the bottom to the top with the horizontal dimension of 10 km by 7 km, the cloud-base height of 2,450 m (15.8°C), and the cloud-top height of 5,400 m (−3°C). The maximum updraft velocity near the cloud top was 13.45 m s−1, and the maximum downdraft velocity occuring in the upper-middle part of the cloud was 4.44 ms−1. The temperature inside the cloud was higher than the outside, with their difference being positively correlated with the cloud water content. The temperature lapse rate inside the cloud was about −6.5°C km−1. The liquid water content and droplet effective radius (Re) increased with increasing height. The cloud droplet number concentration (Nc) increased first then decreased, peaking in the middle and lower part of the cloud, the average values of Nc and Re were 767.9 cm−3 and 5.17 μm, respectively. The cloud droplet spectrum had a multi-peak distribution, with the first appearing at ∼4.5 μm. SS in the cloud first increased then decreased with height. The maximum SS is ∼0.7% appearing at ∼3,800 m. The conversion rate of intra-cloud aerosols to cloud droplets was between 0.2 and 0.54, with the ratio increasing gradually with increasing height. The cloud droplet spectral dispersion and Nc were positively correlated. The aerosol indirect effect (AIE) was estimated to be 0.245 and 0.16, based on Nc and Re, respectively. The cloud droplet dispersion mainly attenuated the AIE, up to ∼34.7%.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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