Spatiotemporal Distributions of Cloud Parameters and the Temperature Response Over the Mongolian Plateau During 2006–2015 Based on MODIS Data

Bao, Shanhu; Letu, Husi; Zhao, Chuanfeng; Tana, Gegen; Shang, Huaming; Wang, Tianxing; Bi, Lijun; Bao, Yuhai; Purevjav, Gomboluudev; He, Jie; Jun, Zhao

doi:10.1109/jstars.2018.2857827

Cited by 19 publications

(17 citation statements)

References 36 publications

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“…The higher coverage of clouds in oceanic areas and lower coverage of clouds in land areas are similar to findings in a number of earlier studies (e.g., Danso et al ., 2019; Zhao et al ., 2019). The TCC distribution is closely related to topographical features, and the relationship between cloud cover distribution and topographic height shows significant regional characteristics (Bao et al ., 2018). The South Korean altitude variation is not high, and therefore, the regional variation of TCC values does not show significant pattern, with 50–56% in the land areas of the country.…”

Section: Spatial Mean Total Cloud Covermentioning

confidence: 99%

Spatiotemporal variability of total cloud cover measured by visual observation stations and their comparison with ERA5 reanalysis over South Korea

Ansari

Bae

Lee

2020

Intl Journal of Climatology

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This analysis investigates the spatiotemporal variation of total cloud cover (TCC) across South Korea from 96 visual observation stations (VOS) during the years 2010-2019. The thin-plate spline interpolation technique is used to interpolate the spatial variation of TCC within the country. The annual average of TCC distribution is closely related to topographical features and does not show excessive variation, with 50-56% over land. The seasonal TCC distribution has the lowest (highest) pattern in spring (summer), with a minimum (maximum) pattern of approximately 45% (95%). The lowest pattern in spring and highest pattern in summer may be due to the combined effect of lower cloud cover with TCC during March, and higher cloud cover with TCC from June to July. Interpolated VOS data are compared with the TCC obtained from ERA5 reanalysis products. The correlation coefficient between VOS and ERA5 is higher than 0.80 on the eastern coast, and approximately 0.20-0.40 on the western coast, indicating a strong and poor sampling effect. The largest variability between VOS and ERA5 is observed during the winter season, with a percentage deviation of −70-20%, possibly due to inaccurate cloud detection under cold low-level atmospheric temperatures or snow cover. In summer, variability is −35-40%, which is slightly less than that in winter, possibly because of the incorrect detection of frequently occurring cumulus clouds. Principal component analysis of the TCC variability used to examine the relationship with other climate parameters. The first principal component, containing the major portion, correlated with precipitation, diurnal temperature, humidity, atmospheric pressure, and wind speed, with correlation coefficients of 0.293, 0.246, −0.360, −0.258, and 0.470, respectively, indicating positive or negative relationship. We believe the outcomes from this study will contribute a better understanding of climate over South Korea and establish links between East Asia and the Pacific Ocean cloud formation.

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Section: Spatial Mean Total Cloud Covermentioning

confidence: 99%

Spatiotemporal variability of total cloud cover measured by visual observation stations and their comparison with ERA5 reanalysis over South Korea

Ansari

Bae

Lee

2020

Intl Journal of Climatology

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“…Clouds are among the most common and important meteorological phenomena, covering over 66 % of the global surface (Rossow and Schiffer, 1991;Carslaw, 2009;Stephens, 2005;Zhao et al, 2019;Wang and Zhao, 2017). The analysis of cloud condition and cloud cover plays a key role in various applications (Papin et al, 2002;Yang et al, 2014;Yuan et al, 2015;Li et al, 2018;Ma et al, 2018;Bao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation

et al. 2020

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Abstract. Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.

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“…Many studies have been carried out to obtain cloud properties from in situ aircraft measurements, ship‐based or ground‐based remote sensing, and satellite remote sensing. Due to the large spatial coverage and long‐term continuous records, satellite remote sensing is widely used in statistical analysis of both regional and global cloud properties, along with the model evaluation studies, compared to other types of measurements (Bao et al, 2018; Jiang et al, 2012; Li et al, 2018; Platnick et al, 2003; Rossow & Schiffer, 1991; Stephens et al, 2002; Winker et al, 2009; Zhao, Chen, et al, 2019). However, large uncertainties could exist in the satellite remote sensing observations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cloud Microphysical Properties Derived From MODIS and Himawari‐8 Using In Situ Aircraft Measurements Over the Southern Ocean

Zhao

Wang

et al. 2020

Earth and Space Science

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Cloud microphysical properties from aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study are used to evaluate the cloud products from the geostationary satellite Himawari-8 (H-8) and the polar-orbiting satellite the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the in situ aircraft observations when aircraft flew horizontally near cloud tops, the cloud droplet effective radius (r e ) and number concentration (N d ) from H-8 (MODIS) are 33% (26%-31%) and 2% (9-13%) larger. Both the H-8 and MODIS retrievals behave similarly for liquid-only and mixed-phase low-level clouds, indicating the weak sensitivity of the satellite cloud retrieval performance to cloud phase. The r e and N d of the cloud profiles from aircraft measurements were also used to compare with the satellite product. It shows that H-8 r e and N d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions (highest 20%) of the clouds. Roughly, the r e overestimation by H-8 decreases from 18% to 3% when considering the upper portions of clouds compared to all cloud layer averages, except for one case with drizzles appeared. In addition, the performance of MODIS r e and N d is highly dependent on the wavelengths the retrieval method uses. The droplet r e retrievals using wavelength of 1.6 μm have much larger biases than that using the other two channels. The potential effects of the cloud vertical variation and the photon penetration depth, the cloud heterogeneity, the cloud droplet size spectra, and the drizzle on satellite retrievals have also been discussed. is a co-corresponding author with Chuanfeng Zhao Key Points:• Himawari-8 (MODIS) cloud product overestimates r e near cloud top by 33% (26-31%) over Southern Ocean • Himawari8 r e and N d agree better with aircraft measurements when considering only the in situ data acquired in the upper portions of clouds • The performance of MODIS retrievals depends on the wavelength, with larger biases at 1.6 μm than 2.1 and 3.7 μm

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Spatiotemporal Distributions of Cloud Parameters and the Temperature Response Over the Mongolian Plateau During 2006–2015 Based on MODIS Data

Cited by 19 publications

References 36 publications

Spatiotemporal variability of total cloud cover measured by visual observation stations and their comparison with ERA5 reanalysis over South Korea

Spatiotemporal variability of total cloud cover measured by visual observation stations and their comparison with ERA5 reanalysis over South Korea

SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation

Evaluation of Cloud Microphysical Properties Derived From MODIS and Himawari‐8 Using In Situ Aircraft Measurements Over the Southern Ocean

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