Application and Evaluation of an Explicit Prognostic Cloud‐Cover Scheme in GRAPES Global Forecast System

Ma, Zhanshan; Liu, Qi‐Jun; Zhao, Chuanfeng; Shen, Xueshun; Wang, Yuan; Jiang, Jonathan H.; Li, Zhe; Yung, Yuk L.

doi:10.1002/2017ms001234

Cited by 56 publications

(37 citation statements)

References 53 publications

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“…Understanding the cloud properties and processes is still not sufficient so that representation of clouds remains as one of the largest uncertainties for future climate prediction in all climate models (Solomon et al, 2007;Jiang et al, 2012;Boucher et al, 2013;Ma et al, 2018). Slingo (1990) suggested that the top-of-the-atmosphere (TOA) radiative forcing by doubled CO 2 can be balanced by a 15-20% increase in the amount of low clouds, 20-35% increase in liquid water path, or 15-20% decrease in cloud mean droplet radius.…”

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confidence: 99%

Fifteen‐year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations

Zhao

Chen

et al. 2019

Intl Journal of Climatology

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Using 15‐year observations obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board both NASA Terra and Aqua from March 2003 to February 2018, this study investigated the spatio‐temporal variations of both macro‐ and micro‐physical cloud properties over China, including cloud fraction (CF), cloud top pressure (CTP), cloud top temperature (CTT), cloud optical thickness (COT), and effective radius (r e) of both liquid water and ice clouds. Multi‐year averaged CF is around 61% over whole China region. However, CF varies with both regions and seasons. The CFs are about 6–8% larger in summer and winter (~64–65%) than in spring and autumn (~58%). By classifying China into five regimes, which are northwestern China (NW), northeastern China (NE), Tibetan Plateau (TP), northern China (N), and southern China (S), there is a clear CF regional distribution pattern. In general, there are large amount of clouds in S and southeast of TP, and small amount in NE, N, NW and most TP. Moreover, there are generally more clouds over ocean than over land, and much more clouds over S than over N. The CFs are larger (smaller) in the afternoon than in the morning over most land (ocean) regions. Furthermore, the largest CF differences between afternoon and morning occur over the TP region in China. COT demonstrates almost the same regional distribution pattern as the CF for all four seasons. Specifically, COT is higher in S than in N, which is most likely associated with the type of clouds and the availability of water vapour. Cloud r e shows larger values in NW and TP than in eastern China regions in all seasons except for summer, which could be related to the heavy aerosol pollution in eastern China regions. Accompanying with the cold cloud tops over TP, a low CTP centre is often located there.

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confidence: 99%

Fifteen‐year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations

Zhao

Chen

et al. 2019

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). Localized and simultaneous cloud conditions can be accurately acquired with a high temporal and spatial resolution of ground-based observed clouds.…”

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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“…However, since the formation of low-level clouds is governed by small-scale turbulent processes, the associated controlling factors of low-cloud fraction (LCF) are complex. Although Ma et al has found a prognostic method of cloud-cover calculation (PROGCS) which has significant advantage over the conventional diagnostic one, the complex controlling factors still are the main sources of the uncertainty in state-of-the-art models [6]. For example, Fan et al found that the aerosol errors have a certain contribution to cloud fraction biases in Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations [7].…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Interannual Variation Characteristics of Low-Cloud Fraction in Different North Pacific Regions

Wang

Zhang

et al. 2019

Atmosphere

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In this study, we use the long-term satellite data to investigate seasonal and interannual variation of low-cloud fraction (LCF) and the associated controlling factors over the eastern and western North Pacific. On the seasonal time scale, the enhanced LCF over the eastern North Pacific in summer is actively coupled with strong estimated inversion strength (EIS) and 700-hPa relative humidity, and the LCF over the western North Pacific in winter is large and mainly caused by increased sensible heat flux and tropospheric low-level cold advection. On the interannual time scale, the increased LCF over the eastern North Pacific in summer is associated with increased EIS and decreased sea surface temperatures, in which the El Niño plays an important role; the enhanced LCF over the western North Pacific in spring and winter has a positive correlation with enhanced sensible heat flux (SHF) and tropospheric low-level cold advection, which can be partly explained by the subpolar frontal zone (SPFZ) intensity.

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Application and Evaluation of an Explicit Prognostic Cloud‐Cover Scheme in GRAPES Global Forecast System

Cited by 56 publications

References 53 publications

Fifteen‐year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations

Fifteen‐year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations

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

Seasonal and Interannual Variation Characteristics of Low-Cloud Fraction in Different North Pacific Regions

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