Spatial Distribution and Seasonal Variation of Cloud over China Based on ISCCP Data and Surface Observations

Li, Yunying; Yu, Rucong; Xu, Youping; Zhang, Xuehong

doi:10.2151/jmsj.2004.761

Cited by 75 publications

(42 citation statements)

References 10 publications

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“…The large positive MBE mainly located in southern China, in which the corresponding RMSE values are relatively large. This phenomenon can be easily explained, because southern China (20-35 • N, 103-120 • E) is the largest cloudy subtropical continental region in China (Yu et al, 2001), which was also confirmed by Li et al (2004) based on multi-year ISCCP data and surface cloud observations. If cloud distribution becomes more complicated, the accuracy of cloud parameters estimates (see Sect.…”

Section: Validation Of Hourly Ssr In the Haihe River Basinmentioning

confidence: 81%

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

et al. 2016

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Abstract. Cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporalresolution SSR retrievals. In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m −2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m −2 (or 3.5 %) and 98.5 W m −2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m −2 (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m −2 (or 19.1 %) and 22.1 W m −2 (or 12.3 %), respectively. The accuracy is comparable to or even higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

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Section: Validation Of Hourly Ssr In the Haihe River Basinmentioning

confidence: 81%

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

et al. 2016

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“…There were some special features in different seasons. Unlike the other three seasons, summer has the highest frequency in the −10% to 0% range, which might due to the high occurrence frequency of cirrus in summer than in the other seasons since MODIS could detect more cirrus than the visual observation (Li et al 2004). Spring had higher frequency in the range of 60−100% (frequency: 4.35%) than the other seasons.…”

Section: Comparison Between Tccs and Tccmmentioning

confidence: 88%

Consistency and differences between remotely sensed and surface observed total cloud cover over China

Lu¹,

Zhang

Cai

2015

International Journal of Remote Sensing

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In this study, we conducted a comparison between surface-observed total cloud cover (TCCs) and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived total cloud cover (TCCm) over China. A statistical method was applied to estimate the average field of view (FOV) of surface observers, and the radius range of FOV was 20-25, 25-35, 35-50, and 25-45 km for spring, summer, autumn, and winter, respectively. More differences would be added in the comparison when the satellite's FOV was smaller or larger than the average FOV. Monthly mean TCCs was 74.78%, 74.41%, 66.5%, and 74.06% for each season and the corresponding TCCm was 75.27%, 78.34%, 73.82%, and 82.12%. The correlation between two data sets was stronger in spring (0.727) and summer (0.736) than in autumn (0.710) and winter (0.667). Over 60% of the differences were within the −10% to 10% range, and more differences occurred for smaller TCCs. As a special feature, we found that the dust, haze, and snow cover over specific regions in China were the possible causes of the significant differences. Generally, these two data sets were in good agreement over China, and can complement each other especially in those significant difference cases to provide more accurate TCC data sets.

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“…The mid-to low-level divergence difference (or largescale rising motion that lies between) and the static stability are two primary large-scale dynamic and thermodynamic factors governing EC stratus clouds Li and Gu 2006;Zhang et al 2013). The dynamic component is examined by showing a vertical cross section for the cold-season mean divergence at each layer (Fig.…”

Section: A Simulated Dynamic and Thermodynamic Environmental Conditimentioning

confidence: 99%

“…The EC stratus clouds have strong relations with the orography-induced ambient environment Li and Gu 2006;Zhang et al 2013). They usually form under a stable stratification featured with moistening at the middle and low levels.…”

Section: Introductionmentioning

confidence: 99%

Simulations of Stratus Clouds over Eastern China in CAM5: Sensitivity to Horizontal Resolution

Zhang

Chen

2014

Journal of Climate

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This paper evaluates the simulations of stratus clouds over eastern China (EC) in the Community Atmosphere Model, version 5 (CAM5), with an emphasis on the impact of changing horizontal resolutions on the performance. CAM5 in all experiments generally satisfactorily simulates the cloud radiative features over EC, including the spatial distributions of the continental shortwave cloud radiative forcing (SWCF) and stratus regimes, the responses of SWCF to the dynamic and thermodynamic ambient environment, and several relations in the environmental fields that are favorable to the stratus formation. Meanwhile, all experiments suffer from similar biases. Models tend to underestimate the stratus amount because of a corresponding underestimate of stratus occurrence frequency, while the stratus amount when present (AWP) is generally higher than that in the observation. Models also simulate similar errors in the environmental fields. The differences between low-and high-resolution experiments are distinct. An increase of resolution enhances the SWCF in southern China, but the skill deteriorates in the Sichuan basin. Correspondingly, the stratus amount increases in southern China from low-to high-resolution experiments, mainly because of more stratus occurrences, which are found to be related to the better represented environmental fields in the high-resolution experiments, especially the dynamic component. Several relations in the ambient environment are also slightly improved in the high-resolution experiments. Meanwhile, the reason for the decrease of stratus AWP within the Sichuan basin, which is mainly responsible for the decreased stratus amounts and weaker SWCF from low-to high-resolution experiments, is also discussed.

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Spatial Distribution and Seasonal Variation of Cloud over China Based on ISCCP Data and Surface Observations

Cited by 75 publications

References 10 publications

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

Consistency and differences between remotely sensed and surface observed total cloud cover over China

Simulations of Stratus Clouds over Eastern China in CAM5: Sensitivity to Horizontal Resolution

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