Assessment on Main Kinds of Satellite Cloud Climate Datasets

Liu Jian; Wang Xijin

doi:10.11898/1001-7313.20170602

Vol.28, NO.6, 2017

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Liu Jian, Wang Xijin. Assessment on main kinds of satellite cloud climate datasets. J Appl Meteor Sci, 2017, 28(6): 654-665. DOI: 10.11898/1001-7313.20170602.

Citation:

Liu Jian, Wang Xijin. Assessment on main kinds of satellite cloud climate datasets. J Appl Meteor Sci, 2017, 28(6): 654-665. DOI: 10.11898/1001-7313.20170602.

Liu Jian, Wang Xijin. Assessment on main kinds of satellite cloud climate datasets. J Appl Meteor Sci, 2017, 28(6): 654-665. DOI: 10.11898/1001-7313.20170602.

Citation:

Liu Jian, Wang Xijin. Assessment on main kinds of satellite cloud climate datasets. J Appl Meteor Sci, 2017, 28(6): 654-665. DOI: 10.11898/1001-7313.20170602.

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Assessment on Main Kinds of Satellite Cloud Climate Datasets

DOI: 10.11898/1001-7313.20170602

Liu Jian^{1
,
,},
Wang Xijin²

1.
National Satellite Meteorological Center, Beijing 100081
2.
Nanjing University of Information Science & Technology, Nanjing 210044

Received Date: 2017-07-10
Rev Recd Date: 2017-10-10
Publish Date: 2017-11-30

Abstract

Abstract

Cloud is not only a key parameter that affects the radiation balance between earth and atmosphere system, but also is an important index to research atmosphere cycle and climate change. Cloud information can be achieved by surface observation, but it is limited by the spatial and temporal distribution of stations. Only satellite observations can provide a continuous synoptic survey of the state of the atmosphere over the entire globe, and satellite remote sensing also has advantages in observation area and time frequency. The operational weather satellite sensors can supply data records as long as more than 40 years, provide major support for cloud climate research. Whereas polar-orbiting cross-track scanning sensors generally only provide daily global coverage at particular local times, geostationary satellites are placed at particular longitudes along the equator and permit higher-frequency temporal sampling.Building the cloud climate dataset is related with some factors, such as recalibration, stable retrieval algorithm and validation. Based on long term satellite data, several cloud climate datasets, such as ISCCP, Patmos-x, CLARA, MODIS-ST, HIRS and so on are built selecting different instruments and different retrieval algorithm. Spatial and temporal resolutions of these cloud climate datasets are also different. Focusing on different properties of these cloud climate datasets including instruments and retrieval algorithm, references are cited to show the accuracy of these cloud climate datasets. Applications of these cloud climate data in weather and climate analysis are also introduced. As an example, the Tibetan Plateau is selected to analyze the difference between Patmos-x and CLARA-A2 that has the same satellite data source and high similar cloud detection algorithm. In long term, the changing trend is similar. The difference between these two cloud datasets is the spatial resolution:For Patmos-x, it's 0.1°, while for CLARA-A2, it's 0.25°. Compared with CLARA-A2, the Patmos-x has smaller cloud amount at day-time and has larger cloud amount at night-time. Based on NOAA-18 and Aqua data, Patmos-x, CLARA-A2 and Aqua/MODIS cloud amount during 2005 and 2015 are compared. Results show that the difference between three kinds of cloud amount is smaller in day-time, especially in summer. The difference increases in night-time, especially in winter and spring. The main cause may come from different observation ability, retrieval algorithm and observation time of different satellites.
- satellite;
- cloud;
- climate data

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References(66)

Figures and Tables

Fig. 1 Day-time equator observation times for satellites

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Fig. 2 Monthly mean cloud fraction at day-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015

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Fig. 3 Monthly mean cloud fraction at night-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015

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Fig. 4 Monthly mean cloud fraction at day-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015

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Fig. 5 Monthly mean cloud fraction at night-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015

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Table 1 Some kinds of cloud climate dataset information

数据集名称	空间分辨率	时间分辨率	时间范围	主要数据源
ISCCP	2.5°×2.5°(C和D系列) 30 km×30 km(DX数据)	3 h, 日, 月	1983—2009年	NOAA，GMS，GOES，METEOSAT
Patmos-x	0.1°×0.1°	每日2次，月	1979年至今	NOAA，Metop
CLARA-A1	0.25°×0.25°	日，月	1982—2009年	NOAA
CLARA-A2	0.25°×0.25°	日，月	1982—2015年	NOAA，Metop
MODIS-ST	1 km×1 km，5 km×5 km	每日2次，月	2000年至今 2003年至今	EOS/Terra EOS/Aqua
HIRS		月	1980—2015年	NOAA，Metop

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