Calculation and Validation Method of Cloud Amount by High Spatial Resolution Satellite Data

Liu Jian; Zhang Liyang

Vol.22, NO.1, 2011

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(1): 35-45

Liu Jian, Zhang Liyang. Calculation and validation method of cloud amount by high spatial resolution satellite data. J Appl Meteor Sci, 2011, 22(1): 35-45.

Citation:

Liu Jian, Zhang Liyang. Calculation and validation method of cloud amount by high spatial resolution satellite data. J Appl Meteor Sci, 2011, 22(1): 35-45.

Liu Jian, Zhang Liyang. Calculation and validation method of cloud amount by high spatial resolution satellite data. J Appl Meteor Sci, 2011, 22(1): 35-45.

Citation:

Liu Jian, Zhang Liyang. Calculation and validation method of cloud amount by high spatial resolution satellite data. J Appl Meteor Sci, 2011, 22(1): 35-45.

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Calculation and Validation Method of Cloud Amount by High Spatial Resolution Satellite Data

Liu Jian^,,
Zhang Liyang

Received Date: 2009-11-03
Rev Recd Date: 2010-07-27
Publish Date: 2011-02-28

Abstract

Abstract

Cloud plays an important role in earth-atmosphere radiation balance system, atmospheric circulation and climate change. Surface observation is a regular method to obtain cloud amount data but it is limited by time and place. International Satellite Cloud Climatology Project (ISCCP) offers cloud parameters product with better quality, but the best spatial resolution is just 30 km. Based on re-calibration and accurate re-location to NOAA daily data during 1998—2008, total cloud amount are calculated with improved cloud detection and radiation calculation method, and validated by ISCCP and surface regular observation data. The temporal and spatial resolution (daily and 0.01°×0.01°) of this cloud amount data is much better than ISCCP product. The sub cloud pixel covered problem is also resolved. Compared with ISCCP DX cloud detection data, validation result shows that clear pixel detection consistence reaches 0.70, cloud pixel detection consistence reaches 0.60, and total cloud detection consistence reaches 0.57. For cloud amount, the coefficient between the calculated cloud amount and surface observation is higher than 0.70. The main differences between cloud amount of ISCCP and calculated data come from two aspects. First, ISCCP method doesn't consider sub-pixel problem reasonably. If one pixel is covered by cloud, ISCCP method regards its cloud amount as one while with the radiation calculation method, clear and completely cloudy cover radiation is calculated, and then every pixel cloud amount according to its radiation value is calculated. Second, different spatial resolution and targets influence the evaluation of the two sets of data. Limited by observation angles and time, ground and satellite observations are not the same. The validation shows that the calculated long time series cloud parameters with high temporal and spatial resolution have good quality, and could play important role in weather analysis and climate change research.
- satellite data;
- cloud amount;
- high spatial resolution;
- long time series

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References(15)

Figures and Tables

Fig. 1 The temperal distribution of calculated cloud detection accuracy (a), cloud detection hit rate (b), clear detection hit rate (c), Threat Score (d) in 1998

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Fig. 2 The temporal distribution of calculated cloud detection accuracy (a), cloud detection hit rate (b), clear detection hit rate (c) and Threat Score (d) in 2007

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Fig. 3 Evaluation parameters for calculated cloud detection and ISCCP DX data in 1998

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Fig. 4 Evaluation parameters for calculated cloud detection and ISCCP DX data in 2007

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Fig. 5 The temporal distrubution of CCA relation coefficients of retrieval cloud amount and ground observation

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Fig. 6 Comparison of visible image (a), ground observation (b) and retrieval cloud amount of NOAA (c) on 12 July 2007

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Fig. 7 The relationship of the brightness temperature bias about clear (a) and cloudy (b) to retrieval cloud amount

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Table 1 Contingency table for NOAA and ISCCP cloud detection

	NOAA云像元	NOAA晴空像元
ISCCP云像元	A	B
ISCCP晴空像元	C	D

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Table 2 Difference analysis between retrieval cloud amount and ground observation

月份	有效数据天数/d	平均偏差值/%	偏差的标准方差
1	31	4.46	0.06
2	28	3.64	0.04
3	28	6.91	0.04
4	30	5.54	0.04
5	27	7.47	0.05
6	30	15.13	0.03
7	31	16.03	0.02
8	31	14.92	0.03
9	30	11.13	0.05
10	29	7.17	0.06
11	29	0.80	0.03
12	31	3.33	0.06

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