Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

被引:13
|
作者
Zhu, Weiwei [1 ]
Wu, Bingfang [1 ,2 ]
Yan, Nana [1 ]
Ma, Zonghan [1 ,2 ]
Wang, Linjiang [1 ,2 ]
Liu, Wenjun [1 ]
Xing, Qiang [1 ]
Xu, Jiaming [1 ,2 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
关键词
sunshine duration; total cloud amount; FY-2G; Three-River Headwaters Region; GLOBAL SOLAR-RADIATION; 3-RIVER HEADWATERS REGION; DAILY EVAPOTRANSPIRATION; MODEL;
D O I
10.3390/atmos11010026
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R-2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Estimating daily sunshine duration over the UK from geostationary satellite data
    Good, Elizabeth
    WEATHER, 2010, 65 (12) : 324 - 328
  • [2] A model for the estimation of the daily global sunshine duration from meteorological geostationary satellite data
    Kandirmaz, H. M.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2006, 27 (22) : 5061 - 5071
  • [3] EMPIRICAL METHOD FOR ESTIMATING SUNSHINE FROM TOTAL CLOUD AMOUNT
    REDDY, SJ
    SOLAR ENERGY, 1974, 15 (04) : 281 - 285
  • [4] A Method to Estimate Sunshine Duration Using Cloud Classification Data from a Geostationary Meteorological Satellite (FY-2D) over the Heihe River Basin
    Wu, Bingfang
    Liu, Shufu
    Zhu, Weiwei
    Yu, Mingzhao
    Yan, Nana
    Xing, Qiang
    SENSORS, 2016, 16 (11):
  • [5] Estimation of monthly sunshine duration using satellite derived cloud data
    Kaba, Kazim
    Erdi, Erdem
    Avci, Mutlu
    Kandirmaz, H. Mustafa
    THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (07) : 5727 - 5743
  • [6] A SHORT INVESTIGATION INTO RELATIONSHIP BETWEEN DURATION OF SUNSHINE AND TOTAL CLOUD AMOUNT
    WEBSTER, FB
    METEOROLOGICAL MAGAZINE, 1969, 98 (1160): : 87 - &
  • [7] AN AUTOMATED METHOD OF ESTIMATING TOTAL CLOUD AMOUNT FROM MESO-SCALE SATELLITE DATA
    MILLER, DB
    BOOTH, AL
    MILLER, RE
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 1970, 51 (03) : 285 - &
  • [8] CLANN: Cloud amount neural network for estimating 3D cloud from geostationary satellite imager
    Lin, Han
    Li, Jun
    Min, Min
    Zhang, Feng
    Wang, Keyue
    Wu, Qunyong
    REMOTE SENSING OF ENVIRONMENT, 2025, 318
  • [9] ESTIMATING CLOUD AMOUNT AND HEIGHT FROM SATELLITE INFRARED RADIATION DATA
    RAO, PK
    DECOTTIS, AG
    KOFFLER, R
    TRANSACTIONS-AMERICAN GEOPHYSICAL UNION, 1971, 52 (04): : 224 - &
  • [10] Generation of a typical meteorological year using sunshine duration data
    Ecevit, A
    Akinoglu, BG
    Aksoy, B
    ENERGY, 2002, 27 (10) : 947 - 954