Advanced Search
Article Contents

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China


doi: 10.1007/s00376-022-1464-0

  • Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring–summer and higher in autumn–winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.
    摘要: 碳中和目标背景下,中国未来将显著增加光伏等新能源在能源结构中的占比。光伏太阳能的间接性和不稳定性是太阳能并网利用中的重大挑战之一。除了发展新能源储能等技术之外,发展太阳能短临预报技术是提高太阳能利用率的经济有效途径。我国新一代静止气象卫星风云四号(FY-4A)的发射给太阳能短临预报(<3小时)提供了新的观测手段。本文利用FY-4A多通道反射率数据建立了地表太阳辐照度估算和短临预报系统。该系统由两个关键部分组成,第一部分为基于物理晴天模型和经验云天模型构建的地表太阳辐照度混合估算方法,第二部分为地表太阳辐照度短临预报模型,其核心是通过块状匹配法推导出云运动矢量,进而预报未来3小时内地表太阳辐照度场。该系统目前能够同时实现水平面总辐射和法向直接辐射的估算和短临预报。验证结果表明:水平面总辐射估算值的准确性高于法向直接辐射,二者归一化均方根误差分别为22.4%和45.4%。30-180分钟预测范围内水平面总辐射和法向直接辐射预测值的归一化均方根误差分别在25.1%-30.8%和48.1%-53.4%之间。地表太阳辐照度估算结果的准确性取决于云的出现频率,即春夏较低,秋冬较高。本研究工作表明新一代静止气象卫星在地表太阳辐射短时临近预报中的广阔应用前景,将显著促进我国光伏太阳能能源发展和利用。该系统在华北地区具备良好的性能,未来进一步改进将侧重于对地表太阳辐照度估算模型的校准并推广其在整个中国地区的适用性。
  • 加载中
  • Figure 1.  (a) Geographical location of Xianghe observation site, and (b) the instrument.

    Figure 2.  Monthly proportion of four sky conditions at Xianghe in 2018.

    Figure 3.  (a) The function relation between FY-4A reflectance and the cosine of SZA under the clear-sky condition, and (b) the dynamic range for Xianghe station from the near infrared channel of the FY-4A satellite in 2018.

    Figure 4.  Density plot of REST2 model GHI (a) and DNI (b) versus surface measurements under clear-sky conditions. The black line denotes a 1:1 line. The red lines indicates linear regression results.

    Figure 5.  Density plot of the SSI estimation model GHI (a) and DNI (b) versus SSI measurements under all-sky conditions in 2018. The black line denotes a 1:1 line. The red lines indicate liner regression results.

    Figure 6.  Density plot of the SSI estimation model GHI (a–d) and DNI (e–h) versus observed SSI under different sky conditions. The black line denotes a 1:1 line. The red lines indicate linear regression results.

    Figure 7.  Monthly mean of observed (grey line) and model (orange line) surface solar irradiance GHI (a) and DNI (b) in 2018. Shaded regions indicate one standard error of the mean.

    Figure 8.  An example of FY-4A-based (a–c) nowcasting and (d–f) observation for CI, GHI, and DNI at 0240 UTC on 1 December 2018.

    Figure 9.  The nRMSE (%) and nMBE (%) of SSI under different sky conditions with forecast horizons from 30–180 min.

    Table 1.  Statistical indexes of instantaneous FY-4A SSI in the present study and previous studies.

    No.ReferenceSiteSSIRMSEnRMSEMBEnMBE
    1Present studyXianghe, ChinaGHI106.622.4 28.05.9
    DNI201.945.4107.2 24.1
    2Chen et al. (2020)Shanghai, ChinaGHI 34.7 11.4
    3Jia et al. (2021)Chengde, ChinaGHI120.1 25.9 –4.7–1.0
    DNI230.5 49.3%–97.3 –20.8
    DownLoad: CSV

    Table 2.  Statistical indexes (the mean value, RMSE, nRMSE, MBE, and nMBE) under all-sky conditions with forecast horizons from 0–180 min (units: W m–2).

    SSIMetric0 min30 min60 min90 min120 min150 min180 min
    GHImean475.9469.4479.9510.9500.9494.8485.6
    RMSE106.6117.8125.5138.5144.4143.2149.5
    nRMSE22.425.126.227.128.828.930.8
    MBE28.034.528.327.214.918.93.7
    nMBE5.97.35.95.33.03.80.8
    DNImean444.7430.3438.0452.7447.2441.7419.4
    RMSE201.9207.3208.3220.2222.0228.3232.6
    nRMSE45.448.147.648.649.651.753.4
    MBE107.2105.098.599.287.890.881.1
    nMBE24.124.422.521.919.620.618.6
    DownLoad: CSV
  • Amillo, A. G., T. Huld, and R. Müller, 2014: A new database of global and direct aolar radiation using the eastern meteosat satellite, models and validation. Remote Sensing, 6, 8165−8189, https://doi.org/10.3390/rs6098165.
    Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, 2016: Review of photovoltaic power forecasting. Solar Energy, 136, 78−111, https://doi.org/10.1016/j.solener.2016.06.069.
    Antonanzas-Torres, F., R. Urraca, J. Polo, O. Perpiñán-Lamigueiro, and R. Escobar, 2019: Clear sky solar irradiance models: A review of seventy models. Renewable and Sustainable Energy Reviews, 107, 374−387, https://doi.org/10.1016/j.rser.2019.02.032.
    Arbizu-Barrena, C., J. A. Ruiz-Arias, F. J. Rodríguez-Benítez, D. Pozo-Vázquez, and J. Tovar-Pescador, 2017: Short-term solar radiation forecasting by advecting and diffusing MSG cloud index. Solar Energy, 155, 1092−1103, https://doi.org/10.1016/j.solener.2017.07.045.
    Bai, B., Y. H. Wang, C. Fang, S. Q. Xiong, and X. M. Ma, 2021: Efficient deployment of solar photovoltaic stations in China: An economic and environmental perspective. Energy, 221, 119834, https://doi.org/10.1016/j.energy.2021.119834.
    Beyer, H. G., J. P. Martinez, M. Suri, J. L. Torres, E. Lorenz, S. C. Müller, C. Hoyer-Klick, and P. Ineichen, 2009: D 1.1.3 Report on Benchmarking of Radiation Products. Management and Exploitation of Solar Resource Knowledge. Available from http://www.mesor.org/docs/MESoR_Benchmarking_of_radiation_products.pdf.
    Burandt, T., B. Xiong, K. Löffler, and P.-Y. Oei, 2019: Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors. Applied Energy, 255, 113820, https://doi.org/10.1016/j.apenergy.2019.113820.
    Chen, X. M., Y. Li, and R. Z. Wang, 2020: Performance study of affine transformation and the advanced clear-sky model to improve intra-day solar forecasts. Journal of Renewable and Sustainable Energy, 12, 043703, https://doi.org/10.1063/5.0009155.
    Cros, S., M. Albuisson, M. Lefèvre, C. Rigollier, and L. Wald, 2004: HelioClim: A long-term database on solar radiation for Europe and Africa. Proceedings of Eurosun 2004, Freiburg, Germany, PSE GmbH.
    Cros, S., N. Sébastien, O. Liandrat, and N. Schmutz, 2014: Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting. Proceedings of SPIE 9242, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII, Amsterdam, Netherlands, SPIE,
    Damiani, A., and Coauthors, 2018: Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements. Atmospheric Measurement Techniques, 11, 2501−2521, https://doi.org/10.5194/amt-11-2501-2018.
    Gallucci, D., and Coauthors,, 2018: Nowcasting surface solar irradiance with AMESIS via motion vector fields of MSG-SEVIRI Data. Remote Sensing, 10, 845, https://doi.org/10.3390/rs10060845.
    Gueymard, C. A., 2008: REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation – Validation with a benchmark dataset. Solar Energy, 82, 272−285, https://doi.org/10.1016/j.solener.2007.04.008.
    Gueymard, C. A., and R. George, 2005: Gridded aerosol optical depth climatological datasets over continents for solar radiation modeling. Proceedings of Solar World Congress, Orlando, USA, International Solar Energy Society. [Available online from https://www.semanticscholar.org/paper/GRIDDED-AEROSOL-OPTICAL-DEPTH-CLIMATOLOGICAL-OVER-Gueymard-George/a3e7dad6035e6a35afdccf9bf4b98319436c3014]
    Hammer, A., D. Heinemann, E. Lorenz, and B. Lückehe, 1999: Short-term forecasting of solar radiation: A statistical approach using satellite data. Solar Energy, 67, 139−150, https://doi.org/10.1016/S0038-092X(00)00038-4.
    Huang, C. L., J. Z. Li, W. W. Sun, Q. X. Chen, Q.-J. Mao, and Y. Yuan, 2021: Long-term variation assessment of aerosol load and dominant types over Asia for air quality studies using multi-sources aerosol datasets. Remote Sensing, 13, 3116, https://doi.org/10.3390/rs13163116.
    Huang, G. H., Z. Q. Li, X. Li, S. L. Liang, K. Yang, D. D. Wang, and Y. Zhang, 2019: Estimating surface solar irradiance from satellites: Past, present, and future perspectives. Remote Sensing of Environment, 233, 111371, https://doi.org/10.1016/j.rse.2019.111371.
    IRENA, 2020: Renewable Capacity Statistics 2020: International Renewable Energy Agency (IRENA), Abu Dhabi. [Available online from https://irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020]
    Jia, D. Y., J. J. Hua, L. P. Wang, Y. T. Guo, H. Guo, P. P. Wu, M. Liu, and L. W. Yang, 2021: Estimations of global horizontal irradiance and direct normal irradiance by using Fengyun-4A satellite data in northern China. Remote Sensing, 13, 790, https://doi.org/10.3390/rs13040790.
    Jiang, H., N. Lu, J. Qin, W. J. Tang, and L. Yao, 2019: A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable and Sustainable Energy Reviews, 114, 109327, https://doi.org/10.1016/j.rser.2019.109327.
    Kallio-Myers, V., A. Riihelä, P. Lahtinen, and A. Lindfors, 2020: Global horizontal irradiance forecast for Finland based on geostationary weather satellite data. Solar Energy, 198, 68−80, https://doi.org/10.1016/j.solener.2020.01.008.
    Kleissl, J., 2013: Solar Energy Forecasting and Resource Assessment. Academic Press,
    Lamsal, D., V. Sreeram, Y. Mishra, and D. Kumar, 2018: Kalman filter approach for dispatching and attenuating the power fluctuation of wind and photovoltaic power generating systems. IET Generation, Transmission & Distribution, 12, 1501−1508, https://doi.org/10.1049/iet-gtd.2017.0663.
    Letu, H., T. Y. Nakajima, T.X. Wang, H. Z. Shang, R. Ma, K. Yang, A. J. Baran, J. Riedi, H. Ishimoto, M. Yoshida, C. Shi, P. Khatri, Y. H. Du, L. f. Chen, and J. C Shi, 2021: A new benchmark for surface radiation products over the East Asia-Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite. Bull. Amer. Meteor. Soc, 103, E873−888, https://doi.org/10.1175/BAMS-D-20-0148.1.
    Li, M. Q., E. Virguez, R. Shan, J. L. Tian, S. Gao, and D. Patiño-Echeverri, 2022: High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system. Applied Energy, 306, 117996, https://doi.org/10.1016/j.apenergy.2021.117996.
    Li, T., A. Li, and X. P. Guo, 2020: The sustainable development-oriented development and utilization of renewable energy industry-A comprehensive analysis of MCDM methods. Energy, 212, 118694, https://doi.org/10.1016/j.energy.2020.118694.
    Liu, M. Q., X. A. Xia, D. S. Fu, and J. Q. Zhang, 2021: Development and validation of machine-learning clear-sky detection method using 1-min irradiance data and sky imagers at a polluted suburban site, Xianghe. Remote Sensing, 13, 3763, https://doi.org/10.3390/rs13183763.
    Mouhamet, D., A. Tommy, A. Primerose, and L. Laurent, 2018: Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas. Solar Energy, 169, 565−576, https://doi.org/10.1016/j.solener.2018.05.032.
    Nonnenmacher, L., and C. F. M. Coimbra, 2014: Streamline-based method for intra-day solar forecasting through remote sensing. Solar Energy, 108, 447−459, https://doi.org/10.1016/j.solener.2014.07.026.
    Peng, Z., and Coauthors, 2020: Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China. Journal of Quantitative Spectroscopy and Radiative Transfer, 240, 106672, https://doi.org/10.1016/j.jqsrt.2019.106672.
    Pfeifroth, U., S. Kothe, J. Trentmann, R. Hollmann, P. Fuchs, J. Kaiser, and M. Werscheck, 2019: Surface Radiation Data Set - Heliosat (SARAH) - Edition 2.1. Available from
    Prăvălie, R., C. Patriche, and G. Bandoc, 2019: Spatial assessment of solar energy potential at global scale. A geographical approach. Journal of Cleaner Production, 209, 692−721, https://doi.org/10.1016/j.jclepro.2018.10.239.
    Randles, C. A., and Coauthors, 2017: The MERRA-2 aerosol reanalysis, 1980 Onward. Part I: System description and data assimilation evaluation. J. Climate, 30, 6823−6850, https://doi.org/10.1175/JCLI-D-16-0609.1.
    Razagui, A., K. Abdeladim, K. Bouchouicha, N. Bachari, S. Semaoui, and A. Hadj Arab, 2021: A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures. Solar Energy, 221, 148−161, https://doi.org/10.1016/j.solener.2021.04.024.
    Rigollier, C., M. Lefèvre, and L. Wald, 2004: The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77, 159−169, https://doi.org/10.1016/j.solener.2004.04.017.
    Senatla, M., and R. C. Bansal, 2018: Review of planning methodologies used for determination of optimal generation capacity mix: The cases of high shares of PV and wind. IET Renewable Power Generation, 12, 1222−1233, https://doi.org/10.1049/iet-rpg.2017.0380.
    Shi, H. R., and Coauthors, 2021: Surface brightening in eastern and central China since the implementation of the clean air action in 2013: Causes and implications. Geophys. Res. Lett., 48, e2020GL091105, https://doi.org/10.1029/2020GL091105.
    Sun, X. X., J. M. Bright, C. A. Gueymard, X. Y. Bai, B. Acord, and P. Wang, 2021: Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis. Renewable and Sustainable Energy Reviews, 135, 110087, https://doi.org/10.1016/j.rser.2020.110087.
    Wang, F., Z. Zhen, C. Liu, Z. Q. Mi, B.-M. Hodge, M. Shafie-Khah, and J. P. S. Catalão, 2018: Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Conversion and Management, 157, 123−135, https://doi.org/10.1016/j.enconman.2017.11.080.
    Wang, P., R. van Westrhenen, J. F. Meirink, S. van der Veen, and W. Knap, 2019: Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations. Solar Energy, 177, 47−58, https://doi.org/10.1016/j.solener.2018.10.073.
    Wild, M., D. Folini, F. Henschel, N. Fischer, and B. Müller, 2015: Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Solar Energy, 116, 12−24, https://doi.org/10.1016/j.solener.2015.03.039.
    Xian, D., P. Zhang, L. Gao, R. J. Sun, H. Z. Zhang, and X. Jia, 2021: Fengyun meteorological satellite products for earth system science applications. Adv. Atmos. Sci., 38, 1267−1284, https://doi.org/10.1007/s00376-021-0425-3.
    Yang, L. W., X. Q. Gao, Z. C. Li, D. Y. Jia, and J. X. Jiang, 2019: Nowcasting of surface solar irradiance using Fengyun-4 satellite observations over China. Remote Sensing, 11, 1984, https://doi.org/10.3390/rs11171984.
    Yang, L. W., X. Q. Gao, J. J. Hua, P. P. Wu, Z. C. Li, and D. Y. Jia, 2020: Very short-term surface solar irradiance forecasting based on Fengyun-4 geostationary satellite. Sensors, 20, 2606, https://doi.org/10.3390/s20092606.
    Zhang, J. Q., X. A. Xia, H. R. Shi, X. M. Zong, and J. Li, 2020: Radiation and aerosol measurements over the Tibetan Plateau during the Asian summer monsoon period. Atmospheric Pollution Research, 11, 1543−1551, https://doi.org/10.1016/j.apr.2020.06.017.
    Zhu, T. T., H. Zhou, H. K. Wei, X. Zhao, K. J. Zhang, and J. X. Zhang, 2019: Inter-hour direct normal irradiance forecast with multiple data types and time-series. Journal of Modern Power Systems and Clean Energy, 7, 1319−1327, https://doi.org/10.1007/s40565-019-0551-4.
    Zou, L., L. C. Wang, J. R. Li, Y. B. Lu, E. Gong, and Y. Niu, 2019: Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models. Journal of Cleaner Production, 224, 304−324, https://doi.org/10.1016/j.jclepro.2019.03.268.
  • [1] Li Zhenjun, 1998: Estimation of Cloud Motion Using Cross-Correlation, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 277-282.  doi: 10.1007/s00376-998-0046-0
    [2] Yaodeng CHEN, Jie SHEN, Shuiyong FAN, Deming MENG, Cheng WANG, 2020: Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1222-1238.  doi: 10.1007/s00376-020-0080-0
    [3] Mengqi LIU, Jinqiang ZHANG, Hongrong SHI, Disong FU, Xiang'ao XIA, 2022: Data-driven Estimation of Cloud Effects on Surface Irradiance at Xianghe, a Suburban Site on the North China Plain, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2213-2223.  doi: 10.1007/s00376-022-1414-x
    [4] Xiaqiong ZHOU, Johnny C. L. CHEN, 2006: Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 342-354.  doi: 10.1007/s00376-006-0342-5
    [5] Dazhi YANG, Wenting WANG, Xiang'ao XIA, 2022: A Concise Overview on Solar Resource Assessment and Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1239-1251.  doi: 10.1007/s00376-021-1372-8
    [6] QIAO Fangli, ZHANG Shaoqing, YIN Xunqiang, 2005: Study of Initial Vorticity Forcing for Block Onset by a 4-Dimensional Variational Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 246-259.  doi: 10.1007/BF02918514
    [7] Binod Kumar BHATTARAI, Berit KJELDSTAD, Trond Morten THORSETH, 2011: Assessment of Erythemal UV Level in Nepal Based on Solar UV Estimates from Total Ozone Mapping Spectrometer, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 789-796.  doi: 10.1007/s00376-010-9222-0
    [8] ZHONG Lingzhi, LIU Liping, DENG Min, ZHOU Xiuji, 2012: Retrieving Microphysical Properties and Air Motion of Cirrus Clouds Based on the Doppler Moments Method Using Cloud Radar, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 611-622.  doi: 10.1007/s00376-011-0112-x
    [9] Zhenhui WANG, Xinxiu SUI, Qing ZHANG, Lu YANG, Hang ZHAO, Min TANG, Yizhe ZHAN, Zhiguo ZHANG, 2017: Derivation of Cloud-Free-Region Atmospheric Motion Vectors from FY-2E Thermal Infrared Imagery, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 272-282.  doi: 10.1007/s00376-016-6098-7
    [10] JIN Ling, Fanyou KONG, LEI Hengchi*, and HU Zhaoxia, 2014: A Methodological Study on Using Weather Research and Forecasting (WRF) Model Outputs to Drive a One-Dimensional Cloud Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 230-240.  doi: 10.1007/s00376-013-2257-2
    [11] WU Bingyi, Mark A. JOHNSON, 2010: Distinct Modes of Winter Arctic Sea Ice Motion and Their Associations with Surface Wind Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 211-229.  doi: 10.1007/s00376-009-8179-3
    [12] Chen ZHOU, Yincheng LIU, Quan WANG, 2022: Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 2124-2136.  doi: 10.1007/s00376-021-1166-z
    [13] YANG Lu, WANG Zhenhui, CHU Yanli, ZHAO Hang, TANG Min, 2014: Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions: The Temporal Difference Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1386-1394.  doi: 10.1007/s00376-014-3165-9
    [14] LI Jiandong, LIU Yimin, WU Guoxiong, 2009: Cloud Radiative Forcing in Asian Monsoon Region Simulated by IPCC AR4 AMIP Models, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 923-939.  doi: 10.1007/s00376-009-8111-x
    [15] HUANG Yanyan, XUE Jishan, WAN Qilin, CHEN Zitong, DING Weiyu, ZHANG Chengzhong, 2013: Improvement of the Surface Pressure Operator in GRAPES and Its Application in Precipitation Forecasting in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 354-366.  doi: 10.1007/s00376-012-1270-1
    [16] Suping NIE, Tongwen WU, Yong LUO, Xueliang DENG, Xueli SHI, Zaizhi WANG, Xiangwen LIU, Jianbin HUANG, 2016: A Strategy for Merging Objective Estimates of Global Daily Precipitation from Gauge Observations, Satellite Estimates, and Numerical Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 889-904.  doi: 10.1007/s00376-016-5223-y
    [17] Xiao Qingnong, Wu Rongsheng, 1995: A Study on Frontal Motion over Orography, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 325-334.  doi: 10.1007/BF02656981
    [18] YUE Caijun, SHOU Shaowen, Xiaofan LI, 2009: Water Vapor, Cloud, and Surface Rainfall Budgets Associated with the Landfall of Typhoon Krosa (2007): A Two-Dimensional Cloud-Resolving Modeling Study, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1198-1208.  doi: 10.1007/s00376-009-8135-2
    [19] Li Yinpeng, Ji Jinjun, 2001: Model Estimates of Global Carbon Flux between Vegetation and the Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 807-818.
    [20] ZHU Weijun, Kevin HAMILTON, 2008: Empirical Estimates of Global Climate Sensitivity: An Assessment of Strategies Using a Coupled GCM, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 339-347.  doi: 10.1007/s00376-008-0339-3

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 20 December 2021
Manuscript revised: 04 March 2022
Manuscript accepted: 17 March 2022
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China

    Corresponding author: Yuan YUAN, yuanyuan83@hit.edu.cn
    Corresponding author: Xiang′ao XIA, xxa@mail.iap.ac.cn
  • 1. Key Laboratory of Aerospace Thermophysics, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150001, China
  • 2. Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 4. National Satellite Meteorological Center, China Meteorological Administration, Beijing 100192, China
  • 5. Key Laboratory of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
  • 6. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute (CEPRI), Beijing 100192, China
  • 7. Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract: Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring–summer and higher in autumn–winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.

摘要: 碳中和目标背景下,中国未来将显著增加光伏等新能源在能源结构中的占比。光伏太阳能的间接性和不稳定性是太阳能并网利用中的重大挑战之一。除了发展新能源储能等技术之外,发展太阳能短临预报技术是提高太阳能利用率的经济有效途径。我国新一代静止气象卫星风云四号(FY-4A)的发射给太阳能短临预报(<3小时)提供了新的观测手段。本文利用FY-4A多通道反射率数据建立了地表太阳辐照度估算和短临预报系统。该系统由两个关键部分组成,第一部分为基于物理晴天模型和经验云天模型构建的地表太阳辐照度混合估算方法,第二部分为地表太阳辐照度短临预报模型,其核心是通过块状匹配法推导出云运动矢量,进而预报未来3小时内地表太阳辐照度场。该系统目前能够同时实现水平面总辐射和法向直接辐射的估算和短临预报。验证结果表明:水平面总辐射估算值的准确性高于法向直接辐射,二者归一化均方根误差分别为22.4%和45.4%。30-180分钟预测范围内水平面总辐射和法向直接辐射预测值的归一化均方根误差分别在25.1%-30.8%和48.1%-53.4%之间。地表太阳辐照度估算结果的准确性取决于云的出现频率,即春夏较低,秋冬较高。本研究工作表明新一代静止气象卫星在地表太阳辐射短时临近预报中的广阔应用前景,将显著促进我国光伏太阳能能源发展和利用。该系统在华北地区具备良好的性能,未来进一步改进将侧重于对地表太阳辐照度估算模型的校准并推广其在整个中国地区的适用性。

    • To achieve carbon peak and neutrality goals, China will increase the share of renewable energy in its energy structure to reduce carbon emissions (Shi et al., 2021; Li et al., 2022). China has the world's largest installed photovoltaic (PV) capacity and growth rate (IRENA, 2020). Until August 2021, China had an installed capacity of 275.1 GW and had surpassed its 2016 capacity by 255.4%. The ratio of installed capacity to China’s full-caliber installed capacity has increased from 4.7% (2016) to 12.0% (August 2021). The share of PV energy in China's energy structure will undoubtedly increase significantly in the coming decades (Burandt et al., 2019; Prăvălie et al., 2019; Li et al., 2020; Bai et al., 2021). The intermittence and instability of solar energy, however, pose a major challenge for the integration of solar PV into the energy grid. Solar energy mainly depends on the availability of surface solar irradiance (SSI), which is highly susceptible to variations in atmospheric components (clouds, aerosols, water vapor, etc.). This results in significant fluctuations in the PV output power. Therefore, accurate forecasting of SSI fluctuations is of great importance for utilizing and developing solar PV and reducing carbon emissions (Lamsal et al., 2018; Senatla and Bansal, 2018).

      The forecast method for SSI is generally divided into three categories. Years- and decades-ahead predictions are mainly made using climate models that are commonly used for solar PV plant deployment and planning (Wild et al., 2015; Zou et al., 2019). For short-term (day/hour-scale) forecasts, regional numerical weather prediction (NWP) simulations are advantageous; the results obtained through these are useful for scheduling and optimizing microgrids and grid-connected power allocation for the next 24–48 hours (Antonanzas et al., 2016; Razagui et al., 2021). For nowcasting (0–3 h ahead), satellite-based methods are widely used, which is important for the safe operation, control, and management of PV plants and is also a prerequisite for grid connection of PV plants (Zhu et al., 2019).

      The satellite-based SSI nowcasting method consists of two components. The first is to estimate SSI as accurately as possible using satellite reflectance measurements. The second is to forecast SSI at the next target time, which requires estimation of the cloud motion vector (CMV). The diverse methods used to estimate SSI from satellites can be divided broadly into physical and semi-empirical methods; both have been widely used for different satellites (e.g., NOAA series, Meteosat Second Generation (MSG), and Himawari-8) (Kallio-Myers et al., 2020; Peng et al., 2020; Letu et al., 2021). The physical method uses atmospheric and surface properties retrieved from satellite measurements and other sources, such as reanalysis products, to drive a radiative transfer model to calculate SSI. This method is based on solid physical foundations, and its uncertainty depends heavily on the availability and accuracy of the input variables. Additionally, this method requires more computing power than empirical methods. The semi-empirical method estimates SSI directly from satellite observations using a simple but effective method. One representative method of this kind in the solar energy community is the semi-empirical method for MSG, called the Heliosat method. This method was originally developed in the last century and has greatly facilitated research and application of solar energy in Europe (Cros et al., 2004; Rigollier et al., 2004). The basic principle is very simple, but its operational implementation is delicate and requires a fair amount of site-specific accounting (Kleissl, 2013). In recent years, machine learning, which can be classified as an empirical method, has developed rapidly for SSI estimation (Damiani et al., 2018; Peng et al., 2020). Empirical methods mainly use satellite observation and generally do not need atmospheric and surface properties. Therefore, they can be extremely useful when atmospheric and surface properties are not reliably available or have large uncertainties (Huang et al., 2019). Another advantage of this method is that SSI estimations can be obtained in the native spatial resolutions of the original satellite measurements. This is very useful for solar energy forecasting because the higher the spatial resolution, the better is the application.

      With regard to CMV derivation, three methods are widely used: phase correlation, optical flow, and block-matching (Amillo et al., 2014; Kallio-Myers et al., 2020; Yang et al., 2020). The phase correlation method obtains motion information from the phase difference of consecutive fields. The basic principle is the shift property of the Fourier transform, which states that translation of pixels in the spatial domain produces proportional phase shifts in the frequency domain (Wang et al., 2018). The optical flow method derives the CMV based on the image gradient in which three assumptions are made. First, the pixel brightness of the image is constant between the two frames. Second, the motion in the image varies little over time. Third, the same or similar motion is shared by the target pixel and its neighboring pixels, i.e., the spatial smoothing is constrained (Nonnenmacher and Coimbra, 2014). The block-matching method assumes that cloud structures are constant along motion trajectories over a short time period. The CMV is calculated through the cloud structure displacement obtained from consecutive images (Hammer et al., 1999). CMV is generally obtained by directly using 2–3 images from geostationary satellites taken within a short period of time. Retrievals of cloud physical properties, such as cloud optical thickness, cloud top height, and cloud effective radius, have been shown to improve the CMV derivation (Wang et al., 2019).

      SSI nowcasting can be achieved using a combination of CMV information and SSI estimates. Numerous SSI nowcasting studies have been conducted based on satellite sensors (e.g., Spinning Enhanced Visible and Infrared Imager (SEVIRI)) (Arbizu-Barrena et al., 2017; Gallucci et al., 2018; Mouhamet et al., 2018; Wang et al., 2019). Fengyun-4A (FY-4A), launched by China in 2016, is a new generation geostationary meteorological satellite. The advanced geosynchronous radiation imager (AGRI) on board the FY-4A can provide images with much higher spatial, temporal, and spectral resolutions than previous imagers. It has great potential for SSI nowcasting, which still needs to be explored (Zhang et al., 2020; Xian et al., 2021). There have been a few attempts, but all were limited in their spatial and temporal coverage and satellite-based detection of cloud motion (Yang et al., 2019; Chen et al., 2020; Yang et al., 2020). More importantly, many issues still require further investigation.

      In this study, we explore the potential of FY-4A in SSI nowcasting. We attempt to answer the following three scientific questions: 1) Can the clear-sky model be used to accurately estimate clear-sky SSI even under heavy pollution conditions. 2) what is the accuracy of SSI estimation expected by using an empirical method to the AGRI onboard the FY-4A, and how can its accuracy be improved. and 3) Can the empirical method be extended to DNI estimation, and what is the expected accuracy. China has a vast absolute area of DNI superb potential [>200 000 km2, (Prăvălie et al., 2019)], which means that accurate estimates and nowcasting of DNI fluctuations are extremely important for the development of solar PV energy in China. Therefore, it is of great significance to explore FY-based DNI estimates and the nowcasting method.

      This paper is organized as follows: Section 2 describes the clear-sky model, SSI estimates model, nowcasting algorithm, and data used in the study. Section 3 presents the results of the evaluation of FY-4A SSI estimates and forecast products. The discussion and conclusions are presented in sections 4 and 5.

    2.   Data and methodology
    • FY-4A is located over the equator at a longitude of 104.7°E. The AGRI onboard the FY-4A takes satellite images of China and its surrounding areas with varying spatial (from 0.5 km to 16 km) and temporal (from 15 min to 2 h) resolutions. To simplify the method and thereby fulfill the operational usage of the method, we only used Level 1 spectral reflectance at the near-infrared channel (0.83 µm) with a spatial resolution of 1 km to calculate the cloud index (CI) and CMV information. The data are available online (http://satellite.nsmc.org.cn/portalsite/default.aspx).

      The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) uses the Goddard Earth Observing System Version 5 data assimilation system. MERRA-2 provides long-term (from 1980 to the present) radiation, meteorological, and atmospheric aerosol products, which have been widely used to study atmospheric environments, climate change, and solar energy (Randles et al., 2017). The hourly MERRA-2 aerosol optical depth at 550 nm (AOD550), Ångström exponent (AE), precipitable water vapor, ozone amount, and surface albedo products (http://disc.sci.gsfc.nasa.gov) are used to drive a parametrization radiative transfer model to calculate clear-sky SSI.

      The SSI measurements at Xianghe (39.75°N, 116.95°E, Fig. 1) in 2018 were used for the evaluation. GHI and DNI were measured using a Kipp and Zonen CM21 pyranometer and CHPI pyrheliometer, respectively (Liu et al., 2021). The SSI measurements were quality controlled through procedures recommended by the Baseline Surface Radiation Network, with uncertainties of approximately 6% and 3% for GHI and DNI, respectively. Only quality-assured measurements were used for the evaluation, which led to different sample numbers of GHI and DNI. Xianghe is located in the Beijing–Tianjin–Hebei region, which is a typical polluted region in China. This region has been gradually increasing the share of solar PV energy in its total energy consumption to balance energy requirements with environmental protection.

      1-minute SSI measurements were used to detect cloud occurrence using a random forest method (Liu et al., 2021). Daily cloud occurrences were used to classify each day into four distinct categories: C1: 0%–10%; C2: 10%–50%; C3: 50%–90%; C4: 90%–100%, roughly corresponding to clear, moderately clear, cloudy, and overcast conditions, respectively. The SSI estimates and nowcasting were evaluated under these four sky conditions to investigate their potential different performances.

      Figure 2 presents the monthly occurrence frequency of these four sky types in 2018. The cloud occurrence frequency showed a seasonal pattern. Skies were much clearer in winter than in other seasons, while overcast skies dominated in summer, especially in July when 27 days were overcast.

      Figure 1.  (a) Geographical location of Xianghe observation site, and (b) the instrument.

      Figure 2.  Monthly proportion of four sky conditions at Xianghe in 2018.

    • Clear-sky SSI estimation is a critical step in empirical SSI algorithms. In this work, clear-sky SSI was calculated using the REST2v5 model because of its excellent performance for supporting SSI estimates and nowcasting in a worldwide test under various conditions (Antonanzas-Torres et al., 2019; Sun et al., 2021). REST2 splits the shortwave spectrum into two bands (290–700 nm and 700–4000 nm), and DNI and diffuse horizontal irradiance (DHI) are parameterized for each band (Gueymard, 2008). The GHI is calculated as the summation of the DNI multiplied by the cosine of the solar zenith angle and DHI.

    • The all-sky SSI values were obtained by multiplying the REST2 clear-sky SSI calculation and the clear sky index ($ {K_{\text{c}}} $: the ratio of observed all-sky SSI to the expected clear-sky SSI). $ {K_{\text{c}}} $ is parameterized as a function of the CI:

      where $ {n_t}(i,j) $ denotes the CI derived from FY-4A reflectance measurements. The relationship between CI and $ {K_{\text{c}}} $ is determined based on the historical data of Xianghe station.

      The CI at a given pixel $ (i,j) $ and time $ t $ is calculated using Eq. (2):

      where $ {\rho _t}(i,j) $ is the FY-4A observed reflectance, and $ {\rho _{t{\text{,c}}}}(i,j) $ and $ {\rho _{t,{\text{g}}}}(i,j) $ represent the reflectance associated with the brightest clouds and clear skies within a given period, respectively. The operational advantage of this all-sky SSI estimation method is that it does not require precise satellite calibration information. As the method is self-calibrating, the upper and lower bounds of the dynamic range at the target pixel are established from the data history (Kleissl, 2013). The upper bound of the dynamic range, $ {\rho _{t{\text{,c}}}}(i,j) $, represents the overcast condition with the brightest clouds. We defined $ {\rho _{t{\text{,c}}}}(i,j) $ as the 95th percentile of all reflectance values throughout the year in the target region (latitude 36°N to 44°N and longitude 112°E to 120°E), which considers potential measurement uncertainty. Given $ {\rho _{t,{\text{g}}}}(i,j) $, the lower bound of the dynamic range is highly dependent on surface reflectance, which shows remarkable temporal variation; it is estimated by an iteration procedure for each month. First, all satellite measurements of reflectance for each pixel in a given month are averaged as an initial threshold value ($ {\rho _{{\text{thres}}}} $). The new average is then calculated from the reflectance smaller than the sum of the initial threshold and a specified bias $ \varepsilon $, which is defined as 0.035 $ {\rho _{t{\text{,c}}}}(i,j) $ here. This new average is used for the next iteration. Then, the iteration proceeds until no reflectance exceeds the threshold by a magnitude of $ \varepsilon $, and the last-iterated $ {\rho _{t,{\text{g}}}}(i,j) $ is used as the lower bound.

      Note that the FY-4A-measured clear-sky reflectance shows a convex parabolic variation with the solar zenith angle (SZA) over the daytime. This means that $ {\rho _t}(i,j) $ at local solar noon would exceed $ {\rho _{t,{\text{g}}}}(i,j) $. Then, the $ {n_t}(i,j) $ obtained based on Eq. (2) would tend to identify the clear-sky pixels at local solar noon as cloud pixels, eventually leading to much lower calculated clear-sky SSI values than actual SSI values. Therefore, this issue was addressed by introducing a parameterization of the FY-4A clear-sky reflectance to SZA, as follows:

      where $ {\rho _{\text{c}}} $ denotes corrected reflectance, $ {\rho _0} $ denotes FY-4A clear-sky reflectance, $ \theta $ represents SZA.

      The corrected reflectance and corresponding dynamic range are given in Fig. 3b.

      Figure 3.  (a) The function relation between FY-4A reflectance and the cosine of SZA under the clear-sky condition, and (b) the dynamic range for Xianghe station from the near infrared channel of the FY-4A satellite in 2018.

      The GHI is given by Eq. (4):

      where $ {\text{GH}}{{\text{I}}_{{\text{cs}}}} $ represents the GHI under clear sky calculated by the REST2 model.

      The DNI is given by Eq. (5) (Pfeifroth et al., 2019), which is determined based on the historical data of Xianghe station:

      where $ {\text{DN}}{{\text{I}}_{{\text{cs}}}} $ represents the DNI under clear sky, which is also calculated using the REST-2 model.

    • The forecast method includes two operational steps: forecasting clear-sky SSI background at the target horizons, and superimposing cloud attenuation ($ {K_{\text{c}}} $) on the background. We assumed that all parameters, except the SZA, are held constant with a forecast horizon up to 6 h. The present values (0 min ahead) provided by MERRA-2 are used as the input predicted values for the prediction of the clear-sky SSI background. The SZA is the main cause of the alteration of clear-sky SSI background. The SZA values at target horizons are obtained according to the time and geographical location information. The FY-4A satellite images were first converted to CI images using the abovementioned empirical method. The CMV was subsequently calculated using the block-matching method and consecutive CI images with time intervals of 20 min. Based on the CMV information, the CI and $ {K_{\text{c}}} $ of the forecast horizon were obtained, and the SSI nowcasting was finally completed.

      The development of clouds is not determined by dissipation and formation for the forecast time of several hours, and cloud tracking methods (e.g., the block-matching method) using images from geostationary satellite show good performance. As a demonstration, we used a rectangular area [112°–120°E, 36°–44°N] as our research subject. The images were divided into multiple square boxes (0.5° × 0.5°), and the motion vectors for each square region were derived using the following procedures. The block-matching method identifies cloud structures according to the pixel intensities in consecutive CI images. Assuming that pixel intensities are constant along motion trajectories over a short time period, the CMV of each square area was subsequently calculated using the cloud structure displacement (Hammer et al., 1999).

      According to the assumption of constant pixel intensities, the vector v used to describe the motion is given by:

      where ni denotes the CI at position x of the image at time ti. The actual and past images were subscribed with 1 and 0, respectively.

      The best vector describing the cloud motion was determined using the displacement vector with the minimum mean square pixel difference (MSE):

    • The accuracy of the SSI estimates and nowcasting with 20-min temporal resolution was evaluated for the Xianghe observations. For spatial matching, we used the SSI estimates and nowcasting for the closest pixel to the Xianghe station. The FY-4A SSI estimates and nowcasting performance were evaluated using the following statistical indices: root-mean-square error (RMSE), mean bias error (MBE), and their normalized counterparts (nRMSE and nMBE).

    3.   Results
    • In this section, we first evaluate the performance of the REST2 model, then the accuracy of the estimated GHI and DNI, and finally, the forecast accuracies for time horizons between 30–180 min. All evaluations are detailed under the four sky conditions.

    • Figure 4 shows the scatter statistics of the clear-sky GHI and DNI estimates based on the REST2 model. The GHI estimates were very close to the ground-based measurements, with nRMSE and nMBE of 4.93% and 1.68%, respectively. The REST2 model tended to slightly overestimate GHI on clear-sky days, and it tended to overestimate DNI by a larger magnitude compared to GHI, with an nMBE value of 8.67%. Large overestimations of DNI were observed in low and moderate DNI conditions. One explanation is that a lower DNI is associated with a high aerosol loading, which is generally underestimated by the MERRA-2 reanalysis (Huang et al., 2021). Notably, the model estimates were very close to the surface measurements when DNI was high. The high output power of solar PV plants depends on high DNI values. Compared to the low and moderate DNI values, the fluctuations of high DNI values will induce heavier impact on the safe operation, control, and management of solar PV plants. Therefore, the accurate calculation of high DNI values is crucial for the utilization of solar PV and is the focus of DNI calculations. Overall, the REST2 model performed well in supporting the GHI and DNI estimates and nowcasting evaluations at the Xianghe station under different conditions.

      Figure 4.  Density plot of REST2 model GHI (a) and DNI (b) versus surface measurements under clear-sky conditions. The black line denotes a 1:1 line. The red lines indicates linear regression results.

    • Figure 5 presents the density plot of FY-4A GHI and DNI under all-sky conditions against surface observations at Xianghe. The estimated GHIs show good correlation with the surface measurements, with nRMSE and nMBE of 22.4% and 5.9%, respectively. The percentage of the estimated GHI values exceeding the measurements by 100 W m–2 was approximately 15.9%. These overestimated GHI values were concentrated at 200–500 W m–2, comprising the dominant contribution to GHI overestimation. Accurate calculation of high GHI values is crucial for solar energy estimation and forecasting. The good performance of the FY-4A GHI estimation model when the GHI is high indicates that the model can support FY-4A GHI estimation and nowcasting under all-sky conditions. Compared with the estimated GHI, the performance of the estimated DNI is worse, and the model needs to be improved in this aspect. In general, the FY-4A DNI was overestimated with an nMBE value of 24.1% (Fig. 5b). The empirical-cloudy model performs well with a high DNI condition and tends to largely overestimate DNI when DNI values are lower than 700 W m–2. One explanation is that if the ground station is covered by the shadows of clouds or surrounding features (namely, the 3D effect), its real-time measured DNI will be lower than that of other locations within the matching pixel in satellite images, thus the measured DNI values will be lower than satellite-derived values (Huang et al., 2019; Jiang et al., 2019). The deviation of clear-sky DNI background and CI is also a contributor to the overestimation.

      Figure 5.  Density plot of the SSI estimation model GHI (a) and DNI (b) versus SSI measurements under all-sky conditions in 2018. The black line denotes a 1:1 line. The red lines indicate liner regression results.

      Table 1 shows the statistical indices of the estimated GHI and DNI in this study compared with those in previous studies based on empirical methods. The nRMSE and nMBE of the GHI estimates in Shanghai obtained by Chen et al. (2020) were 34.7% and 11.4%, respectively, which were higher than those obtained in our work by 12.3% and 5.5%. The nRMSE values of the estimated GHI and DNI in our study were lower by 3.5% and 3.9%, respectively, compared to those obtained by Jia et al. (2021) in their study of northern China. The improvement of our estimation model eliminates the bias related to SZA variation by introducing a parameterization of the FY-4A clear-sky reflectance to SZA.

      No.ReferenceSiteSSIRMSEnRMSEMBEnMBE
      1Present studyXianghe, ChinaGHI106.622.4 28.05.9
      DNI201.945.4107.2 24.1
      2Chen et al. (2020)Shanghai, ChinaGHI 34.7 11.4
      3Jia et al. (2021)Chengde, ChinaGHI120.1 25.9 –4.7–1.0
      DNI230.5 49.3%–97.3 –20.8

      Table 1.  Statistical indexes of instantaneous FY-4A SSI in the present study and previous studies.

      The evaluation of the estimated GHI and DNI for different sky conditions is shown in Fig. 6. The accuracies of GHI and DNI estimations declined with an increase in cloud occurrence (from clear-sky C1 to overcast C4). GHI estimates feature less deviation under clear, moderately clear, and cloudy conditions. The MBE values of GHI varied from –22.4 W m–2 to 23.5 W m–2, and the RMSE values increased from 41.1 W m–2 to 90.5 W m–2 as the sky conditions varied from C1 to C3. The estimation accuracy declined sharply under C4 conditions, with the MBE and RMSE reaching 62.6 W m–2 and 144.5 W m–2, respectively. The performance of the estimated DNI was significantly worse than that of the GHI estimation, featuring larger deviations under all four conditions. The MBE values varied from –9.1 W m–2 to 173.1 W m–2, while the RMSE values increased from 94.2 W m–2 to 253.7 W m–2. The poor performance under the C4 condition had a limited impact on the PV plants because the grid-connected power allocation can be adjusted to suit C4 conditions based on regional numerical models. Therefore, the performance of the GHI and DNI estimates under other sky conditions, especially C1 and C2, is the focus of GHI and DNI estimation and nowcasting and is crucial in the utilization of solar PV.

      Figure 6.  Density plot of the SSI estimation model GHI (a–d) and DNI (e–h) versus observed SSI under different sky conditions. The black line denotes a 1:1 line. The red lines indicate linear regression results.

      To characterize seasonal variations in model accuracy, the monthly mean estimated GHI and DNI and surface measurements were compared in Fig. 7. The monthly mean estimations of GHI and DNI were larger than the surface measurements. The difference between model and observation presented a seasonal pattern with a large discrepancy in spring–summer and a small difference in autumn–winter. The seasonal pattern of the GHI and DNI model discrepancy is related to the seasonal variation in cloud occurrence. In spring and summer, the proportion of days with the C4 condition was higher than 40%, especially in July, with the proportion reaching 90% (as shown in Fig. 2). High cloud occurrence reduces the accuracy of the estimated GHI and DNI. With the decrease in cloud occurrence, the discrepancy between estimated GHI and DNI and their respective measurements was reduced in autumn and winter.

      Figure 7.  Monthly mean of observed (grey line) and model (orange line) surface solar irradiance GHI (a) and DNI (b) in 2018. Shaded regions indicate one standard error of the mean.

    • A case of forecasted and observed CI, GHI, and DNI is presented in Fig. 8, with a 60-min forecast horizon at 0140 UTC 1 December 2018. The CI at 0240 UTC was forecasted from the CI observations and CMV information at 0140 UTC. The estimated GHI and DNI and the CI forecast were used to obtain the GHI and DNI nowcasts at 0240 UTC. The FY-4A-based GHI and DNI nowcasts and the observations had similar spatial distribution patterns. An apparent divergence between forecasts and observations occurred in the red rectangular area with broken clouds (Figs. 8a and 8d) because the CMV derivation using the block-matching method does not consider the formation and dissipation of clouds. Therefore, the CMV forecast method has relatively low predictability under the broken-cloud situation.

      Figure 8.  An example of FY-4A-based (a–c) nowcasting and (d–f) observation for CI, GHI, and DNI at 0240 UTC on 1 December 2018.

      The quality of GHI and DNI nowcasting within 0–3 h ahead, under all-sky conditions, is summarized in Table 2. The statistical indices for 0 min ahead were taken as deviations between the GHI and DNI estimations and the surface measurements. For 0–3-h forecast horizons, the nRMSE values of the GHI and DNI nowcasts increased slowly to 30.8% and 53.4%, respectively, surpassing those at 0 min ahead only by 8.4% and 8.0%, respectively. The nMBE values of GHI and DNI decreased with increasing forecast horizon from 5.9% and 24.1% at 0 min ahead to 0.8% and 18.6% at 3 h ahead, respectively. The GHI and DNI nowcasting methods offset the overestimation of the hybrid estimation method.

      SSIMetric0 min30 min60 min90 min120 min150 min180 min
      GHImean475.9469.4479.9510.9500.9494.8485.6
      RMSE106.6117.8125.5138.5144.4143.2149.5
      nRMSE22.425.126.227.128.828.930.8
      MBE28.034.528.327.214.918.93.7
      nMBE5.97.35.95.33.03.80.8
      DNImean444.7430.3438.0452.7447.2441.7419.4
      RMSE201.9207.3208.3220.2222.0228.3232.6
      nRMSE45.448.147.648.649.651.753.4
      MBE107.2105.098.599.287.890.881.1
      nMBE24.124.422.521.919.620.618.6

      Table 2.  Statistical indexes (the mean value, RMSE, nRMSE, MBE, and nMBE) under all-sky conditions with forecast horizons from 0–180 min (units: W m–2).

      In the FY-4A satellite-based study conducted for Chengde by Yang et al. (2020), the nRMSE values of the GHI and DNI forecasts at 3 h ahead were 36.8% and 58.8%, respectively. The nRMSE of the FY-4A GHI forecasts for Shanghai calculated by Chen et al. (2020) was nearly 60% with a forecast horizon of 3 h. Both of these studies presented higher nRMSE values than the present study, suggesting that the GHI and DNI nowcasting system in this study performed better for forecast horizons up to 3 h. The CMV derivation algorithm used in the reference and our work has a similar performance (Cros et al., 2014). In this study, the correction (the parameterization of FY-4A clear-sky reflectance to SZA before CI calculation) decreases the deviations of the estimations and further improves the performance of the GHI and DNI nowcasting systems.

      Figure 9 shows the nRMSE and nMBE values of GHI and DNI nowcasting under four sky conditions with forecast horizons of 0–3 h. The nRMSE values of GHI nowcasting are between 8% and 22% at 30–180 min ahead, and the increase with growing forecast horizon is relatively small under C1, C2, and C3 conditions. The nRMSE values under C4 increased rapidly as the forecast horizon increased—from 44.3% at 30 min ahead to 52.6% at 180 min. The nRMSE of DNI nowcasting exhibits similar trends, but the values are larger. The nRMSE is about 12%–13% under C1 conditions, with the 150-min forecast horizon having the smallest value. For C2 and C3 conditions, the nRMSE values increased with increasing forecast horizons—from 25.4% and 53.1% at the 30-min horizon to 33.1% and 57.9% at the 180-min horizon, respectively. Under the C4 condition, the nRMSE values of DNI show big changes over the forecast horizons of 30–180 min, ranging from 189.5% to 201.5%.

      Figure 9.  The nRMSE (%) and nMBE (%) of SSI under different sky conditions with forecast horizons from 30–180 min.

      The nowcasting system tends to overestimate DNI. The most severe overestimations were under C4, followed by under the C3, C2, and C1 conditions. The nMBE values of DNI showed small changes under all sky conditions, except under C4. The highest nMBE value of DNI was 31.6% at 30 min ahead under cloudy conditions. Note that the nRMSE and nMBE values of DNI nowcasting are significantly higher under C4 conditions than under other sky conditions due to the low DNI.

    4.   Discussion
    • The benchmarks of nRMSE for GHI and DNI forecasts given by the University of Geneva are 15%–70% and 30%–160%, respectively (Beyer et al., 2009). The nRMSE values of GHI and DNI nowcasts in our study were within these ranges under all-sky conditions, except for C4. The SSI nowcasting system established in this work performed well for forecast horizons up to 180 min. The GHI nowcasting outperformed the DNI nowcasting for all forecast horizons. Furthermore, the new generation geostationary satellite FY-4A is promising for application in SSI nowcasting, but its accuracy still needs to be improved. Improving the SSI nowcasting system consists of two steps: revising the hybrid SSI estimation method and upgrading the CMV derivation algorithms.

      The REST2 model performs well in estimating the clear-sky GHI in heavily polluted areas (e.g., North China). The AOD input quality in the REST2 model was key to clear-sky DNI estimation because DNI is especially sensitive to AOD compared to GHI (Gueymard and George, 2005). The empirical cloudy-sky model was first developed for MSG and is applicable to European regions (Yang et al., 2019), but it requires further revisions for it to be applicable in China using FY-4A data. The empirical coefficients of the cloudy-sky model have been calibrated based on the historical data of Xianghe station and lead to satisfactory results under clear and moderately clear conditions. The further revisions should focus on promoting the performance of the DNI estimation method under cloudy and overcast conditions. More attention should be paid to the calculation of the CI and the empirical coefficient of Eqs. (1) and (5). Long-term clear-sky DNI estimation and surface measurements should be used to fit the most suitable coefficients under different sky conditions. The empirical coefficients can be refined if high-quality SSI measurements are available across the country. Furthermore, the applicability and revision of these empirical coefficients in different regions and seasons in China is also a problem worthy of in-depth study.

      The accuracy of the SSI forecast method depends on the CMV derivation algorithm. In this work, we calculated the CMV using FY-4A single-channel data. Subsequent research can develop multichannel-based CMV derivation algorithms to more accurately characterize the cloud motion. The cloud physical properties, the formation and dissipation of broken clouds, and the alteration of direction and speed in cloud motion should also be considered in future CMV derivation algorithms. For example, the square boxes used to obtain CMV fields are further divided into multiple square sub-boxes, and then intensity vectors for each sub-box, which are used to reflect the formation and dissipation process of clouds, are calculated using the alteration of pixel intensities in consecutive CI images.

    5.   Conclusions
    • A preliminary but effective SSI nowcasting system was developed based on FY-4A Level 1 reflectance data at 0.83 µm. This system was composed of an SSI estimation and forecasting method. A semiphysical SSI estimation method was developed, in which the REST2 model was first used to calculate clear-sky SSI. Subsequently, all-sky SSI was estimated according to the clear-sky SSI and the CI derived from FY-4A reflectance measurements. SSI nowcasting was developed using the CMV obtained using the block-matching method. The main conclusions are as follows:

      The REST2 model performed well in supporting GHI and DNI estimates and nowcasts at Xianghe under different sky conditions. The FY-4A GHI estimates show good correlation with the surface measurements, with nRMSE and nMBE of 22.4% and 5.9%, respectively. The calibrated empirical method can be extended to DNI estimation under clear and moderately clear conditions, but it must be further improved under cloudy and overcast conditions. The FY-4A DNI estimation method tends to severely overestimate DNI when the actual DNI values are lower than 700 W m–2. The difference between the model results and observations presents a seasonal pattern, with a large discrepancy in spring–summer and a small difference in autumn–winter. The seasonal pattern of the GHI and DNI model discrepancy is related to seasonal variations in cloud occurrences.

      The nRMSE values of GHI and DNI nowcasts under all-sky conditions varied within 25.1%–30.8% and 48.1%–55.3%, respectively. The SSI nowcasting system performs well for forecast horizons up to 180 min, but its accuracy still needs improvement. The FY-4A has great potential for supporting SSI nowcasting, which would certainly enable the promotion of PV energy development and carbon emissions reduction in China.

      Further enhancements to the SSI nowcasting system should focus on the calibration of the SSI estimation model and improving its applicability for China and FY-4A. More specifically, the coefficients of the empirical method can be refined if high-quality SSI measurements are available across the country.

      Acknowledgements. This work was supported by the National Natural Science Foundation of China (Grant Nos. 42030608, 41805021, and 51776051), the Beijing Natural Science Foundation (Grant No. 8204072), and Beijing Nova Program (Grant No. Z211100002121077). We would like to thank the National Satellite Meteorological Center and the MERRA-2 teams for providing the data used in this study. The FY-4A data were collected from http://satellite.nsmc.org.cn/portalsite/default.aspx (accessed on 20 June 2021). The MERRA-2 data were collected from http://disc.sci.gsfc.nasa.gov/ (accessed on 12 September 2021).

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return