地表太阳辐射是地球表面主要能量来源，也是太阳能资源评估、生态、水文、气候等模型及相关研究的重要变量。其日内变化影响局地气候模式、太阳能光伏电站集中并网安全等，因此估算日内地表太阳辐射具有重要意义。本文选取黄土高原及其周边为研究区，首先基于新一代静止气象卫星Himawari-8云产品（云光学厚度、云粒子有效半径等）分析了区域云特性时空变化特征。其次基于Libradtran辐射传输模型构建了云透射率参数化模型，将其与Iqbal C晴空模型结合，同时考虑地形阻挡，形成了综合考虑大气、云和地形影响的地表太阳辐射估算模型。在此基础上，利用Himawari-8云及气溶胶产品、MODIS大气产品等，模拟了日内（每10分钟）地表太阳辐射，并基于地面站点实测数据验证模型精度。为进一步评估模型的适用性，模拟了2020年黄土高原及其周边地区日内地表太阳辐射空间分布，在不同大气条件下与Himawari-8地表下行短波辐射数据进行对比，并分析了其时空变化特征，量化了云和地形对地表太阳辐射空间分布的影响。本文的主要研究结论如下：

    （1）黄土高原及其周边地区云频率（CF）、云光学厚度（COT）和云粒子有效半径（CER）存在明显的时空分布差异。其中，CF和COT高值区主要集中在南部，CER则呈现与之相反的空间分布格局。在四个季节中，CF夏季均值最大，COT秋季均值最大，而CER最大均值主要出现在春季和冬季。在日内变化上，CF值在中午高，上午和下午低，而COT的日内变化则呈相反趋势。CER值从上午到下午呈增加趋势，在北京时间17:00达到最大。

    （2）基于Libradtran辐射传输模型，根据不同云相（水云、冰云）分别选择云光学厚度、云粒子有效半径以及太阳天顶角等参数计算得到云透射率查找表，采用自定义函数的方式探究云透射率与关键参数的关系，从而确定云透射率参数化模型，通过最小二乘拟合获取模型参数，所构建参数化模型拟合度达到0.99以上，均方根误差小于0.1。

    （3）结合晴空模型、本文构建的云透射率参数化模型、Himawari-8云及气溶胶产品、MODIS大气产品以及DEM产品等，估算了每10分钟的晴空、云天以及全天空地表太阳辐射，并利用地面站点实测数据验证模型精度。其中，晴空与全天空验证结果相关系数（R）均大于0.9，云天R均大于0.6，晴空、云天以及全天空在七个站点均方根误差（RMSE）均值分别为91.36 W/m²、146.87 W/m²、103.68 W/m²，平均偏差（MBE）均值分别为54.45 W/m²、-65.77 W/m²、30.00 W/m²，结果表明各站点晴空、云天以及全天空地表太阳辐射估算结果与实测数据之间相关性较好，总体精度较好，同时也存在一定的高、低估现象。

    （4）本文所构建模型模拟的空间分布与Himawari-8地表下行短波辐射数据具有较好的一致性，且本文模型能够呈现空间分布细节特征。研究区直接辐射、散射辐射和总辐射具有显著的空间分布差异和季节差异，其中直接辐射和总辐射在空间分布上呈现出西高东低、北高南低的特点，同时在春季值最高，秋冬季最低；而散射辐射的空间分布与之相反，季节上呈现出夏季最高，冬季最低。直接辐射、散射辐射和总辐射日内变化均呈现先增后减的趋势，在中午达到最大值。同时，发现云与高程的交互作用对地表太阳射空间分布的影响最大，因此在复杂地形区域地表太阳辐射估算中考虑高程以及云的影响是十分必要的。

      Surface solar radiation is the primary energy source for the Earth's surface and a crucial variable for assessing solar resources, ecological systems, hydrology, climate models, and related studies. Its diurnal variation impacts local climate models, as well as the safe integration of solar photovoltaic power plants into the grid. Therefore, estimating the diurnal surface solar radiation is of great significance. This study focuses on the Loess Plateau and its surrounding areas. Firstly, this study analyzes the spatiotemporal characteristics of regional cloud properties, based on the cloud products from the new generation geostationary meteorological satellite Himawari-8 (such as cloud optical thickness, cloud effective radius, etc.). Secondly, based on the Libradtran radiative transfer model, a parameterized model of cloud transmittance is constructed, which is combined with the Iqbal C clear sky model, and considering the terrain obstruction, a comprehensive model for estimating surface solar radiation considering the effects of atmosphere, clouds, and terrain is formed. On this basis, using Himawari-8 cloud and aerosol products, MODIS atmospheric products, etc., the diurnal (every 10 minutes) surface solar radiation is simulated, and the model accuracy is verified based on ground station measurements. To further evaluate the model's applicability, the diurnal spatial distribution of surface solar radiation over the Loess Plateau and its surrounding areas in 2020 was simulated and compared with Himawari-8 downward shortwave radiation data under different atmospheric conditions. The study also analyzed the spatiotemporal characteristics and quantified the impacts of clouds and terrain on the spatial distribution of surface solar radiation. The main conclusions of this study are as follows:

      (1) There are significant spatiotemporal distribution differences in cloud frequency (CF), cloud optical thickness (COT), and cloud effective radius (CER) over the Loess Plateau and its surrounding areas. Specifically, regions with high CF and COT values are mainly concentrated in the southern part, while CER exhibits an opposite spatial distribution pattern. Among the four seasons, the highest mean CF values occur in summer, while the mean COT peaks in autumn. The highest mean CER values mainly occur in spring and winter. In terms of the diurnal variation, the CF values are high at midday and low in the morning and afternoon, while the diurnal variation of COT shows the opposite trend. The CER values show an increasing trend from morning to afternoon and reach a maximum at 17:00 Beijing time.

     (2) Based on the Libradtran radiation transfer model, cloud transmittance lookup tables were computed based on different cloud phases (liquid cloud, ice cloud), with parameters including cloud optical thickness, cloud effective radius, and solar zenith angle. A custom function was employed to explore the relationship between cloud transmittance and key parameters, thus determining a parameterized model for cloud transmittance. Model parameters were obtained through least squares fitting, resulting in parameterized models with a fitting degree exceeding 0.99 and root mean square error less than 0.1.

     (3) Combining the clear sky model, the cloud transmittance parameterization model constructed in this study, Himawari-8 cloud and aerosol products, MODIS atmospheric products, as well as DEM products, surface solar radiation under clear sky, cloudy sky, and all-sky conditions was estimated every 10 minutes. The accuracy of the models was validated using ground measurements. The correlation coefficients (R) for validation results between clear-sky and all-sky conditions were all greater than 0.9, while for cloudy-sky conditions, R values were all greater than 0.6. The root mean square errors (RMSE) for clear-sky, cloudy-sky, and all-sky conditions averaged over the seven stations were 91.36 W/m², 146.87 W/m², and 103.68 W/m², respectively. The mean bias errors (MBE) averaged over the seven stations were 54.45 W/m², -65.77 W/m², and 30.00 W/m², respectively. These results indicate a good correlation between the estimated surface solar radiation and ground measurements at each station for clear-sky, cloudy-sky, and all-sky conditions, demonstrating overall good accuracy with some instances of overestimation and underestimation.

     (4) The spatial distribution simulated by the model constructed in this paper shows good consistency with the Himawari-8 surface downward shortwave radiation data,, and it is capable of presenting detailed spatial distribution characteristics. Direct radiation, diffuse radiation, and global radiation exhibit significant spatial and seasonal variations over the study area. Direct and global radiation exhibit a spatial distribution pattern of high values in the west and low values in the east, and high values in the north and low values in the south. Additionally, their values are highest in spring and lowest in autumn and winter. In contrast, the spatial distribution of diffuse radiation is the highest in summer and the lowest in winter. Diurnal variations of direct radiation, diffuse radiation, and global radiation all exhibit a trend of increase followed by decrease, reaching their maximum values around noon. Furthermore, it is observed that the interaction between cloud and elevation has the greatest impact on the spatial distribution of surface solar radiation. Therefore, considering the influence of elevation and cloud is essential for estimating surface solar radiation in complex terrain areas.

[1] Yu L, Zhang M, Wang L, et al. Effects of aerosols and water vapour on spatial-temporal variations of the clear-sky surface solar radiation in China[J]. Atmospheric Research, 2021, 248: 105162.

[2] 齐月, 房世波, 周文佐. 近50年来中国地面太阳辐射变化及其空间分布[J]. 生态学报, 2014, 34(24): 7444-7453.

[3] 曹美燕. 基于随机森林的太阳能辐射预测方法研究[D]. 沈阳: 沈阳建筑大学, 2019.

[4] Tang W, Yang K, Qin J, et al. A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation[J]. Earth System Science Data, 2019, 11(4): 1905-1915.

[5] 何川. 基于机器学习的太阳辐射和参考作物腾发量估算研究[D]. 杨凌: 西北农林科技大学, 2020.

[6] 刘媛媛, 胡琦, 和骅芸, 等. 中国不同时间尺度地表太阳总辐射估算研究[J]. 气候变化研究进展, 2021, 17(2): 175-183.

[7] 张星星, 吕宁, 姚凌, 等. ECMWF地表太阳辐射数据在我国的误差及成因分析[J]. 地球信息科学学报, 2018, 20(2): 254-267.

[8] Iqbal M. Correlation of average diffuse and beam radiation with hours of bright sunshine[J]. Solar Energy, 1979, 23(2): 169-173.

[9] 郁云, 许昌, 曹潇, 等. 云图纹理分析结合SVM的地表太阳辐射预测[J]. 西南师范大学学报(自然科学版), 2017, 42(12): 75-81.

[10] Dong J, Olama M M, Kuruganti T, et al. Novel stochastic methods to predict short-term solar radiation and photovoltaic power[J]. Renewable Energy, 2020, 145(C): 333-346.

[11] Angstrom A. Solar and terrestrial radiation[J]. Quarterly Journal of the Royal Meteorological Society, 1924, 50(210): 121-126.

[12] Prescott J. Evaporation from a water surface in relation to solar radiation [J]. Transactions of the Royal Society of South Australia, 1940,64(3):114-118.

[13] Bahel V, Bakhsh H, Srinivasan R. A correlation for estimation of global solar radiation[J]. Energy, 1987, 12(2): 131-135.

[14] Yao W, Zhang C, Wang X, et al. A new correlation between global solar radiation and the quality of sunshine duration in China[J]. Energy Conversion and Management, 2018, 164: 579-587.

[15] 刘志和, 邱让建, 刘春伟. 基于日照时数的太阳辐射通用模型提升参考作物蒸散估算精度研究[J]. 节水灌溉, 2023, 330(2): 80-88+95.

[16] Hargreaves G H, Samani Z A. Estimating potential evapotranspiration[J]. Journal of the irrigation and Drainage Division, 1982, 108(3): 225-230.

[17] Bristow K L, Campbell G S. On the relationship between incoming solar radiation and daily maximum and minimum temperature[J]. Agricultural and Forest Meteorology, 1984, 31(2): 159-166.

[18] Thornton P E, Running S W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation[J]. Agricultural and Forest Meteorology, 1999, 93(4): 211-228.

[19] Pan T, Wu S, Dai E, et al. Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China[J]. Applied Energy, 2013, 107(7): 384-393.

[20] Kasten F, Czeplak G. Solar and terrestrial radiation dependent on the amount and type of cloud[J]. Solar Energy, 1980, 24(2): 177-189.

[21] Badescu V, Dumitrescu A. New types of simple non-linear models to compute solar global irradiance from cloud cover amount[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2014, 117: 54-70.

[22] Ahamed M S, Guo H, Tanino K. Cloud cover-based models for estimation of global solar radiation: A review and case study[J]. International Journal of Green Energy, 2022, 19(2): 175-189.

[23] Zhang Q, Huang J, Lang S. Development of typical year weather data for Chinese locations[J]. ASHRAE Transactions, 2002, 108(2): 1063-1075.

[24] Chen J L, Li G S, Xiao B B, et al. Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature[J]. Energy Conversion and Management, 2015, 89(15):318-329.

[25] Kaba K, Sarıgül M, Avcı M, et al. Estimation of daily global solar radiation using deep learning model[J]. Energy, 2018, 162: 126-135.

[26] 周晋, 吴业正, 晏刚, 等. 利用神经网络估算太阳辐射[J]. 太阳能学报, 2005, 26(4): 509-512

[27] 侯宁, 张晓通, 魏瑜, 等. 基于随机森林方法的中国地表短波辐射估算[J]. 太阳能学报, 2021, 42(2): 31-36.

[28] 胡斯勒图, 施建成, 李明, 等. 基于卫星数据的地表下行短波辐射估算:方法、进展及问题[J]. 中国科学:地球科学, 2020, 50(7): 887-902.

[29] Fritz S, Rao P K, Weinstein M. Satellite measurements of reflected solar energy and the energy received at the ground[J]. Journal of Atmospheric Sciences, 1964, 21(2): 141-151.

[30] Cano D, Monget J M, Albuisson M, et al. A method for the determination of the global solar radiation from meteorological satellite data[J]. Solar Energy, 1986, 37(1): 31-39.

[31] Rigollier C, Lefèvre M, Wald L. The method Heliosat-2 for deriving shortwave solar radiation from satellite images[J]. Solar Energy, 2004, 77(2): 159-169.

[32] Hammer A, Heinemann D, Hoyer C, et al. Solar energy assessment using remote sensing technologies[J]. Remote Sensing of Environment, 2003, 86(3): 423-432.

[33] Beyer H G, Costanzo C, Heinemann D. Modifications of the Heliosat procedure for irradiance estimates from satellite images[J]. Solar Energy, 1996, 56(3): 207-212.

[34] 陈蔺鸿. 联合Himawari-8与Sentinel-2卫星的日尺度太阳短波辐射估算研究[D]. 兰州: 西北师范大学, 2021.

[35] Wong L T, Chow W K. Solar radiation model[J]. Applied Energy, 2001, 69(3): 191-224.

[36] Tang W, Qin J, Yang K, et al. Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data[J]. Atmospheric Chemistry and Physics Discussions, 2015, 15(23): 35201-35236.

[37] Qin J, Tang W, Yang K, et al. An efficient physically based parameterization to derive surface solar irradiance based on satellite atmospheric products[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(10): 4975-4988.

[38] Ameen B, Balzter H, Jarvis C, et al. Modelling hourly global horizontal irradiance from satellite-derived datasets and climate variables as new inputs with artificial neural networks[J]. Energies (Basel), 2019, 12(1): 148.

[39] Ma R, Letu H, Yang K, et al. Estimation of surface shortwave radiation from Himawari-8 satellite data based on a combination of radiative transfer and deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5304-5316.

[40] 梁益同, 刘可群, 夏智宏. 利用 FY-2C 卫星资料估算太阳辐射研究[J]. 气象科技, 2009, 37(2): 234-238.

[41] Stubenrauch C J, Cros S, Guignard A, et al. A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat[J]. Atmospheric Chemistry and Physics, 2010, 10(15): 7197-7214.

[42] Ramanathan V, Cess R D, Harrison E F, et al. Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment[J]. Science, 1989, 243(4887): 57-63.

[43] 杨溯, 石广玉, 王标, 等. 1961～2009年我国地面太阳辐射变化特征及云对其影响的研究[J]. 大气科学, 2013, 37(5): 963-970.

[44] 达选芳, 李照荣, 王小勇, 等. 有云条件下太阳辐射短临预报订正技术研究[J]. 干旱气象, 2021, 39(6): 1006-1016.

[45] 朱婷婷. 基于地基云图的太阳直接辐照度超短期预测研究[D]. 南京: 东南大学, 2019.

[46] 张定禄. 山地云观测中常见的几个问题[J]. 高原山地气象研究, 2009, 29(1): 76-79.

[47] 武艳. 利用NOAA/AVHRR资料分析21年长江中下游地区云量时空分布与演变特征[D]. 南京: 南京信息工程大学, 2011.

[48] 刘菊菊, 游庆龙, 周毓荃, 等. 基于ERA-Interim的中国云水量时空分布和变化趋势[J]. 高原气象, 2018, 37(6): 1590-1604.

[49] Wang J, Jian B, Wang G, et al. Climatology of cloud phase, cloud radiative effects and precipitation properties over the Tibetan Plateau[J]. Remote Sensing, 2021, 13(3): 363.

[50] 史继花. 祁连山及周边地区不同下垫面云特性变化及其影响因素[D]. 兰州: 西北师范大学, 2021.

[51] Cintineo J L, Pavolonis M J, Sieglaff J M, et al. Evolution of severe and nonsevere convection inferred from GOES-derived cloud properties[J]. Journal of Applied Meteorology and Climatology, 2013, 52(9): 2009-2023.

[52] Senf F, Dietzsch F, Hünerbein A, et al. Characterization of initiation and growth of selected severe convective storms over central Europe with MSG-SEVIRI[J]. Journal of Applied Meteorology and Climatology, 2015, 54(1): 207-224.

[53] Lin D, Wang W, Liu P, et al. FY-4A satellite based cloud microphysical variation analysis of airborne cloud seeding operations in Sichuan basin[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, 42: 125-131.

[54] Shang H, Letu H, Nakajima T Y, et al. Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data[J]. Scientific Reports, 2018, 8(1): 1105.

[55] Minnis P, Young D F, Sun-Mack S, et al. Diurnal, seasonal, and interannual variations of cloud properties derived for CERES from imager data[R]. Norfolk: American Meteorological Society, 2004.

[56] 马润. 基于葵花-8新一代静止气象卫星的地表下行短波辐射估算研究[D]. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2019.

[57] Mace G G, Zhang Q, Vaughan M, et al. A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data[J]. Journal of Geophysical Research: Atmospheres, 2009, 114(8): D26A.

[58] 董自香. SACOL和SGP站卷云时空变化及其与大尺度气象场的关系[D]. 兰州: 兰州大学, 2021.

[59] 王佳华. 多波段全天空成像和辐射观测系统[D]. 武汉: 武汉大学, 2017.

[60] 高太长, 刘磊, 赵世军, 等. 全天空测云技术现状及进展[J]. 应用气象学报, 2010, 21(1): 101-109.

[61] Shields J E, Karr M E, Johnson R W, et al. Day/night whole sky imagers for 24-h cloud and sky assessment: History and overview[J]. Applied Optics, 2013, 52(8): 1605-1616.

[62] 王聪. 基于数值和地基云图的太阳辐射超短期预测研究[D]. 南京: 南京林业大学, 2021.

[63] 田辉, 文军, 马耀明, 等. 复杂地形下黑河流域的太阳辐射计算[J]. 高原气象, 2007(4): 666-676.

[64] 龚力峰. 下行短波辐射遥感产品地形校正与验证中的尺度问题研究[D]. 成都: 电子科技大学, 2015.

[65] 张小萍. 黄土高原沟壑区太阳辐射时空分布特征及估算模型研究[D]. 杨凌: 西北农林科技大学, 2015.

[66] Garnier B J, Ohmura A. A method of calculating the direct shortwave radiation income of slopes[J]. Journal of Applied Meteorology and Climatology, 1968, 7(5): 796-800.

[67] 李占清, 翁笃鸣. 丘陵山地总辐射的计算模式[J]. 气象学报, 1988(4): 461-468.

[68] 曾燕, 邱新法, 潘敖大, 等. 地形对黄河流域太阳辐射影响的分析研究[J]. 地球科学进展, 2008, 167(11): 1185-1193.

[69] Dubayah R. Estimating net solar radiation using Landsat thematic mapper and digital elevation data[J]. Water Resources Research, 1992, 28(9): 2469-2484.

[70] Hay J E, Mckay D C. Estimating solar irradiance on inclined surfaces: A review and assessment of methodologies[J]. International Journal of Solar Energy. 1985, 3(4): 203-240.

[71] Wang T, Yan G, Mu X, et al. Toward operational shortwave radiation modeling and retrieval over rugged terrain[J]. Remote Sensing of Environment, 2018, 205: 419-433.

[72] Zhang S, Li X, She J, et al. Assimilating remote sensing data into GIS-based all sky solar radiation modeling for mountain terrain[J]. Remote Sensing of Environment, 2019, 231: 111239.

[73] 武永利, 相栋. FY2号气象卫星估算地面太阳辐射研究[J]. 自然资源学报, 2013, 28(12): 2117-2126.

[74] 李彬. 起伏地形上云阴影畸变对地表太阳辐射影响研究[D]. 南京: 南京信息工程大学, 2016.

[75] 王凯利. 黄土高原蒸散发与植被变化响应关系研究[D]. 郑州: 华北水利水电大学, 2023.

[76] Yan M J, He Q Y, Yamanaka N, et al. Location, geology and landforms of the Loess Plateau[J]. Restoration and Development of the Degraded Loess Plateau, China, 2014: 3-21.

[77] 王东. 黄土高原干旱时空特征及对植被生长潜在风险评估[D]. 兰州: 兰州大学, 2023.

[78] 肖蓓, 崔步礼, 李东昇, 等. 黄土高原不同气候区降水时空变化特征[J]. 中国水土保持科学, 2017, 15(1): 51-61.

[79] 史莹莹. 西北半干旱区沙尘气溶胶光学和微物理特性研究[D]. 兰州: 兰州大学, 2017.

[80] Fang W, Huang S, Huang Q, et al. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China[J]. Remote Sensing of Environment, 2019, 232(10): 111290.

[81] Bessho K, Date K, Hayashi M, et al. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites[J]. Journal of the Meteorological Society of Japan. Ser. II, 2016, 94(2): 151-183.

[82] 张慧子. 基于Himawari-8卫星数据的青藏高原地区云微物理参数和地表短波辐射的时空分布研究[D]. 桂林: 桂林理工大学, 2020.

[83] 邵天彬. 中国华北地区气溶胶对云—降水的影响[D]. 兰州: 兰州大学, 2023.

[84] Coy L, Wargan K, Molod A M, et al. Structure and dynamics of the quasi-biennial oscillation in MERRA-2[J]. Journal of Climate, 2016, 29(14): 5339-5354.

[85] Farr T G, Rosen P A, Caro E, et al. The shuttle radar topography mission[J]. Reviews of Geophysics, 2007, 45(2): 1-13.

[86] Letu H, Nakajima T Y, Wang T, et al. A new benchmark for surface radiation products over the East Asia–Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite[J]. Bulletin of the American Meteorological Society, 2022, 103(3): E873-E888.

[87] Li R, Wang D, Liang S. Comprehensive assessment of five global daily downward shortwave radiation satellite products[J]. Science of Remote Sensing, 2021, 4(10): 100028.

[88] Yang Y, Zhao C, Fan H. Spatiotemporal distributions of cloud properties over China based on Himawari-8 advanced Himawari imager data[J]. Atmospheric Research, 2020, 240: 104927.

[89] 王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(1): 116-134.

[90] 刘建军, 陈葆德. 基于CloudSat卫星资料的青藏高原云系发生频率及其结构[J]. 高原气象, 2017, 36(3): 632-642.

[91] Yan M J, He Q Y, Yamanaka N, et al. Location, geology and landforms of the Loess Plateau[J]. Restoration and Development of the Degraded Loess Plateau, China, 2014: 3-21.

[92] Zhao H, He H, Wang J, et al. Vegetation restoration and its environmental effects on the Loess Plateau[J]. Sustainability, 2018, 10(12): 4676.

[93] 张宝庆, 田磊, 赵西宁, 等. 植被恢复对黄土高原局地降水的反馈效应研究[J]. 中国科学:地球科学, 2021, 51(7):1 080-1091.

[94] Zhao Q, Ma X, Liang L, et al. Spatial-temporal variation characteristics of multiple meteorological variables and vegetation over the Loess Plateau region[J]. Applied Sciences, 2020, 10(3): 1000.

[95] Yang Z, Zhang Q, Hao X. Evapotranspiration trend and its relationship with precipitation over the loess plateau during the last three decades[J]. Advances in Meteorology, 2016, 2016(3): 6809749.

[96] Zhao, W, Fang, X, Daryanto, S, et al. Factors influencing soil moisture in the Loess Plateau, China: A review[J]. Earth and Environmental Science Transactions of the Royal Society of Edinburgh, 2018, 109(3-4): 501-509.

[97] 周喜讯, 张华, 荆现文. 中国地区云量和云光学厚度的分布与变化趋势[J]. 大气与环境光学学报, 2016, 11(1): 1-13.

[98] 高星星. 中国典型地区气溶胶光学特性及其气候效应[D]. 兰州: 兰州大学, 2018.

[99] Albrecht B A. Aerosols, cloud microphysics, and fractional cloudiness[J]. Science, 1989, 245(4923): 1227-1230.

[100] Li Y, Yi B, Min M. Diurnal variations of cloud optical properties during day-time over China based on Himawari-8 satellite retrievals[J]. Atmospheric Environment, 2022, 277: 119065.

[101] Roeckner E, Schlese U, Biercamp J, et al. Cloud optical depth feedbacks and climate modelling[J]. Nature, 1987, 329(6135): 138-140.

[102] Li X, Che H, Wang H, et al. Spatial and temporal distribution of the cloud optical depth over China based on MODIS satellite data during 2003-2016[J]. Journal of Environmental Sciences, 2019, 80: 66-81.

[103] Harrison R G. Cloud formation and the possible significance of charge for atmospheric condensation and ice nuclei[J]. Space Science Reviews, 2000, 94(1-2): 381-396.

[104] 杨大生, 王普才. 中国地区夏季云粒子尺寸的时空分布特征[J]. 气候与环境研究, 2012, 17(4): 433-443.

[105] Coopman Q, Riedi J, Finch D P, et al. Evidence for changes in arctic cloud phase due to long‐range pollution transport[J]. Geophysical Research Letters, 2018, 45(19): 10709-10718.

[106] Zhao C, Chen Y, Li J, et al. Fifteen‐year statistical analysis of cloud characteristics over China using Terra and Aqua Moderate Resolution Imaging Spectroradiometer observations[J]. International Journal of Climatology, 2019, 39(5): 2612-2629.

[107] Zhao, C F, Klein S A, Xie S, et al. Aerosol first indirect effects on non-precipitating low-level liquid cloud properties as simulated by CAM5 at ARM sites[J]. Geophysical Research Letters, 2012, 39(8): L08806.

[108] Qiu Y, Zhao C, Guo J, et al. 8-Year ground-based observational analysis about the seasonal variation of the aerosol-cloud droplet effective radius relationship at SGP site[J]. Atmospheric Environment, 2017, 164: 139-146.

[109] Jung W S, Panicker A S, Lee D I, et al. Estimates of aerosol indirect effect from Terra MODIS over republic of Korea[J]. Advances in Meteorology, 2013, 2013(1): 1-8.

[110] Douglas A R. The Effects of aerosol-cloud interactions on warm cloud properties[D]. Madison: The University of Wisconsin-Madison, 2020.

[111] Li Z, Wang Y, Guo J, et al. East Asian study of tropospheric aerosols and their impact on regional clouds, precipitation, and climate (EAST‐AIRCPC)[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(23): 13026-13054.

[112] 刘唯佳. 基于云参量的太阳辐射短时临近预报模型的构建及预报评估[D]. 南京: 南京信息工程大学, 2021.

[113] Emde C, Buras-Schnell R, Kylling A, et al. The LibRadtran software package for radiative transfer calculations (version 2.0.1)[J]. Geoscientific Model Development, 2016, 9(5): 1647-1672.

[114] Ma H, Liang S, Shi H, et al. An optimization approach for estimating multiple land surface and atmospheric variables from the geostationary advanced Himawari imager top-of-atmosphere observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(4): 2888-2908.

[115] 刘兴冉, 张宏晔, 闫海明, 等. 区域复杂地形晴日太阳辐射估算研究[J]. 太阳能学报, 2022, 43(8): 174-180.

[116] Li P, Zhong L, Ma Y, et al. Estimation of 1 km downwelling shortwave radiation over the Tibetan Plateau under all-sky conditions[J]. Atmospheric Chemistry and Physics Discussions, 2023, 2023: 1-36.

[117] 李小军, 辛晓洲, 彭志晴. 2003～2012年中国地表太阳辐射时空变化及其影响因子[J].太阳能学报, 2017, 38(11): 3057-3066.