太阳能被认为是最具发展前景的清洁能源之一，为缓解地球日益严重的能源短缺问题，对太阳能资源的评估和大规模利用仍迫在眉睫。山区拥有十分丰富的太阳能资源，由于其地势高、空气干洁，且不易受人为影响，十分适合太阳能资源开发。但这些地区易受地形条件限制，且辐射观测站和气象观测站稀疏，太阳能资源往往难以评估。目前，国内外地表太阳辐射的估算模型已较为成熟，但有着高精度的模型大多需要结合辐射站或气象站的记录数据（如日照时长等），在气象站较为稀缺的山区等地存在使用限制。基于此背景，本文利用国际基线地表辐射观测网络（Baseline Surface Radiation Network, BSRN）及中国气象局（China Meteorological Administration, CMA）提供的多年地面观测数据，结合多源遥感及再分析数据，在Google Earth Engine（GEE）云计算平台构建了亚热带、干旱带、温带及亚寒带普适的全天空全地形地表太阳辐射估算经验模型，综合大数据和遥感技术弥补山区等地气象站稀缺的弊端。基于所构建模型，从太阳能资源的丰富性及稳定程度评估了三个气候区站点上的太阳能资源情况。本文的主要结论如下：

（1）基于GEE平台构建了量化周围地形阻挡的算法，并结合平台MODIS、MERRA-2等大气数据集，构建了考虑地形遮蔽、大气和地表的多次反射所带来的辐射衰减的晴空地表太阳辐射估算模型。模型验证结果表明：晴空地表太阳辐射估算模型的模拟精度较高。在地形复杂区昆仑山提孜那甫河流域三个具有不同地形开阔度（Sky View Factor，SVF：KD=0.79；XHX=0.85；MMK=0.96）的气象站上，模型的均方根误差RMSE分别为1.10 MJ/m²，1.69 MJ/m²，2.49 MJ/m²；平均偏差ME（MRE）分别为0.39 MJ/m²（3.24%），0.73 MJ/m²（12.76%），1.88 MJ/m²（15.88%），由此可见，模型在视野一般开阔的地形下仍较为准确地模拟晴空总太阳辐射。

（2）以晴空总太阳辐射为基值，选择云属性（云量、云水路径、云光学厚度、云粒子有效半径）和地表温度作为太阳辐射的影响因子，构建亚热带、干旱带、温带及亚寒带气候区的全天空太阳辐射经验模型。研究利用BSRN的全球地面实测数据，在上述三类气候区分别构建全天空地表太阳辐射估算模型并验证了模型精度。结果表明，亚热带气候区Semi-Model模型在BSRN站点上表现最好，ME（MRE）和RMSE分别为-0.03 MJ/m²（10.21%）和3.31 MJ/m²；温带、亚寒带气候区模型Temp-Model次之，误差分别为0.16 MJ/m²（16.97%）和3.44 MJ/m²。干旱带气候区模型Arid-Model精度相对较差，误差分别为0.09 MJ/m²（9.48%）和4.52 MJ/m²；接着在中国区域CMA站点进一步验证模型的适用性。在亚热带站点上，模型的ME（MRE）和RMSE为0.89 MJ/m²（26.69%）和4.12 MJ/m²；干旱带模型的误差分别为-0.72 MJ/m²（18.11%）和3.94 MJ/m²；温带及亚寒带模型的误差分别为-0.62 MJ/m²（9.20%）和4.85 MJ/m²。

（3）将所构建的模型与国际上被广泛使用的太阳辐射估算经验模型（Black模型）分别在BSRN和CMA站点上进行对比验证。结果表明，本文提出的经验模型精度较高，适用性较好，且在站点上总体表现优于Black模型，其中在亚热带、干旱带、温带及亚寒带气候区BSRN站点上，本模型相较于Black模型平均RMSE分别减少了0.48 MJ/m²、-0.35 MJ/m²、-0.09 MJ/m²。在三个气候区的CMA站点上，平均RMSE分别减少了1.12 MJ/m²、0.50 MJ/m²、0.22 MJ/m²。

（4）基于三种太阳辐射估算经验模型从太阳能资源丰富性和稳定程度评估了三个气候区的太阳能资源。结果表明，干旱气候区的太阳能资源较其他两个气候区丰富且稳定性更强，所有站点上的年总太阳辐射量（SC）均值为6512 MJ/m²，各月平均稳定度（ST）为0.51，太阳能资源利用潜力巨大；其次是亚热带气候区，SC均值为5033 MJ/m²，平均ST为0.29；温带、亚寒带气候区的SC均值为4369 MJ/m²，平均ST为0.26。

Solar energy is one of the most promising clean energy sources. To mitigate the growing global energy shortage, assessing and utilizing solar resources on a large scale is urgent. Mountainous regions have abundant solar resources due to their high altitude, clean air, and minimal human interference, making them ideal for solar energy development. However, these areas often face challenges due to complex terrain and sparse radiation and meteorological stations, making resource assessment difficult. Existing solar radiation estimation models, both domestic and international, are mature but often require data from radiation or meteorological stations, such as sunshine duration, which limits their use in mountainous areas with few stations. To address this, we used long-term ground observation data from the Baseline Surface Radiation Network (BSRN) and the China Meteorological Administration (CMA), along with multi-source remote sensing and reanalysis data, building three empirical models on the Google Earth Engine (GEE) cloud platform. These models could estimate surface solar radiation under various sky and terrain conditions in subtropical, arid, temperate and subarctic climates. By leveraging big data and remote sensing, it overcomes the issue of sparse meteorological stations in mountainous regions. Using the constructed model, we evaluated the solar resources at stations in three climate zones based on richness and stability of solar energy. The main conclusions of this study are as follows:

(1) Based on the GEE platform, we developed an algorithm to quantify surrounding terrain obstruction. By integrating atmospheric datasets such as MODIS and MERRA-2 available on the platform, we constructed a clear-sky surface solar radiation estimation model. This model accounts for radiation attenuation due to terrain shading, multiple reflections between the atmosphere and surface. The model validation results indicate high simulation accuracy. At three meteorological stations in the Kunlun Mountains' Tizinafu River Basin, characterized by different terrain openness (Sky View Factor, SVF: KD=0.79; XHX=0.85; MMK=0.96), the model's root mean square error (RMSE) values were 1.10 MJ/m², 1.69 MJ/m², and 2.49 MJ/m², respectively. The mean error (ME) values were 0.39 MJ/m² (3.24%), 0.73 MJ/m² (12.76%), and 1.88 MJ/m² (15.88%), respectively. Thus, the model accurately simulates total clear-sky solar radiation even in moderately open terrain.

(2) Using clear-sky total solar radiation as a baseline, we selected cloud properties (cloud cover, cloud water path, cloud optical thickness, and effective radius of cloud particles) and surface temperature as factors influencing solar radiation. We then constructed empirical models for all-sky solar radiation in subtropical, arid, temperate, and subarctic climate zones. The study utilized global ground-based observation data from the Baseline Surface Radiation Network (BSRN) to develop and validate these models. The results indicate that the Semi-Model for the subtropical climate zone performed the best at BSRN sites, with ME (MRE) of -0.03 MJ/m² (10.21%) and RMSE of 3.31 MJ/m². The Temp-Model for the temperate and subarctic climate zones followed, with errors of 0.16 MJ/m² (16.97%) and 3.44 MJ/m², respectively. The Arid-Model for the arid climate zone had relatively lower accuracy, with errors of 0.09 MJ/m² (9.48%) and 4.52 MJ/m². The model's applicability was further validated at China Meteorological Administration (CMA) sites across subtropical, arid, temperate, and subarctic climate zones. At subtropical sites, the model's ME (MRE) and RMSE were 0.89 MJ/m² (26.69%) and 4.12 MJ/m², respectively. For the arid zone model, the errors were -0.72 MJ/m² (18.11%) and 3.94 MJ/m². For the temperate and subarctic zone models, the errors were -0.62 MJ/m² (9.20%) and 4.85 MJ/m², respectively.

(3) This study further compares our model with the widely used international empirical model for estimating solar radiation (the Black model) at BSRN and CMA sites. The results demonstrate that the empirical model proposed in this paper has higher accuracy and better applicability, generally outperforming the Black model at the stations. Specifically, compared to the Black model, at BSRN sites in the subtropical, arid, temperate and subarctic climate zones, our model reduces the average RMSE by 0.48 MJ/m², -0.35 MJ/m², and -0.09 MJ/m², respectively. At the CMA stations in the three climate zones, the average RMSE is reduced by 1.12 MJ/m², 0.50 MJ/m², and 0.22 MJ/m², respectively.

(4) Based on three empirical models for solar radiation estimation, the solar energy resources of three climate zones were evaluated in terms of abundance and stability. Results indicate that the solar energy resource in arid climate zones is more abundant and stable compared to the other two climate zones. The annual total solar radiation (SC) averaged 6512 MJ/m² across all sites, with a monthly average stability (ST) of 0.51, suggesting significant potential for solar energy utilization. Subtropical climate zones exhibit the next highest solar resource potential, with an SC mean of 5033 MJ/m² and an average ST of 0.29. Temperate and subarctic climate zones show an SC mean of 4369 MJ/m² and an average ST of 0.26.

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