|
|||||
|
|
| 河西走廊东部太阳能分布特征及指数预报 | |
| 引用本文： | 钱莉,林纾,杨永龙,兰晓波,闫国华.河西走廊东部太阳能分布特征及指数预报[J].资源科学,2010,32(12):2419-2426. |
| 作者姓名： | 钱莉 林纾 杨永龙 兰晓波 闫国华 |
| 作者单位： | 1. 中国气象局兰州干旱气象研究所干旱气候变化与减灾重点实验室,兰州730020;甘肃省武威市气象局,武威733000 2. 兰州区域气候中心,兰州,730020 3. 甘肃省武威市气象局,武威,733000 |
| 基金项目： | 财政部、国家发改委和中国气象局联合项目：“甘肃省风能资源详查和评价”。 |
| 摘 要： | 利用线性回归和线性相关分析方法对近30a年河西走廊东部民勤代表站的太阳总辐射资料进行研究。结果表明:河西走廊东部年平均太阳总辐射为6166.2MJ/m2,年平均太阳总辐射总体呈上升趋势,进入21世纪后,年太阳总辐射值稳定在(6000～6400)MJ/m2的高值区间上,说明全球增暖的气候背景下,河西走廊东部太阳能总辐射值稳定且有所增大,这为该区域内太阳能资源开发提供了有利的气候背景。太阳总辐射存在明显的季节变化,夏季最强,次强为春季,最弱的是冬季。太阳总辐射的月际变化呈单峰型,最大月份出现在6月,最小月出现在12月。尝试利用ECMWF数值预报产品作为预报因子库,采用press算子普查影响太阳总辐射变化的主要因子,对一特定地区和特定季节影响太阳总辐射变化的主要因素有温度、水汽压、相对湿度、比湿、温度露点差、露点温度等。这些因子不但与表征太阳辐射强弱的温度有关,还与空气中水汽含量的多少有关。采用最优子集回归精选因子,建立逐日太阳总辐射BP神经网络分月预报模型,并将其预报产品根据不同季节服务对象的不同划分预报等级指数,提出服务对策建议。业务试用结果表明:BP神经网络预报模型具有较强的非线性处理能力,能较好地表征日太阳总辐射的变化,预报拟合率和准确率均达较高水平,业务系统与MICAPS对接,实现全自动化,可制作一周内太阳辐射指数预报,为太阳辐射精细化预报提供了重要的技术支撑,也为开展太阳能气象指数预报提供了一种好的思路和方法。 |
| 关 键 词： | 太阳总辐射 变化规律 BP神经网络 太阳能指数 预报 |
Characteristics of Solar Energy Distribution and Index Forecasting across the Eastern Hexi Corridor |
|
| QIAN Li,LIN Shu,YANG Yonglong,LAN Xiaobo and YAN Guohua.Characteristics of Solar Energy Distribution and Index Forecasting across the Eastern Hexi Corridor[J].Resources Science,2010,32(12):2419-2426. | |
| Authors: | QIAN Li LIN Shu YANG Yonglong LAN Xiaobo and YAN Guohua |
| Institution: | Lanzhou Institute of Arid Meteorology China Meteorological Administration, Key Laboratory of Arid Climatic Change and Reducing Disaster in Gansu Province, Key Open Laboratory of Arid Climate Change and Disaster Reduction CMA, Lanzhou 730020, China; Wuwei Meteorological Bureau, Wuwei 733000, China;Northwest Regional Climate Center, Lanzhou 730020, China;Wuwei Meteorological Bureau, Wuwei 733000, China;Wuwei Meteorological Bureau, Wuwei 733000, China;Wuwei Meteorological Bureau, Wuwei 733000, China |
| Abstract: | Spatial and temporal distributions of solar radiation over the eastern Hexi Corridor were examined using around 30a solar radiation time series and linear regression and correlation analysis. Results showed the average solar radiation was roughly 6166.2 MJ/m2 over the eastern Hexi Corridor. The average solar radiation showed a generally increasing trend, particularly since 1989. The year of solar radiation beginning to increase seemed to generally correspond to climate warming trends over this region. This suggested that the solar radiation over the Hexi corridor was essentially stable and somewhat increased in the context of global warming, which indeed provides favorable climate conditions for utilization of solar energy resources. The solar radiation had also obvious seasonal and monthly variations. Factors affecting solar radiation were analyzed with press (Prediction square sum) guidelines. The primary factors affecting solar radiation were found to be temperature, vapor pressure, relative humidity, specific humidity, temperature and dew point difference, and dew-point temperature. These factors were highly related to both the temperature which is capable of characterizing solar radiation intensity and the water vapor content in the atmosphere as well. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecast products, a BP neural network prediction model for various months of daily solar radiation was established with the selected factors for optimal subset regression, with indexing its forecasting products. Preliminary results showed that the BP neural network prediction model is of strong capability to deal with non-linear mechanisms. This enables a better characterization of changes in solar radiation, suggesting the forecasting fitting rate and accuracy reaching a relatively high level. The business system docked with MICAPS, achieving full automation. This can produce weekly-based index forecasting of solar radiation, providing important technical support for precise forecasting of solar radiation and creating a new opportunity to realize meteorological index forecasting of solar radiation. |
| Keywords: | Solar radiation Change characteristics BP neural network Solar energy index Forecasting |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
| 点击此处可从《资源科学》浏览原始摘要信息 | |
| 点击此处可从《资源科学》下载免费的PDF全文 | |