向友珍,吴立峰,张富仓,范军亮,鲁向晖,王荚文.基于常规气象资料估算南方地区日辐射总量方法比较[J].农业机械学报,2016,47(10):181-192,155.
Xiang Youzhen,Wu Lifeng,Zhang Fucang,Fan Junliang,Lu Xianghui,Wang Jiawen.Comparison of Total Radiation Estimation Methods in South Area Based on Conventional Meteorological Data[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(10):181-192,155.
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基于常规气象资料估算南方地区日辐射总量方法比较   [下载全文]
Comparison of Total Radiation Estimation Methods in South Area Based on Conventional Meteorological Data   [Download Pdf][in English]
投稿时间:2016-05-25  
DOI:10.6041/j.issn.1000-1298.2016.10.024
中文关键词:  地表总辐射  温度  模型  支持向量机  估算方法
基金项目:国家高技术研究发展计划(863计划)项目(2011AA100504)、国家自然科学基金项目(51509208)和江西省教育厅科学技术研究项目(GJJ151123)
作者单位
向友珍 西北农林科技大学 
吴立峰 南昌工程学院 
张富仓 西北农林科技大学 
范军亮 西北农林科技大学 
鲁向晖 南昌工程学院 
王荚文 南昌工程学院 
中文摘要:日地表总辐射量(Rs)是作物生长模型和参考作物蒸发蒸腾量估算的重要基础数据,但我国只有约1/20的气象站能够直接观测Rs。由于气温资料很容易获得,使用基于基本气象资料的经验模型是估算Rs的常用方法。以1982—2014年南方20个气象站的气象资料为基础,对Bristow—Campbell (B—C)方法和Hargreaves(Harg)方法各6种不同形式重新进行了参数率定,并对以上方法和支持向量机15种参数输入形式进行了适用性评价,结果表明:支持向量机模型整体好于B—C方法和Harg方法。其中,以最高温度(Tmax)、最低温度(Tmin)、相对湿度(RH)和降水量(P)为输入变量的支持向量机模型精度最高,其20站平均R2达到0.80、RMSE平均为3.20MJ/(m2·d),且在包含降雨量资料后,不存在Rs为负或大于地外总辐射量(Ra)的问题。仅有温度资料时,支持向量机模型的20站平均R2为0.74,RMSE为3.72MJ/(m2·d)。不同输入变量对支持向量机模型预报Rs的精度影响不同,输入变量为Tmax和Tmin优于输入变量为ΔT;而除温度资料外,当拥有相对湿度和降水量资料时,模型优劣依次表现为RH+P、RH、P。经验模型中B—C方法的M1和M3以及Harg方法的M10和M12模型精度较好,其R2为0.69~0.70、RMSE在4.00MJ/(m2·d)左右,但M10和M12模型对气象资料要求更高,除日温度差外,需要降水量资料,同时还存在有降水时日Rs严重高估或负值问题。
Xiang Youzhen  Wu Lifeng  Zhang Fucang  Fan Junliang  Lu Xianghui  Wang Jiawen
Northwest A&F University,Nanchang Institute of Technology,Northwest A&F University,Northwest A&F University,Nanchang Institute of Technology and Nanchang Institute of Technology
Key Words:global solar radiation  temperature  model  support vector machine  estimation method
Abstract:Global solar radiation (Rs) is an important elementary datum for crop modeling and reference evapotranspiration (ETo) estimation, but only 1/20 of Chinese weather stations can observe it directly. It is a common method for estimating Rs to use empirical model based on temperature data, which are easy to get. Based on the temperatures of 20 weather stations in south of China from 1982 to 2014, parameters of six different forms of Bristow—Campbell (B—C) and Hargreaves (Harg) methods were calibrated, and the applicability of above mentioned methods and fifteen support vector machine (SVM) parameter input forms were evaluated. The results showed that SVM model was better than B—C method and Harg method as a whole. The SVM model with maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH) and precipitation (P) as input variables had the highest precision. On average, R2 and RMSE from the twenty weather stations were 0.80 and 3.20MJ/(m2·d), respectively, even when it included precipitation data, Rs was not negative and even greater than the extraterrestrial total radiation (Ra). R2 from the twenty weather stations was 0.74 on average, and RMSE was 3.72MJ/(m2·d) when based on temperature data. Different input variables had different influences on the SVM model forecasted Rs, the input variables of Tmax and Tmin were superior to ΔT. In addition to temperature data, when the model had the relative humidity and rainfall data, it was showed that RH+P > RH > P. Among the empirical models, the B—C model’s M1 and M3, and the Harg models’ M10 and M12 were preferable, their R2 were 0.69~0.70, RMSE was about 4.0MJ/(m2·d). While the M10 and M12 had higher request to the meteorological data, which needed the data of dayly temperature and precipitation. There existed the dayly Rs overestimation or negative problems when it rained.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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