不同日照情况下太阳日辐射估算方法研究

张佩; 高苹; 谢小萍; 拉巴; 江旭; 陈诗瑶; 吴洪颜

doi:10.12357/cjea.20210470

不同日照情况下太阳日辐射估算方法研究以江苏为例

doi: 10.12357/cjea.20210470

张佩^1,,
高苹¹,
谢小萍¹,
拉巴²,
江旭³,
陈诗瑶⁴,
吴洪颜^1, ,

1.
江苏省气象局　南京　210008
2.
拉萨市气象局　拉萨　850000
3.
美国塔夫茨大学　波士顿　02155
4.
福建省烟草公司龙岩市公司　龙岩　364000

基金项目: 江苏省“333工程”高层次人才培养科研项目(BRA2019348)、拉萨市科技计划项目(LSKJ202002)和江苏省气象局科技项目(KM201905)资助

详细信息

作者简介:
张佩, 主要研究方向为农业气象。E-mail: 78073954@qq.com

通讯作者:
吴洪颜, 主要研究方向为农业气象。E-mail: 306916487@qq.com

中图分类号: S161.1
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- 被引次数: 0
出版历程
- 收稿日期: 2021-07-19
- 录用日期: 2021-08-27
- 网络出版日期: 2021-11-11
- 刊出日期: 2022-02-08

Estimation method of daily global radiation under different sunshine conditions: A case study of Jiangsu Province

1.
Jiangsu Meteorological Bureau, Nanjing 210008, China
2.
Lahsa Meteorological Bureau, Lahsa 850000, China
3.
Tufts University, Boston 02155, U.S.A
4.
Longyan Company of Fujian Provincial Tobacco Corporation, Longyan 364000, China

Funds: This study was supported by the Research Project for High Level Talent of “333 Project” of Jiangsu Province (BRA2019348), the Science and Technology Project of Lhasa (LSKJ202002) and the Science and Technology Project of Jiangsu Meteorological Bureau (KM201905).

More Information

Corresponding author: E-mail: 306916487@qq.com

摘要

摘要: 太阳辐射是影响农田生态系统碳交换和能量收支等的关键因子。为了准确估算不同日照情况下的太阳日辐射量, 更好地开展农田生态系统的相关研究, 本文以江苏省为例, 利用淮安、吕泗和南京3个辐射观测站2005—2020年逐日气象资料和辐射资料, 以逐日日照时数是否为0, 将研究样本划分为无日照和有日照两类, 梳理了可观测得到的24个气象因子、3个地理因子, 通过相关分析确定了不同日照情况下太阳日辐射的高度相关因子; 选取3个站2005—2016年奇数年份的逐日资料样本作为建模集, 采用基于最小二乘法的逐步回归方式, 分别以太阳日辐射(GR)和日大气透明系数(太阳日辐射与天空辐射的比值, GR/SR)作为因变量建立了不同日照情况下的太阳日辐射估算模型; 选取3个站2005—2016年偶数年份的逐日资料样本为组间验证集、2017—2020年的逐日资料样本为组外验证集。通过比较模型的拟合效果及其对建模集、组间验证集和组外验证集太阳日辐射的估算效果, 最终确定太阳日辐射估算的最佳模型。结果表明: 1)无论是在无日照情况还是有日照情况下, 太阳日辐射都与各气象因子普遍呈现极显著相关(P<0.01)。其中在有日照情况下, 太阳日辐射与日照因子呈最强的相关性, 而在无日照情况下, 太阳日辐射与日最高地表温度表现出最强的相关性, 两者之间的相关系数高于其他气温类因子。2)无日照情况下应选择以太阳日辐射为因变量、以日最高地表温度和日露点温度为自变量的估算模型, 模型的决定系数R²为0.650, 对太阳日辐射的估算准确度接近75%; 在有日照情况下选择以日大气透明系数为因变量、以日日照百分率和日日照时数为自变量集的估算模型, 模型的决定系数R²可达0.769, 对太阳日辐射的估算准确度平均为87.60%。基于该分段估算模型, 江苏地区不同日照情况下的太阳日辐射估算准确度平均可达84.71%, 异常点占比为2.04%。引入准确的太阳辐射量将利于更好地开展作物生长和产量模拟、土壤水分蒸散估算等研究, 最终为农田生态系统的相关研究提供基础。
- 太阳日辐射 /
- 日照时数 /
- 逐步回归 /
- 日照情况 /
- 估算模型
Abstract: Global radiation is a key factor affecting carbon exchange and the surface energy budget of agroecosystems. To accurately estimate the daily global radiation (GR) under different sunshine conditions and to improve the research carried out on agroecosystems, this study used daily meteorological and radiation data collected between 2005 and 2020 at three radiation observation stations in Jiangsu Province, namely Huai’an, Lüsi, and Nanjing, to divide the research samples into two categories, namely with and without sunshine, according to whether the number of hours of sunshine per day was zero. In total, 24 observable meteorological factors and 3 geographical factors were identified, with the main factors influencing GR under different sunshine conditions being determined using correlation analysis. Daily data from the three stations collected during odd-numbered years between 2005 and 2016 were selected as the modeling dataset, and the least-squares stepwise regression method was adopted to establish the GR estimation models for conditions with and without sunshine, with GR and the daily atmospheric transparency coefficient (ratio of GR to sky radiation [SR], GR/SR) representing the dependent variables. Daily data samples from the three stations collected during even-numbered years between 2005 and 2016 were selected as the between-group verification set, while daily data samples collected from 2017 to 2020 were selected as outside-group verification sets. The optimal GR estimation model for Jiangsu Province was determined by comparing the model fits and the estimation effects of the original models with the between-group and the outside-group verification sets. The results showed that first, GR was significantly correlated with most of the meteorological factors (P<0.01) regardless of the presence of sunshine. GR under sunshine conditions had the strongest correlation with sunshine factors, while GR under without sunshine condition had the strongest correlation with the daily maximum ground temperature (T_GMax). Furthermore, the correlation coefficient between GR and T_G_Max was higher than the correlation between GR and other temperature factors. Second, the estimation model with GR as the dependent variable and T_G_Max and daily dew point temperature as the independent variables was selected when the daily sunshine duration was zero; the coefficient of determination (R²) of this model was 0.650, and the estimation accuracy of GR was close to 75%. The estimation model with GR/SR as the dependent variable and daily percentage of sunshine and sunshine duration as the independent variables was selected when the daily sunshine duration was greater than zero; the R² of this model reached 0.769 and the average estimation accuracy of GR was 87.60%. On the basis of subsection of estimation models, the average accuracy of GR under different sunshine conditions in Jiangsu reached 84.71%, and the proportion of outliers in the total sample was 2.04%. The introduction of accurate GR estimation is greatly beneficial to carry out research on crop growth and yield simulation and soil moisture estimation, and ultimately provide a basis for related research on agroecosystems.
- Daily global radiation /
- Sunshine hours /
- Stepwise regression /
- Sunshine condition /
- Estimation method

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图 1 江苏省3个辐射观测站和69个国家气象观测站空间分布

Figure 1. Spatial distribution of 3 radiation observation stations and 69 national meteorological observation stations in Jiangsu Province

下载: 全尺寸图片幻灯片

图 2 无日照(A, n=1515)和有日照(B, n=5050)天气情况下太阳日辐射与环境因子的相关性

C_T: 逐日平均总云量; C_L: 逐日平均低云量; P: 逐日平均气压; P_Max: 逐日最高气压; P_Min: 逐日最低气压; e: 逐日平均水汽压; e_Max: 逐日最高水汽压; e_Min: 逐日最低水汽压; T: 逐日平均气温; T_Max: 逐日最高气温; T_Min: 逐日最低气温; T_R: 逐日气温日较差; T_G: 逐日平均地表温度; T_GMax: 逐日最高地表温度; T_GMin: 逐日最低地表温度; T_DP: 逐日露点温度; S: 逐日日照时数; S_R: 逐日日照百分率; RH: 逐日相对湿度; R: 逐日降水量; E_vp: 逐日蒸发量; L_o: 经度; L_a: 纬度; H: 海拔; GR: 逐日太阳日辐射。C_T: daily total cloud cover; C_L: daily low cloud cover; P: daily average atmospheric pressure; P_Max: daily maximum atmospheric pressure; P_Min: daily minimum atmospheric pressure; e: daily average vapour pressure; e_Max: daily maximum vapour pressure; e_Min: daily minimum vapour pressure; T: daily average temperature; T_Max: daily maximum temperature; T_Min: daily minimum temperature; T_R: daily diurnal temperature range; T_G: daily average surface temperature; T_GMax: daily maximum ground temperature; T_GMin: daily minimum ground temperature; T_DP: daily dew point temperature; S: daily sunshine duration; S_R: daily percentage of sunshine; RH: daily relative humidity; R: daily precipitation; E_vp: daily evaporation; L_o: longitude; L_a: latitude; H: elevation; GR: daily global radiation.

Figure 2. Correlation between daily global radiation and environmental factors under without sunshine (A, n=1515) and sunshine (B, n=5050) conditions

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图 3 无日照(A, F1和F2模型)和有日照(B, F3和F4模型)天气情况下对建模集和验证集太阳日辐射的估算准确度

Figure 3. Estimation accuracies of models for daily global radiation estimation under without sunshine (A, model F1 and F2) and sunshine (B, F3 and F4 models) conditions based on establishment and validation datasets

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图 4 建模集和验证集基于模型F1和F4的太阳日辐射模拟值-实际值的散点分布图(a: 无日照建模集基于模型F1; b: 有日照建模集基于模型F4; c: 无日照组间验证集基于模型F1; d: 有日照组间验证集基于模型F4; e: 无日照组外验证集基于模型F1; f: 有日照组外验证集基于模型F4)

Figure 4. Scatter distribution diagrams of actual and simulated daily global radiation of models F1 and F4 based on establishment and validation datasets (a: model F1 based on establishment dataset under without sunshine condition; b: model F4 based on establishment dataset under sunshine condition; c: model F1 based on between-group validation dataset without sunshine condition; d: model F4 based on between-group validation dataset under sunshine condition; e: model F1 based on outside-group validation dataset without sunshine condition; f: model F4 based on outside-group validation dataset under sunshine condition)

下载: 全尺寸图片幻灯片

表 1 有无日照天气情况下基于气象因子和天空辐射的太阳日辐射回归估算模型结构

Table 1. Structure of regression estimation models for daily global radiation under with and without sunshine conditions based on meteorological factors and radiation

模型 Model		自变量 Independent variable	因子数量 Number of factors	因变量 Dependent variable
无日照 Without sunshine	F1	气象因子 Meteorological factors	25	太阳日辐射 Daily global radiation (GR)
无日照 Without sunshine	F2	气象因子、天空辐射 Meteorological factors and sky radiation	24	大气透明系数 Daily atmospheric transparency coefficient (GR/SR)
有日照 Under sunshine	F3	气象因子 Meteorological factors	25	太阳日辐射 Daily global radiation (GR)
有日照 Under sunshine	F4	气象因子、天空辐射 Meteorological factors and sky radiation	24	大气透明系数 Daily atmospheric transparency coefficient (GR/SR)

下载: 导出CSV

表 2 有无日照天气情况下基于气象因子和天空辐射的太阳日辐射回归估算模型

Table 2. Estimation models of daily global radiation under without sunshine and sunshine conditions based on meteorological factor and radiation

模型 Model		模型表达式 Model expression	R²	F	P
无日照 Without sunshine	F1	GR=0.427T_GMax−0.322 T_DP−0.643	0.650	1405.750	0.000
无日照 Without sunshine	F2	GR/SR=−0.030C_T−0.003RH−0.001R+0.001T_GMax+0.673	0.382	234.219	0.000
有日照 Under sunshine	F3	GR=1.437S+5.401	0.592	7324.189	0.000
有日照 Under sunshine	F4	GR/SR=1.281S_R−0.067S+0.226	0.769	8405.086	0.000
GR为太阳日辐射, GR/SR为大气透明系数, T_GMax为最高地表气温, T_DP为露点温度, C_T为总云量, RH为相对湿度, R为降水量, S为日照时数, S_R为日照百分率。GR is the daily global radiation; GR/SR is the daily atmospheric transparency coefficient (ratio of global radiation to sky radiation); T_GMax is the daily maximum ground temperature; T_DP is the daily dew point temperature; C_T is the daily total cloud cover; RH is the daily relative humidity; R is the daily precipitation; S is the daily sunshine duration; S_R is the daily relative sunshine duration.

下载: 导出CSV

表 3 有无日照天气情况下不同太阳日辐射回归估算模型在不同数据集估算效果

Table 3. Estimation effects of models of daily global radiation under without sunshine and sunshine conditions in different datasets %　

模型 Models		建模集 Establishment dataset		组间验证集 Between-group validation dataset		组外验证集 Outside-group validation dataset		平均 Average
模型 Models		异常点比例 Proportion of outliers	估算准确度 Estimation accuracy	异常点比例 Proportion of outliers	估算准确度 Estimation accuracy	异常点比例 Proportion of outliers	估算准确度 Estimation accuracy	异常点比例 Proportion of outliers	估算准确度 Estimation accuracy
无日照 Without sunshine	F1	7.11	75.11	6.22	74.41	3.37	73.98	5.89	74.57
无日照 Without sunshine	F2	11.59	68.29	11.68	69.51	6.64	69.28	10.51	69.00
有日照 Under sunshine	F3	0.79	77.31	0.88	77.20	1.84	76.94	1.09	77.18
有日照 Under sunshine	F4	0.83	87.93	0.66	88.44	1.28	85.87	0.88	87.60

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