Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China.
Solar radiation data are essential for designing solar energy devices. However, the measurement of solar radiation is not easily available due to the cost and techniques involved [
The widely used correlations for estimating solar radiation are mainly based on sunshine duration and air temperature. In fact, the models estimating solar radiation from sunshine duration are generally more accurate than those involving other meteorological observations [
Two common approaches estimating solar radiation from air temperature use the methods of Hargreaves and Samani [
However, the performance of the HS and its modifications varies significantly in different locations [
China has extensive territory with complex topography, and hence many different climates with distinct features were found [
The measured data of monthly average daily global solar radiation (
Information of the stations used in this study.
No. | Station | Lat. (°N) | Long. (°E) | Alt. (m) | Ave. |
Climate [ |
---|---|---|---|---|---|---|
1 | Shanghai | 31.40 | 121.48 | 6.0 | 6.20 | Humid |
2 | Chongqing | 29.58 | 106.47 | 259.1 | 6.41 | Humid |
3 | Haikou | 20.03 | 110.35 | 13.9 | 6.43 | Humid |
4 | Shantou | 23.40 | 116.68 | 2.9 | 6.53 | Humid |
5 | Dalian | 38.90 | 121.63 | 91.5 | 6.75 | Humid |
6 | Nanchang | 28.60 | 115.92 | 46.7 | 6.96 | Humid |
7 | Chengdu | 30.67 | 104.02 | 506.1 | 7.20 | Humid |
8 | Changsha | 28.22 | 112.92 | 68.0 | 7.22 | Humid |
9 | Emeishan | 29.52 | 103.33 | 3047.4 | 7.22 | Humid |
10 | Guilin | 25.32 | 110.30 | 164.4 | 7.29 | Humid |
11 | Guangzhou | 23.17 | 113.33 | 41.0 | 7.38 | Humid |
12 | Guiyang | 26.58 | 106.73 | 1223.8 | 7.45 | Humid |
13 | Fuzhou | 26.08 | 119.28 | 84.0 | 7.54 | Humid |
14 | Nanning | 22.63 | 108.22 | 121.6 | 7.55 | Humid |
15 | Hangzhou | 30.23 | 120.17 | 41.7 | 7.63 | Humid |
16 | Ganzhou | 25.85 | 114.95 | 123.8 | 7.72 | Humid |
17 | Yichang | 30.70 | 111.30 | 133.1 | 7.85 | Humid |
18 | Mianyang | 31.45 | 104.75 | 486.3 | 7.85 | Humid |
19 | Wuhan | 30.62 | 114.13 | 23.1 | 8.02 | Humid |
20 | Hefei | 31.87 | 117.23 | 27.9 | 8.08 | Humid |
21 | Gushi | 32.17 | 115.67 | 57.1 | 8.24 | Humid |
22 | Nanjing | 32.00 | 118.80 | 7.1 | 8.67 | Humid |
23 | Mengzi | 23.38 | 103.38 | 1300.7 | 9.98 | Humid |
24 | Kunming | 25.02 | 102.68 | 1892.4 | 10.51 | Humid |
25 | Heihe | 50.25 | 127.45 | 166.4 | 11.42 | Humid |
26 | Lijiang | 26.87 | 100.22 | 2392.4 | 11.56 | Humid |
27 | Jinghong | 22.00 | 100.78 | 582.0 | 11.57 | Humid |
28 | Jinan | 36.60 | 117.05 | 170.3 | 8.96 | Semihumid |
29 | Tianjin | 39.08 | 117.07 | 2.5 | 9.84 | Semihumid |
30 | Xian | 34.30 | 108.93 | 397.5 | 10.08 | Semihumid |
31 | Changchun | 43.90 | 125.22 | 236.8 | 10.47 | Semihumid |
32 | Beijing | 39.80 | 116.47 | 31.3 | 10.65 | Semihumid |
33 | Shenyang | 41.73 | 123.45 | 44.7 | 10.68 | Semihumid |
34 | Zhengzhou | 34.72 | 113.65 | 110.4 | 10.78 | Semihumid |
35 | Juxian | 35.58 | 118.83 | 107.4 | 11.06 | Semihumid |
36 | Jiamusi | 46.82 | 130.28 | 81.2 | 11.31 | Semihumid |
37 | Harbin | 45.75 | 126.77 | 142.3 | 11.49 | Semihumid |
38 | Houma | 35.65 | 111.37 | 433.8 | 12.58 | Semihumid |
39 | Yanan | 36.60 | 109.50 | 958.5 | 12.94 | Semihumid |
40 | Chaoyang | 41.55 | 120.45 | 169.9 | 13.33 | Semihumid |
41 | Yushu | 33.02 | 97.02 | 3681.2 | 14.85 | Semihumid |
42 | Naqu | 31.48 | 92.07 | 4507.0 | 14.93 | Semihumid |
43 | Chengdu | 31.15 | 97.17 | 3306.0 | 15.93 | Semihumid |
44 | Aletai | 47.73 | 88.08 | 735.3 | 12.04 | Semiarid |
45 | Tongliao | 43.60 | 122.27 | 178.5 | 12.13 | Semiarid |
46 | Lanzhou | 36.05 | 103.88 | 1517.2 | 12.19 | Semiarid |
47 | Hailaer | 49.22 | 119.75 | 610.2 | 12.22 | Semiarid |
48 | Guyuan | 36.00 | 106.27 | 1753.0 | 12.42 | Semiarid |
49 | Taiyuan | 37.78 | 112.55 | 778.3 | 13.10 | Semiarid |
50 | Xilinhaote | 43.95 | 116.07 | 989.5 | 13.24 | Semiarid |
51 | Datong | 40.10 | 113.33 | 1067.2 | 13.28 | Semiarid |
52 | Xining | 36.72 | 101.75 | 2295.2 | 13.39 | Semiarid |
53 | Yining | 43.95 | 81.33 | 662.5 | 13.83 | Semiarid |
54 | Lasa | 29.67 | 91.13 | 3648.7 | 14.32 | Semiarid |
55 | Wulumiqi | 43.78 | 87.65 | 935.0 | 10.30 | Arid |
56 | Hetian | 37.13 | 79.93 | 1374.5 | 12.30 | Arid |
57 | Yinchuan | 38.48 | 106.22 | 1111.4 | 12.74 | Arid |
58 | Kashi | 39.47 | 75.98 | 1288.7 | 12.80 | Arid |
59 | Tulufan | 42.93 | 89.20 | 34.5 | 13.23 | Arid |
60 | Geermu | 36.42 | 94.90 | 2807.6 | 13.90 | Arid |
61 | Erlianhaote | 43.65 | 111.97 | 964.7 | 14.23 | Arid |
62 | Hami | 42.82 | 93.52 | 737.2 | 14.92 | Arid |
63 | Geer | 32.50 | 80.08 | 4278.0 | 15.53 | Arid |
64 | Ruoqiang | 39.03 | 88.17 | 888.3 | 15.87 | Arid |
65 | Dunhuang | 40.15 | 94.68 | 1139.0 | 16.08 | Arid |
The HS model [
Following Hargreaves and Samani’s pioneer work, Samani [
The characteristic underlying equations (
Among these factors, precipitable water has a considerable effect on solar radiation and then affects
A common method to calculate global solar radiation that is used by many models is to first determine
The models’ performance is assessed by four common statistical indicators, namely, mean percentage error (MPE, %), mean bias error (MBE, MJ/m2), root mean square error (RMSE, MJ/m2), and Nash-Sutcliffe equation (NSE), calculated from the estimated and measured values of
The empirical coefficients of the four models at each station are reported in Table
Empirical coefficients of the four models at the 65 stations in China.
No. | Stations | HS | Samani | Chen | Equation ( | |||||
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1 | Shanghai | 0.1610 | 1.0880 | −0.2761 | 0.0203 | −0.0320 | 0.4812 | −0.0111 | 0.0010 | 0.3856 |
2 | Chongqing | 0.1028 | 0.0850 | −0.0089 | 0.0016 | 0.2427 | −0.3569 | 0.0734 | 0.0024 | −0.0510 |
3 | Haikou | 0.1543 | 0.8163 | −0.2148 | 0.0171 | 0.2027 | −0.1231 | −0.2070 | 0.0061 | 0.5269 |
4 | Shantou | 0.1588 | 0.3037 | −0.0489 | 0.0040 | 0.2306 | −0.1837 | 0.3433 | 0.0042 | −0.7028 |
5 | Dalian | 0.1852 | 0.3425 | −0.0434 | 0.0029 | 0.1448 | 0.1052 | 0.1552 | 0.0006 | 0.0597 |
6 | Nanchang | 0.1390 | 0.5998 | −0.1712 | 0.0150 | 0.5675 | −1.1313 | 0.4093 | 0.0013 | −0.7748 |
7 | Chengdu | 0.1086 | −0.2956 | 0.1030 | −0.0064 | 0.2227 | −0.3068 | 0.1165 | 0.0015 | −0.0889 |
8 | Changsha | 0.1232 | −0.5742 | 0.1820 | −0.0117 | 0.4003 | −0.7456 | 0.1615 | 0.0024 | −0.2203 |
9 | Emeishan | 0.1503 | 0.0141 | 0.0254 | −0.0009 | 0.3205 | −0.4588 | 0.1563 | −0.0029 | 0.0076 |
10 | Guilin | 0.1255 | 0.1556 | −0.0366 | 0.0043 | 0.4751 | −0.9459 | 0.4317 | 0.0005 | −0.8567 |
11 | Guangzhou | 0.1278 | −0.6600 | 0.1893 | −0.0111 | 0.4454 | −0.8640 | 0.4590 | 0.0022 | −1.0344 |
12 | Guiyang | 0.1074 | −0.6792 | 0.2013 | −0.0128 | 0.2876 | −0.4926 | 0.0677 | 0.0022 | 0.0096 |
13 | Fuzhou | 0.1321 | 1.0571 | −0.2530 | 0.0172 | 0.3340 | −0.5548 | 0.0209 | 0.0024 | 0.1684 |
14 | Nanning | 0.1351 | −1.7651 | 0.4796 | −0.0299 | 0.4274 | −0.8042 | 0.3184 | 0.0032 | −0.7019 |
15 | Hangzhou | 0.1296 | −0.6555 | 0.2023 | −0.0130 | 0.2336 | −0.2872 | 0.0970 | 0.0011 | 0.0388 |
16 | Ganzhou | 0.1351 | −0.1039 | 0.0302 | 0.0001 | 0.6017 | −1.2976 | 0.4718 | 0.0012 | −1.0032 |
17 | Yichang | 0.1197 | −0.6137 | 0.1804 | −0.0110 | 0.2701 | −0.4218 | −0.1108 | 0.0022 | 0.5341 |
18 | Mianyang | 0.1107 | −0.1189 | 0.0525 | −0.0029 | 0.1855 | −0.2098 | 0.1030 | 0.0016 | −0.0564 |
19 | Wuhan | 0.1285 | 2.1273 | −0.4857 | 0.0294 | −0.0494 | 0.5039 | 0.0520 | 0.0016 | 0.1414 |
20 | Hefei | 0.1312 | 0.7351 | −0.1454 | 0.0087 | 0.0596 | 0.2036 | 0.0604 | 0.0007 | 0.1709 |
21 | Gushi | 0.1431 | 0.9199 | −0.1792 | 0.0103 | −0.0096 | 0.4388 | 0.0044 | 0.0010 | 0.3528 |
22 | Nanjing | 0.1332 | 1.1027 | −0.2193 | 0.0123 | 0.0238 | 0.3224 | 0.0538 | 0.0006 | 0.2078 |
23 | Mengzi | 0.1465 | −0.1047 | 0.0480 | −0.0022 | 0.1948 | −0.1528 | 0.1797 | −0.0014 | −0.0177 |
24 | Kunming | 0.1430 | 0.0348 | 0.0181 | −0.0007 | 0.1983 | −0.1807 | 0.1864 | −0.0012 | −0.0828 |
25 | Heihe | 0.1598 | 0.1533 | 0.0064 | −0.0005 | 0.0418 | 0.3994 | 0.1448 | −0.0011 | 0.0555 |
26 | Lijiang | 0.1589 | 0.0983 | 0.0040 | 0.0001 | 0.2920 | −0.4541 | 0.2112 | −0.0024 | −0.0715 |
27 | Jinghong | 0.1351 | 0.1076 | 0.0072 | −0.0004 | 0.0823 | 0.1816 | 0.1006 | −0.0014 | 0.2315 |
28 | Jinan | 0.1493 | 0.2295 | −0.0176 | 0.0010 | 0.1439 | 0.0163 | 0.1880 | −0.0004 | −0.0960 |
29 | Tianjin | 0.1543 | −0.0542 | 0.0421 | −0.0021 | 0.1631 | −0.0275 | 0.1689 | −0.0003 | −0.0345 |
30 | Xian | 0.1225 | 0.0192 | 0.0208 | −0.0010 | 0.1055 | 0.0539 | 0.0264 | 0.0007 | 0.2746 |
31 | Changchun | 0.1611 | −0.5361 | 0.1377 | −0.0067 | 0.1179 | 0.1401 | 0.1206 | −0.0009 | 0.1492 |
32 | Beijing | 0.1560 | −0.2924 | 0.0867 | −0.0041 | 0.1284 | 0.0900 | 0.1472 | −0.0008 | 0.0630 |
33 | Shenyang | 0.1472 | −0.1274 | 0.0524 | −0.0025 | 0.1635 | −0.0533 | 0.1464 | −0.0003 | 0.0100 |
34 | Zhengzhou | 0.1367 | 0.2959 | −0.0268 | 0.0011 | 0.0701 | 0.2190 | 0.0701 | 0.0000 | 0.2193 |
35 | Juxian | 0.1413 | 0.3678 | −0.0399 | 0.0017 | 0.0724 | 0.2297 | 0.0812 | 0.0003 | 0.1868 |
36 | Jiamusi | 0.1462 | −0.0224 | 0.0331 | −0.0016 | 0.0786 | 0.2274 | 0.0662 | −0.0007 | 0.2783 |
37 | Harbin | 0.1492 | −0.2103 | 0.0660 | −0.0030 | 0.0921 | 0.1941 | 0.0808 | −0.0004 | 0.2382 |
38 | Houma | 0.1296 | 0.5198 | −0.0584 | 0.0022 | 0.0485 | 0.2877 | 0.0495 | −0.0001 | 0.2883 |
39 | Yanan | 0.1276 | 0.4752 | −0.0513 | 0.0019 | 0.0864 | 0.1486 | 0.0855 | 0.0002 | 0.1428 |
40 | Chaoyang | 0.1433 | 0.1449 | −0.0001 | 0.0000 | 0.1400 | 0.0120 | 0.1085 | −0.0003 | 0.1357 |
41 | Yushu | 0.1416 | −0.0654 | 0.0246 | −0.0007 | 0.2338 | −0.3557 | 0.1959 | −0.0003 | −0.2057 |
42 | Naqu | 0.1426 | 0.1176 | 0.0014 | 0.0000 | 0.1981 | −0.2147 | −0.1381 | −0.0031 | 1.0668 |
43 | Chengdu | 0.1360 | 0.4305 | −0.0393 | 0.0013 | 0.2177 | −0.3266 | 0.2212 | 0.0000 | −0.3417 |
44 | Aletai | 0.1679 | −0.5058 | 0.1160 | −0.0049 | 0.1449 | 0.0800 | 0.2071 | −0.0003 | −0.1306 |
45 | Tongliao | 0.1479 | −0.3715 | 0.0895 | −0.0038 | 0.1124 | 0.1237 | 0.0921 | −0.0005 | 0.2065 |
46 | Lanzhou | 0.1366 | −0.5793 | 0.1132 | −0.0045 | 0.2112 | −0.2604 | 0.1530 | 0.0003 | −0.0689 |
47 | Hailaer | 0.1642 | 0.2512 | −0.0084 | 0.0001 | 0.0255 | 0.4852 | 0.1453 | −0.0009 | 0.0662 |
48 | Guyuan | 0.1478 | 0.4469 | −0.0526 | 0.0023 | 0.2455 | −0.3442 | 0.1270 | −0.0009 | 0.0951 |
49 | Taiyuan | 0.1361 | 0.0527 | 0.0144 | −0.0006 | 0.1010 | 0.1274 | 0.0960 | −0.0003 | 0.1554 |
50 | Xilinhaote | 0.1571 | 0.2633 | −0.0112 | 0.0002 | 0.0295 | 0.4646 | 0.0966 | −0.0006 | 0.2274 |
51 | Datong | 0.1474 | −0.3479 | 0.0763 | −0.0029 | 0.0975 | 0.1820 | 0.0963 | −0.0003 | 0.1952 |
52 | Xining | 0.1469 | 0.1327 | 0.0001 | 0.0001 | 0.1986 | −0.1893 | 0.0889 | −0.0007 | 0.2290 |
53 | Yining | 0.1491 | 0.2021 | −0.0066 | 0.0002 | 0.1175 | 0.1177 | 0.1081 | 0.0001 | 0.1501 |
54 | Lasa | 0.1696 | 0.5127 | −0.0522 | 0.0019 | 0.2408 | −0.2698 | 0.2120 | −0.0003 | −0.1509 |
55 | Wulumuqi | 0.1553 | 0.1945 | −0.0121 | 0.0008 | 0.2261 | −0.2280 | 0.0757 | 0.0010 | 0.2271 |
56 | Hetian | 0.1565 | 0.6063 | −0.0725 | 0.0029 | 0.0564 | 0.3517 | 0.3131 | −0.0017 | −0.4670 |
57 | Yinchuang | 0.1613 | 0.4334 | −0.0363 | 0.0012 | −0.0032 | 0.5878 | 0.0095 | −0.0005 | 0.5592 |
58 | Kashi | 0.1517 | 0.3119 | −0.0261 | 0.0010 | 0.1455 | 0.0223 | 0.1281 | 0.0001 | 0.0789 |
59 | Tulufan | 0.1522 | 0.1287 | 0.0058 | −0.0003 | 0.1190 | 0.1213 | 0.2036 | −0.0006 | −0.1536 |
60 | Geermu | 0.1770 | −2.7052 | 0.4167 | −0.0150 | 0.1885 | −0.0427 | 0.0684 | −0.0005 | 0.4149 |
61 | Erlianhaote | 0.1736 | 0.4895 | −0.0400 | 0.0012 | 0.0461 | 0.4814 | 0.1089 | −0.0009 | 0.2598 |
62 | Hami | 0.1640 | 0.2464 | −0.0056 | 0.0000 | 0.0190 | 0.5611 | 0.1433 | −0.0007 | 0.1118 |
63 | Geer | 0.1690 | 0.0684 | 0.0091 | −0.0002 | 0.2882 | −0.4701 | 0.2318 | −0.0004 | −0.2479 |
64 | Ruoqiang | 0.1489 | 0.4433 | −0.0349 | 0.0010 | 0.0271 | 0.4865 | 0.1127 | −0.0006 | 0.1754 |
65 | Dunhuang | 0.1550 | 0.5495 | −0.0459 | 0.0013 | 0.0076 | 0.5923 | 0.0906 | −0.0005 | 0.2816 |
The minimum, maximum, and average values of the statistical indicators for the four models at the 65 stations in China.
Error | Model | Wet region | Semiwet region | Semi-arid region | Arid region | Overall | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Min. | Max. | Ave. | Min. | Max. | Ave. | Min. | Max. | Ave. | Min. | Max. | Ave. | Min. | Max. | Ave. | ||
MPE | HS | −0.8776 | 8.7307 | 2.5114 | −0.2146 | 0.9753 | 0.2416 | −0.1027 | 0.5700 | 0.2023 | −0.2488 | 1.2711 | 0.1362 | −0.8776 | 8.7307 | 1.1600 |
Samani | −2.6927 | 3.9379 | 1.1221 | −2.9563 | 4.3446 | 0.8382 | −5.6928 | 4.0134 | 0.0326 | −4.8029 | 3.8377 | −0.9011 | −5.6928 | 4.3446 | 0.5254 | |
Chen | −0.8200 | 2.3224 | 0.8990 | 0.0623 | 0.6648 | 0.2495 | 0.0623 | 0.5936 | 0.2474 | 0.0757 | 0.5148 | 0.2490 | −0.8200 | 2.3224 | 0.5189 | |
Equation ( |
−0.4714 | 1.4145 | 0.4072 | −0.1984 | 0.7587 | 0.1067 | −0.2414 | 0.3613 | 0.1086 | −0.4718 | 0.4250 | 0.0361 | −0.4718 | 1.4145 | 0.2199 | |
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MBE | HS | −0.2237 | 0.6223 | −0.0024 | −0.1528 | 0.4181 | 0.1358 | −0.0962 | 0.4891 | 0.1759 | −0.1936 | 0.4919 | 0.2205 | −0.2237 | 0.6223 | 0.0995 |
Samani | −0.4043 | 0.5132 | 0.0285 | −0.4681 | 0.6410 | 0.1842 | −1.0949 | 0.6886 | 0.0646 | −0.8158 | 0.8158 | −0.0861 | −1.0949 | 0.8158 | 0.0536 | |
Chen | −0.2592 | 0.5091 | −0.0316 | −0.1040 | 0.4187 | 0.1107 | −0.0409 | 0.3427 | 0.1378 | −0.0711 | 0.4193 | 0.1431 | −0.2592 | 0.5091 | 0.0617 | |
Equation ( |
−0.0536 | 0.1681 | 0.0511 | −0.0710 | 0.0747 | 0.0184 | −0.0659 | 0.0934 | 0.0159 | −0.0867 | 0.0919 | 0.0201 | −0.0867 | 0.1681 | 0.0318 | |
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RMSE | HS | 0.6398 | 2.3068 | 1.4972 | 0.3878 | 1.2721 | 0.7589 | 0.3489 | 1.3869 | 0.7508 | 0.5161 | 1.3956 | 0.9760 | 0.3489 | 2.3068 | 1.1009 |
Samani | 0.5503 | 1.8496 | 1.0371 | 0.2622 | 1.1235 | 0.7129 | 0.3624 | 1.2429 | 0.8792 | 0.3661 | 1.2611 | 0.9003 | 0.2622 | 1.8496 | 0.9074 | |
Chen | 0.5085 | 1.9254 | 1.1351 | 0.2976 | 1.2655 | 0.6849 | 0.3005 | 1.2403 | 0.7090 | 0.3962 | 1.4056 | 0.8036 | 0.2976 | 1.9254 | 0.8961 | |
Equation ( |
0.2688 | 1.1429 | 0.6579 | 0.2356 | 0.8538 | 0.4382 | 0.1524 | 0.8140 | 0.4234 | 0.2301 | 0.8039 | 0.5204 | 0.1524 | 1.1429 | 0.5408 | |
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NSE | HS | 0.2311 | 0.9837 | 0.7651 | 0.7575 | 0.9931 | 0.9540 | 0.9284 | 0.9960 | 0.9715 | 0.9297 | 0.9933 | 0.9656 | 0.2311 | 0.9960 | 0.8805 |
Samani | 0.5717 | 0.9818 | 0.8872 | 0.8169 | 0.9957 | 0.9616 | 0.8644 | 0.9969 | 0.9582 | 0.9424 | 0.9966 | 0.9690 | 0.5717 | 0.9969 | 0.9314 | |
Chen | 0.5730 | 0.9817 | 0.8708 | 0.7962 | 0.9945 | 0.9616 | 0.9288 | 0.9968 | 0.9740 | 0.9470 | 0.9960 | 0.9761 | 0.5730 | 0.9968 | 0.9284 | |
Equation ( |
0.8324 | 0.9930 | 0.9534 | 0.8908 | 0.9975 | 0.9821 | 0.9418 | 0.9988 | 0.9889 | 0.9768 | 0.9987 | 0.9887 | 0.8324 | 0.9988 | 0.9724 |
Statistical performance of the four models at the 65 stations (nos. 1–27 in the humid region, nos. 28–43 in the semihumid region, nos. 44–54 in the semiarid region, and nos. 55–65 in the aid region).
Figure
For clarity, the estimates of (
Comparison between the estimates of the four models and measured data at eight representative stations.
Also, it can be found that with precipitable water increasing, namely, from the arid region to the humid region, the advantage of the new model over the HS, Samani, and Chen models becomes more prominent. In terms of NSE, (
This work stems from the fact that air temperature is commonly measured at many stations around the world, and the performance of the HS and its modifications varies significantly in different locations. To estimate monthly average daily global solar radiation from air temperature with better accuracy in all climatic regions, a new modification to the HS model is proposed. The new model is validated by comparing with the HS model and its two modifications against the measured data at 65 meteorological stations in China. The study demonstrates that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Therefore, it can be recommended for estimating monthly average daily global solar radiation.
Admittedly, a limitation of this study is that the new model developed here is site-dependent, so when it is utilized in locations other than its based region, it is better to calibrate the empirical coefficients against the local data first. Future efforts should be directed to explore the correlation of the model’s empirical coefficients with common factors and then develop a model for general application.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the Key Laboratory of Renewable Energy and Gas Hydrate, Chinese Academy of Sciences (no. y107jc), the National Natural Science Foundation of China (no. 50506025), and the Science and Technology Planning Project of Guangdong Province, China (no. 2012A010800024). The authors would like to thank the National Meteorological Information Centre, China Meteorological Administration, for its data support.