Techniques to obtain improved predictions of global radiation from sunshine duration

Data for 42 stations in different parts of the world in the northern hemisp here have been employed to partition monthly averaged daily global radiatio n (H) over bar and sunshine duration a in a bid to obtain improved fits to Angstrom's correlation. It has been found that regression fits to the corre lation using data for biannual groups of months from March-August (months 3 -8) and September-February (months 9-2), or March-September (months 3-9) an d October-February (months 10-2), give an improvement in the rms error over the year, which is 25% or higher than the errors for annual fits for half of the cases. In no case is there an increase in rms error from the partiti oning. It is found that biannual regression parameters for a pyranometer station m ay be used to predict with good accuracy global radiation for locations hun dreds of kilometers away from the station if the climate, altitude and lati tude are similar. A use of the seasonal partitioning of data leads to the following relations with station independent coefficients for (n) over bar/(N) over bar (H) over bar/(H) over bar(O), = 0.29 cos Phi + 0.49 (n) over bar/(N) over b ar for months 10-2. and (H) over bar/(H) over bar(O), = 0.29 cos Phi + 0.54 (n) over bar/(N) over b ar for months 3-9 These give better estimates for India than popular station independent form ulae. It has been shown that if the coefficients of (n) over bar/(N) over bar are considered to be dependent on the climate as well, even more accurate esti mates are obtained for the central and northern portions of the Indian subc ontinent. The average rms is found to be 2.5%. (C) 1999 Elsevier Science Lt d. All rights reserved.