Statistical downscaling with Bayesian inference: Estimating global solar radiation from reanalysis and limited observed data

Abstract

Daily global solar radiation (SR) is one of essential weather inputs for crop, hydrological, and other simulation models to calculate biomass production and potential evapotranspiration. The availability of long-term observed SR data is, however, limited, especially in developing countries. This hinders climate applications in various sectors in these countries. To overcome this difficulty, we here propose a method to infer the reasonable daily SR condition for past decades from global reanalysis and limited observed SR data. The method consists of the regression-based statistical downscaling method and two empirical models for estimating the SR condition (i.e. the S-model and the DTR/RH-model). These empirical models were independent in terms of the variables explaining the SR condition. The regression models were trained on the basis of the SR conditions estimated by the S-model and the DTR/RH-model instead of the observed SR data. The Markov Chain Monte Carlo (MCMC) technique was applied to determine the parameter values of these models that guide the models to provide SR conditions that are close in value to each other at both the site and domain-mean scales. After that, we computed the SR condition over the 30 years from 1978 through 2007 at 17 sites in the Vietnam Mekong Delta area using the determined parameter values. The inferred SR condition was close in value to the corresponding observations available from the literature. This suggests that the proposed method yielded a reasonable inference of the SR condition at the sites despite the limited availability of observed SR data. The provided estimates of the daily SR condition over the past 30 years are useful for climate applications in agricultural, hydrological, and other sectors in this area.

Estimating uncertainty in daily weather interpolations: a Bayesian framework for developing climate surfaces

“…Bayesian methods are capable of generating a full posterior distribution of all unknown model parameters (Clark, ) and are becoming more common in climatological analyses (Fischer et al , ; Iizumi et al , ; Ruggieri, ). Thus, a Bayesian interpolation results in a distribution of meteorological values for each prediction location for each time.…”

Applicability of empirical solar radiation models to altered climate

“…For these reasons, the DTR-SR model was selected for quite a few portions of the ocean for the GCM. Furthermore, the dependence of the performance of the RH-SR model on relative humidity values may limit its applicability to other reanalysis datasets, as well as GCMs, because there is generally a large variation in values of relative humidity across reanalysis datasets (e.g., Iizumi et al, 2011b). One limitation of these empirical models is that they do not reduce SR bias of the GCM against reanalysis data.…”

“…By contrast, it is well-known that the temporal variation of SR correlates highly with those of other climatic variables, such as diurnal temperature range (DTR) and relative humidity (RH), as well as sunshine duration and cloud cover (Baigorria and Brown, 2001;Iizumi et al, 2008;2011b;Mahmood and Hubbard, 2002;Menges et al, 2006;Shinohara et al, 2007). Though several studies have used empirical models to estimate SR, global studies (e.g., Yang et al, 2006) have been limited.…”

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

“…Commonly, relative sunshine hours (RSH) is a linear function of the clearness index [18] , [19] , [20] , the quadratic function of relative sunshine hours [21] , [22] , [23] , [24] , [25] , and polynomials of relative sunshine hours and temperature, relative humidity, precipitation and latitude [17] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . The linear, quadratic and polynomial SPP models in literature [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] are none geometric and uncoordinated; hence, it is associated with ample truncation errors, which affect the accuracy of the SPP model. Therefore, there is a need to develop a robust SPP model in geometric and coordinate forms, which minimizes the truncation error in the bid to improve the accuracy of the coordinated SPP models (mercatorian and spatial SPP models).…”