Long-term changes in surface solar radiation and their effects on air temperature in the Shanghai region

2014 ◽  
Vol 35 (12) ◽  
pp. 3385-3396 ◽  
Author(s):  
Jianming Deng ◽  
Yunlin Zhang ◽  
Boqiang Qin ◽  
Kun Shi
2020 ◽  
Author(s):  
Runze Zhao ◽  
Kaicun Wang ◽  
Guocan Wu ◽  
Chunlue Zhou

<p>The change of its annual cycle is extremely important due to global warming. A widely used method to analyze the changes of temperature annual cycle is based on the decomposition to phase, amplitude and baseline terms. Solar radiation as the leading energy source of temperature changes can directly influence temperature annual cycle. In this study, we investigate the phase, amplitude and baseline of temperature and solar radiation annual cycle after Fourier transform during 1960-2016 in China. The results show that annual cycle of maximum, minimum and mean surface air temperature are advancing in time (-0.08, -0.27 and -0.33 days per ten years), decreasing in range (-0.07, -0.25 and -0.18 degrees per ten years) and rising in baseline (0.20, 0.34 and 0.25 degrees per ten years). To further quantify the effect of surface solar radiation to temperature, we remove the effect from its original time series of maximum and mean temperature, based on a linear regression. The compare of raw and adjusted temperature shows that surface solar radiation advancing the time by 0.19 and 0.19 days per ten years, reduces the range by 0.14 and 0.13 degrees per ten years, and reduces the baseline by 0.08 and 0.04 degrees per ten years, for surface maximum and mean daily air temperature. The result can explain parts of seasonal temperature variation. Effect of surface solar radiation is most obvious Yunnan-Guizhou Plateau for maximum phase. The low phase value in this area is corrected and well-match with other same latitude area after adjusted.</p>


2011 ◽  
Vol 71-78 ◽  
pp. 4374-4381 ◽  
Author(s):  
Kuo Tsang Huang ◽  
Wen Sheng Ou

The energy generation efficiency of Building Intergraded Photovoltaic Systems (BIPV) system relies much on the panel’s surface solar radiation received. In the projection of annual power generation of photovoltaic panels, local global solar radiation plays a pivotal role for reliable estimation process. The purpose of this paper is to develop an hourly typical solar radiation year (TSRY) as fundamental meteorological database for utilizing the estimation process. The TSRY should be interpretable to local long-term climate variations, thus, ten years' hourly meteorological data were gathered to formulate a typical year by means of modified Sandia method herein. A total of four cities' hourly typical years from northern to southern Taiwan were established in this paper. Orientation and inclination effect of the PV panel were also discussed in terms of daily averaged global solar radiation that cumulate from TSRY.


2019 ◽  
Vol 28 (1) ◽  
pp. 25-34
Author(s):  
Katarzyna Rozbicka ◽  
Martyna Zawistowska

The aim of the work is to evaluate thermal sensations based on the average daily temperature of air and to determine thermal stimuli, using interdependent variability of air temperature (average, maximum and minimum). The data from the weather station Ursynów – SGGW was used for the analysis in the period 1961–2016. The analysis showed that with the highest frequency (74%) there are thermal sensations “saving” (“slightly cold”, “cool”, “warm”). In the case of thermal stimuli with the greatest frequency, changes from day to day were described as “neutral”, not exceeding 2°C . Based on the analysis of the long-term period trend of the number of days in the year, it can be stated an increase in the number of days with the thermal stress “very warm”, which is results from a positive statistically significant trend and also a decrease in number of days with thermal stimuli “sharp”.


2021 ◽  
Vol 13 (3) ◽  
pp. 907-922
Author(s):  
Fei Feng ◽  
Kaicun Wang

Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval, and reanalysis, getting best-estimated long-term variations in Rs are sorely needed for climate studies. It has been shown that Rs data derived from sunshine duration (SunDu) can provide reliable long-term variability, but such data are available at sparsely distributed weather stations. Here, we merge SunDu-derived Rs with satellite-derived cloud fraction and aerosol optical depth (AOD) to generate high-spatial-resolution (0.1∘) Rs over China from 2000 to 2017. The geographically weighted regression (GWR) and ordinary least-squares regression (OLS) merging methods are compared, and GWR is found to perform better. Based on the SunDu-derived Rs from 97 meteorological observation stations, which are co-located with those that direct Rs measurement sites, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2=0.97 and standard deviation =11.14 W m−2, while GWR driven by only cloud fraction produces similar results with R2=0.97 and standard deviation =11.41 W m−2. This similarity is because SunDu-derived Rs has included the impact of aerosols. This finding can help to build long-term Rs variations based on cloud data, such as Advanced Very High Resolution Radiometer (AVHRR) cloud retrievals, especially before 2000, when satellite AOD retrievals are not unavailable. The merged Rs product at a spatial resolution of 0.1∘ in this study can be downloaded at https://doi.org/10.1594/PANGAEA.921847 (Feng and Wang, 2020).


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