Abstract
Surface incoming shortwave (solar) radiation data are an important component of many scientific analyses, but direct measurements are not commonly available. Estimates can be obtained from gridded meteorological analysis or reanalysis systems, such as the Global Data Assimilation Systems (GDAS) and Modern Era Retrospective Reanalysis System (MERRA-2), or calculated using empirical models dependent on meteorological variables such as air temperature. The purpose of this analysis was to compare multiple methods for estimating daily shortwave radiation in a tropical highland environment in Ethiopia. Direct solar radiation outputs of GDAS and MERRA-2, topographically corrected outputs of the two analysis systems, and empirically estimated solar radiation values calculated with the systems’ air temperature data were compared to see which produced the most reliable radiation values. GDAS appeared to underestimate the seasonal variability, resulting in low correlation (R2) with in situ data and large mean bias error (MBE). In comparison, MERRA-2 did not underestimate variability, but produced larger bias than the empirical model estimates. There was an improvement in correlation and reduction in MBE when using the GDAS air temperature predictions in the empirical model, but the opposite was true for MERRA-2. The empirical model using station air temperature data (stationT) produced the highest correlation across all four stations, with best performance at the lower elevation sites. The direct shortwave radiation outputs of MERRA-2 produced comparable correlation values, with larger R2 at stations at higher elevation. Topography possibly influenced these results, as MERRA-2 performed comparably to stationT at the stations in moderate terrain, but not in steeper terrain. This work can serve as a starting point for analyses in other tropical highland regions, where continuous in situ solar radiation data are rarely available.
Highlights
1.1 History & importance of solar radiation dataData on incoming solar radiation at the surface are required for many applications, including surface energy balance analyses, generation of evaporation and transpiration estimates, and site selection for solar energy production
The maximum, minimum, mean, and standard deviation was determined for each model, as well as the correlation to in situ data, root mean square error (RMSE) and mean bias error (MBE) (Table 3)
Solar radiation estimates are a critical component of many scientific analyses, but in situ data can be hard to obtain
Summary
1.1 History & importance of solar radiation dataData on incoming solar radiation at the surface are required for many applications, including surface energy balance analyses, generation of evaporation and transpiration estimates, and site selection for solar energy production. For other environments and applications, it is necessary to integrate some kind of meteorological predictors to the model, in the form of sunshine-hour or cloud cover data or as direct meteorological inputs of temperature, precipitation, humidity, and/or other relevant atmospheric conditions [4, 5]. The number of sunshine hours and cloud cover are intuitive inputs for a surface downwelling shortwave radiation estimate, but they are difficult to measure and are not commonly recorded at most weather stations [4]. Since daily minimum and maximum air temperature are almost always recorded by professional grade weather stations, many empirical models have been developed to estimate solar radiation as a function of air temperature [6, 7]. Even in cases where the estimates provided by different methods are relatively similar, even moderate differences in performance can have a significant impact when the radiation estimates are used as an input to a crop model [8]
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