Interactive comment on “ Two decades of distributed global radiation time series across a mountainous semiarid area ( Sierra Nevada , Spain ) ”

Abstract. The main drawback of the reconstruction of high-resolution
distributed global radiation (Rg) time series in mountainous semiarid
environments is the common lack of station-based solar radiation registers.
This work presents 19 years (2000–2018) of high-spatial-resolution (30 m) daily, monthly, and annual global radiation maps derived using the
GIS-based model proposed by Aguilar et al. (2010) in a mountainous area in
southern Europe: Sierra Nevada (SN) mountain range (Spain). The model was
driven by in situ daily global radiation measurements, from 16 weather
stations with historical records in the area; a 30 m digital elevation model;
and 240 cloud-free Landsat images. The applicability of the modeling scheme
was validated against daily global radiation records at the weather
stations. Mean RMSE values of 2.63 MJ m−2 d−1 and best
estimations on clear-sky days were obtained. Daily Rg at weather
stations revealed greater variations in the maximum values but no clear
trends with altitude in any of the statistics. However, at the monthly and
annual scales, there is an increase in the high extreme statistics with the
altitude of the weather station, especially above 1500 m a.s.l. Monthly
Rg maps showed significant spatial differences of up to 200 MJ m−2 per month that clearly followed the terrain configuration. July and
December were clearly the months with the highest and lowest values of
Rg received, and the highest scatter in the monthly Rg values was
found in the spring and fall months. The monthly Rg distribution was
highly variable along the study period (2000–2018). Such variability,
especially in the wet season (October–May), determined the interannual
differences of up to 800 MJ m−2 yr−1 in the incoming global
radiation in SN. The time series of the surface global radiation datasets
here provided can be used to analyze interannual and seasonal variation
characteristics of the global radiation received in SN with high spatial
detail (30 m). They can also be used as cross-validation reference data for
other global radiation distributed datasets generated in SN with different
spatiotemporal interpolation techniques. Daily, monthly, and annual
datasets in this study are available at https://doi.org/10.1594/PANGAEA.921012 (Aguilar et al., 2021).


General comments: The manuscript describes a high spatial resolution global radiation dataset over the Sierra Nevada region in Spain, based on a solar radiation model. Such high-resolution datasets are rare; this is the novelty of the data. My concerns are: -The applicability of a monthly and annual resolution, though because of missing data in the station data series it is understandable.
-There are many solar radiation models out there. It is not clearly stated why this model is chosen, whether there are better, up-to-date models. I would suggest at least a comparison to other models' skill.
-Why is the daily missing data need to be generated? Since the global radiation has C1 high variability in mountainous regions, especially in low valleys with fog occurrence, incorporating data based on another station can distort calculations.
-An English language revision is required.
Other specific comments/questions: -General remark: please refrain from using sentences that are 4-5 lines long, break them up into separate ones.
-L18, L259,L269: dispersion => use instead scatter or spread, to not cause confusion -L20: "at the wet season," => in the wet season -L29-30: Rephrase the second part of the sentence, it is not understandable.
-L30-34: too long sentence -L73: actor => members -L80-82 (and L330): Monthly solar radiation data is only suitable for eyeballing surface energy budget components, and most definitely won't help with runoff in a mountainous area.
-L93: end of sentence dot is missing -L94-94: please, rephrase the sentence with a different word structure.
-L95-97: I don't understand the sentence. -L202-203: A high correlation coefficient is expected since the global radiation has a clear intra-annual course. Instead of a simple linear correlation for the whole dataset, the annual course should be removed and then calculate the correlation.
- Figure 3, Figure 4: Beside the station IDs, the altitude of the stations could also be shown, so one doesn't have to scroll back-and-forth to analyse the figure on their own. Or the figures/columns could be ordered by altitude, so it would be more informative as there is no seemingly order in the current figures/columns.
-L225-228: Station 853 has lower RMSE than 802 or 860, though it is situated far away from the other stations, so the "leave one out method" for validation would affect it the most. (comparing 858 and 860, which are both high altitude station, the verification scores are still worse for 860 which is surrounded by 3 other stations) How is the statement in these lines are then supported?

C3
Printer-friendly version Discussion paper -L231, L233,L292,L299 (I'm sure I left some out): "appreciated" is not the correct word to be used here, perhaps use "shown" instead.
-L230-235: too long sentence -L231-232: a stable minimum value is attributed to the occasional cloudy days, it is expected to have low variations.
-L245: I get what you mean by curved evolution, but it should be rephrased as it means something different -L245: Why is there a difference between the curves from January to July, and from August to December? (it only looks linear because of the temporal resolution, but the second semester's global radiation are lower than the first. Is it because of precipitation?) -L258: "allow to draw the same conclusions as those" Was it assumed to be otherwise?
-L262: Which months constitute the "wet season"? - Figure 6: The color scale is not fortunate in terms of values. Using the same scale for winter and summer months is not a good idea as low radiation values disappear from the map. Perhaps use two scales, one for the winter semester and one for the summer semester.
- Figure 8: What is the grey area mean? What do the different grayscale colours mean? The timeseries is too long for a good figure. Variations in the data can barely be observed. It would be better to split the figure into two periods.
- Figure 11: Do the grayscale colours correspond to the percentiles? If so, please note it in the caption.
-L319-327: The paragraph refers in general long term solar radiation data, but one should be careful with it, and highlight the ones that are representative to this particular dataset. C4