The reviewed study assesses the overall quality of Sunshine Duration (SDU) estimates through geostationary satellite data over Brazil. This is done by comparing CMSAF’s  product, composed by Meteosat measurements and DISSM/INPE’s product, obtained from GOES series’ data, against SDU estimates from in-situ measurements from meteorological stations. The analysis is based on usual statistical metrics, guided by a climate regions separation and leveraging data enhancements by the way the results are displayed.

General Comments:

1. The data presented, introduces the overall behavior of SDU products on climate regions in South America. I acknowledge the contribution of this study to the progress in satellite estimates of SDU in the region. This kind of assessment is meaningful for a broad range of economic and infrastructure activities developed in the study region.
2. The authors revisited the literature properly and brought to discussion the available measurements over the study region and their respective limitations. All the results were discussed accordingly. Additionally, the long description of the regions’ aspects was relevant for this discussion.
3. Their results were presented in a clear, concise, and well-structured way. Some of the figures embedded a lot of relevant information from the way the data was presented. It was a smart choice to do so. Their interpretation was coherent and supported by the data presented. Substantial conclusions were reached in accordance with their analysis.

Specific Comments:

1. Particularly in Figures 4, 6, 7 and 8, it was not completely clear to me why just the middle month of every season of the year were shown. Is there a specific reason for this choice? It might be better to clear that out in the text.
2. Figures 6 and 7 are very illustrative of the products’ general behavior. In most of those Difference vs. Ground-truth plots there are clusters of counts close to the 0.0 line, centered around larger SDU, which means the products present better performance with longer periods of clear sky. However, on “g” plots (mostly 6g but 7g as well), the data seems more spread and less clustered. Does that suggest that the products account for cloudiness better in July for most of the regions of interest?

Technical Corrections:

This study evaluates two satellite-based data records of sunshine duration for the region of Brazil. The first product is a Meteosat-based climate data record by CM SAF, the other product is a GOES-based product by DISSM / INPE. Both datasets are compared against in-situ measurements from ground stations all over Brazil. The analyses is done for different climatic regions and seasons.   

General comments

• Sunshine duration is an important parameter as there are long-time series of measurements and it is easy to communicate to the general public, as this parameter is well known and easy to understand. This study helps to estimate the quality of satellite-based sunshine duration and shows that satellite-based products can be a good complement to station-based data.
• The study is very well structured and written. It gives an excellent overview about the two different retrieval techniques used to derive sunshine duration from satellite. The applied approach is clearly explained and the results are easy to understand and well presented by meaningful figures. Results and figures support the conclusions in an adequate way.

Specific comments

• It might be good to include a paragraph describing some of the specific challenges, which have to be considered while comparing satellite-data to station-based data. For the Meteosat-based product Brazil is right at the outer edge of the observed region, which comes along with larger uncertainties in the data due to different effects (parallax effect, larger footprint, different atmospheric effects, …). Thus, the observing geometry is different to a GOES-based product. How representative are station-based measurements for a grid point of 4 km by 4 km (or even larger for the CM SAF product at the edge of the Meteosat disc)?
• CM SAF released SARAH version 3.0 in May 2023. Especially with some improvements in underlying auxiliary data, such as water vapour and surface albedo, it is expected to perform better in the region of Brazil.

Technical corrections

• line 206: ‘summer’ could be confusing for some readers, as we are on the southern hemisphere
• Fig. 8: I learned that it should be avoided, wherever possible, to use rainbow colour scales. Maybe you consider this at least for the next time.