the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2024
Solar radiation estimation in West Africa: impact of dust conditions during 2021 dry season
Abstract. The anticipated increase in solar energy production in West Africa requires high-quality solar radiation estimates, which is affected by meteorological conditions and in particular the presence of desert dust aerosols. This study examines the impact of incorporating desert dust into solar radiation and surface temperature estimations. The research focuses on a case study of a dust event in March 2021, which is characteristic of the dry season in West Africa. Significant desert aerosol emissions at the Bodélé depression are associated with a Harmattan flow that transports the plume westwards. Simulations of this dust event were conducted using the WRF meteorological model alone, as well as coupled with the CHIMERE chemistry-transport model, using three different datasets for the dust aerosol initial and boundary conditions (CAMS, GOCART, MERRA2). Results show that considering desert dust reduces estimation errors in global horizontal irradiance (GHI) by about 75 %. The dust plume caused an average 18 % reduction in surface solar radiation during the event. Additionally, the simulations indicated a positive bias in aerosol optical depth (AOD) and PM10 surface concentrations. The choice of dataset for initial and boundary conditions minimally influenced GHI, surface temperature, and AOD estimates, whereas PM10 concentrations and aerosol size distribution were significantly affected. This study underscores the importance of incorporating dust aerosols into solar forecasting for better accuracy.
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RC1: 'Comment on egusphere-2024-1604', Anonymous Referee #1, 05 Aug 2024
General comments
In this study by Clauzel et al. the effect of including desert dust in estimating surface solar radiation using atmospheric modeling (WRF coupled with CHIMERE chemistry-transport model) is investigated during a dust event in March 2021 for West Africa. Their results show the importance of including dust in estimating surface solar radiation. By using three different datasets for dust initial and boundary conditions the authors demonstrate their influence in reproducing surface solar radiation and temperature and aerosol parameters. The objectives of the study are quite straightforward and are addressed by a thorough analysis. The increased accuracy of the surface solar radiation estimates that the authors provide here for West Africa by including the dust effects is of significance for atmospheric modeling the solar energy sector too.
I consider the topic and results of this manuscript to fit the scope of ACP. However, I have some general and major comments (please see below 1-2) which should be addressed prior to publication.
- It is not clear to me in Section 2.2.3 and specifically in Line 241 “ci is the dust aerosol concentration” and Table 2 “aerosol size distribution” if the concentration of only the dust aerosol component or the total aerosol concentration was used assuming only dust. Please, clarify and discuss this choice in terms of the notable modeled AOD overestimation. In terms of assimilation, AOD is assimilated. Could you please also provide information regarding CAMS product, which version is used? The one after update of 2023? (https://www.ecmwf.int/en/newsletter/176/news/major-upgrade-cams-forecasts-atmospheric-composition, https://confluence.ecmwf.int/display/COPSRV/Implementation+of+IFS+cycle+48r1+for+CAMS) If the previous one was used, a DOD underestimation is reported and should be discussed in context of the results. I also recommend checking the DOD to AOD ratio to support the hypothesis in Lines 280-284.
- The 3.8 Discussions section needs to be elaborated (see specific comments below) and it lacks comparing the results with the previous literature in the topic. Please, provide (where applicable) relevant references. For example, there are studies showing the direct impact of aerosols in GHI under higher aerosol loads.
2) Specific comments
Line 205: For Table S1 is there any reference for the provided Dust RI?
Regarding information in Table S2, could the authors discuss the results in case of larger dust particles than 40μm or non-spherical are considered in the modeling approach?
Lines 351-352: I cannot find the reference in the manuscript, please provide the reference and specify also if this negative bias is for clear-sky or all-sky CAMS radiation service products?
Lines 554-557: Please provide references for this statement and elaborate.
Lines 594-595: May I miss something here, but I don't get the point of interpolating AERONET lower limit 0.05 to model lower limit 0.01. In addition, I am not seeing anything in Fig. 8 at bins below 0.05 even for the model. Why not interpolate between 0.048-10.0 μm?
Lines 597-598: Could you elaborate this sentence, and explain to the reader how the AOD measurement at 875nm influences here?
Lines 763-764: "MERRA2 dataset might be more accurate" Could this statement be put into perspective regarding the AOD and surface PM10 results?
Lines 808-811: I think that a comment and relevant references should be added here regarding comparing AOD which is a total column property with surface PM10 concentrations, where the vertical distribution plays an important role.
Lines 812-813: I think there is a discrepancy here with the conclusion of Section 3.6 Lines 716-719:
"Therefore, the differences in AOD and dust concentration may be attributed to the dust flows at the boundaries of the domain and are not linked to differences in simulated dust emissions within the domain."
Please, clarify.
Lines 813-816: Again, I think there is a discrepancy here with Section's 3.7 conclusion:
"... these differences in eastern dust fluxes appear to account for the uncertainties of the simulated aerosol concentrations (see 3.5) and AODs (see 3.3)."
Could you please elaborate on this?
3) Technical corrections
Figure 6: For Tamanrasset corrcoef is 0.18? or 0.81?
Supplementary materials
Lines 6-8: The number of equations needs correction
Citation: https://doi.org/10.5194/egusphere-2024-1604-RC1 -
RC2: 'Comment on egusphere-2024-1604', Anonymous Referee #2, 07 Aug 2024
Solar energy forecasting plays a crucial role in energy planning and management. This interesting paper deals with the impacts of AOD on the simulation of temperature and solar irradiance using WRF coupled with CHIMERE over the Sahelian zone in West Africa. The paper is well-written and structured, with a well-explained methodology. The results of this study show that WRF-CHIMERE performs better in simulating GHI and temperature over the studied domain than WRF-only. Moreover, the lateral boundary condition provided by AOD also impacts the output of the WRF-CHIMERE. However, I have a general comment on the paper.
The comparison between WRF-CHIMERE and WRF-only is not appropriate. The configuration of the WRF-only does not incorporate the optimized configuration for solar energy applications. I suggest the authors use the WRF option with WRF-Solar for this purpose. WRF-Solar has all the features designed for solar energy applications, and some modules were introduced to make this tool robust. For instance, the GHI values from the Fast All-sky Radiation Model for Solar Applications (FARMS) in WRF-Solar are better than the traditional ones in the WRF model (Gueymard et al., 2018). I also suggest using the Thompson microphysics scheme for the WRF-only experiment. I recommend the authors use the recommended configurations in WRF for solar energy applications (https://ral.ucar.edu/solutions/products/wrf-solar).
Gueymard, C. A., and P. A. Jimenez, 2018: Validation of real-time solar irradiance simulations over Kuwait using WRF-Solar. 12th Int. Conf. on Solar Energy for Buildings and Industry, Rapperswil, Switzerland, International Solar Energy Society, https://doi.org/10.18086/eurosun2018.09.14.
This is optional: the authors can also use AOD in WRF-Solar to compare with WRF-CHIMERE; this would provide interesting results for the region.
It is difficult for readers to obtain straightforward information from the plotted figures. I suggest writing down the names of different experiments or observed data from the different panels.
The discussion section needs enhancement by providing references to support your claims.
These are detail comments
Line 52: The citation should be (IEA, 2022).
Line 66: It is solar irradiance, not solar radiation. There is a difference between them. Please, if you refer to GHI, use solar irradiance, not solar radiation. Change this throughout the manuscript.
Lines 143-144: It is hard to see that on Fig.1a. It should be FS1.
Line 349: You could also add Sawadogo et al., 2024:
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Fig.2: I suggest putting the title of the station for each panel. It will be easier for the reader, without having to read the caption.
Fig.3: Same comment as in Fig.2. In addition, use one color bar for all of them. It would be nice to have the spatial correlation of different experiments based on the CAMS reference dataset.
Line 452: Please check the value of 115 W.m−2.
Lines 452-453: These values refer to the WRF-only simulation, right? In this case, this should be clearly stated in the sentence.
Lines 456-458: This statement needs some references. You can use Sawadogo et al., 2024, where they show that CAMS data has a huge bias under the Harmattan period, hence for dust events over Burkina Faso.
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Fig.4: I suggest that wrf_chimere-G, wrf_chimere-M, and wrf_chimere-C refer to the WRF-CHIMERE simulations using GOCART, MERRA2, and CAMS and should be used throughout the manuscript to be consistent.
Lines 477-478: Why do the simulated temperatures differ among the experiments during nighttime? This needs to be discussed.
Line 485: Please provide a scientific explanation for why the impact of dust aerosols on temperature is particularly pronounced at nighttime.
Lines 554-557: Please provide some references to back up this claim.
Lines 592-606: This part should be in the methodology section.
For Figure 8, I would like to know the model time output. This is missing in the manuscript.
Lines 771-773: It is hard to understand this sentence. Please rephrase it.
Lines 779-783: The performance of CAMS in simulating solar irradiance during high AOD episodes is low.
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Lines 801-803: This is not true. ERA5 does not dynamically simulate aerosols but incorporates its radiative effects through prescribed monthly climatologies from the GOCART model.
Citation: https://doi.org/10.5194/egusphere-2024-1604-RC2 -
AC1: 'Comment on egusphere-2024-1604 : final response', Léo Clauzel, 27 Sep 2024
Dear editors,
We are pleased to send you our final response. You'll find it in the attached document, which lists the comments made by the reveiwers, our responses to each comment and the changes made to the manuscript.
We thank the reviewers for their thorough reading of the manuscript and their courageous comments.
Should you have any questions, please do not hesitate to contact me.
Best regards
Status: closed
-
RC1: 'Comment on egusphere-2024-1604', Anonymous Referee #1, 05 Aug 2024
General comments
In this study by Clauzel et al. the effect of including desert dust in estimating surface solar radiation using atmospheric modeling (WRF coupled with CHIMERE chemistry-transport model) is investigated during a dust event in March 2021 for West Africa. Their results show the importance of including dust in estimating surface solar radiation. By using three different datasets for dust initial and boundary conditions the authors demonstrate their influence in reproducing surface solar radiation and temperature and aerosol parameters. The objectives of the study are quite straightforward and are addressed by a thorough analysis. The increased accuracy of the surface solar radiation estimates that the authors provide here for West Africa by including the dust effects is of significance for atmospheric modeling the solar energy sector too.
I consider the topic and results of this manuscript to fit the scope of ACP. However, I have some general and major comments (please see below 1-2) which should be addressed prior to publication.
- It is not clear to me in Section 2.2.3 and specifically in Line 241 “ci is the dust aerosol concentration” and Table 2 “aerosol size distribution” if the concentration of only the dust aerosol component or the total aerosol concentration was used assuming only dust. Please, clarify and discuss this choice in terms of the notable modeled AOD overestimation. In terms of assimilation, AOD is assimilated. Could you please also provide information regarding CAMS product, which version is used? The one after update of 2023? (https://www.ecmwf.int/en/newsletter/176/news/major-upgrade-cams-forecasts-atmospheric-composition, https://confluence.ecmwf.int/display/COPSRV/Implementation+of+IFS+cycle+48r1+for+CAMS) If the previous one was used, a DOD underestimation is reported and should be discussed in context of the results. I also recommend checking the DOD to AOD ratio to support the hypothesis in Lines 280-284.
- The 3.8 Discussions section needs to be elaborated (see specific comments below) and it lacks comparing the results with the previous literature in the topic. Please, provide (where applicable) relevant references. For example, there are studies showing the direct impact of aerosols in GHI under higher aerosol loads.
2) Specific comments
Line 205: For Table S1 is there any reference for the provided Dust RI?
Regarding information in Table S2, could the authors discuss the results in case of larger dust particles than 40μm or non-spherical are considered in the modeling approach?
Lines 351-352: I cannot find the reference in the manuscript, please provide the reference and specify also if this negative bias is for clear-sky or all-sky CAMS radiation service products?
Lines 554-557: Please provide references for this statement and elaborate.
Lines 594-595: May I miss something here, but I don't get the point of interpolating AERONET lower limit 0.05 to model lower limit 0.01. In addition, I am not seeing anything in Fig. 8 at bins below 0.05 even for the model. Why not interpolate between 0.048-10.0 μm?
Lines 597-598: Could you elaborate this sentence, and explain to the reader how the AOD measurement at 875nm influences here?
Lines 763-764: "MERRA2 dataset might be more accurate" Could this statement be put into perspective regarding the AOD and surface PM10 results?
Lines 808-811: I think that a comment and relevant references should be added here regarding comparing AOD which is a total column property with surface PM10 concentrations, where the vertical distribution plays an important role.
Lines 812-813: I think there is a discrepancy here with the conclusion of Section 3.6 Lines 716-719:
"Therefore, the differences in AOD and dust concentration may be attributed to the dust flows at the boundaries of the domain and are not linked to differences in simulated dust emissions within the domain."
Please, clarify.
Lines 813-816: Again, I think there is a discrepancy here with Section's 3.7 conclusion:
"... these differences in eastern dust fluxes appear to account for the uncertainties of the simulated aerosol concentrations (see 3.5) and AODs (see 3.3)."
Could you please elaborate on this?
3) Technical corrections
Figure 6: For Tamanrasset corrcoef is 0.18? or 0.81?
Supplementary materials
Lines 6-8: The number of equations needs correction
Citation: https://doi.org/10.5194/egusphere-2024-1604-RC1 -
RC2: 'Comment on egusphere-2024-1604', Anonymous Referee #2, 07 Aug 2024
Solar energy forecasting plays a crucial role in energy planning and management. This interesting paper deals with the impacts of AOD on the simulation of temperature and solar irradiance using WRF coupled with CHIMERE over the Sahelian zone in West Africa. The paper is well-written and structured, with a well-explained methodology. The results of this study show that WRF-CHIMERE performs better in simulating GHI and temperature over the studied domain than WRF-only. Moreover, the lateral boundary condition provided by AOD also impacts the output of the WRF-CHIMERE. However, I have a general comment on the paper.
The comparison between WRF-CHIMERE and WRF-only is not appropriate. The configuration of the WRF-only does not incorporate the optimized configuration for solar energy applications. I suggest the authors use the WRF option with WRF-Solar for this purpose. WRF-Solar has all the features designed for solar energy applications, and some modules were introduced to make this tool robust. For instance, the GHI values from the Fast All-sky Radiation Model for Solar Applications (FARMS) in WRF-Solar are better than the traditional ones in the WRF model (Gueymard et al., 2018). I also suggest using the Thompson microphysics scheme for the WRF-only experiment. I recommend the authors use the recommended configurations in WRF for solar energy applications (https://ral.ucar.edu/solutions/products/wrf-solar).
Gueymard, C. A., and P. A. Jimenez, 2018: Validation of real-time solar irradiance simulations over Kuwait using WRF-Solar. 12th Int. Conf. on Solar Energy for Buildings and Industry, Rapperswil, Switzerland, International Solar Energy Society, https://doi.org/10.18086/eurosun2018.09.14.
This is optional: the authors can also use AOD in WRF-Solar to compare with WRF-CHIMERE; this would provide interesting results for the region.
It is difficult for readers to obtain straightforward information from the plotted figures. I suggest writing down the names of different experiments or observed data from the different panels.
The discussion section needs enhancement by providing references to support your claims.
These are detail comments
Line 52: The citation should be (IEA, 2022).
Line 66: It is solar irradiance, not solar radiation. There is a difference between them. Please, if you refer to GHI, use solar irradiance, not solar radiation. Change this throughout the manuscript.
Lines 143-144: It is hard to see that on Fig.1a. It should be FS1.
Line 349: You could also add Sawadogo et al., 2024:
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Fig.2: I suggest putting the title of the station for each panel. It will be easier for the reader, without having to read the caption.
Fig.3: Same comment as in Fig.2. In addition, use one color bar for all of them. It would be nice to have the spatial correlation of different experiments based on the CAMS reference dataset.
Line 452: Please check the value of 115 W.m−2.
Lines 452-453: These values refer to the WRF-only simulation, right? In this case, this should be clearly stated in the sentence.
Lines 456-458: This statement needs some references. You can use Sawadogo et al., 2024, where they show that CAMS data has a huge bias under the Harmattan period, hence for dust events over Burkina Faso.
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Fig.4: I suggest that wrf_chimere-G, wrf_chimere-M, and wrf_chimere-C refer to the WRF-CHIMERE simulations using GOCART, MERRA2, and CAMS and should be used throughout the manuscript to be consistent.
Lines 477-478: Why do the simulated temperatures differ among the experiments during nighttime? This needs to be discussed.
Line 485: Please provide a scientific explanation for why the impact of dust aerosols on temperature is particularly pronounced at nighttime.
Lines 554-557: Please provide some references to back up this claim.
Lines 592-606: This part should be in the methodology section.
For Figure 8, I would like to know the model time output. This is missing in the manuscript.
Lines 771-773: It is hard to understand this sentence. Please rephrase it.
Lines 779-783: The performance of CAMS in simulating solar irradiance during high AOD episodes is low.
Sawadogo, W., Bliefernicht, J., Fersch, B., Salack, S., Guug, S., Diallo, B., ... & Kunstmann, H. (2023). Hourly global horizontal irradiance over West Africa: A case study of one-year satellite-and reanalysis-derived estimates vs. in situ measurements. Renewable Energy, 216, 119066.
Lines 801-803: This is not true. ERA5 does not dynamically simulate aerosols but incorporates its radiative effects through prescribed monthly climatologies from the GOCART model.
Citation: https://doi.org/10.5194/egusphere-2024-1604-RC2 -
AC1: 'Comment on egusphere-2024-1604 : final response', Léo Clauzel, 27 Sep 2024
Dear editors,
We are pleased to send you our final response. You'll find it in the attached document, which lists the comments made by the reveiwers, our responses to each comment and the changes made to the manuscript.
We thank the reviewers for their thorough reading of the manuscript and their courageous comments.
Should you have any questions, please do not hesitate to contact me.
Best regards
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