Abdoulaye Bouya Diop¹, Abdoulaye Sy^1,2, Malick Wade¹, Abdoul Karim Mbodj¹, Abdou Karim Farota¹, Adoum Mahamat Moussa^1,3, Babacar Niang¹, Aicha Dia Diop¹, Bara Ndiaye¹, Bouya Diop^1*, Amadou Thierno Gaye⁴, Aboubakary Diakhaby¹

Show more
¹ LSAO-MED Gaston Berger University, Saint Louis, Senegal.
² CERDI Clermont Auvergne University, Clermont Auvergne, France.
³ University of Tchad, Djamena, Tchad.
⁴ LPAOSF ESP Cheikh Anta Diop Univesity, Dakar, Senegal.
Show less

Abstract: This work concerns the 10? North, 20? North African band. Area renowned for having some of the poorest countries in the world. It is also home to the Sahelian strip and part of the Sahara. The countries in this zone have a relatively low electrification rate compared to the enlightened country. To solve this problem, these countries want to turn to renewable energies such as photovoltaics (renewable energy obtained through solar radiation). Therefore, understanding the behavior of irradiation and cloudiness in the 10 - 20 band becomes necessary. The application of the empirical orthogonal functions to the different cloud layers and to the irradiation reveals a seasonality of the latter, in particular compared to the first modes of the empirical orthogonal functions (EOF1). Indeed, by filtering in time and space to isolate solar radiation and cloudiness, the EOF1 expresses respectively 94.3% of the variation of descending solar radiation in clear sky in the 10 - 20 band. Note 65.7% for global radiation, 54.4% for cloudiness at 450 hPa, 69.6% for cloudiness between 800 hPa and 450 hPa, 76.6% for low cloudiness, 61.4% for total cloudiness. These results allow us to say that we have generally good sunshine over several months, with little cloud cover in this band. However, since this zone is considered to be part of the main aerosol emission source zones, it is important in their temporal consideration of the optical depth. By doing the wavelet analysis on the optical depth data from Dakar and Banizoumbou, we note that the average dust presence spectrum over the entire period from 1997 to 2019 in the two stations with a slight shift compared to the peaks and the maximum observed value. A sign that there is a strong presence of dust in this area and that it should be taken into account for any photovoltaic installation in this area.

Keywords: Sahel, Irradiation, Cloudiness, Aerosol, EOF, Wavelet

Cite this paper: Diop, A. , Sy, A. , Wade, M. , Mbodj, A. , Farota, A. , Moussa, A. , Niang, B. , Diop, A. , Ndiaye, B. , Diop, B. , Gaye, A. and Diakhaby, A. (2022) Characterization of Spatio-Temporal Variability of Irradiation, Nebulosity and Aerosols Optical Depth in 10˚ North-20˚ North African Band. American Journal of Climate Change, 11, 155-171. doi: 10.4236/ajcc.2022.112008.

References

[1]   Antoine, J.-P. (2018). Wavelet Analysis. From Oil Exploration to Relics of the Big Bang. Review of Scientific Questions, 178, 367-410.

[2]   deMenocal, P., Ortiz, J., Guilderson, T., et al. (2000). Abrupt Onset and Termination of the African Humid Period: Rapid Climate Responses to Gradual Insolation Forcing. Quaternary Science Reviews, 19, 347-361.
https://doi.org/10.1016/S0277-3791(99)00081-5

[3]   Deremble, B. (2010). Low Frequency Atmospheric Dynamics: Influence of an Ocean Temperature Front. Thesis, Pierre and Marie Curie Sorbonne University Paris 6, Paris.
https://www.theses.fr/2010PA066277

[4]   Diawara, H., Berthe, T., Bengaly, S., Gaidukova, E. V., Sangare, K., & Diarra, S. (2021). Impact of Climate Change on the Water Balance of the Sankarani River Basin in West Africa. International Journal of Environment, Agriculture and Biotechnology, 6, 119-126.
https://doi.org/10.22161/ijeab.66.14

[5]   Emeterio, J.-L. S., Lacaze, B., & Méring, C. (2012). Detection of Vegetation Cover Changes in the Sahel during the Period 1982-2002 from NDVI and Precipitation Data. Remote Sensing Review, 10, 2-3.

[6]   Gabor, D. (1946). Theory of Communication: Part 1: The Analysis of Information. Journal of the Institute of Electrical Engineering, 93, 429-457.
https://doi.org/10.1049/ji-3-2.1946.0074

[7]   Julianto, M. T., Dhimas, S., Sopaheluwakan, A., Nurdiati, S., & Septiawan, P. (2021). Identification of Global Warming Contribution to the El Niño Phenomenon Using Empirical Orthogonal Function Analysis. Agromet, 35, 11-19.
https://doi.org/10.29244/j.agromet.35.1.11-19

[8]   Lorenz, E. N. (1956). Empirical Orthogonal Functions and Statistical Weather Prediction. Statistical Forecasting Project Rep. 1, MIT Department of Meteorology, 49.

[9]   Marega, O., Emeterio, J.-L. S., Fall, A., & Andrieu, J. (2021). Remote Sensing Mapping of Vegetation Land Cover Change with Regard to Rainfall Variability in the Sahel: A Multiscale Approach. Physio-Géo, 16, 1-28.
https://doi.org/10.4000/physio-geo.11977

[10]   Martiny, N., Roucou, P., & Adde, A. (2014). Impact of Climate on Health: Regional Dust Modeling for Meningitis Epidemics in the Sahel. 27th Colloquium of the International Association of Climatology, Dijon, July 2014, 657-662.

[11]   Monahan, A., Fyfe, J. C., Ambaum, M. H. P., Stephenson, D. B., & North, G. R. (2009). Empirical Orthogonal Functions: The Medium is the Message. Journal of Climate, 22, 6501-6514.
https://doi.org/10.1175/2009JCLI3062.1

[12]   Munagapati, H., & Tiwari, V. M. (2021). Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data. Remote Sensing, 13, 265.
https://doi.org/10.3390/rs13020265

[13]   Nouaceur, & Zeineddine. (2020). Rain Resumption and Floods Multiplication in Western Sahelian Africa. Physio-Géo, 15, 89-109.

[14]   Nouaceur, Z. (2004). Haze, Dust Haze, Dust Blower and Sandstorm Specific Types of Weather in Dry Regions. Brume sèche, brume de poussière, chasse-sable et tempête de sable. Des types de temps spécifiques des régions sèches, 2, 11.

[15]   Olauson, J. (2018). ERA5: The New Champion of Wind Power Modelling? Renewable Energy, 126, 322-331.
https://doi.org/10.1016/j.renene.2018.03.056

[16]   Paturel, J. E., Servat, E., Delatre, M. O., & Lubes-niel, H. (1998). Analysis of Rainfall Long Series in Non-Sahelian West and Central Africa within a Context of Climate Variability. Hydrological Sciences Journal, 43, 937-946.

[17]   Rajot, J. L., Toure, A., Guillon, R., Zibogarba, P. C., Bichet, V., & Marticorena, B. (2011). Terrigen Dust in the Sahel—A Climatic or Anthropogenic Marker? In Ngaoundéré (CMR): International Colloquium: Water, Climate and Environmental Sciences for Sustainable Development in Africa (p. 50). Ngaoundere (CMR).

[18]   Sicard, B. (1992). Influences of Aridity on the Biology of Sudano-Sahelian Rodents.

[19]   Sy, A., Diop, B., Farota, A. K., Sarr, D., Dia, A., Diop, A. B., Pohl, B., & Duroure, C. (2018). Role of Indian Fluxes in the Intraseasonal 10-30 Days Variability of the African Monsoon. Journal of Earth Science & Climatic Change, 9, Article ID: 1000495.
https://doi.org/10.4172/2157-7617.1000495

[20]   Taïbi, A. N. (2017). Desertification on Both Sides of the Largest Desert in the World. The Sahara and Its Northern and Southern Margins; A Context Favorable to Desertification? In: J. Andrieu, Ed., Africa, from the Sahel and the Sahara, to the Mediterranean (pp. 259-270), Desertification.

[21]   Wang, C., Graham, R. M., Wang, K., Gerland, S., & Granskog, M. A. (2019). Comparison of ERA5 and ERA-Interim Near-Surface Air Temperature, Snowfall and Precipitation over Arctic Sea Ice: Effects on Sea Ice Thermodynamics and Evolution. The Cryosphere, 13, 1661-1679.
https://doi.org/10.5194/tc-13-1661-2019