Open Access
Issue
E3S Web Conf.
Volume 170, 2020
6th International Conference on Energy and City of the Future (EVF’2019)
Article Number 02002
Number of page(s) 6
Section Factories of Future
DOI https://doi.org/10.1051/e3sconf/202017002002
Published online 28 May 2020
  1. Trigo, R., Xoplaki, E., Zorita, E., Luterbacher, J., Krichak, S.O., Alpert, P., Jacobeit, J., Sàenz, J., Fernàndez, J., Gonzàlez-Rouco, F., Garcia-Herrera, R., Rodo, X., Brunetti, M., Nanni, T., Maugeri, M., Turkex, M., Gimeno, 18 L., Ribera, P., Brunet, M., Trigo, I.F., Crepon, M., Mariotti, A., Chapter 3 Relations between variability in the Mediterranean region and mid-latitude variability. In: Developments in Earth and Environmental Sciences, vol. 4. Elsevier, pp. 179–226 (2006). [Google Scholar]
  2. Hong, Y., Hsu, K.-L., Sorooshian, S., Gao, X., Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteorol. 43, 1834–1853 (2004). [CrossRef] [Google Scholar]
  3. Ebert, E.E., Janowiak, J.E., Kidd, C., Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Am. Meteorol. Soc. 88, 47–64 (2007) [Google Scholar]
  4. Thies, B., Nauss, T., & Bendix, J., Precipitation process and rainfall intensity differentiation using Meteosat Second Generation SEVIRI data. Journal of Geophysical Research, 113, 19 (2008). [Google Scholar]
  5. Lazri, M., Ameur, S.,. A satellite rainfall retrieval technique over northern Algeria based on the probability of rainfall intensities classification from MSG-SEVIRI. J. Atmos. Sol. Terr. Phys. 147, 106–120 (2016). [Google Scholar]
  6. Levizzani, V., Satellite rainfall estimations: new perspectives for meteorology and climate from the EURAINSAT project, Ann. Geophys., 46, 363–372, http://www.ann-geophys.net/46/363/2003/ (2003). [Google Scholar]
  7. Nauss, T., Kokhanovsky, A.A., Assignment of rainfall confidence values using multispectral satellite data at mid-latitudes: first results. Adv. Geosci. 10, 99–102 (2007). [CrossRef] [Google Scholar]
  8. Roebeling, R.A., Holleman, I., SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res. 114 (2009). [Google Scholar]
  9. Lazri, M., Ameur, S., Mohia, Y., Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall. Adv. Space Res. 53, 138–155 (2014). [Google Scholar]
  10. Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., Ratier, A., 2002. An introduction to Meteosat Second Generation (MSG). Bull. Am. Meteorol. Soc. 83, 977–992 (2002). [Google Scholar]
  11. Lazri, M., Ameur, S., Brucker, J.M., Testud, J., Hamadache, B., Hameg, S., Ouallouche, F., Mohia, Y., Identification of raining clouds using a method based on optical and microphysical cloud properties from Meteosat second generation daytime and nighttime data. Appl. Water Sci. 3, 1–11 (2013). [Google Scholar]
  12. Bensafi N., Lazri M., Ameur S., Novel WkNN-based technique to improve instantaneous rainfall estimation over the north of Algeria using the multispectral MSG SEVIRI imagery, Journal of Atmospheric and Solar-Terrestrial Physics, doi.org/10.1016/j.jastp.2018.12.004 (2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.