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Research Article | Open Access |

Comparison between Rayleigh and Mie Scattering Assumptions for Z-R Relation and Rainfall Rate Estimation with TRMM/PR Data

Author 1: Kohei Arai
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 2, No. 8 · Published 2013

DOI: 10.14569/IJARAI.2013.020801

Abstract

Comparison of the rain rate estimated with the assumptions of Rayleigh and Mie scattering is made. We analyzed the different relationships between the radar reflective factor and rain rate (so-called Z-R relationship) with both scattering models for different DSD (droplet size distribution) and rainfall types as the wavelength is 2.2cm which is in accord with the band of TRMM/PR. Meanwhile we introduced a discrete ordinates method to retrieve the Z-R relationship for Mie scattering assumption. It is found that the retrieval result can be represented as the sum of some simple Z-R relationships. By the analysis of the Z-R relationships estimated from Rayleigh and Mie scattering assumptions in the rain types, we found that the difference of Z-R relationships between Rayleigh and Mie scattering in the thunderstorm that represents the larger raindrop size is larger than that in the drizzle that represent the smaller raindrop size.

Keywords

How to Cite this Article

Arai, K. (2013). Comparison between Rayleigh and Mie Scattering Assumptions for Z-R Relation and Rainfall Rate Estimation with TRMM/PR Data. International Journal of Advanced Research in Artificial Intelligence, 2(8). https://doi.org/10.14569/IJARAI.2013.020801

Arai, Kohei. "Comparison between Rayleigh and Mie Scattering Assumptions for Z-R Relation and Rainfall Rate Estimation with TRMM/PR Data." International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 8, 2013, https://doi.org/10.14569/IJARAI.2013.020801.

@article{Arai2013,
  title     = {Comparison between Rayleigh and Mie Scattering Assumptions for Z-R Relation and Rainfall Rate Estimation with TRMM/PR Data},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {2},
  number    = {8},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJARAI.2013.020801},
  url       = {https://doi.org/10.14569/IJARAI.2013.020801}
}

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