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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110310
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.
Abstract: Method for rainfall rate estimation with satellite based microwave radiometer data is proposed. A method to consider the geometric relationship of the observed ice particles and microwave radiometer in the estimation of precipitation is shown, and its validity is shown by comparing it with precipitation radar data on the ground. Observations at high altitudes, such as ice particles, differ greatly in the location of the observation point projected on the ground surface and in the upper troposphere where the observations exist. This effect was insignificant when the precipitation was small because ice particles were often absent, but it was found that the effect was large when the precipitation was large. In other words, the proposed method is effective and effective for Advanced Microwave Scanning Radiometer (AMSR) data in Houston, which was shown as an example of a highly developed convective rain cloud with an In the case of Kwajalein, the effect is insignificant. In addition, the proposed method requires an assumption of ice particle height, and it is necessary to make assumptions based on climatic values. In addition, microwaves in the 89 GHz band, which are considered to be sensitive to ice particles, are not only sensitive to ice particles, so it must be taken into account that they are also affected by the presence of non-ice particles.
Kohei Arai, “Method for Rainfall Rate Estimation with Satellite based Microwave Radiometer Data” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110310
@article{Arai2020,
title = {Method for Rainfall Rate Estimation with Satellite based Microwave Radiometer Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110310},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110310},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Kohei Arai}
}