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AMT | Articles | Volume 12, issue 10
Atmos. Meas. Tech., 12, 5613–5637, 2019
https://doi.org/10.5194/amt-12-5613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 5613–5637, 2019
https://doi.org/10.5194/amt-12-5613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Oct 2019

Research article | 23 Oct 2019

A Gaussian mixture method for specific differential phase retrieval at X-band frequency

Guang Wen et al.

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Cited articles

Anagnostou, E. N., Krajewski, W. F., and Smith, J.: Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates, J. Atmos. Ocean. Tech., 16, 206–215, 1999. a
Aydin, K., Bringi, V., and Liu, L.: Rain-rate estimation in the presence of hail using S-band specific differential phase and other radar parameters, J. Appl. Meteorol., 34, 404–410, 1995. a
Balakrishnan, N. and Zrnić, D. S.: Estimation of rain and hail rates in mixed-phase precipitation, J. Atmos. Sci., 47, 565–583, 1990. a
Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or unrecognized potential?, Adv. Water Res., 51, 357–366, 2013. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, 2006. a
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In this study, we propose a probabilistic method based on the Gaussian mixture model to estimate the slope of a data profile. The Gaussian mixture method (GMM) not only obtains the expected value of the slope by differentiating the conditional expectation of the data, but also yields the variance of the slope regarding the errors in the calculation of the first derivative.
In this study, we propose a probabilistic method based on the Gaussian mixture model to estimate...
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