Articles | Volume 17, issue 15
https://doi.org/10.5194/amt-17-4675-2024
https://doi.org/10.5194/amt-17-4675-2024
Research article
 | 
13 Aug 2024
Research article |  | 13 Aug 2024

Improving the Gaussianity of radar reflectivity departures between observations and simulations using symmetric rain rates

Yudong Gao, Lidou Huyan, Zheng Wu, and Bojun Liu

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Cited articles

Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. 
Bannister, R. N., Chipilski, H. G., and Martinez-Alvarado, O.: Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales, Q. J. R. Meteorol. Soc., 146, 1–48, https://doi.org/10.1002/qj.3652, 2020. 
Baron, P., Kawashima, K., Kim, D., Hanado, H., Kawamura, S., Maesaka, T., Nakagawa, K., Satoh, K., and Ushio, T.: Nowcasting Multiparameter Phased-Array Weather Radar (MP-PAWR) Echoes of Localized Heavy Precipitation Using a 3D Recurrent Neural Network Trained with an Adversarial Technique, J. Atmos. Ocean. Technol., 40, 803–821, https://doi.org/10.1175/JTECH-D-22-0109.1, 2023. 
Bishop, C. H.: The GIGG-EnKF: ensemble Kalman filtering for highly skewed non-negative uncertainty distributions, Q. J. R. Meteorol. Soc., 142, 1395–1412, https://doi.org/10.1002/qj.2742, 2016. 
Bishop, C. H.: Data assimilation strategies for state-dependent observation error variances, Q. J. R. Meteorol. Soc., 145, 217–227, https://doi.org/10.1002/qj.3424, 2019. 
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Short summary
A symmetric error model built by symmetric rain rates handles the non-Gaussian error structure of the reflectivity error. The accuracy and linearization of rain rates can further improve the Gaussianity.
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