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

Related authors

Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor
Lidou Huyan, Yudong Gao, Zheng Wu, and Bojun Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-72,https://doi.org/10.5194/amt-2023-72, 2023
Preprint withdrawn
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Global sensitivity analysis of simulated remote sensing polarimetric observations over snow
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024,https://doi.org/10.5194/amt-17-4737-2024, 2024
Short summary
On the temperature stability requirements of free-running Nd:YAG lasers for atmospheric temperature profiling through the rotational Raman technique
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024,https://doi.org/10.5194/amt-17-4687-2024, 2024
Short summary
Limitations in wavelet analysis of non-stationary atmospheric gravity wave signatures in temperature profiles
Robert Reichert, Natalie Kaifler, and Bernd Kaifler
Atmos. Meas. Tech., 17, 4659–4673, https://doi.org/10.5194/amt-17-4659-2024,https://doi.org/10.5194/amt-17-4659-2024, 2024
Short summary
A new non-linearity correction method for the spectrum from the Geostationary Inferometric Infrared Sounder on board Fengyun-4 satellites and its preliminary assessments
Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui
Atmos. Meas. Tech., 17, 4613–4627, https://doi.org/10.5194/amt-17-4613-2024,https://doi.org/10.5194/amt-17-4613-2024, 2024
Short summary
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024,https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary

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. 
Download
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.