Articles | Volume 17, issue 15
https://doi.org/10.5194/amt-17-4675-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-4675-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2024
Research article |
| 13 Aug 2024
| 13 Aug 2024
Improving the Gaussianity of radar reflectivity departures between observations and simulations using symmetric rain rates
Key Laboratory of Core Tech on Numerical Model-AI Integrated Forecast for Hazardous Precipitation, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
Lidou Huyan
Key Laboratory of Core Tech on Numerical Model-AI Integrated Forecast for Hazardous Precipitation, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
Zheng Wu
Key Laboratory of Core Tech on Numerical Model-AI Integrated Forecast for Hazardous Precipitation, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
Bojun Liu
Chongqing Meteorological Observatory, Chongqing 401147, China
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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.
Chang, P., Zhang, J., Tang, Y., Tang, L., Lin, P., Langston, C., Kaney, B., Chen, C., and Howard, K.: An Operational Multi-Radar Multi-Sensor QPE System in Taiwan, B. Am. Meteorol. Soc., 102, E555–E577, https://doi.org/10.1175/BAMS-D-20-0043.1, 2021.
Cuomo, J. and Chandrasekar, V.: Use of Deep Learning for Weather Radar Nowcasting, J. Atmos. Ocean. Technol., 38, 1641–1656, https://doi.org/10.1175/JTECH-D-21-0012.1, 2021.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. R. Meteorol. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
Ek, M. B., Mitchell, K. E., Rogers, E., Lin, Y., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational Mesoscale Eta Model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Gao, Y.: Data used in the publication: Improving the Gaussianity of Radar Reflectivity Departures between Observations and Simulations by Using the Symmetric Rain Rate, figshare [data set], https://doi.org/10.6084/m9.figshare.25093508.v2, 2024.
Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation, Q. J. R. Meteorol. Soc., 137, 2024–2037, https://doi.org/10.1002/qj.830, 2011.
Gleiter, T., Janjić, T. and Chen, N.: Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model, Q. J. R. Meteorol. Soc., 148, 1035–1056, https://doi.org/10.1002/qj.4245, 2022.
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., and Mile, M.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. R. Meteorol. Soc., 144, 1218–1256, https://doi.org/10.1002/qj.3179, 2018.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Janjić, T., McLaughlin, D., Cohn, S. E., and Verlaan, M.: Conservation of mass and preservation of positivity with ensemble-type Kalman filter algorithms, Mon. Weather Rev., 142, 755–773, https://doi.org/10.1175/MWR-D-13-00056.1, 2014.
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance, S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston, P.: On the representation error in data assimilation, Q. J. R. Meteorol. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130, 2018.
Johnson, A., Wang, X., and Jones, T.: Impacts of assimilating GOES-16 ABI channels 9 and 10 clear air and cloudy radiance observations with additive inflation and adaptive observation error in GSI-EnKF for a case of rapidly evolving severe supercells, J. Geophys. Res.-Atmos., 127, e2021JD036157, https://doi.org/10.1029/2021JD036157, 2022.
Jung, Y., Xue, M., Zhang, G. F., and Straka, J. M.: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part II: Impact of polarimetric data on storm analysis, Mon. Weather Rev., 136, 2246–2260, https://doi.org/10.1175/2007MWR2288.1, 2008.
Kain, J. S.: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Li, S. Y., Huang, X. L., Wu, W., Du, B., and Jiang, Y. H.: Evaluation of CMPAS precipitation products over Sichuan, China, Atmos. Ocean. Sci. Lett., 15, 100129, https://doi.org/10.1016/j.aosl.2021.100129, 2022,
Liu, C. S., Xue, M., and Kong, R.: Direct Variational Assimilation of Radar Reflectivity and Radial Velocity Data: Issues with Nonlinear Reflectivity Operator and Solutions, Mon. Weather Rev., 148, 1483–1502, https://doi.org/10.1175/MWR-D-19-0149.1, 2020.
Liu, C., Li, H., Xue, M., Jung, Y., Park, J., Chen, L., Kong, R., and Tong, C.: Use of a Reflectivity Operator Based on Double-Moment Thompson Microphysics for Direct Assimilation of Radar Reflectivity in GSI-Based Hybrid En3DVar, Mon. Weather Rev., 150, 907–926, https://doi.org/10.1175/MWR-D-21-0040.1, 2022.
Lopez, P.: Direct 4D-Var assimilation of NCEP stage IV radar and gauge precipitation data at ECMWF, Mon. Weather Rev., 139, 2098–2116, https://doi.org/10.1175/2010MWR3565.1, 2011.
Migliorini, S. and Candy, B.: All-sky satellite data assimilation of microwave temperature sounding channels at the Met Office, Q. J. R. Meteorol. Soc., 145, 867–883, https://doi.org/10.1002/qj.3470, 2019.
NCAR: The NCAR Command Language, Version 6.6.2, UCAR/NCAR/CISL/TDD [code], Boulder, Colorado, https://doi.org/10.5065/D6WD3XH5, 2019.
Pan, Y., Gu, J. X., Xu, B., Shen, Y., Han, S., and Shi, C. X.: Advances in multi-source precipitation merging research, Adv. Meteorol. Sci. Technol., 8, 143–152, https://doi.org/10.3969/j.issn.2095-1973.2018.01.019, 2018 (in Chinese).
Shahabadi, M. B. and Buehner, M.: Toward All-Sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada's Global Deterministic Weather Prediction System, Mon. Weather Rev., 149, 3725–3738, https://doi.org/10.1175/MWR-D-21-0044.1, 2021.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Model Version 4, figshare, https://doi.org/10.6084/m9.figshare.7369994.v4, 2019.
Snyder, C. and Zhang, F. Q.: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter, Mon. Weather Rev., 131, 1663–1677, https://doi.org/10.1175//2555.1, 2003.
Stensrud, D. J., Wicker, L. J., Xue, M., Dawson, D. T., Yussouf, N., Wheatley, D. M., Thompson, T. E., Snook, N. A., Smith, T. M., Schenkman, A. D., Potvin, C. K., Mansell, E. R., Lei, T., Kuhlman, K. M., Jung, Y., Jones, T. A., Gao, J., Coniglio, M. C., Brooks, H. E., and Brewster, K. A.: Progress and challenges with warn-on-forecast, Atmos. Res., 123, 2–16, https://doi.org/10.1016/j.atmosres.2012.04.004, 2013.
Stoelinga, M. T.: Simulated equivalent reflectivity factor as currently formulated in RIP: Description and possible improvements. University of Washington Tech. Rep., 5 pp., https://www.researchgate.net/publication/242107593_Simulated _equivalent_reflectivity_factor_as_currently_formulated_in_ RIP_Description_and_possible_improvements (last access: 1 August 2024), 2005.
Sun, J. Z. and Crook, N. A.: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments, J. Atmos. Sci., 54, 1642–1661, https://doi.org/10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2, 1997.
Sun, J. Z., Xue, M., Wilson, J. M., Zawadzki, I., Ballard, S. P., Onvlee-Hooimeyer, J., Joe, P., Barker, D. M., Li, P., Golding, B., Xu, M., and Pinto, J.: Use of NWP for nowcasting convective precipitation: Recent progress and challenges, B. Am. Meteorol. Soc., 95, 409–426, https://doi.org/10.1175/BAMS-D-11-00263.1, 2014.
Sun, Y. Q. and Zhang, F. Q.: A New Theoretical Framework for Understanding Multiscale Atmospheric Predictability, J. Atmos. Sci., 77, 2297–2309, https://doi.org/10.1175/JAS-D-19-0271.1, 2020.
Thompson, G., Field, P. R., Rasmussen, M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme, Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
Tong, M. J. and Xue, M.: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments, Mon. Weather Rev., 133, 1789–1807, https://doi.org/10.1175/MWR2898.1, 2005.
Waller, J. A., Dance, S. L., and Nichols, N. K.: On diagnosing observation-error statistics with local ensemble data assimilation, Q. J. R. Meteorol. Soc., 143, 2677–2686, https://doi.org/10.1002/qj.3117, 2017.
Xue, M., Jung, Y., and Zhang, G. F.: Error modeling of simulated reflectivity observations for ensemble Kalman filter assimilation of convective storms, Geophys. Res. Lett., 34, L10802, https://doi.org/10.1029/2007GL029945, 2007.
Zhu, Y. Q., Gayno, G., Purser, R. J., Su, X. J., and Yang, R. H.: Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP, Mon. Weather Rev., 147, 2603–2620, https://doi.org/10.1175/MWR-D-18-0228.1, 2019.
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.
A symmetric error model built by symmetric rain rates handles the non-Gaussian error structure...
