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Volume 15
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10.3390/rs15030672
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Article

Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting

by
Hong Zheng
1,2,
Yaodeng Chen
1,2,*,
Shiwei Zheng
1,
Deming Meng
3 and
Tao Sun
4
1
Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3
Key Laboratory of Mesoscale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
4
Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 672; https://doi.org/10.3390/rs15030672
Submission received: 25 November 2022 / Revised: 6 January 2023 / Accepted: 18 January 2023 / Published: 23 January 2023
(This article belongs to the Section Atmospheric Remote Sensing)
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Abstract

Radar reflectivity assimilation is often used to initialize hydrometeors, to which Numerical Weather Prediction (NWP) is highly sensitive. To better initialize hydrometeors, this study further developed the background error covariance (BEC) with vertical and multivariable correlations of hydrometeor control variables (H-BEC) in the WRF three-dimensional variational data assimilation system (WRFDA-3DVar). The impacts of the H-BEC are discussed using single radar reflectivity tests and series of cycling data assimilation and forecasting experiments for five multi-type convective rainfall cases. The conclusions are summarized as follows: (1) The vertical correlations can speed up the minimization of the cost function, whereas the multivariable correlations further accelerate this minimization; (2) The vertical correlations slightly improve the precipitation forecasting and only in the first hour, while multivariate correlations lead to a larger improvement and persist into the third hour; (3) The application of H-BEC leads to a more reasonable thermodynamic and dynamical structure of the initial field, thereby improving the capability of short-term precipitation forecasting.
Keywords: precipitation forecasting; data assimilation; radar reflectivity; hydrometeors; background error covariance precipitation forecasting; data assimilation; radar reflectivity; hydrometeors; background error covariance

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MDPI and ACS Style

Zheng, H.; Chen, Y.; Zheng, S.; Meng, D.; Sun, T. Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting. Remote Sens. 2023, 15, 672. https://doi.org/10.3390/rs15030672

AMA Style

Zheng H, Chen Y, Zheng S, Meng D, Sun T. Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting. Remote Sensing. 2023; 15(3):672. https://doi.org/10.3390/rs15030672

Chicago/Turabian Style

Zheng, Hong, Yaodeng Chen, Shiwei Zheng, Deming Meng, and Tao Sun. 2023. "Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting" Remote Sensing 15, no. 3: 672. https://doi.org/10.3390/rs15030672

APA Style

Zheng, H., Chen, Y., Zheng, S., Meng, D., & Sun, T. (2023). Radar Reflectivity Assimilation Based on Hydrometeor Control Variables and Its Impact on Short-Term Precipitation Forecasting. Remote Sensing, 15(3), 672. https://doi.org/10.3390/rs15030672

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