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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-67
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-67
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 25 Apr 2019

Model description paper | 25 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

A Radar Reflectivity Operator with Ice-Phase Hydrometeors for Variational Data Assimilation (RadZIceVarv1.0) and Its Evaluation with Real Radar Data

Shizhang Wang1,2 and Zhiquan Liu2 Shizhang Wang and Zhiquan Liu
  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
  • 2National Center of Atmospheric Research, Boulder, 80301, USA

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadZIceVar) were developed for variational data assimilation (DA). RadZIceVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3DVAR) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadZIceVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3D analysis of rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. The deficiencies in the analysis using this operator could be caused by the poor quality of the background fields and the use of the static background error covariance, and these issues can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadZIceVar also improved the short-term (2 h–5 h) precipitation forecasts compared to those of the experiment without DA.

Shizhang Wang and Zhiquan Liu
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Status: open (until 26 Jun 2019)
Status: open (until 26 Jun 2019)
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Shizhang Wang and Zhiquan Liu
Shizhang Wang and Zhiquan Liu
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Latest update: 20 May 2019
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Short summary
A reflectivity operator was developed for directly assimilating radar reflectivity involving contributions from ice species with the variational data assimilation method. Its current version was implemented in WRFDA 3.9.1. This operator allows for not only the dry snow/graupel but also the wet species so that it can effectively obtain the rainwater, snow, and graupel analysis which improved the short-term precipitation forecasts compared to those of the experiment without DA.
A reflectivity operator was developed for directly assimilating radar reflectivity involving...
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