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Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method

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Pan XD, Tian XJ, Li X, Xie ZH, Shao AM, Lu CY. Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method. Journal of Geophysical Research, 2012, 117(D17113), doi:10.1029/2012JD017684.

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Type Of Reference JOUR
Title Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method
Authors Pan, Xiaoduo| Tian, Xiangjun| Li, Xin| Xie, Zhenghui| Shao, Aimei| Lu, Chunyan|
Secondary Title Journal of Geophysical Research: Atmospheres
Abstract Doppler radar observations with high spatial and temporal resolution can effectively improve the description of small-scale structures in the initial condition and enhance the mesoscale and microscale model skills of numerical weather prediction (NWP). In this paper, Doppler radar radial velocity and reflectivity are simultaneously assimilated into a weather research and forecasting (WRF) model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method (referred to as PODEn3DVar), which therefore forms the PODEn3DVar-based radar assimilation system (referred to as WRF-PODEn3DVar). The main advantages of WRF-PODEn3DVar over the standard WRF-3DVar are that (1) the PODEn3DVar provides flow-dependent covariances through the evolving ensemble of short-range forecasts, and (2) the PODEn3DVar analysis can be obtained directly without an iterative process, which significantly simplifies the assimilation. Results from real data assimilation experiments with the WRF model show that WRF-PODEn3DVar simulation yields better rainfall forecasting than radar retrieval, and radar retrieval is better than the standard WRF-3DVar assimilation, probably because of the flow-dependence character embedded in the WRF-PODEn3DVar.
Date 2012///
Year 2012
DOI 10.1029/2012JD017684
Database Provider Wiley Online Library
volume 117
Issue Number D17
Start Page n/a
Language en
Issn 2156-2202
Url http://onlinelibrary.wiley.com/doi/10.1029/2012JD017684/abstract
Access Date 2013/10/04/06:20:16
Keywords data assimilation| Doppler radar| proper orthogonal decomposition| rainfall| weather research and forecasting model|
file_attachments2 http://onlinelibrary.wiley.com/doi/10.1029/2012JD017684/full
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