Doppler Radar Data Assimilation in KMA's Operational Forecasting

Xiao, Qingnong; Sun, Juanzhen; Lee, Wen-Chau; Kuo, Ying-Hwa; Barker, Dale; Lim, Eunha; Won, Duk-Jin; Lee, Mi-Seon; Lee, Woojin; Cho, Joo-Young; Lee, Dong‐Kyou; Lee, Hee-Sang

doi:10.1175/bams-89-1-39

Cited by 36 publications

(21 citation statements)

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“…Recent studies have shown that assimilation of radar data can improve the short-term forecasts of convective systems by incorporating the initial conditions of mesoscale storm structures (Weygandt et al, 2002;Sun, 2005;Dawson and Xue, 2006;Hu et al, 2006;Zhao et al, 2006;Xiao et al, 2007Xiao et al, , 2008Pu et al, 2009;Li and John, 2010;Kawabata et al, 2011). In particular, both the four-dimensional variational (4DVar) data assimilation method (e.g.…”

Section: Introductionmentioning

confidence: 99%

PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW)

Zhang

Tian

Sun

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T The Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as PODEn4DVar) is a hybrid assimilation method that exploits the strengths of both the ensemble Kalman filter (EnKF) and the 4DVar assimilation method. Its feasibility and validity have been demonstrated using ideal models through observing system simulation experiments (OSSEs). In this study, we further utilise this approach to build a PODEn4DVar-based radar data assimilation scheme (PRAS). In a PRAS, radar observations including radial velocity and reflectivity, after some necessary data preprocessing, are assimilated directly to improve model initialisation. A group of single-observation-based OSSEs are first designed to generally evaluate the validity of PRAS. Subsequently, a group of comparison experiments are also carried out between PRAS and an LETKF-based radar assimilation scheme (LRAS), which shows that PRAS is able to produce results better than (at least as good as) LRAS. Thirdly, to evaluate the potential impact for PRAS in the operational context, a group of cycling assimilation experiments of radar data are performed, which demonstrates that PRAS can gradually improve the accuracy of analysis field by cycling assimilation. Finally, a heavy convective-rainfall case study was selected to investigate the performance of PRAS in assimilating real radar observations and the impacts of assimilating radar observations on numerical forecasts, with the Weather Research and Forecasting (WRF) model as our forecasting model. The results show that significant improvements in predicting heavy rainfall can be achieved due to the improved initial conditions for the convective system's dynamics and microphysics after assimilating the radar observations with PRAS. In summary, the results show that the PODEn4DVar is a promising method for atmospheric data assimilation.

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Section: Introductionmentioning

confidence: 99%

PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW)

Zhang

Tian

Sun

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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“…They found a positive impact, mainly on the first 3-h of forecasting. Xiao et al (2008) verified the benefit of Doppler radar data assimilation on operational forecasting for the Korean Meteorological Administration (KMA). Zhao et al (2006) developed a three-and-half-dimensional variational data assimilation (3.5D-VAR) system for radar wind data to initialize the Navy's Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) model.…”

Section: Introductionmentioning

confidence: 75%

Effect of Doppler Radial Velocity Data Assimilation on the Simulation of a Typhoon Approaching Taiwan: A Case Study of Typhoon Aere (2004)

Lin¹,

Lin²,

Xiao³

et al. 2011

Terr. Atmos. Ocean. Sci.

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Compared to conventional data, radar observations have an advantage of high spatial and temporal resolutions, and Doppler radars are capable of capturing detailed characteristics of flow fields, including typhoon circulation. In this study, the possible improvement of short-term typhoon predictions near Taiwan, particularly with regard to related rainfall forecasts over the mountainous island, using Doppler radial wind observations is explored. The case of Typhoon Aere (2004) was chosen for study, and a series of experiments were carried out using the Penn State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model Version 5 (MM5) with its three-dimensional variational (3D-VAR) data assimilation system.The results show that once the Doppler radial velocities were assimilated into the model, the typhoon's circulation intensified within one hour. However, when Typhoon Aere approached from the east and only the western half of its core area could be observed by the radar, the assimilation caused the typhoon to deflect southward due to the incomplete and uneven data coverage. In another experiment in which Doppler radar data assimilation did not start until Typhoon Aere moved closer, such that its entire core region could be observed. A similar track deflection was avoided. Overall, the assimilation of Doppler radial velocity data reduced the intensity error (in wind speed) by about 25%. Furthermore, the improvements in location, intensity, and circulation structure of Typhoon Aere lead to better rainfall prediction over the island of Taiwan.

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“…However, it possesses a number of advantages over 3D-Var including the ability to: a) use observations at the exact times that they are observed, which suits most asynoptic data; b) implicitly use flow-dependent background errors, which ensures the analysis quality for fast developing weather systems; and c) use a forecast model as a constraint, which enhances the dynamic balance of the final analysis. Almost all observations can be assimilated into WRF 3/4D-Var analysis, including the observations from GTS data stream, satellite (Liu & Barker, 2006) and Doppler radar (Xiao et al, 2005;2007;Xiao et al, 2008b). Recently, Liu et al (2008;2009) …”

Section: Wrf Variational (Wrf-var) Data Assimilationmentioning

confidence: 99%

Doppler Radar Data Assimilation in KMA's Operational Forecasting

Abstract: mode at KMA has been very smooth and successful. With great efforts from all parties, KMA is running the advanced system operationally and benefiting Korea. The WRF 3D-Var Doppler radar data-assimilation system now resides in both NCAR and KMA and serves as a very useful tool for research.

Cited by 36 publications

References 0 publications

PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW)

PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW)

Effect of Doppler Radial Velocity Data Assimilation on the Simulation of a Typhoon Approaching Taiwan: A Case Study of Typhoon Aere (2004)

Improvements of Hurricane Forecast with Vortex Initialization using WRF Variational (WRF-Var) Data Assimilation

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