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The development of GRAPES_RAFS and its applications Xu Zhifang Hao Min Zhu Lijuan Gong Jiangdong Chen Dehui National Meteorological Center, CMA Wan Qilin.

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Presentation on theme: "The development of GRAPES_RAFS and its applications Xu Zhifang Hao Min Zhu Lijuan Gong Jiangdong Chen Dehui National Meteorological Center, CMA Wan Qilin."— Presentation transcript:

1 The development of GRAPES_RAFS and its applications Xu Zhifang Hao Min Zhu Lijuan Gong Jiangdong Chen Dehui National Meteorological Center, CMA Wan Qilin Guangzhou Institute of Tropical and Marine Meteorology, CMA (24 October, 2011, for Workshop-NWP Nowcasting in Boulder-USA)

2 Outline 1 Introduction 2 RAFS Based on GRAPES_Meso 3 Results of some experiments 4 Plans for future work

3 1 Introduction

4 The requirements are increasing in short- term forecasts of the severe high impact weather which occurred more frequently in recent years in China. 1 Introduction Sever thunderstorm in He- nan ( 河南 ) on June, 2009 Mud-rock flow on August 07, 2010 in Zhou-qu ( 舟曲 )

5 1 Introduction (cont.) Many data sets from different obs. systems (AWS, aircraft, GPS, radars, satellites) have been frequently (hourly or shorter) are available in real time. (Meng Zhaolin , 2011)

6 2 RAFS Based on GRAPES_Meso

7 Flow Chart of Grapes_RAFS Data processing Observation DATA Assimilation Cycle Firstguess Forecast Digital Filter initialization DFI GRAPES_ MODEL GRAPES-Model background GRAPES_ 3DVAR GRAPES-3DVar Cloud Analysis nudging

8 Data Assimilation – Radio sonde data (wind, temperature, humidity, pressure) – AWS data (pressure) – VAD winds (Doppler Radar) – GPS/PW – Ship reports (pressure) – Aircraft (wind, temperature) – FY-2C/2D cloud-drift winds – Cloud Analysis (Radar, Satellite data, surface observation et al.) Data can be used in GRAPES_3DVAR :

9 3 Results of some experiments

10 3.1 Design of the experiments

11 – GRAPES_Meso: 15km L31 – GRAPES_3DVAR (model grid space) – 1-hourly cycle rapid analysis – 12 hour forecast at 03,06,09,15,18,21UTC – 24 hour forecast at 00,12UTC – Data used: GTS, local radiosonde, Doppler radar VAD, AWS, Aircraft 3.1 Design of the experiments

12 VADRadiosonde SYNOPAIREP Horizontal distribution of one case

13 3.2 Verification

14 left : observation (radar reflectivity) right : model forecasting ( Initial time : 0300GMT 3 June, 2009 ) The position prediction of strong convective system is very close to the observation.

15 The moving direction of the system is close to observation left : observation (radar composite reflectivity) right : model forecasting ( Initial time : 0600GMT 3 June, 2009 )

16 Initial Time : 0000GMT Initial Time : 1200 GMT (TS score) TS-Verification of 6h accumulated precipitation over whole Chinafor the period of June-August 2009 Inter-comparison between GRAPES_RAFS (RUC) with operational GRAPES_Meso (OPT): TS of RAFS is better than that of OPT for all thresholds: 0.5mm, 5mm, 10mm, above 15mm.

17 TS-Verification of 24h accumulated precipitation over whole China for the period of June-August 2009 Inter-comparison between GRAPES_RAFS (RUC) with operational GRAPES_Meso (OPT): TS of RAFS is better than that of OPT for all thresholds: 1mm, 10mm, 25mm, above 50mm. 1mm 10mm 25mm 50mm (TS score)

18 (Wang Yu,2007) Sub-domains for verifications 1 North-Eastern 2 Xinjiang 3 East of West-Northern 4 North of China 7 M. & D. basins of Yangtz River 8 South of China 5 Tibetan Plateau 6 East of West-Southern R.

19 TS-Verification of 6h accumulated precipitation forecasts over whole China for the period of June-August 2011: RAFS vs OPT Threat Score Bias Threat ScoreBias

20 3.3 Recent updates (1)

21 Old vertical correlation: too big from bottom to top V. Potential Humidity S. function Pressure

22 new vertical correlation: reduced significantly S. function Humidity Pressure V. potential

23 Red: with old background covariance ; Grey: with new background covariance (mm) TS-Verification of 6h accumulated precipitation over whole China Period: I. Time: 06Z Period: I. Time: 18Z Period: I. Time: 06Z Period: I. Time: 18Z

24 Red: with old background covariance ; Grey: with new back-ground covariance (mm) TS-Verification of 24h accumulated precipitation over whole China for August (left), July (right) 2009

25 Configurations GRAPES_Meso km L31 with m-top at 10 hPa Radiation: no-change Cumulus: Betts-Miller- Janjic Microphy.: WSM-6 (soon by 2-momment (Liu, 2010)) Cloud: no-change Land surface: NOAH PBL: no-change q-adv.: PRM(Xiao, 2002) GRAPES_Meso km L31 with m-top at 10 hPa Radiation: RRTM LW & Dudhia SW Cumulus: SAS Microphy.: WSM-6 Cloud: Xu & Randall diagnostic cloud Land surface: SLAB PBL: MRF PBL q-adv.: QMSL

26 Grey: with GRAPES_V2.5; Pink: with GRAPES_V (mm) TS-Verification of 6h accumulated precipitation over whole China Period: I. Time: 06Z Period: I. Time: 18Z Period: I. Time: 06Z Period: I. Time: 18Z

27 Grey: with GRAPES_V2.5; Pink: with GRAPES _V (mm) TS-Verification of 24h accumulated precipitation over whole China for July (left), August (right) 2009

28 4 Plans for future work

29  To Improve QC scheme and assimilation approach of local intensified observation.  To make better use of satellite data.  To perform the cloud analysis.  To improve boundary layer and cloud microphysical process in GRAPES model.  To add 1DVAR assimilation of precipitations

30 Thanks for your attention


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