1. GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005

2. Outline Background System design Preliminary results – hydrometeor retrieval and model hot start Further development Summary

3. What is GRAPES Global / Regional Assimilation and Prediction System Chinese new generation numerical weather prediction system consisting of : DA, Unified dynamic core, Model physics Background

4. Exploit the potentials of GRAPES To improve the warning of mesoscale severe weather events in advance of 3-6hr To promote the application of remote sensing and in situ data to monitoring meso scale weather systems To meet the needs of high quality weather services for Beijing Olympic Games 2008 Motivation

5. Outline Background System design Current status Further development Summary

6. System Design Data input Data Analysis GRAPES-Meso Extrapolation and forecasting Display and dissemination Validation System Structure

7. Data Input Conventional observation ( RA & Synop ) AWS Weather Radar Satellite Profiler Lightning positioning GPS Air craft

8. Data Analysis Quick look at basic elements: (Qlable) usage: Initializing NWP First Guess of SA, CA Background of system id and fcst Surface Analysis (SA) usage: Initializing NWP System id and fcst display Cloud Analysis (CA) usage: NWP hot start System id and fcst display System Design

9. Quick look at basic elements ( Qlabel ) Based on GRAPES 3DVar Observational data: Raob, Synop, Profiler, GPS, Radar(VAD), First Guess: Last analysis, NWP Spatial resolution ~ 1km Update frequency ~ 3hr currently, 1hr later System Design

10. Surface Analysis Analyzed variables: V 10m, T 2m, q, p s Observational data: Synop, AWS, Qlabel products Analysis algorithm: successive correction+variational adjustment Spatial resolution ~ 1km Update frequency: 3hr now, 1hr later System Design

11. Cloud Analysis Utilization: model hot start; convective system identification Input data: Qlabel products, synop, Radar, satellite Resolution ( model grids) Analysis procedure: System Design

12. Cloud Analysis 3-D cloud analysis （ cloud cover 、 cloud top 、 cloud ceiling 、 cloud classification, vertical velocity in cloud ） Observational data （ Synop., Aeroplane ， plofiler,radar, satellite ） Algorithm: successive correction with variational adjustments Schematic CA

13. Model Start Options Time-n Time GRAPES Forecast CA Analyses GRAPES NudgingGRAPES Forecast Eta GRAPES LBC for all runs Dynamically balanced, Cloud-consistent CA LII “Cold Start” “Warm Start” “Hot Start” (no CA analysis) (pre-forecast nudging to a series of CA analyses..) (Directly using the balanced CA analysis)

14. Data input Data Analysis GRAPES-Meso Validation Current Status Extrapolation And forecasting Display and Dissemination

15. Outline Background System design Preliminary results – hydrometeor retrieval and model hot start Further development Summary

16. Retrieval of cloud hydrometeor based on radar observation Basic assumption: 1, Cloud and rainfall are stationary in short time period and horizontal advection is negligible. 2, Vertical variation of rainfall is determined by collection ( saturated) and evaporation ( unsaturated) so that the vertical variations of q c and q v may be derived. 3, In the saturated area the increase of q r is the results of condensation.

17. Ⅰ Derive q r from radar reflect factor z Ⅱ Compute V t from q r Ⅲ Ⅲ Compute saturation specific humidity

18. Ⅳ Compute condensation function Ⅴ Compute vertical variation of rain flux Ⅵ Compute q c and q v from rain flux Ⅶ Ⅶ Compute vertical velocity in saturated area

19. Selected case: 2003/07/04 heavy rain event in Haihe river basin

20. Model set up Horizontal resolution: 0.04 lat/long Domain size: 201*201 centered at Hefei city Vertical layers: 30 with equidistance Z top =15km Model Physics : Explicit cloud: Kesller ’ s Radiation: RRTM for long wave Dudhia for short wave Surface layer:Monin – Obukhov PBL: MRF

21. Model initialization: Cold start: operational analysis Hot start: q c q r q v w c retrieved other variables – taken from operational analysis dynamic adjustment by “ pre-forecast ” model integration

22. Reflectivity retrieved q c Cross section of q c

23. Prediction of rainfall rate (mm/10min) Prediction and observation of rain fall

24. Cross section of cloud and vertical motion

25. Cloud and precipitation

26. Further development Dynamic adjustment to depress the high frequency fluctuation due to the unbalance between cloud-related parameters and large scale environment; Utilization of data of radar net work Fusion of radar data with data by satellite and other equipments

27. Summary A new nowcasting system based on Chinese new generation NWP system and dense mesoscale observational data is being developed; The radar data have the potential to retrieve the cloud parameters; Model hot start may improve the prediction if the storm is better initialized; The problem of unbalance between cloud- related parameters and large scale environment is not solved yet.

28. Thank you for your attention!