GRAPESBased Nowcasting System design and Progress Jishan Xue

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

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

Background 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 Motivation Exploit the potentials of GRAPES • To improve the warning of mesoscale severe weather events in advance of 3 -6 hr • 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

Outline • • • Background System design Current status Further development Summary

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

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

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

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

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

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

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

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

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

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

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 qc and qv may be derived. 3, In the saturated area the increase of qr is the results of condensation.

Ⅰ Derive qr from radar reflect factor z Ⅱ Compute Vt from qr Ⅲ Compute saturation specific humidity

Ⅳ Compute condensation function Ⅴ Compute vertical variation of rain flux Ⅵ　Compute qc and qv from rain flux Ⅶ　Compute vertical velocity in saturated area

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

Model set up • • • Horizontal resolution: 0. 04 lat/long Domain size: 201*201 centered at Hefei city Vertical layers: 30 with equidistance Ztop=15 km Model Physics : Explicit cloud: Kesller’s Radiation: RRTM for long wave Dudhia for short wave Surface layer: Monin –Obukhov PBL: MRF

• Model initialization: Cold start: operational analysis Hot start: qc qr qv wc retrieved other variables –taken from operational analysis dynamic adjustment by “pre-forecast” model integration

retrieved qc Reflectivity Cross section of qc

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

Cross section of cloud and vertical motion

Cloud and precipitation

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

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 cloudrelated parameters and large scale environment is not solved yet.

Thank you for your attention!