Nowcasting-Oriented Data Assimilation in GRAPES Briefing of GRAPES-SWIFT

Jishan Xue1 Feng Yerong2 Zitong Chen31, State key Laboratory of Sever Weather, CAMS, CMA2, Guangdong Provincial Observatory , GRMC, CMA 3, Guangzhou Institute of Tropical and Oceanic meteorology, CMAContributors: Wan Qilin3, Chen Dehui1, Liu Yan1, Liu Hongya1

Outline

MotivationSystem structureGRAPES and its High Resolution assimi.-pred. cycleSevere weather integrated forecast toolsSome tests and real time runningUnsolved issues and plan for further development

MotivationCombine the high resolution NWP products ( GRAPES) and nowcasting technologies (SWIFT) to improve severe weather forecasts within 6 hoursProvide a new tool for the weather services for Olympic Games 2008 BeijingPromote the further development of meso NWP technologies driven by expanded application of NWP

Global-Regional Assimilation and PrEdiction SystemSchematic description of GRAPESChinese new generation NWP systemsVariational data assimilation: 3DVar-available, 4DVar-being developed;Non-hydrostatic model with options of global and regional configurationsUsed in various applications ranging from severe weather events, general circulation modeling, environmental issues,

System compositionData inputCycle of Hourly Assi. Fcst.6 hour NWPId. of Conv Storm ( QPE )TREC Wind ( Movement Esti.)Extrapolation and ForecastingDisplay and ValidationGRAPESSever weather integrated forecast tool (SWIFT)

GRAPES cycle of hourly assimi.-fcst. and PredictionNon-hydrostatic model with spatial res. 13km (1km finally)3DVar for analysisDigital filter controlling noisy oscillation1 hour time windowData ingested: Temp Synop Doppler Radar AWS AIRep Wind profiler Two test beds: Beijing area (for BO2008) Pearl river delta

Cycle of Hourly Assimilation and ForecastIDFI

Test of Hydrometeors initializationmodelmodelvarqcqr.datISIadjustmentIDFInudgmodelpostvar3DVRadar,SatelliteParameters to be nudged : qc , qr, qi, qs, qh, qg (skipped in this presentation)

Severe Weather Integrated Forecast ToolRadar based approachesAutomatically monitoring data inflow and quick response High res. (1:5000) GIS coupledMeso scale precipitation systems as the essential objective to detect and predictMain components: Storm cell (SC) identification and QPE Estimation of movement of the cells (TREC wind) Extrapolation of SC, QPF

Main components of SWIFTCurrently available:Identification of SC (storm cell)Potential of intense convectiontornado , hail, thunderstormTREC wind (estimation of SC movement)SC Tracking and forecastingQuantitative precipitation estimationQPEQuantitative precipitation forecast (QPF)To be developed:Potential of lightningForecasts of storm-genesis and dissipationUrban water logging forecastDebris flow forecast

Rapid Update VS Rapid ResponseDataSourceRadar DataMosaic ProcessorMosaic OutputTRECQPEQPFTREC QPE QPF outputTriggered upon data arrival

Nowcasting AlgorithmsSC identification:

SC defined by a radar echo with reflectivity reaching specified thresholdsCorrelation between storm cell and observed severe weather events.

Estimation of movement

Spatial consistency checkSpecial treatment for missing data areaAdjustment based on continuity hypothesisTracking radar echo by correlation

Redar reflectivityData of AWSGRAPES outputFY2CTREC WindAdjust. Based on cons. Of massZ-R relationOIQPECorrected TRECAdv. extrapolation of echo1h QPFCorre. Of TREC and model fcst.2 and 3h QPFGenes. Disp. Adjust.Extrapolation and forecasting algorithms

Extrapolation and forecasting algorithms

TREC winds are used for extrapolation within 1 hourTREC winds are also used to find the model levels on which the NWP wind fits the movement of CS ( 500hpa or higher in most cases ) Forecast of CS with weighting mean of NWP and TREC Statistical approach with NWP products as predictors

1 hourWeight of TRECWeight of NWP

Guangdong Meteorological BureauPearl River Delta TrialsRadar

Distribution of auto weather stations(>=700)Auto weather stations

200608130710 case2006081307101020060813071022006081307103

Quantitative Precipitation ForecastQPF200608130710

Radar Mosaic--STS Bilis

1-h QPF

1

2-h QPF

2

3-h QPF

3

Further development

Radar and satellite data ingested in real time systemData quality controlCombine well NWP products with nowcasting technologies

The endThank you for attention