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Nowcasting-Oriented Data Assimilation in GRAPES
Briefing of GRAPES-SWIFT
Jishan Xue1 Feng Yerong2 Zitong Chen3
1, State key Laboratory of Sever Weather, CAMS, CMA
2, Guangdong Provincial Observatory , GRMC, CMA
3, Guangzhou Institute of Tropical and Oceanic meteorology, CMA
Contributors: Wan Qilin3, Chen Dehui1, Liu Yan1, Liu Hongya1
Outline
Motivation System structure GRAPES and its High Resolution assimi.-pred. cycle Severe weather integrated forecast tools Some tests and real time running Unsolved issues and plan for further development
Motivation
Combine the high resolution NWP products ( GRAPES) and n
owcasting technologies (SWIFT) to improve severe weather
forecasts within 6 hours Provide a new tool for the weather services for Olympic Ga
mes 2008 Beijing Promote the further development of meso NWP technologi
es driven by expanded application of NWP
Global-Regional Assimilation and PrEdiction System
Schematic description of GRAPES
Chinese new generation NWP systems
Variational data assimilation: 3DVar-available, 4DVar-being de
veloped;
Non-hydrostatic model with options of global and regional co
nfigurations
Used in various applications ranging from severe weather eve
nts, general circulation modeling, environmental issues,……
System composition
Data input
Cycle of Hourly Assi. Fcst. 6 hour
NWP
Id. of Conv Storm ( QPE )
TREC Wind
( Movement Esti.)
Extrapolation and
Forecasting
Display and Validation
GRAPES
Sever weather integrated forecast tool (SWIFT)
GRAPES cycle of hourly assimi.-fcst. and PredictionGRAPES cycle of hourly assimi.-fcst. and Prediction
Non-hydrostatic model with spatial res. 13km (1km finally)
3DVar for analysis Digital filter controlling noisy oscill
ation 1 hour time window Data ingested: Temp Synop Doppler Radar AWS AIRep Wind profiler Two test beds: Beijing area (for BO2008) Pearl river delta
Cycle of Hourly Assimilation and Forecast
IDFI
Test of Hydrometeors initialization
model
modelvar
qcqr.dat
ISI
adjustment
IDFI
nudg
model
postvar 3DV
Radar,
Satellite
Parameters to be nudged : qc , qr, qi, qs, qh, qg (s
kipped in this presentation)
Severe Weather Integrated Forecast Tool
Radar based approaches Automatically monitoring data inflow and quick res
ponse High res. (1:5000) GIS coupled Meso scale precipitation systems as the essential ob
jective to detect and predict Main components: Storm cell (SC) identification and QPE Estimation of movement of the cells (TREC wind) Extrapolation of SC, QPF
Main components of SWIFT Currently available:1. Identification of SC (storm cell)2. Potential of intense convection ( tornado , hai
l, thunderstorm )3. TREC wind (estimation of SC movement)4. SC Tracking and forecasting5. Quantitative precipitation estimation ( QPE )6. Quantitative precipitation forecast (QPF) To be developed:1. Potential of lightning2. Forecasts of storm-genesis and dissipation3. Urban water logging forecast4. Debris flow forecast
monitoringmonitoring
controlcontrol
Rapid Update VS Rapid ResponseRapid Update VS Rapid Response
DataSource Radar Data
Mosaic Processor
Mosaic Output
TREC QPE QPF
TREC QPE QPF output
DisplayDisplay
Triggered upon data arrival
数据流
1.触发机制2.统一调度
Nowcasting Algorithms
SC identification:
SC defined by a radar echo with reflectivity reaching specified thresholds
Correlation between storm cell and observed severe weather events.
Estimation of movement
Spatial consistency check
Special treatment for missing data area
Adjustment based on continuity hypothesis
Tracking radar echo by correlation
Redar reflectivity
Data of AWS
GRAPES output
FY2C
TREC Wind
Adjust. Based on cons. Of mass
Z-R relation
OI
QPE
Corrected TREC
Adv. extrapolation of echo
1h QPF
Corre. 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 hour TREC 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 predictors1
hour
Weight of TREC
Weight of NWP
韶关
梅州
阳江
广州
广 东 省 气 象 局Guangdong Meteorological Bureau
汕头
深圳
Pearl River Delta Pearl River Delta TrialsTrials
RadarRadar
广州
湛江
韶关
汕头
Distribution of auto weather stations(>=700)
Auto weather stations
200608130710 case
200608130710 每隔 10 分钟外推
200608130710 的 2 小时外推
200608130710 的 3 小时外推
Quantitative Precipitation ForecastQuantitative Precipitation Forecast
QPF200608130710 预报
Radar Mosaic
--STS Bilis
1-h QPF
1 小时后的回波
2-h QPF
2 小时后的回波
3-h QPF
3 小时后的回波
Further development
Radar and satellite data ingested in re
al time system Data quality control Combine well NWP products with no
wcasting technologies
The end
Thank you for attention