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