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
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
Background
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
Outline
• Background• System design• Current status• Further development• Summary
System Design
Data input Data Analysis GRAPES-Meso
Extrapolationand forecasting
Display and dissemination
Validation
System Structure
Data Input
• Conventional observation ( RA & Synop ) • AWS• Weather Radar• Satellite• Profiler• Lightning positioning• GPS• Air craft
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
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
Surface Analysis
• Analyzed variables: V10m, T2m, q, ps
• Observational data: Synop, AWS, Qlabel products
• Analysis algorithm: successive correction+variational adjustment
• Spatial resolution ~ 1km
• Update frequency: 3hr now, 1hr later
System Design
Cloud Analysis
• Utilization: model hot start; convective system identification
• Input data: Qlabel products, synop, Radar, satellite
• Resolution ( model grids)
• Analysis procedure:
System Design
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 OptionsTime-n Time
GRAPES Forecast
CA Analyses
GRAPES Nudging GRAPES Forecast
GRAPES 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)
Data input Data Analysis GRAPES-Meso
Validation
Current Status
Extrapolation
And forecasting
Display and
Dissemination
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
43.1 17.5log( )rZ q ( 43.1) /17.510 /Z
rq
Ⅱ Compute Vt from qr
0.1255.40t rv a q
0.40( )a p p
ⅢⅢ Compute saturation specific humidity
17.27( 273.16)38000035.86exp T
vs p Tq
Ⅳ Compute condensation function
2 2( /1000.0)( )v p vvs
p v vs v
L R c R TgqR c R T q L
F
Ⅴ Ⅴ Compute vertical variation of rain fluxCompute vertical variation of rain flux
( )1 r tq vr zI
Ⅵ Compute qc and qv from rain flux
7 /8c rPRA q q
0.875/( )
v vs
c r r
q q
q I q
0.65( )( )vs v rPRE q q q
0.65
0.0
/( ( ) )
c
v vs r r
q
q q I q
ⅦⅦ Compute vertical velocity in saturated area
/rw I F
Selected case:
2003/07/04 2003/07/04 heavy rain heavy rain
event in Haihe river basinevent 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=15km
• 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
Reflectivity
retrieved qc
Cross section of qc
Prediction of rainfall rate (mm/10min)
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 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
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.
Thank you for Thank you for your attention!your attention!
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