application of cloud analysis in grapes_rafs lijuan zhu [1], dehui chen [1], zechun li [1], liping...
TRANSCRIPT
Application of Cloud Analysis in GRAPES_RAFS
Lijuan ZHU[1], Dehui CHEN[1], Zechun LI[1], Liping LIU[2], Zhifang XU[1], Ruixia LIU[3]
[1]National Meteorological Centre (NMC)[2]Chinese Academy of Meteorological Sciences (CAMS)
[3]National Satellite Meteorological Center (NSMC)
China Meteorological Administration (CMA) , Beijing, 100081
(25 October 2011, for workshop-NWP nowcasting in Boulder-USA)
Outline
• 1 Motivations
• 2 Cloud Analysis in GRAPES
• 3 Data used by C.A. of GRAPES
• 4 Preliminary results
• 5 Summary
1 Motivations
1 Motivations
• There are a lot of data sets which are yet difficult to be directly assimilated, but could be fused for the model initialization for some reasons of technique approaches or computation effectiveness.
• These data sets are available, such as the satellite images or retrieved cloud products, surface visual + instrumental observations of cloud, visibility, lightning and so on, specially the radar reflectivity.
CMA’s Radar Network: CINRAD
The observations of ~158 radars, which have been deployed in whole China (most along with East coast line) , are available to be used.
1 Motivations
• In other hand, a “cold-start” GRAPES is poor to provide the initial information of cloud for the microphysical scheme, and the associated moisture field and vertical motions.
• It is naturally motivated for us to fuse the available data sets for generating a more reasonable initial field with a detailed 3D cloud specification to produce the meso-scale cloud analysis products, and to improve short-time H.I.W. forecasts.
2 Cloud Analysis in GRAPES
Cloud Analysis in GRAPES_RAFS ( 1)Cloud analysis scheme from ADAS of ARPS Model developed by
CAPS,OU ( Xue et al., MAP, 2003 ; Hu, Xue et al., MWR, 2006 ) based on LAPS (Albers et al., 1996)
Fusion of all cloud, precipitation observations
Synop Satellite IR ,VIS
Radar Ref
Background moisture
Cloud field
Cloud amount
Cloud base
Cloud thick
Cloud type
…
…
Hydrom.
Background observations
3D cloud field , cloud amout
Cloud type
Cloud water, cloud ice
Qc on cloud type (Cumulus)
Precipitation type
Precipitation (qr, qs , qh, …)
Be nudged
( )A
f tt
( ) ( )o
Af t A A
t
dynamical relaxation factor
And then the cloud analyzed information can be included by nudging method for the model initialization
Cloud Analysis in GRAPES_RAFS ( 2)
Cloud analysis can be called every 1 hour or every 3 hours.
Changes in the original C.A.
• (1) Correction in the code about Synop application to modify the background cloud base specification (barnes interpolation weights ):
original modified
(2) The introduction of saturation on ice-surface scheme
Org: only water surface saturation Modified by adding ice surface saturation
with ice surface saturationOrg: water surface saturation only
TRMM
(3) Permitting cloud water, cloud ice as well
NCEP’s RUC: more suitable to stratus-cumulus (smaller upward motion in cloud), which dominate in most cases in China;
Original scheme: more focused on deep convective cumulus (stronger upward motion in cloud)(4) Quality control of radar reflectivity
Ground Clutter, Clear air echo, etc.
TRMM
Cloud Water
original modified
Cloud Ice
TRMM
original modified
3 Data used by C.A. of GRAPES
Data used
Background: 3D grid fields of RH, Temperature, Pressure, surface temperature from 3DVAR analysis
SYNOP: Cloud base ,Cloud amount
Radar 3D Mosaic Reflectivity
Composite reflectivity over whole China or domain specified;
Satellite
FY-2 IR TBB FY-2 VIS CTA
SAT advantage: to specify the cloud top
FY-2 Geostationary satellite, FY2D/2E , every 30min , but just hourly data used by RAFS
Data use ( cont.)
4 Preliminary results
Specification of the experiment
• Case : a Tropical Storm landed on Guangdong coast line
• Model: 15km GRAPES using T213 for 3DVAR FG and BC
• Background analysis: 3DVAR analysis downscaling to cloud analysis mesh of 5km as background of C.A.
• Initial Time : Aug. 6, 2009 at 00UTC
b. cloud modified c. base
used IR TBB used radar reflect. used visible image
Impact on cloud cover analysis
IR TBB Obs.
Corrected the cloud base
Before After
Cloud top compared to MODIS
MODIS Cloud analysis
Cloud Type
Radar Ref 1 St:Stratus 2 Sc:Stratocumulus3 Cu:Cumulus 4 Ns:Nimbostratus5 Ac:Altocumulus 6 AS:Altostratus7 Cs:Cirrostratus 8 Ci:Cirrus9 Cc:Cirrocumulus 10 Cb :Cumulonimbus
Compared to cloudsat
cloudsatCloud analysis
Height(km)
Analyzed hydrometeors
Radar reflectivity(Ob) Cloud water Cloud ice
Qr Qs
Impact on forecast
3h forecastRadar obs
With cloud analysis Without cloud analysis
With cloud analysis 6h forecast
12h forecast
Radar obs
Radar obs
Without cloud analysis
Without cloud analysis
With cloud analysis
All china <10mm <25mm <50mm <100mm
Warm start 0.395 0.203 0.068 0.017
Warm start+cloud analysis
0.398 0.206 0.066 0.033
TS-verification of 6H Precipitation forecasts (for July 5~30, 2009)
5 Summary
Conclusion and discussion
• The cloud analysis scheme ADAS has been adapted to GRAPES_RAFS, and with some modifications.
• The preliminary experiments have showed the positive impacts. It still needs much further assessments.
• The quality control of the radar reflectivity is still a big challenge for real time application, not only due to the reflectivity quality itself, but also due to effectively receive the data in time.
Conclusion and discussion (cont.)
• The cloud analysis is a complicated issue. It is particularly necessary to adapt it according the stratus-cumulus which dominate in most cases in China.
• A lot of works are ongoing for real-time implementation of RAFS with C.A. at NMC/CMA.
Thanks!