application of cloud analysis in grapes_rafs lijuan zhu [1], dehui chen [1], zechun li [1], liping...

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!