Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data

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Simulation of radar reflectivities in the UK Met Office model: comparison with

CloudSat Data

Alejandro Bodas-Salcedo, M.E. Brooks and M. Webb

GERB Science Team Meeting, Abingdon, 3 May 2007


Outline

• Introduction

• The A-Train and CloudSat

• Our approach

• Description of the simulator: subcomponents

• Global forecast model: comparison with observations

• Conclusions and future work


Relevance of clouds in the ARB

The vertical distribution and overlap of cloud layers determine the magnitude and vertical profile of radiative heating, which then exerts an influence in the large-scale circulation.

ATM Radiation Budget ATM CRFs


Impact on ocean heat transport

By modulating the distribution of heating between the atmosphere and the surface, clouds influence the circulation of the oceans.

(Glecker, GRL, 2005)


Feedback loop

These large-scale impacts are connected to cloud physical properties through a feedback loop.

(Stephens et al., BAMS, 2002)

This loop involves a wide range of spatio-temporal scales => the Unified Model appears to be an adequate framework to link interactions at different scales


A new perspective on clouds and the SARB

(http://cloudsat.atmos.colostate.edu/mission/formation_flying)


Synergy between active and passive sensing

(ESA SP-1257(1), 2001)


CloudSat

- Launch April 28th 2006. Operations began on June 2nd.

- Nadir pointing, 94GHz radar.

- 500m vertical resolution, oversampled at 240m.

- 1.4km x 2.5 km horizontal resolution

- Sensitivity ~-28 dBZ

- Dynamic range: 80 dBZ

- Calibration: 2 dBZ


Our approach

To facilitate the exploitation of CloudSat and CALIPSO data in numerical models, we are developing a system that allows to simulate the signal that CloudSat/CALIPSO would see in a model-generated world.

CFMIP CloudSat/CALIPSO Simulator (C3S):

LMD/IPSL, LLNL, CSU, UW, Met Office

Flexible tool to simulate active instruments in models (climate,

forecast, cloud-resolving) This 'model-to-satellite' approach has proven successful in recent

years, with the development of the ISCCP simulator1 and the simulation of satellite channel radiances2.

1: (Klein and Jakob, 1999; Webb et al., 2001)2: (Ringer et al., 2003)


Subcomponents

C3S MAIN SCOPS SG

PRECIPC3S SUB-GRID

CLOUDSAT CALIPSO SUMMARY STATISTICS


Case study I: analysis chart

• 2006/07/07 Transect trough a mature extra-tropical system

• Analysis chart valid at 18 UTC

• CloudSat overpass from 15:14:38 to 15:21:01

B

A

.


Case study I: MSG composite

RGB 321 (1.6 , 0.8 , 0.6 ) 1330 UTC: turquoise clouds contain ice crystals, whilst white clouds are water clouds (inc. fog). Vegetation creates a green signal and sandy areas are pink. Snow covered ground is turquoise.

B

A


Case study I: Ze

AB

1/120

1/55


Case study II: analysis chart

• 2006/12/09 Transect trough a mature extra-tropical system



A

B


Case study II: MSG composite

A

B


Case study II: Ze

AB


Case study III: analysis chart

• 2006/12/14 Transect trough a quasi-stationary front



A

B


Case study III: MSG composite

A

B


Case study III: Ze

AB


Cloud/Precipitation occurrence


North Atlantic statistics


Conclusions and future work

Tool to simulate radar reflectivities in the UM

New perspective on clouds and precipitation

Comparisons with global forecast model: The overall vertical structure of ML systems is well represented

LS precipitation is also generally well captured in the occluded sector

Cloud top height matches very well the obs.

Indications of too much cirrus/cirrostratus

Indications of too much drizzle production

Need to develop more quantitative, statistically-based approaches

Developing a community simulator: CFMIP CloudSat/CALIPSO Simulator (C3S) (LMD/IPSL, LLNL, CSU)

Flexible tool to simulate active instruments in models (climate, forecast,

cloud-resolving)


Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data

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