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Page 1© Crown copyright 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
Page 2© Crown copyright Outline Introduction The A-Train and CloudSat Our approach Description of the simulator: subcomponents Global forecast model: comparison with observations Conclusions and future work
Page 3© Crown copyright 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 BudgetATM CRFs
Page 4© Crown copyright 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)
Page 5© Crown copyright 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
Page 6© Crown copyright A new perspective on clouds and the SARB (http://cloudsat.atmos.colostate.edu/mission/formation_flying)
Page 7© Crown copyright Synergy between active and passive sensing (ESA SP-1257(1), 2001)
Page 8© Crown copyright CloudSat - Launch April 28 th 2006. Operations began on June 2 nd. - 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
Page 9© Crown copyright 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 simulator 1 and the simulation of satellite channel radiances 2. 1: (Klein and Jakob, 1999; Webb et al., 2001) 2: (Ringer et al., 2003)
Page 10© Crown copyright Subcomponents C3S MAIN SCOPS SG PRECIP C3S SUB-GRID CLOUDSATCALIPSOSUMMARY STATISTICS
Page 11© Crown copyright 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.
Page 12© Crown copyright 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
Page 13© Crown copyright Case study I: Z e AB 1/120 1/55
Page 14© Crown copyright Case study II: analysis chart 2006/12/09 Transect trough a mature extra-tropical system Analysis chart valid at 12 UTC CloudSat overpass from 14:57:10 to 15:03:53 A B
Page 15© Crown copyright Case study II: MSG composite A B
Page 16© Crown copyright Case study II: Z e AB
Page 17© Crown copyright Case study III: analysis chart 2006/12/14 Transect trough a quasi-stationary front Analysis chart valid at 18 UTC CloudSat overpass from 15:12:36 to 15:15:53 A B
Page 18© Crown copyright Case study III: MSG composite A B
Page 19© Crown copyright Case study III: Z e AB
Page 20© Crown copyright Cloud/Precipitation occurrence
Page 21© Crown copyright North Atlantic statistics
Page 22© Crown copyright 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)
Page 23© Crown copyright
The University of Reading Helen Dacre UM user 2009 Forecasting the transport of pollution using a NWP model ETEX Surface Measurement Sites.
© University of Reading Radiative effects of persistent aircraft contrails: a case study Richard Allan Environmental Systems.
Yuying Zhang, Jim Boyle, and Steve Klein Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Jay Mace University.
The University of Reading Helen Dacre AMS 2010 Air Quality Forecasting using a Numerical Weather Prediction Model ETEX Surface Measurement Sites.
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
1 Copyright © 2013 Elsevier Inc. All rights reserved. Chapter 38.
© Crown copyright 2006Page 1 CFMIP II Plans Mark Webb (Met Office Hadley Centre) Sandrine Bony (IPSL) Rob Colman (BMRC) with help from many others… CFMIP/ENSEMBLES.
© University of Reading Monitoring and understanding current changes in the global energy & water cycles Richard Allan.
Proposed new uses for the Ceilometer Network
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 2.1 Chapter 2.
DO WE KNOW HOW STORMS WILL CHANGE IN A WARMING CLIMATE? William B. Rossow NOAA CREST at The City College of New York June 2013.
Robin Hogan, Julien Delanoë, Nicky Chalmers, Thorwald Stein, Anthony Illingworth University of Reading Evaluating and improving the representation of clouds.
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1 03/0045a © Crown copyright Evaluating water vapour in HadAM3 with 20 years of satellite data Richard P. Allan Mark A. Ringer Met Office, Hadley Centre.
Comparisons between GERB and the Met Office NWP model Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading Sean Milton,
Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.
Addition 1’s to 20.
© University of Reading Richard Allan Department of Meteorology, University of Reading Thanks to: Jim Haywood and Malcolm.
Satellite Evaluation work at Reading Julien Delanoë/Thorwald Stein/Robin Hogan Collaborations: Richard Forbes (ECMWF)/ Alejandro Bodas-Salcedo (MetOffice)
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