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This study focuses on estimating heating profiles associated with deep convection using satellite observations. The study explores different algorithms and their validation with total heating derived from soundings.
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Heating Profiles Associated with Deep Convection Estimated from TRMM and Future Satellites Robert HouzeUniversity of Washington Wei-Kuo TaoGoddard Space Flight Center 19th Scientific Steering Group Session of the Global Energy and Water Cycle Experiment, January 23, 2007, Honolulu, HI
Latent heating only Total heating Relationship of Cloud Water Budgets to Heating Profile Calculations Austin and Houze 1973 Houze et al. 1980 Houze 1982, 1989 Takayabu 2002 Shige et al. 2006
Houze 1982 PREMISE Can also relate all components of heating profiles to water budget of convective clouds Total Heating = Latent + Eddy + Radiative
Idealized life cycle of tropical MCS Houze 1982
Strat. LH Rad. Conv. LH Conv. Eddy Strat. Eddy Contributions to Total Heating by Convective Cloud System Houze 1982
TOTAL HEATING These combine to give “top-heavy” heating profile Includes latent, eddy, and radiative “top heavy” Houze 1982
Think of the following schematic as a composite of the water budget of an MCS over its whole life cycle Ac As After Houze et al. (1980)
As As Ac Stratiform water budget equation Rain not simply related to condensation
As As Ac Convective region water budget equation
As As Ac For each height category i Sum over all cloud height categories i with the constraint “Spectral Method”…Shige et al. (each height cat.)
TRMM Latent Heating Workshops 1st Workshop: NASA Goddard Space Flight Center Greenbelt, May 10-11 2001 (W. Olson, R. Johnson and W.-K. Tao) 18 participants 2nd Workshop: NCAR Boulder CO, October 10-11 2001 (M. Moncrieff, A. Hou and W.-K. Tao) ~ 30 participants after 9.11 3rd Workshop: Nara Japan, September 10-11 2004 (E. Smith, S. Shige and W.-K. Tao) 20 participants 4th Workshop: Seattle Washington, May 17-19 2006(R. Houze, E. Smith and W.-K. Tao) 26 participants Tao, W.-K., R. Houze, Jr., and E. Smith, 2007: Summary of the 4th TRMM Latent Heating Workshop, Bull. Amer. Meteor. Soc., (submitted)
Can’t measure directly Vertically integrated Convective Stratiform Heat Budget At a level z Total heating Radiation + Eddy fluxes Latent Heating LH(z) = + NOTE: Pc&Psprovide a constraint when determining LH(z)
TRMM Observations Precipitation Radar (PR)—Pc, Ps,Echo Height TRMM Microwave Imager (TMI)—multifrequency microwave radiances LH algorithms designed to be physically consistent with some combination of these observations
Five Different Latent Heating Algorithms Convective - Stratiform Heating -CSH(Tao/Lang et al. 1993, 2000) PR* or TMI* (Cloud model generated look-up table) Spectral Latent Heating -SLH(Shige/Takayabu et al. 2003, 2006) PR (Cloud model generated look-up table) Goddard Profiling -GPROF(Olson et al. 1999, 2006) TMI or PR/TMI Combined (Cloud model generated look-up table) Hydrometeor Heating -HH(Yang/Smith et al. 1999) PR or PR/TMI Combined (Hydrometeor mass conservation) Precipitation Radar Heating -PRH(Satoh/Noda 2001) PR (Hydrometeor mass conservation)
How do we validate latent heating algorithms? Check consistency of derived latent heating profiles with total heating determined from soundings.
Soundings Total Heating, Q1 Yanai et al. 1973, and others
SCSMEX KWAJEX LBA ARM SGP ARM Testbeds
At testbed sites: • Determined Q1 profiles from soundings • Checked consistency with CRM Q1 with sounding data • Checked consistency of algorithm Q1 or LH with sounding data
Mean profiles at testbed sites KWAJEX SCSMEX LBA Zhang, Johnson, Schumacher
OBS CSH SLH Workshop Results SCSMEX Result CSH & SLH similar for all four testbeds CRM Simulated Retrieved & Observed LH Q1 Eddy QR QR
Recent Applications • Improve NWP (Rajendran et al. 2004) • Validate Climate Model (Chen,Del Genio, Chen, 2006) • Assimilate Q1 heating profiles into Global Model(Hou and Zhang 2006)
Improved 72 h forecast GPCP Rain (satellite observations): 8 February 1998 (12 UTC ) 45N 30N 15N EQ 15S 30S 45S 60E 120E With LH algorithm GM IDL 120W 6OW GM 72-Hour Forecast with ECPS ( FSU - GSM T126L14) 45N 30N 15N EQ 15S 30S 45S GM 60E 120E IDL 120W 6OW GM Control 72-Hour Forecast Control Run ( FSU - GSM T126L14) 45N 30N 15N EQ 15S 30S 45S GM 60E 120E IDL 120W 6OW GM 0 2 5 10 15 20 35 (mm day ) -1 Rajendran et al. (2004, J. Meteor. Soc. Japan)
Climate Model ValidationMean & ENSO 1998 DJF anomalies at altitude of peak latent heating for the CSH product & GCM Mean Anomalies CSH GISS Chen, Del Genio, Chen (2006, J. Climate)
Current Products Available in TSDIS (Experimental) CSH (3A25 - PR) - 0.5 x 0.5 degree, Monthly GPROF (2A12 - TMI) - Orbital, Instantaneous HH (2B31 - Combined) - Orbital, Instantaneous Latent Heating Products (Beta - V1 in 2007) 0.5 x 0.5 degree from daily - weekly - seasonal Convective and Stratiform region Regimes (e.g., African Monsoon) 19 vertical layers: 0.5, 1, 2, ……….18 km LH, Q1-QR and Q1
5th TRMM/GPM Latent Heating Workshop (April or May 2007 - DC Area) Soliciting the requirements from large-scale modeling and dynamic community
Latent heating profiles • Can be determined if the cloud water budget parameters are known • But radiative heating profiles • Also related through the cloud water budget
As Recall IWC ZAC Ac IWC As XAC Dimensions and hydrometeor content of anvils are determined by the cloud dynamics
400 hPa K/day 200 hPa streamfunction response to CRF assumed proportional to TRMM rain by Schumacher et al. (2004) Using empirically derived values of the water budget parameters b and es, the stratiform regions’ anvil mass is proportional to the stratiform rain As =(bRs)/es
Summary • Algorithms for determining LH(z) are being developed by TRMM/GPM community, experimental products in TSDIS • Profiles of latent heating, eddy, and radiative heating profiles not independent but rather closely interrelated through cloud water budgets. • Cloud water budget is a powerful way to determine the consistency of latent, radiative, and eddy heating. • If latent and radiative heating determined independently, physical inconsistencies could arise. Cloud water budget provides a means for establishing consistency. • Water budget parameters need to be determined--via fieldstudies supported by high resolution modeling