A Cloud Resolving Modeling Study of Tropical Convective and Stratiform Clouds C.-H. Sui and Xiaofan Li.

Slides:



Advertisements
Similar presentations
R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike.
Advertisements

Moisture Transport in Baroclinic Waves Ian Boutle a, Stephen Belcher a, Bob Plant a Bob Beare b, Andy Brown c 24 April 2014.
Simulating cloud-microphysical processes in CRCM5 Ping Du, Éric Girard, Jean-Pierre Blanchet.
Robert Houze University of Washington (with contributions from B. Smull) Winter MONEX Summer MONEX Presented at: International Conference on MONEX and.
Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.
The Problem of Parameterization in Numerical Models METEO 6030 Xuanli Li University of Utah Department of Meteorology Spring 2005.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
To perform statistical analyses of observations from dropsondes, microphysical imaging probes, and coordinated NOAA P-3 and NASA ER-2 Doppler radars To.
Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields Yoav Yair 1, Barry Lynn 1, Colin Price 2,
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
Cirrus Production by a Mesoscale Convective System Sampled During TWP-ICE: Analysis via Water Budget Equations Jasmine Cetrone and Robert Houze University.
Understanding Atmospheric Heating associated with Deep Convection Robert Houze University of Washington ARM Radiative Heating Profile Workshop, 8-9 January.
Hydrometeors Injected into the Large-scale Environment by Tropical Cloud Systems Robert A. Houze & Courtney Schumacher Co-PIs ARM Science Team Meeting,
Understanding Atmospheric Heating in the GPM Era Robert Houze University of Washington 6 th GPM International Planning Workshop, 6-8 November 2006, Annapolis,
1 Radar Displays PPI - Plan position Indicator Maps the received signals on polar coordinates in plan view. The antenna scans 360° at fixed elevation angle.
Relationship of Cloud Water Budgets to Heating Profile Calculations Austin and Houze 1973 Houze et al Houze 1982 Relationship of Cloud Water Budgets.
GFS Deep and Shallow Cumulus Convection Schemes
The tropical convective cloud population Peking University Seminar, Beijing, 4 July 2011 Robert Houze University of Washington.
Mesoscale Convective System Heating and Momentum Feedbacks R. Houze NCAR 10 July 2006.
AGU Annual Meeting, San Francisco, 11 December 2013.
Climate model grid meshes are too coarse to explicitly simulate storm system winds and therefore must rely on simplified models referred to as parameterizations.
Impact of Graupel Parameterization Schemes on Idealized Bow Echo Simulations Rebecca D. Adams-Selin Adams-Selin, R. D., S. C. van den Heever, and R. D.
Understanding the effects of aerosols on deep convective clouds Eric Wilcox, Desert Research Institute, Reno NV Tianle.
Using TWP-ICE Observations and CRM Simulations to Retrieve Cloud Microphysics Processes Xiping Zeng 1,2, Wei-Kuo Tao 2, Shaocheng Xie 3, Minghua Zhang.
Water Budget and Precipitation Efficiency of Typhoons Morakot (2009) Ming-Jen Yang 1, Hsiao-Ling Huang 1, and Chung-Hsiung Sui 2 1 National Central University,
A Further Look at Q 1 and Q 2 from TOGA COARE* Richard H. Johnson Paul E. Ciesielski Colorado State University Thomas M. Rickenbach East Carolina University.
Case Study Example 29 August 2008 From the Cloud Radar Perspective 1)Low-level mixed- phase stratocumulus (ice falling from liquid cloud layer) 2)Brief.
The Role of Polarimetric Radar for Validating Cloud Models Robert Cifelli 1, Timothy Lang 1, Stephen Nesbitt 1, S.A. Rutledge 1 S. Lang 2, and W.K. Tao.
Momentum Budget of a Squall Line with Trailing Stratiform Precipitation: Calculation with a High-Resolution Numerical Model. Yang, M.-J., and R. A. Houze.
Water Budget and Precipitation Efficiency of Typhoon Morakot (2009) Hsiao-Ling Huang 1, Ming-Jen Yang 1, and Chung-Hsiung Sui 2 1 National Central University,
WMO workshop, Hamburg, July, 2004 Some aspects of the STERAO case study simulated by Méso-NH by Jean-Pierre PINTY, Céline MARI Christelle BARTHE and Jean-Pierre.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
Yanjun Jiao and Colin Jones University of Quebec at Montreal September 20, 2006 The Performance of the Canadian Regional Climate Model in the Pacific Ocean.
Characterization of tropical convective systems Henri Laurent IRD/LTHE Cooperation with Brazil CTA (Centro Técnico Aeroespacial) CPTEC (Centro de Previsião.
Comparison of Evaporation and Cold Pool Development between Single- Moment (SM) and Multi-moment (MM) Bulk Microphysics Schemes In Idealized Simulations.
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998).
Schematic diagram of the convective system life cycle size evolution Lifetime=  (A e Initiation ) Mass flux or condensation process in the initiation.
Dual-Aircraft Investigation of the inner Core of Hurricane Norbert. Part Ⅲ : Water Budget Gamache, J. F., R. A. Houze, Jr., and F. D. Marks, Jr., 1993:
Water Budget and Precipitation Efficiency of Typhoons Ming-Jen Yang 楊明仁 National Central University.
On the Definition of Precipitation Efficiency Sui, C.-H., X. Li, and M.-J. Yang, 2007: On the definition of precipitation efficiency. J. Atmos. Sci., 64,
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget SCOTT A. BRAUN J. Atmos. Sci., 63,
MJO Insights from the S-PolKa radar in DYNAMO Robert A. Houze, Jr. H. C. Barnes, S. W. Powell, A. K. Rowe, M. Zuluaga University of Washington Symposium.
Yuqing Wang and Chunxi Zhang International Pacific Research Center University of Hawaii at Manoa, Honolulu, Hawaii.
Precipitation efficiency and its dependence on physical factors A look into the cloud response to climate warming Chung-Hsiung Sui 1 Institute of Hydrological.
APR CRM simulations of the development of convection – some sensitivities Jon Petch Richard Forbes Met Office Andy Brown ECMWF October 29 th 2003.
Analysis of High-Resolution WRF Simulations During A Severe Weather Event Jason A. Otkin* Cooperative Institute for Meteorological Satellite Studies, University.
Remote sensing and modeling of cloud contents and precipitation efficiency Chung-Hsiung Sui Institute of Hydrological Sciences National Central University.
Chien Wang Massachusetts Institute of Technology A Close Look at the Aerosol-Cloud Interaction in Tropical Deep Convection.
Cheng-Zhong Zhang and Hiroshi Uyeda Hydroshperic Atmospheric Research Center, Nagoya University 1 November 2006 in Boulder, Colorado Possible Mechanism.
Background – Building their Case “continental” – polluted, aerosol laden “maritime” – clean, pristine Polluted concentrations are 1-2 orders of magnitude.
T.Nasuno, H.Tomita, M.Satoh, S. Iga, and H.Miura Frontier Research Center for Global Change WMO International Cloud Modeling Workshop July 12-16, 2004,
A modeling study of cloud microphysics: Part I: Effects of Hydrometeor Convergence on Precipitation Efficiency. C.-H. Sui and Xiaofan Li.
Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes.
Part II: Implementation of a New Snow Parameterization EXPLICIT FORECASTS OF WINTER PRECIPITATION USING AN IMPROVED BULK MICROPHYSICS SCHEME Thompson G.,
WRF model runs of 2 and 3 August
GEORGE H. BRYAN AND HUGH MORRISON
Simulation of the Arctic Mixed-Phase Clouds
Water Budget of Typhoon Nari(2001)
Coupled atmosphere-ocean simulation on hurricane forecast
Three-category ice scheme
Sensitivity of WRF microphysics to aerosol concentration
Ulrike Romatschke, Robert Houze, Socorro Medina
Conrick, R., C. F. Mass, and Q. Zhong, 2018
Tong Zhu and Da-Lin Zhang 2006:J. Atmos. Sci.,63,
Scott A. Braun, 2002: Mon. Wea. Rev.,130,
High-Resolution Simulation of Hurricane Bonnie (1998)
Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget. J. Atmos. Sci., 63, Gamache, J. F., R. A. Houze.
Xu, H., and X. Li, 2017 J. Geophys. Res. Atmos., 122, 6004–6024
Li, Z., P. Zuidema, P. Zhu, and H. Morrison, 2015
Presentation transcript:

A Cloud Resolving Modeling Study of Tropical Convective and Stratiform Clouds C.-H. Sui and Xiaofan Li

Introduction Partition of clouds and precipitation into convective and stratiform types is an important part of the study towards understanding of clouds and associated microphysics and thermodynamics and their impacts on tropical hydrological and energy cycles. Churchill and Houze (1984) used the radar observational data to study convective-stratiform rain participation. Alder and Negri (1988) developed a convective-stratiform technique for analysis of satellite infrared data locates all local minima in the brightness temperature field. A version of the separation method used in the Goddard Cumulus Ensemble (GCE) model is adopted in Sui et al. (1994).

Model and experiment The 2-D version of the model used by Sui et al. (1994, 1998) and further modified by Li et al. (1999) is used in this study. The cloud microphysics parameterization schemes are referred to Li et al. (1999, 2002b) and Sui and Li (2002). Based on the 6-hourly TOGA COARE observations within the Intensive Flux Array (IFA) region. The model is integrated from 1992/12/19/0400 LST to 1993/01/09/0400 LST (21 days total). The horizontal domain is 768 km, the grid mesh of 1.5 km and time step of 12 seconds.

Mean cloud budgets in stratiform and convective regions based on an existing separation method The cloud budget equation: The vertically integrated cloud budget equation: q x = (q c, q r, q i, q s, q g ), is a mean density, which is function of height; w Tx = (w Tr, w Ts, w Tg ) are terminal velocity for q r, q s, q g ; S qx are the source and sink of various hydrometeor species; D’s are turbulent dissipation terms; [CONV qx ] is hydrometeor convergence; is surface rain rate.

Cloud Ratio: CR = ( [q i ] + [q s ] + [q g ] ) / ( [q c ] + [q r ] ) Ice Water Phase: IWP = [q i ] + [q s ] + [q g ] Liquid Water Phase: LWP = [q c ] + [q r ] Rate Ratio: RR = ( [P DEP ] + [P SDEP ] + [P GDEP ] ) / [P CND ] Cloud Microphysics Precipitation Efficiency: CMPE = P s / ( [P DEP ] + [P SDEP ] + [P GDEP ] + [P CND ] )

A new separation method for stratiform and convective regions and the corresponding mean cloud budgets Rate Ratio (RR)Cloud Ratio (CR)

CR > 1CR < 1 IWP > LWP  stratiform clouds developed IWP < LWP  convective clouds developed

Sui, 1994 (fcrsc1, fcrcc1) RR (fcrsc2,fcrcc2, 0.1, +) CR (fcrsc3,fcrcc3, 0.4, o) StratiformConvective

Stratiform CR = 0.83 RR = 0.44 CMPE = 0.87 Convective CR = 0.26 RR = 0.07 CMPE = 0.62 Sui, 1994 The LWP and associated microphysical conversion rates are significantly larger in the convective regions than in the stratiform regions, whereas the IWP and associated microphysical conversion rates do not change much in the two regions.

(a) stratiform, CR = 2.1, RR = 2.1, CMPE = 0.48 (b) mixture conditions, CR = 0.6, RR = 0.3, CMPE = 1.01 (c) convective, CR = 0.13, RR = 0.03, CMPE = 0.72

Cloud Ratio: 1. It is specifically linked to microphysical processes. 2. It does not need cloud information from surrounding areas for determining rain types. 3. IWP and LWP are now available routinely from satellite measure- ments by sensors like TRMM.

Budget analysis for dominant processes changing the cloud ratio Process 1Process 2 Process 3 Process 4 Process 1 denoting the contribution from the convergence of cloud hydrometeors. Process 2 is determined by the conversion between LWP and IWP. Process 3 are the condensation and deposition time scales. Ex: P CND occurs slower(faster) than the P DEP, CR increases (decreases), and stratiform clouds develop faster (slower) than the convective clouds. Process 4 always enhance CR since rainfall and the P REVP consume raindrop.

Process 1

Process 1 has the same order of magnitude as the sum of the other processes.

CC= 0.76 CC= 0.08 CC= 0.25 The Process 3 and Process 4 are insensitivity to CR, they have no significant impacts on the variations of clouds. Process 2A causes the decrease of CR and the development of convective clouds, Process 2C cause the increase of CR and the development of stratiform clouds. 2A : P GMLT (+) 2B : P SACW (o) 2C : P GACW ( Δ)

IWP LWP disspating stage with the development of anvil clouds The relative amounts of IWP and LWP may depend on the vertical profiles of upward motion.

0800 LST 20 (CR=0.2) 2200 LST 20 (CR=0.8) 0400 LST 23 (CR=2.1, anvil cloud) CRC (+), CRM (o), CRS (Δ)

Summary The conversion between LWP and IWP determines the variation of cloud ratio through the melting of graupels and the accretion of cloud water by graupels. ConvectiveMixedStratiform CR< ~ 1.0> 1.0 RR< ~ 1.0> 1.0