The role of moist processes in the intrinsic predictability of Indian Ocean cyclones

The role of moist processes in short‐range forecasts of Indian Ocean tropical cyclones (TCs) track and intensity and upscale error cascade from cloud‐scale processes affecting the intrinsic predictability of TCs was investigated using the Weather Research and Forecasting model with parameterized and explicitly resolved convection. Comparing the results from simulations of four Indian Ocean TCs at 10 km resolution with parameterized convection and convection‐permitting simulations at 1.1 km resolution, both reproduced the observed TC tracks and intensities significantly better than simulations at 30 km resolution with parameterized convection. “Identical twin” experiments were performed by introducing random perturbations to the simulations for each TC. Results show that moist convection plays a major role in intrinsic error growth that ultimately limits the intrinsic predictability of TCs, consistent with past studies of extratropical cyclones. More specifically, model intrinsic errors start to build up from the regions of convection and ultimately affect the larger scales. It is also found that the error at small scale grows faster compared to the larger scales. The gradual increase in error energy in the large scale is a manifestation of upscale cascade of error energy from convective to large scale. Rapid upscale error growth from convective scales limits the intrinsic predictability of the TCs up to 66 h. The intrinsic predictability limit estimated by the 10 km resolution runs is comparable to that estimated by the convection‐permitting simulations, suggesting some usefulness of high‐resolution (~10 km) models with parameterized convection for TC forecasting and predictability study.

[1]  H. Morrison,et al.  Impact of Microphysics Scheme Complexity on the Propagation of Initial Perturbations , 2012 .

[2]  B. Goswami,et al.  Influence of moist processes on track and intensity forecast of cyclones over the north Indian Ocean , 2011 .

[3]  P. Mukhopadhyay,et al.  Predictability of Indian summer monsoon weather during active and break phases using a high resolution regional model , 2010 .

[4]  Fuqing Zhang,et al.  Factors Affecting the Predictability of Hurricane Humberto (2007) , 2010 .

[5]  Fuqing Zhang,et al.  Evolution of Multiscale Vortices in the Development of Hurricane Dolly (2008) , 2010 .

[6]  Adam J. Clark,et al.  Growth of Spread in Convection-Allowing and Convection-Parameterizing Ensembles , 2010 .

[7]  Fuqing Zhang,et al.  Initial Development and Genesis of Hurricane Dolly (2008) , 2010 .

[8]  Ping Liu,et al.  An MJO Simulated by the NICAM at 14- and 7-km Resolutions , 2009 .

[9]  Hiroaki Miura,et al.  Diurnal Cycle of Precipitation in the Tropics Simulated in a Global Cloud-Resolving Model , 2009 .

[10]  Fuqing Zhang,et al.  Effects of Moist Convection on Hurricane Predictability , 2009 .

[11]  Bin Wang,et al.  Asian summer monsoon simulated by a global cloud‐system‐resolving model: Diurnal to intra‐seasonal variability , 2009 .

[12]  Renate Hagedorn,et al.  Strategies: Revolution in Climate Prediction is Both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction , 2009 .

[13]  M. Satoh,et al.  Predictability Aspects of Global Aqua-planet Simulations with Explicit Convection , 2008 .

[14]  Jimy Dudhia,et al.  A New Method for Representing Mixed-phase Particle Fall Speeds in Bulk Microphysics Parameterizations , 2008 .

[15]  Fuqing Zhang,et al.  A Probabilistic Analysis of the Dynamics and Predictability of Tropical Cyclogenesis , 2008 .

[16]  Fuqing Zhang,et al.  Tracking Gravity Waves in Baroclinic Jet-Front Systems , 2008 .

[17]  Yuqing Wang.  How Do Outer Spiral Rainbands Affect Tropical Cyclone Structure and Intensity , 2008 .

[18]  Nguyen Van Sang,et al.  Tropical‐cyclone intensification and predictability in three dimensions , 2008 .

[19]  I. Kang,et al.  The Impacts of Convective Parameterization and Moisture Triggering on AGCM-Simulated Convectively Coupled Equatorial Waves , 2008 .

[20]  Mark A. Saunders,et al.  Normalized Hurricane Damage in the United States: 1900–2005 , 2008 .

[21]  Hiroaki Miura,et al.  A Madden-Julian Oscillation Event Realistically Simulated by a Global Cloud-Resolving Model , 2007, Science.

[22]  Cathy Hohenegger,et al.  Predictability and Error Growth Dynamics in Cloud-Resolving Models , 2007 .

[23]  R. Rotunno,et al.  Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics , 2007 .

[24]  R. Elsberry,et al.  Accuracy of atlantic and eastern north pacific tropical cyclone intensity forecast guidance , 2007 .

[25]  Wen-Chau Lee,et al.  Hurricane Intensity and Eyewall Replacement , 2007, Science.

[26]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[27]  Cathy Hohenegger,et al.  Predictability Mysteries in Cloud-Resolving Models , 2006 .

[28]  Philip J. Rasch,et al.  Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals , 2006 .

[29]  A. Thorpe,et al.  Predictability of Extratropical Cyclones: The Influence of Initial Condition and Model Uncertainties , 2006 .

[30]  Fuqing Zhang,et al.  Mesoscale predictability of an extreme warm-season precipitation event , 2006 .

[31]  C. Schär,et al.  Embedded Cellular Convection in Moist Flow past Topography , 2005 .

[32]  T. Krishnamurti,et al.  The Hurricane Intensity Issue , 2005 .

[33]  Chidong Zhang,et al.  Madden‐Julian Oscillation , 2005 .

[34]  Christopher A. Davis,et al.  The Role of “Vortical” Hot Towers in the Formation of Tropical Cyclone Diana (1984) , 2004 .

[35]  Christoph Schär,et al.  Convection-resolving precipitation forecasting and its predictability in Alpine river catchments , 2004 .

[36]  R. Rotunno,et al.  Effects of Moist Convection on Mesoscale Predictability , 2003 .

[37]  B. Goswami,et al.  Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations , 2003 .

[38]  Chris Snyder,et al.  Mesoscale Predictability of the “Surprise” Snowstorm of 24–25 January 2000 , 2002 .

[39]  Bin Wang,et al.  Effects of Convective Heating on Movement and Vertical Coupling of Tropical Cyclones: A Numerical Study* , 2001 .

[40]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[41]  Kerry A. Emanuel,et al.  Thermodynamic control of hurricane intensity , 1999, Nature.

[42]  G. Holland.  The Maximum Potential Intensity of Tropical Cyclones , 1997 .

[43]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[44]  P. R. Julian,et al.  Observations of the 40-50-day tropical oscillation - a review , 1994 .

[45]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[46]  Kerry A. Emanuel,et al.  Predictability of Mesoscale Rainfall in the Tropics , 1993 .

[47]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[48]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[49]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[50]  S. L. Rosenthal.  Numerical Simulation of Tropical Cyclone Development with Latent Heat Release by the Resolvable Scales I: Model Description and Preliminary Results , 1978 .

[51]  P. R. Julian,et al.  Detection of a 40–50 Day Oscillation in the Zonal Wind in the Tropical Pacific , 1971 .

[52]  Fuqing Zhang,et al.  Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei‐Yu front of China , 2007 .

[53]  Ming Xue,et al.  Sensitivity Analysis of Convection of the 24 May 2002 IHOP Case Using Very Large Ensembles , 2006 .

[54]  Frédéric Fabry,et al.  The Spatial Variability of Moisture in the Boundary Layer and Its Effect on Convection Initiation: Project-Long Characterization , 2006 .

[55]  Zaviša I. Janić.  Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model , 2001 .

[56]  John S. Kain,et al.  Convective parameterization for mesoscale models : The Kain-Fritsch Scheme , 1993 .

[57]  K. Emanuel,et al.  The Representation of Cumulus Convection in Numerical Models , 1993 .