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A Numerical Study of a TOGA COARE Super Cloud Cluster – Preliminary results

A Numerical Study of a TOGA COARE Super Cloud Cluster – Preliminary results. Peter M.K. Yau and Badrinath Nagarajan McGill University. Outline. Motivation & Objectives Case Overview Modeling Strategy Results & Conclusions Future work. Motivation.

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A Numerical Study of a TOGA COARE Super Cloud Cluster – Preliminary results

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  1. A Numerical Study of a TOGA COARE Super Cloud Cluster – Preliminary results Peter M.K. Yau and Badrinath Nagarajan McGill University

  2. Outline • Motivation & Objectives • Case Overview • Modeling Strategy • Results & Conclusions • Future work

  3. Motivation • MJO associated with supercloud clusters. Processes organizing warm-pool convection a “zeroth-order problem” (Webster & Lucas 1992) • Organizing mechanisms (OM) particularely at meso-and synoptic scale not well understood (Yanai et al 2000, Gabrowksi 2003). • Improved understanding of OM on various scales should lead to: • better representation of convection in models • reduced forecast errors at the medium range • better representation and understanding of the role of convection on water vapor distribution in the vertical

  4. Objective Use a real data multi-grid (15-5-1 km) numerical modeling approach to • simulate supercloud clusters (SCCs) over TOGA COARE • diagnose the processes that: • organize MCSs, • cause clustering of MCSs, and • study the impact of convection on water vapor distribution in the vertical

  5. OLR (W m-2) • Once a day • Averaged • 5S - 5N • OLR < 215 • W m-2 • Shaded • Focus of this study on SCC A Case Overview– IOP of TOGA COARE 1Nov 92 Yanai et al (2000) 1Dec 92 1Jan 93 1Feb 93 28 Feb 93

  6. The 6 DEC. 92 – 6 JAN. 93 SUPER CLOUDCLUSTER • Time cluster: • Lifetime > 24 h • MCS: • Lifetime < 24 h IFA MCSs Time cluster Time Longitude • Data Used: • Hourly GMS • Infrared data • 0-10S average • Areas < 235 K • precipitating • (GATE/COARE • convection)

  7. EVOLUTION of IFA time cluster (11-13 DEC 92) mm h-1 • Data Used: • Precipitation retrieved from • SSM/I, VIS/IR satellite data • Sheu et al (1996), Curry et al • (1999) • 3 hourly/ 30 km resolution

  8. EVOLUTION of IFA time cluster (11-13 DEC 92) mm/h • Data Used: • Precipitation retrieved from • SSM/I, VIS/IR satellite data • Sheu et al (1996), Curry et al • (1999) • 3 hourly/ 30 km resolution

  9. Schematics of Nakazawa (1988) Madden & Julian (1994)

  10. Time cluster: • Lifetime > 24 h IFA 1 2 Westward propagating 3 4 Eastward propagating 5 6 7 Time 8 9 10 11 12 13 14 15 16 Longitude Propagation of Time Clusters

  11. IFA IFA 1 2 3 4 5 6 7 Time 8 9 10 11 12 13 14 15 16 Longitude Westward propagating Eastwardpropagating PROPAGATION OF TIME CLUSTERS 200 hPa

  12. Time Evolution of Domain Average Brightness Temperature Early morning minimum Afternoon minimum (land) Afternoon minimum (ocean)

  13. Brightness temperature minimum occurs: • Early morning for 8 time clusters, • Afternoon for 4 time clusters • Suggests that most of the time clusters are indeed MCSs

  14. Organizing Mechanisms • Large scale flow features (e.g., 2-day waves) • Vertical wind shear (Le Mone et al 1999) • Mid-level mesovortices (Nagarajan et al 2004) – Dec. 15, 1992 • Mapes gravity-wave mechanism

  15. TIME CLUSTERS & 2-DAY PERIODICITY IFA 1 2 3 4 1-4, 7-9, 11-13 associated with 2-day wave (Chen et. al 1996, Takayabu et. al 1996) 5 6 7 Time 8 9 10 11 12 13 14 15 16 Longitude K Westward propagating Eastwardpropagating

  16. TIME CLUSTERS & VERTICAL SHEAR* (wind speed) *Areal & Temporal Averages Temporal average: Duration of the time cluster Areal average: 0-10S, longitudinal extent of time cluster

  17. Summary During the lifetime of the SCC (6Dec-6Jan): • Identified 16 time clusters consisting of eastward & westward propagating cloud clusters. • Convection generally associated with 2-day wave activity • Convection occurred in a weak vertical wind shear environment except between 20-28 Dec 1992.

  18. The Model • Canadian mc2 model (Benoit et al. 1997) • Fully compressible equations • Semi-Lagrangian, semi-implicit numerics • One-way nesting of lateral boundary conditions • RPN1 physics package 1 Recherche en Prevision du Numerique

  19. 1-month long time series Time series based on last 24 h of each 27h long simulation. 00 UTC/6 Dec. 92 03UTC/7 Dec. 92 00 UTC/7 Dec. 92 03 UTC/8 Dec. 92 00 UTC/6 Jan. 93 • 00 UTC chosen because of high availability of • rainfall data for assimilation • Time integration strategy follows guichard et al. • (2003)

  20. 130E 160E 190E 10N 3900 km EQ 10S 3900 km MC2 MODEL DOMAIN IFA Grid Size: 549 x 279 x 40, Horizontal grid length: 15 km Model Top: 26 km

  21. Modeling Strategy • Model Parameters: • KF CPS (deep convection), BM CPS (shallow convection), Kong and Yau (1997) explicit bulk 2-ice microphysics, time step(90 s) • Initial Conditions: • ECMWF operational analysis (0.5 o) enhanced: • radiosonde data (Cieleski et al 2003), • temperature & moisture profiles modified by 1D-VAR rainfall rate assimilation scheme (Nagarajan et al. 2006) and • ABL moistening due to diurnal SST warming (Nagarajan et al 2001, 2004). • 6-hourly lateral boundary conditions

  22. IFA averaged surface precipitation rate Missing data

  23. IFA averaged surface sensible and latent heat flux

  24. Horizontal size distribution of clouds (Model Domain) Missing data Wielicki & Welch (1986)

  25. Domain-averaged surface precipitation rate (140-180E, 0-10S) Missing data

  26. IFA Av. RH RH Height (km) (h)

  27. Conclusions • The IFA-mean and temporal variability of: • surface fluxes of latent and sensible heat, surface precipitation reasonable • Large scale : • Simulated surface precipitation overpredicted • Horizontal size of cloud clusters are reasonably simulated. • Month long mesoscale simulation captures reasonably the life cycle of the super cloud cluster.

  28. Future Work • Nesting to higher resolutions (5 km and 1 km) with new three-moment 4 - ice microphysics (Milbrandt and Yau 2005a,b) • Diagnose mechanisms that organized the super cloud cluster • Diagnose processes for water vapor and temperature distributions

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