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Track Forecasting of 2001 Atlantic Tropical Cyclones Using a Kilo-Member Ensemble

Track Forecasting of 2001 Atlantic Tropical Cyclones Using a Kilo-Member Ensemble. 8:30 AM April 30, 2002 Jonathan Vigh Master’s Student Colorado State University Department of Atmospheric Science. Acknowledgments . Wayne Schubert, graduate advisor Mark DeMaria Scott Fulton Rick Taft

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Track Forecasting of 2001 Atlantic Tropical Cyclones Using a Kilo-Member Ensemble

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  1. Track Forecasting of 2001 Atlantic Tropical Cyclones Using a Kilo-Member Ensemble 8:30 AM April 30, 2002 Jonathan Vigh Master’s Student Colorado State University Department of Atmospheric Science

  2. Acknowledgments • Wayne Schubert, graduate advisor • Mark DeMaria • Scott Fulton • Rick Taft • Funding • Significant Opportunities in Atmospheric Research and Science (SOARS) Program, fellowship • American Meteorological Society, fellowship

  3. What causes track error? • Inaccurate spatial and temporal sampling -> analysis uncertainty • Incomplete representation of physical processes • Discretization and truncation error • Atmosphere’s inherent chaotic nature • Instability • Nonlinear interactions between various spatial scales (Leslie et al., 1998)

  4. What is an ensemble? • A collection deterministic realizations obtained by varying: • Model (numerics, resolution, or physics) • Perturbing model parameters • Initial analysis fields • Time of analysis • Generation method (adjoint or 4DVAR methods) • Stochastic or bred perturbations

  5. Benefits of an ensemble • With many realizations over properly perturbed initial conditions, the subspace of dynamical pathways can be sampled • Ensemble mean is generally more accurate than a single deterministic forecast (Leith, 1974) • Ensemble allows estimation of higher moment statistics of the forecast • Forecast uncertainty • Bifurcation of dynamical pathways • Probability density functions

  6. MBAR: Multigrid Barotropic Model • Modified barotropic vorticity equation • Finite difference, multigrid methods (Fulton, 2001) • 3 Nested grids on a square 6000 km domain h1 = 125 km, h2 = 63 km, h3 = 31 km • Efficient and accurate multigrid methods makes a kilo-scale operational model feasible • Accuracy comparable to LBAR in only 1/38 of the computing time • Each 120-hr track forecast takes ~2.5 seconds • A 1980 member ensemble takes approximately 1.3 hours on a 1 GHz Intel PC

  7. Perturbations in initial and background fields • Operational MRF ensembles • 5 independent breeding cycles are used in the analysis cycle to estimate subspace of fastest growing analysis errors (Toth and Kalnay, 1997) • Adding and subtracting these vectors from the analysis yields 10 + control initial and background fields for MBAR • 00Z at 2 degree resolution

  8. Perturbations to vertical averaging • Four deep layer vertical averages of wind field simulate uncertainties in steering layer depth • Pressure weighted averages of following layers (mb): • Shallow (850 - 700) • Medium (850 – 350) • Deep (850 – 200) • Entire (1000 – 100)

  9. Perturbations to vertical decomposition of vertical modes • In 2D barotropic vorticity models, ultra-long Rossby waves experience excessive retrogression (Wiin-Nielsen, 1959) • Inclusion of inverse Rossby radius in the prognostic equation can fix this • Uncertainties in the vertical decomposition of the tropical atmosphere are handled by perturbing equivalent phase speed • 50 ms-1 • 150 ms-1 • 300 ms-1

  10. Perturbations to vortex size/strength • Simulates uncertainties in the size and strength of the vortex • Weak or small TS (vmax = 15 ms-1) • Weak or medium sized hurricane (wmax = 35 ms-1) • Strong or large hurricane (vmax = 50 ms-1) • For a barotropic model, the size of the outer circulation is important factor in the track forecast

  11. Perturbations to storm motion vector • Simulates uncertainties in the initial storm location and direction • Motion vector added to wind field of bogus vortex with exponentially decaying blending radius • No motion perturbation • Fast and to right • Slow and to right • Fast and to left • Slow and to left

  12. Cross-multiplication across the five perturbation classes • 11 initial and background fields (180) • 4 deep layer averages (495) • 3 vertical decompositions (660) • 3 vortex sizes/strengths (660) • 5 motion vectors (396) --------------------------------------------- 1980 ensemble members 26 sub-ensembles

  13. So what does the kilo-ensemble look like? • Chantal • Dean • Erin • Iris • Michelle • Olga

  14. ChantalAugust 17 and 18 • Well handled by the ensemble • A fairly weak storm embedded in trade flow • For first several days of forecast, a tight envelope, indicating high confidence

  15. DeanAugust 23 • Example of the challenges of recurvature off the East Coast • Total ensemble mean lagged behind actual path • Ensemble ‘swarm’ stretched out in the direction of recurvature

  16. ErinSeptember 3 • A storm which weakened to a tropical depression, then later strengthened to a strong hurricane • Ensemble mean successfully predicted path, although significant cross-track spread developed

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