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Tropical Cyclone Intrinsic Variability and Predictability

Explore the unpredictability, boundaries, and predictability limits of tropical cyclone forecasts. Learn about intrinsic and environmental factors impacting storm dynamics for better predictions.

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Tropical Cyclone Intrinsic Variability and Predictability

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  1. Tropical Cyclone Intrinsic Variability & Predictability Gregory J. Hakim University of Washington 6 March 2013 Q: What is the TC predictability limit? A: We do not know. 67th IHC/Tropical Cyclone Research Forum

  2. Weather Predictability Limits Lorenz (1982) No theory or predictability limits exist for the TC forecast problem  no basis for projecting improvements; devoting resources; etc.

  3. Predictability of First and Second Kindapplied to tropical cyclone prediction Two types of predictability (Lorenz 1975): • First kind: initial conditions • E.g. weather forecasts with fixed SST • Second kind: boundary conditions • E.g. ENSO; CO2, aerosol, orbital, etc. forcing on climate Applied to tropical cyclones: • First kind: “intrinsic” TC-scale initial conditions • Internal storm dynamics • Second kind: environmental “boundary conditions” • SST, shear, dry air intrusions, etc.

  4. Motivation Tropical cyclone forecasts: • Track: steady improvement • better large-scale models & data assimilation • Intensity: much slower improvement • despite improved large-scale environment • cf. ``environmental control'’ Emanuel et al. (2004) Why? Need to understand intrinsic variability. • variability independent of the environment • what aspects are predictable? What timescales? • data assimilation key to realizing predictability, but first need to know limits.

  5. Method • Idealized numerical modeling • Necessary to control environment • CM1 model (George Bryan) • Axisymmetric and 3D (not shown; similar to axi) • Simulate statistically steady state • Extremely long simulations (500 days) • Robust sampling • Variability: EOFs & regression • Predictability: inverse modeling & analogs

  6. Maximum Wind Speed • “superintensity” is a transient effect • wide range of intensity in steady state

  7. Azimuthal wind variability • Bursts of stronger wind that move inward • Dominant period ~4-8 days

  8. Azimuthal wind leading EOFs • EOF1: radial shift of RMW • EOF2: intensity pulsing at RMW

  9. RMW variability linked to far field • Bands of stronger/weaker wind move radially inward • Eyewall replacement cycles

  10. Structure of Variability

  11. Predictability • Autocorrelation • Analogs (divergence of similar states) • Linear inverse modeling Estimate M statistically (least squares) Verify forecasts from independent data

  12. Predictability: LIM azi wind radial wind Predictability limits: • Clouds: ~12-18 hours • Azimuthal wind: ~ 2-3 days cloud water temperature

  13. Analog Forecasts • Fully nonlinear model • Similar results to LIM • larger initial error due to limited sample

  14. Comparison against operational forecasts (NHC) • Coincidence? • Already at predictability limit? • Intrinsic variability dominates error?

  15. Conclusions • Intrinsic variability • Promotes understanding how environment affects storms • convective bands form in the environment and move inward • Intrinsic predictability • ~48-72 hours • environment can add or subtract from this limit • compares closely with operational forecast errors • Basic research needed! Hakim, G. J., 2011: The mean state of axisymmetric hurricanes in statistical equilibrium. J. Atmos. Sci., 68, 1364--1376. Hakim, G. J., 2013: The variability and predictability of axisymmetric hurricanes in statistical equilibrium. J. Atmos. Sci., 70, in press. Brown, B. R., and G. J. Hakim, 2013: Variability and predictability of a three-dimensional hurricane in statistical equilibrium. J. Atmos. Sci.,70, accepted.

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