Tropical Cyclone Intrinsic Variability & Predictability

Tropical Cyclone Intrinsic Variability & Predictability

Gregory J. HakimUniversity of Washington

67th IHC/Tropical Cyclone Research Forum

6 March 2013

Q: What is the TC predictability limit?A: We do not know.

Weather Predictability Limits

No theory or predictability limits exist for the TC forecast problem no basis for projecting improvements; devoting resources; etc.

Lorenz (1982)

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.

MotivationTropical 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.

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

Maximum Wind Speed

• “superintensity” is a transient effect• wide range of intensity in steady state

Azimuthal wind variability

• Bursts of stronger wind that move inward

• Dominant period ~4-8 days

Azimuthal wind leading EOFs

• EOF1: radial shift of RMW• EOF2: intensity pulsing at RMW

RMW variability linked to far field

• Bands of stronger/weaker wind move radially inward• Eyewall replacement cycles

Structure of Variability

Predictability• Autocorrelation• Analogs (divergence of similar states)• Linear inverse modeling

Estimate M statistically (least squares)Verify forecasts from independent data

Predictability: LIM

Predictability limits:• Clouds: ~12-18 hours• Azimuthal wind: ~ 2-3 days

radial wind azi wind

temperature cloud water

Analog Forecasts

• Fully nonlinear model• Similar results to LIM

– larger initial error due to limited sample

Comparison against operational forecasts (NHC)

• Coincidence?• Already at predictability limit?• Intrinsic variability dominates error?

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.