r. n. hoffman , c. grassotti, j. m. henderson, s. m. leidner, g. modica, and t. nehrkorn
DESCRIPTION
Controlling the Evolution of a Simulated Hurricane through Optimal Perturbations: Initial Experiments Using a 4-D Variational Analysis System. R. N. Hoffman , C. Grassotti, J. M. Henderson, S. M. Leidner, G. Modica, and T. Nehrkorn Atmospheric and Environmental Research Lexington, MA. - PowerPoint PPT PresentationTRANSCRIPT
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RICC, June 2005
Controlling the Evolution of a Simulated Hurricane through
Optimal Perturbations: Initial Experiments Using a 4-D
Variational Analysis SystemR. N. Hoffman, C. Grassotti, J. M.
Henderson, S. M. Leidner, G. Modica, and T. Nehrkorn
Atmospheric and Environmental Research
Lexington, MA
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RICC, June 2005
Thanks• Supported by
NIAC
– NASA Institute for Advanced Concepts
• Tools & data
– MM5/4d-VAR
– NCAR/NCEP gridded data
H. Iniki 1992 (NWS image)
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RICC, June 2005
Today’s talk
• Experiments to control hurricanes
• A different approach to weather control–not just hurricanes
• Based on the sensitivity of the atmosphere
• The same reason why it is so difficult to predict the weather!
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RICC, June 2005
Theoretical basis
• The earth’s atmosphere is chaotic
• Chaos implies a finite predictability time limit no matter how well the atmosphere is observed and modeled
• Chaos also implies sensitivity to small perturbations
• A series of small but precise perturbations might control the evolution of the atmosphere
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Objectives of our project• Develop a method to calculate
the atmospheric perturbations needed to control a hurricane
• Quantify the size of the perturbations needed to do this
• Estimate the requirements of a weather control system
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Current NWP operational practice
• NWP centers have developed forecast techniques that capitalize on the sensitivity of the atmosphere
1. 4D variational data assimilation
2. Generation of ensembles
3. Adaptive observations
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Current Practice 1: 4D variational data assimilation• 4D-Var fits all available observations
during a time window (6 or 12 hours) with a model forecast
• The fit to the observations is balanced against the fit to the a priori or first guess from a previous forecast
• We use a variant of 4D-Var in our experiments
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Why hurricanes?
• Public interest: Threat to life and property
• History: Project Stormfury (1963)
• Sensitive to initial conditions
• MM5/4d-VAR: Available tools
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Our Case Study Hurricane Iniki (1992)• Landfall at Kauai at 0130 UTC
12 September
• Hurricane Iniki from 0600 UTC 11 September to 1200 UTC 12 September 1992 is shown in the following movie
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RICC, June 2005
Iniki Simulation
750-hPa Relative Humidity
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Determination of perturbations
• Optimal control theory
• 4d-Var methodology baseline
• Modified control vector: temperature only
• Refined cost function: property damage
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RICC, June 2005
Mesoscale model• The MM5 computation grid is 200 by
200, with a 20 km grid spacing, and ten layers in the vertical
• Physics are either– Simplified parameterizations of the
boundary layer, cumulus convection, stratiform cloud, and radiative transfer; or
– Enhanced parameterizations of these physical processes and a multi-layer soil model
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RICC, June 2005
4D variational data assimilation• 4D-Var adjusts initial conditions
to fit all available observations during a 6 or 12 hour time window
• The fit to the observations is balanced against the fit to the a priori or first guess from a previous forecast
• We use a variant of 4D-Var in our experiments
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RICC, June 2005
Standard 4D-Var cost function
• J is the cost function
• P is the perturbed forecast
• G is the goal
– G is the target at t=T and the initial unperturbed state at t=0
• S is a set of scales
– S depends only on variable and level
• x is temperature or a wind component
• i, j, and k range over all the grid points
J = ∑xijkt [(Pxijk(t)–Gxijk(t))/Sxk]2
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RICC, June 2005
• Optimal is defined as simultaneously minimizing both the goal mismatch and the size of the initial perturbation as measured by the sum of squared differences
‘Optimal’ Defined
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Modified control vector
• Control vector can be restricted by variable and by geographic region
– Temperature only
– Locations far from the eye wall
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RICC, June 2005
Refined cost function
• JD = ∑ijt Dij(t) Cij
• C is the replacement cost
• D is the fractional wind damage– D = 0.5 [1 + cos(π(V1-V)/(V1-V0))]
•D=0 for V<V0 = 25 m/s
•D=1 for V>V1 = 90 m/s
– Evaluated every 15 min. for hours 4–6
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Minimization
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RICC, June 2005
Experiments• Hurricane Andrew MM5 simulations
starting at 00 UTC 24 Aug 1992
• Initial conditions from an earlier 6 h forecast; NCEP reanalysis; bogus vortex
• 4d-Var over 6 h (ending 06 UTC 24 Aug); 20 km grid; temperature increments only; simple physics
• Simulations for unperturbed vs. controlled; 20 km simple physics vs. 7 km enhanced physics
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RICC, June 2005
Initial conditionsProperty values
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RICC, June 2005
Temperature perturbations
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RICC, June 2005
Surface wind field evolution
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RICC, June 2005
Control vector sensitivity
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RICC, June 2005
Temperature increments
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RICC, June 2005
Temperature perturbations(controlled minus unperturbed)
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RICC, June 2005
Time evolutionof perturbations
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RICC, June 2005
Surface wind field evolution
Unperturbed
Controlled
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RICC, June 2005
Time evolution 00 UTC
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RICC, June 2005
Time evolution 06 UTC
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RICC, June 2005
Time evolution 12 UTC
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RICC, June 2005
Time evolution 18 UTC
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High resolution
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Summary
• Perturbations calculated by 4d-Var
• Control path, intensity of simulated hurricane
• Power requirements are huge– Higher resolution, longer lead times
may help
– Very large scale SSP could meet the requirements
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RICC, June 2005
Use in Forecasting
• 4dVAR can assess the likelihood of a specific event that requires immediate action, such as damaging winds along the Hudson Valley
• Exigent, adj:
– 1 : requiring immediate aid or action
– 2 : requiring or calling for much
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Background cost function• Exigent forecasting: Normal
NWP Jb should be used. Related to the probability of the initial conditions.
• Weather control: Jb should be replaced with the cost in terms of available resources of generating the perturbations.
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Hurricane WxMod
• Energetics
– Biodegradable oil
– Pump cold water up to the surface
• Dynamic perturbations
– Stormfury: cloud seeding
– Space based heating
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RICC, June 2005
Space based heating
• Solar reflectors: bright spots on the night side and shadows on the day side
• Space solar power (SSP): microwave downlink could provide a tunable atmospheric heat source
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RICC, June 2005 Space solar power
NASA artwork by Pat Rawlings/SAIC
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RICC, June 2005
Microwave spectrum
• Water and oxygen are the main gaseous absorbers– H2O lines at 22, 183 GHz
– H2O continuum
– O2 lines at 60, 118 GHz
• Frequency and bandwidth control the heating profile
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RICC, June 2005
Microwave heating rates
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Power requirements
• Heating rates calculated for 1500 W/m2
• Equal to 6 GW/(2 km)2
• Current experiments require similar heating rates over an area 100s times larger
• Longer lead times, higher resolution will reduce these requirements significantly.
• By changing the storm’s environment at longer lead times can we prevent its forming, track, or intensity.
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The future• More realistic experiments:
resolution, physics, perturbations
• Future advances in several disciplines will lead to weather control capabilities– The first experiments will not be space
based control of landfalling hurricanes!
• Can legal and ethical questions be answered
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RICC, June 2005
More complicating factors• The control must be effected at
significant time lags
• The difficulty of effecting control
• The problem of defining “optimal”
– For inhabitants of New Orleans, eliminating a hurricane threat to that city may take precedence over all else
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Future WxMod
• Improved models, observations, and assimilation systems will advance to the point where forecasts are:– much improved, and
– include an estimate of uncertainty
• Thus allowing advance knowledge that a change should be detectable in particular cases
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end
• Contact: – rhoffman at aer dot com
– www.niac.usra.edu
• Background: – R. N. Hoffman. Controlling the global
weather. Bull. Am. Meteorol. Soc., 83(2):241--248, Feb. 2002.