Institute forMathematicsApplied toGeoscience

Geophysical Statistics Project - GSP

Data Assimilation Research Section - DAReS

Turbulence Numerics Team - TNT

Your typical ensemble

Ensemble mean = waste of time

GSP - statistically blending winds

QuikSCATLaunched June 1, 1999

~800 km altitude (~100 min orbit)1800 km-wide swath

about 400,000 measurements/day

graphics by JPL

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GSP - Statistically Blending Winds

6144 ‘observations’ every 6 hours(unrealistically smooth)

~ 40,000 observations every 6 hours(dense, but ‘gappy’)

Resulting wind field with proper energy decay

Gradient (important to ocean circulation)

energy falls off too much

energy falls offperhaps not enough

GSP

The striping indicates the areas sampled by

the scatterometer.

Animation of E-W wind field created by synthesizing gridded winds and scatterometer (satellite) winds.

Technique remains faithful to the data.

Low standard deviation in data-

dense regions.

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Data Assimilation Research Section - DAReS

Our computational challenge is to run MANY (~100) instances of the numerical models (CAM, WRF, ...)

simultaneously.

Simply running one numerical weather prediction model has been driving supercomputer research.

Data assimilation exploits the information in observations to ‘steer’ a numerical model.

Put another way, it ‘confronts’ a numerical model with observations.

Our challenge is to run MANY of these.

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Our ‘MACHINE’

Data Assimilation Research Testbed : DART

* Many low-order models: Lorenz 63, L84, L96, etc.

* Global 2-level PE model (from NOAA/CDC)

* NCAR’s CAM 2.0 & 3.0 (global spectral model)

* NCAR’s WRF (regional)

* GFDL FMS B-Grid GCM (global grid point model)

Forward Operators and Datasets

Many linear, non-linear forward operators for low-models

U, V, T, Ps, Q, for realistic models

Radar reflectivity, GPS refractivity for realistic models

Observations from BUFR files (NCEP reanalysis flavor)

Can create synthetic (i.e perfect model) observations for all

Data Assimilation Research Section - DAReS

Turbulence is one of the last unsolved classical physics

problems.GASpAR

Geophysical-Astorphysical spectral element adpative refinement.

GASpAR

Flexible framework for accurate simulation of turbulence

Numerical methods that minimize dissipation.

Objects are structured to facilitate parallel computation.

Dynamic refinement gives a speedup of 5-10X over fixed grids with comparable accuracy.

Objected-oriented h-adapted code for simulating turbulent flows.

Hierarchical.

How does GASpAR do it?

Elements Fields Equation Solvers

Spectral Element Method

operators

GBLASBases

Mortar objects

Adaptive refinement based on error estimates

Mortar Objects

An unambiguous representation of the field at parent/child boundaries based on

interpolation.

Dynamically adaptive geophysicalfluid dynamics simulation

using GASpAR

Simulation of three vortices

Refinement done on a component of

velocity.

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