Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter

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Performance Characteristics of a
Pseudo-operational Ensemble Kalman Filter
Greg Hakim Ryan Torn University of
Washington http//www.atmos.washington.edu/hakim
April 2006, EnKF Wildflower Meeting
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Outline

• Issues for limited-area EnKFs.
• Boundary conditions.
• Nesting.
• Multiscale prior covariance.
• UW pseudo-operational system.
• Performance characteristics.
• Analysis of Record (AOR) test.
• Experiments using the UW RT data.
• Sensitivity targeting.
• Observation impact thinning.

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Boundary Conditions

• Obvious choice global ensemble, but
• Often ensembles too small.
• Undesirable ensemble population techniques.
• Different resolution, grids, etc.
• Flexible alternatives (Torn et al. 2006).
• Mean random draws from N(0,B).
• Mean scaled random draws from climatology.
• error boundary layer shallow due to obs.

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Nesting

• Grid 1 global ensemble BCs.
• E.g. draws from N(0,B) or similar.
• Grid 2 ensemble BCs from grid 1.
• One-way nesting straightforward.
• Cycle on grid 1, then on grid 2.
• Two-way many choices little experience.
• Note Hxb different on grids 1 and 2.
• Issues at grid boundaries.

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The Multiscale Problem

• Sampling error
• noise in obs est prior covariance.
• Ad hoc remedies
• localization
• Confidence intervals.
• Multiscale problem.
• Noise on smallest scales may dominate.
• Need for scale-selective update?

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Surface Temperature Covariance
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Mesoscale Example cov(V, qrain)
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Real Time Data Assimilation at the University of
Washington
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Objectives of System

• Evaluate EnKF in a region of sparse in-situ
  observations and complex topography.
• Estimate analysis forecast error.
• Sensitivity targeting thinning.

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Model Specifics

• WRF Model, 45 km resolution, 33 vertical levels
• 90 ensemble members
• 6 hour analysis cycle
• ensemble forecasts to t24 hrs at 00 and 12 UTC
• perturbed boundaries using fixed covariance
  perturbations from WRF 3D-VAR

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Observations
Obs. Type Variables 00 UTC 06 UTC 12 UTC 18 UTC
Surface Altimeter 430 420 420 440
Rawindsonde u, v, T, RH 1000 0 1000 0
ACARS u, v, T 1650 1390 740 1860
Cloud Wind u, v 2030 1740 1670 1510
Total 5110 3550 3830 3810
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Probabilistic Analyses
sea-level pressure
500 hPa height
Large uncertainty associated with shortwave
approaching in NW flow
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Microphysical Analyses
20 February 2005, 00 UTC
model analysis
composite radar
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Ensemble Forecasts
Analysis
24-hour forecast
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Verification
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Temperature Verification
12 hour forecast
24 hour forecast
UW EnKF GFS CMC UKMO NOGAPS
ECMWF
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U-Wind Verification
12 hour forecast
24 hour forecast
UW EnKF GFS CMC UKMO NOGAPS
ECMWF
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Moisture Verification (Td)
12 hour forecast
24 hour forecast
UW EnKF GFS CMC UKMO NOGAPS
ECMWF
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No Assimilation Verification
Winds
Temperature
UW EnKF No Observations Assimilated
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Moving Toward the Mesoscale
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Analysis of Record
Hourly surface analyses. EnKF covariances. Availab
le t30 minutes. 15 km resolution.
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Hurricane Katrina at 10 km
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Sensitivity Analysis

• Basic premise
• how do forecasts respond to changes in initial
  boundary conditions, the model?
• Applications
• targeted observations network design.
• targeted state estimation (thinning).
• basic dynamics research.

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Adjoint approach
Given J, a scalar forecast metric, one can show
that
adjoint of resolvant

• Need to run an adjoint model backward in time.
• Complex code lots of approximations
• Does not account for state estimation or errors.

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Ensemble Approach

• Adjoint sensitivity weighted by initial-time
  error covariance.
• Can evaluate rapidly without an adjoint model!
• Can show this gives response in J, including
  state estimation.

With Brian Ancell (UW)
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Sensitivity from the UW Real-time system
Case study removing one observation. Metric
average MSL pressure over western WA
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Sensitivity Demonstration
How would a forecast change if buoy 46036 were
removed?
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Overview of Case
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Overview of Case
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Overview of Case
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Overview of Case
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Overview of Case
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12 UTC 5 Feb Sensitivity
Sea-level pressure
850 hPa temperature
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12 UTC 5 Feb. Analysis Change
Analysis Change
Forecast Sensitivity
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Forecast Differences

• Assimilating the surface pressure observation at
  buoy 46036 leads to a stronger cyclone.
• Predicted Response 0.63 hPa
• Actual Response 0.60 hPa

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Summary of 10 Cases
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Observation Impact

• Adaptively sampling the obs datastream
• Thin by assimilating only high-impact obs.

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Observations Ranked by Impact
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Ob-Type Contributions to Metric
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Metric Prediction Verification
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Summary

• BCs flexibility weak influence.
• UW real-time system gov. center quality.
• Moisture field better than most.
• Surface AOR 10 km.
• Sensitivity analysis.
• Ensemble targeting easy flexible.
• Adaptive DA (thinning).

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AOR Opportunities

• No propagate update
• nested high resolution single member.
• assimilate using coarse-grid stats.
• can be done now.
• Deterministic propagation
• as above, but evolve high-res state.
• Full filter
• evolve assimilate entire ensemble.
• 4DVAR with EnKF statistics.
• at least 3--5 years out.

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AOR Challenges

• True multiscale conditions (lt15 km).
• Scale-dependent sampling errors?
• Bias estimation and removal.
• EnKF allows state-dependent bias estimation.
• Model error estimation removal.
• Parameter estimation model calibration.
• Satellite radiance assimilation.
• Kalman smoothing.

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IR Temperature Analyses
30 March 2005, 12 UTC
model analysis
IR satellite image
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Global Perturbations
Randomly choose Ne draws and scale to desired
variance.
Create a number of draws from N(0,B)
Add to deterministic boundary condition and
calculate tendency

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Height Verification
12 hour forecast
24 hour forecast
UW EnKF GFS CMC UKMO NOGAPS
ECMWF
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Surface Obs. and Rawindsondes
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Observation Densities
aircraft obs.
cloud winds
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Ensemble inliers/outliers
inlier
outlier