Transcript Mizzi

Comparison of GSI-based ETKF,
LETKF and DART-EnKF Hybrids from
the MMM Regional Hybrid Testbed
Arthur P. Mizzi
([email protected])
NCAR/MMM
HFIP Ensemble Design Subgroup Meeting
October 31, 2011
Teleconference
Boulder, CO
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Overview:
1. Introduction to the MMM Regional Hybrid
Testbed (MRHT).
2. Results from the study of ETKF inflation factor
schemes and data reduction experiments.
3. Preliminary results from the study of the
GSI/ETKF, GSI/LETKF, and GSI/DART-EnKF
regional hybrids.
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MMM Regional Hybrid Testbed:
1. A community resource to facilitate introduction to
and testing of variational-hybrid cycling strategies.
2. 80 member, low resolution (200km), CONUS
domain, initial ensemble for the Hurricane Dean
(August 15, 2007 to September 15, 2007) test case.
3. 10 member, higher resolution (45km) ensemble for
the same test case.
4. Script to generate initial ensembles.
5. Observations for the test case in prep.bufr,
ob.ascii, and obs.seq formats.
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MMM Regional Hybrid Testbed:
6. Cycling script using:
(i) GSI or WRFDA regional hybrids or DART for updating
the ensemble mean (other assimilation algorithms can
be easily added),
(ii) ETKF, LETKF, or DART-EnKF for updating the
ensemble perturbations (other perturbation update
strategies - like the Whitaker EnKF - can be easily
added),
(iii) Wang et al. (2003), Wang et al. (2007), Bowler et al.
(2008), and MMM experimental ensemble spread
inflation algorithms, and
(iv) WRF-ARW as the forecast (other models - like HWRF
- can be easily added).
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MMM Regional Hybrid Testbed:
7. Script for calculating hybrid single observation
increments.
8. Post-processing scripts to display:
(i) Single observation increments,
(ii) Inflation factor, prior, and posterior ensemble spread
time series,
(iii) Vertical profile and time series plots of the verification
of the analyses and forecasts against observation in
observation space, and
(iv) Spread/error verification diagnostics.
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MMM Regional Hybrid Testbed:
9. Available on web at https://svn-mmm-hybridtestbed.cgd.ucar.edu with the appropriate
password.
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GSI/ETKF Regional Hybrid Cycling
Experiments:
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20-member ensemble.
12-hr cycling (Aug. 15 to Sep. 11, 2007)
CONUS low resolution grid (200km)
ETKF with Wang et al. (2003), Wang et al.
(2007), Bowler et al. (2008), and MMM
experimental inflation schemes.
β=0.75, H=750 km, V=20 grid pts.
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GSI/ETKF Regional Hybrid Cycling
Experiments cont.:
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Verification in observation space against
radiosonde and surface synoptic observations.
Statistical significance testing with Student T-test
and Wilcoxon Sign Test.
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Comparison of GSI/ETKF Regional Hybrid
with 3DVAR and Ensemble 3DVAR
• GSI/ETKF regional hybrid gave best fit to observations.
• GSI/ETKF differences from the other schemes were
statistically significant.
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Comparison ETKF Inflation Schemes
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WG03 and BW08 gave large inflation.
TRNK gave moderate inflation.
WG07 gave small inflation due to ρ-factor.
WG03, WG07, and BW08 had oscillation.
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Comparison Ensemble Spread from
Different Inflation Schemes
• WG03, WG07, and BW08 gave comparable ensemble spread
and oscillation.
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• TRNK gave lower ensemble spread and damped oscillation.
Comparison of Hybrid using Different
ETKF Inflation Schemes with 3DVAR
FCST-RMSE
ANAL-RMSE
Lowest FCSTRMSE
• TRNK provided best fit of 12-hr forecasts to the observations
for the non-surface variables.
• TRNK results were significant different from the other
schemes.
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• Results for the surface variables were mixed.
Results Summary for the ETKF
Inflation Scheme Study:
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Compared to 3DVAR and ensemble-3DVAR, the
GSI/ETKF regional hybrid gave statistically
significant improvements of the fit to observations
for the 12-hr forecasts.
Compared to WG03, WG07, and BW08, the
TRNK inflation scheme gave statistically
significant improvements of the fit to observations
for the 12-hr forecasts.
All schemes had oscillations in the inflation factor
and ensemble spread (due to changes in number of
ETKF observations for one cycle to the next).
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Summary of Results for ETKF Inflation
Scheme Study cont. (Not Presented):
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Holding the number of ETKF observations constant
from one cycle to the next eliminated the oscillations.
The ETKF observation reduction experiments showed
that:
 Small reductions did not have a significant impact on
forecast skill.
 Moderate reductions significantly improved the forecast
skill.
 Large reduction significantly degraded the forecast skill.
 Those results were due to the contraction and expansion of
spread in the ETKF.
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GSI ETKF/LETKF/DART-EnKF
Regional Hybrid Cycling Experiments:
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60 member ensemble.
12-hr cycling (Aug. 15 to Sep. 11, 2007)
CONUS low resolution grid (200km)
ETKF – Wang et al. (2003), Wang et al. (2007)
and MMM TRNK inflation schemes.
LETKF – Loc = 3000 km, Inf = 1.036 Szunyogh et
al. (2005).
EnKF – Prior_Inf = 2,0, Inf_damping = 0.9,
Inf_sd_initial = Inf_sd_lower_bound = 0.6
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ETKF/LETKF/DART-EnKF Hybrids:
Ensemble Spread – (Pre-Results)
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WG03 and WG07 gave large ensemble spread.
TRNK gave second largest ensemble spread.
DART-EnKF gave third largest ensemble spread.
LETKF gave smallest ensemble spread.
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ETKF/LETKF/DART-EnKF Hybrids:
UPR ANAL RMSE – (Pre-Results)
• GSI/TRNK, GSI/WG07, GSI/LETKF, and DETR gave
comparable fit of their analyses to the observations.
• GSI/DART-EnKF gave slightly degraded fit of its
analyses to the observations.
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ETKF/LETKF/DART-EnKF Hybrids:
UPR ANAL BIAS – (Pre-Results)
• All hybrids and DETR gave comparable bias in their
analyses.
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ETKF/LETKF/DART-EnKF Hybrids:
UPR 12-hr FCT RMSE – (Pre-Results)
• All hybrids and DETR gave comparable fit of their 12-hr
forecasts to the observations.
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ETKF/LETKF/DART-EnKF Hybrids:
UPR 12-hr FCST BIAS – (Pre-Results)
• For u, v, and T at 850 mb, all hybrids and DETR gave
comparable bias in their 12-hr forecasts.
• For T at 500 mb, GSI/LETKF and GSI/DART-EnKF gave
lower bias than the other schemes.
• For q, GSI/TRNK gave lower bias than the other schemes. 20
Summary for Comparison of GSI/ETKF,
GSI/LETKF, and GSI/DART-EnKF
Regional Hybrids:
 The different hybrids gave differing amounts of ensemble
spread.
 The fit of the analyses to observations was comparable for
GSI/TRNK, GSI/WG07, GSI/LETKF, and DETR and was
slightly degraded for GSI/DART-EnKF.
 All schemes gave comparable analysis bias.
 All schemes gave comparable fit for their 12-hr forecasts to
observations.
 Generally, all schemes gave comparable forecast bias,
except that GSI/DART-EnKF and/or GSI/LETKF gave
lower bias for T and/or q.
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