62nd Interdepartmental Hurricane Conference CIMSS TC Intensity Satellite Consensus SATCON

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62nd Interdepartmental Hurricane
ConferenceCIMSS TC Intensity Satellite
Consensus (SATCON)

• Derrick Herndon and Chris Velden

ADT
SATCON
CIRA AMSU
CIMSS AMSU
Funding Supported by SPAWAR PEO C4ISpace/PMW
180 and the ONR Marine Meteorology program via
NRL.
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Motivation

• Importance of getting current TC intensity right
• Intensification trends
• CI and short term change in statistical forecast
  models
• Climatology (Basin Best Tracks)
• Estimates of TC intensity can vary by more than
  40 knots
• Hurricane Lili 2002 current intensity estimates
  5 hours prior
• to landfall varied from 83 - 125 kts
• Hurricane Isabel 2003 intensity estimates varied
  from 77 -
• 105 kts 17 hours prior to landfall.
• Hurricane Charley 2004 rapid intensification
• Several objective TC intensity methods exist
• Goal is to assist forecasters in assessing
  current intensity by combining the best aspects
  of individual objective estimates into a single
  estimate

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Members ADT
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Members ADT
Strengths Time-averaging results in
consistency Temporal frequency - every 1/2
hour Method based on the reliable Dvorak
Technique Global coverage with few gaps
(eclipses) Familiarity Weaknesses Sensitive to
scene type identification IR signature not
strongly related to intensity Time-averaging can
miss rapid intensity changes
IR image from NRL TC Page
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Members CIMSS AMSU
Channel 8
150 mb
Channel 7
250 mb
55 Knots
Channel 6
550 mb
AMSU Tb Anomaly vertical cross section for
Katrina 2005
70 Knots
TC Pressure Anomaly Magnitude
125 knots
AMSU Channel 8 Anomaly Magnitude
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Members CIMSS AMSU
Strengths Tb Anomaly magnitude directly related
to intensity No dependence on previous
estimate TC-relative MSW training allows for
motion component Weaknesses Resolution
requires sub-sampling corrections Dependence on
ancillary data (RMW, P_env) Polar orbiter pass
coverage
IR image from NRL TC Page
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Members CIRA AMSU
Strengths Temperature anomaly directly related
to TC intensity CLW useful in accounting for
attenuation Provides objective estimate of
critical wind radii Higher latitudes (Best Track
training) Weaknesses CLW may not correct for
all of the attenuation Reliance on model data for
boundary conditions No sub-sampling correction
for resolution
IR image from NRL TC Page
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CIMSS SATCON

• Combine estimates into a single SATellite
  CONsensus (SATCON) estimate
• Assign weights using known statistical
  performance in a given situation for each method.
  Actual weights are RMSE error for the method.
• ADT performs best in clear eye scenes
• CIMSS AMSU performs best for weaker storms and
  when eye is large for stronger storms
• CIRA AMSU performs best when eye is large and
  position of AMSU-A matches TC position

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Next Step Information Sharing

• What relationships might exist between the
  parameters of
• the member algorithms?
• Can these parameters be shared across the
  algorithms to
• improve the individual members?
• After all corrections are made re-define the
  weights
• and produce a weighted consensus of the
  corrected
• members

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Information Sharing ADT to AMSU
Get Estimate of Eye Size
IR can be used to estimate eye size CIMSS AMSU
uses eye size information to correct resolution
sub-sampling Use RMW to adjust MSW?
Compare to AMSU-A FOV resolution
Adjust AMSU pressure if needed
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Information Sharing CIRA to ADT

• Use CIRA T-max to correct ADT non-
• eye scene estimates
• Emerging eyes
• Post-ERC
• Excessive weakening after eye fills

NRL-MRY
ADT MSLP Error (mb)
NRL-MRY
CIRA AMSU Tmax (K)
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Information Sharing CIMSS to CIRA
CIRA MSLP Error (mb)
AMSU-B 89 Ghz (K)
Position bias used to correct CIMSS AMSU can be
used for CIRA as well. A strong relationship
exists between AMSU-B 89 Ghz signal (convolved)
within the AMSU-A FOV and CIRA estimate error for
both MSLP (shown) and MSW
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Information Sharing
ADT currently does not use any estimate of
environmental pressure. ATCF messages used by
CIMSS includes P_env

• Storm Motion Component
• Both ADT and CIRA AMSU developed using Best
  Track MSW data.
• The component of the MSW imparted by storm motion
  is intrinsic to
• this data set.
• Storm motions which deviate from the Best Track
  data average
• (about 11 knots) are not accounted for by these
  members.
• Apply 50 of anomalous motion component to the
  ADT and CIRA.
• Especially important for recurving storms moving
  gt 30 knots

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Examples
ADT determines scene is an eye scene CIMSS AMSU
near Nadir. Eye is large compared to AMSU
resolution CIRA is sub-sampled
NRL-MRY
ADT 28 CIMSS AMSU 47 CIRA AMSU 25
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Examples
ADT determines scene is a CDO scene CIMSS AMSU
position near limb. Eye is small CIRA AMSU
position located near true TC center
NRL-MRY
ADT 22 CIMSS AMSU 34 CIRA AMSU 44
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Examples
ADT determines scene is a SHEAR scene CIMSS
AMSU indicates no sub-sampling present CIRA
AMSU no sub-sampling due to position offset
NRL-MRY
ADT 18 CIMSS AMSU 41 CIRA AMSU 41
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Examples Katrina 2005
ADT CDO
Eye emerges in IR
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1999-2006 Stats (Pressure)
Dependent sample. Values in millibars.
Validation is aircraft recon (buoys) pressure /-
3 hours from estimate time - bias method was
too weak.
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1999-2006 Stats (MSW)
Dependent sample. Values in knots. Validation
is best track msw coincident with aircraft recon
/- 3 hours from estimate time. A - bias
method was too weak.
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1999-2006 Stats Compare to Simple
Dependent sample. MSW validation in knots. MSLP
validation in millibars. - bias method was
too weak. SIMPLE is simple average of the 3
members
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1999-2006 Compare to Dvorak
MSW validation in knots vs. Best Track. MSLP
validation in millibars vs. recon. Neg. bias
method was too weak. Dvorak is average of TAFB
and SAB estimates
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2007 Stats (MSW)
Independent sample. Values in knots. Validation
is best track msw coincident with aircraft recon
/- 3 hours from estimate time. - bias method
was too weak.
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2007 Stats (MSLP)
Independent sample. Values in millibars.
Validation is recon MSLP /- 3 hours from
estimate time. - bias method was too weak.
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2007 Compare to Dvorak
Independent validation. MSW validation in knots.
MSLP validation in millibars. - bias method
was too weak. Dvorak is average of SAB and TAFB
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Future Work

• Address MW latency
• Add error bars for estimate confidence
• Continue cross-platform information sharing
• Add additional TC intensity methods
  (SSMI / TRMM ?)
• Interactive Web Interface

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SATCON HOMEPAGE
http//cimss.ssec.wisc.edu/tropic2/real-time/satco
n
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References
Brueske K. and C. Velden 2003 Satellite-Based
Tropical Cyclone Intensity Estimation Using the
NOAA-KLM Series Advanced Microwave Sounding
Unit (AMSU). Monthly Weather Review Volume
131, Issue 4 (April 2003) pp. 687697 Demuth J.
and M. Demaria, 2004 Evaluation of Advanced
Microwave Sounding Unit Tropical-Cyclone
Intensity and Size Estimation Algorithms.
Journal of Applied Meteorology Volume 43, Issue
2 (February 2004) pp. 282296 Herndon D. nd C.
Velden, 2004 Upgrades to the UW-CIMSS AMSU-based
TC intensity algorithm. Preprints, 26th
Conference on Hurricanes and Tropical
Meteorology, Miami, FL, Amer. Meteor. Soc.,
118-119. Kossin J. 2007 Estimating Hurricane
Wind Structure in the Absence of Aircraft
Reconnaissance Weather and Forecasting Volume 22,
Issue 1 (February 2007) pp. 89101 Olander T.
2007 The Advanced Dvorak Technique Continued
Development of an Objective Scheme to Estimate
Tropical Cyclone Intensity Using Geostationary
Infrared Satellite Imagery. Wea. and Forecasting
Volume 22, Issue 2 (April 2007) pp.
287298 Velden C. 2006 The Dvorak Tropical
Cyclone Intensity Estimation Technique A
Satellite-Based Method that Has Endured for over
30 Years. Bulletin of the American Meteorological
Society Volume 87, Issue 9 (September 2006) pp.
11951210
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Members ADT
Uses IR imagery to assess eye temperature,
curvature and cloud region temperature. Linear
regression scheme developed by matching recon
MSLP estimates to important IR parameters. Logic
based on the subjective Dvorak Method developed
in the 1970s. Each TC image is classified
according to scene type which drives the logic
structure leading to the intensity estimate
IR image from NRL TC Page
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Members CIMSS AMSU
Microwave sounder which includes channels for
measuring brightness temperatures (Tb) in the
550-150 mb layer. AMSU-A (temperature) and
AMSU-B (moisture) 1998-present Resolution 50 km
at nadir to 100 km at the limb Multiple
regression scheme using Tb anomaly magnitude
from the 3 AMSU-A channels and 1 AMSU-B
channel Corrections applied to account for
sub-sampling, hydrometeor scattering and scan
geometry. Trained versus recon MSLP and
TC-relative MSW
IR image from NRL TC Page
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AMSU Sub-sampling Corrections
Correcting for resolution
Correcting for position
Get Estimate of Eye Size
AMSU-B 89 Ghz
AMSU-A FOV
TC Center
Compare to AMSU-A FOV resolution
Portion of TC eyewall is within the AMSU-A FOV
indicating the AMSU-A pixel location is offset
from true TC center. Find AMSU-B
center-weighted (convolved) Tb. Used as
regression Term.
Adjust AMSU pressure if needed
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Members CIRA AMSU
AMSU-A Tb are used to produce a statistical
temperature retrieval at 23 pressure levels along
with CLW which is used to correct for attenuation
due to hydrometers Data is smoothed to a 12 X 12
degree 22 km grid. Geopotential height field is
derived using Hydrostatic Equation then
integrated downward using boundary conditions
from NCEP model to get surface pressure field.
MSW is estimated from pressure gradient wind. TC
intensity is determined using derived pressure
field, tangential winds at 5 km height, AMSU FOV
resolution and RMW at height of 3 km
IR image from NRL TC Page
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Members CIRA AMSU
CIRA AMSU TC intensity predictors MSW Tangential
wind at height of 5 km Maximum temperature
anomaly Average CLW within 100 km radius Percent
of CLW gt 0.5 mm within 300 km RMW at height of 3
km AMSU-A FOV resolution MSLP Pressure anomaly
estimated from 600 km radius to center
IR image from NRL TC Page
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