Transcript A Multi-platform (i.e, Satellite) Tropical Cyclone Surface Wind Analysis John Knaff, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA Mark DeMaria, NOAA/NESDIS/StAR, RAMMB, Fort Collins,

A Multi-platform (i.e, Satellite) Tropical
Cyclone Surface Wind Analysis
John Knaff, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA
Mark DeMaria, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA
Debra Molenar, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USA
Buck Sampson, Naval Research Laboratory, Monterey, CA, USA
Matthew Seybold, NOAA/NESDIS/OSDPD, Suitland, MD, USA
Graciously Presented by
Andrew Burton ,Australian BoM, Perth, WA, Australia
Need
• Estimates of tropical cyclone (TC) surface wind
structure is a routinely analyzed and forecast quantity.
• However, there are few tools to estimate tropical
cyclone wind structure in the absence of aircraft
reconnaissance
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Cloud drift winds
Scatterometer wind vectors
SSM/I wind speeds
AMSU
Etc…
• and the existing tools fail to provide a complete picture
of the surface wind field, particularly near the center of
strong TCs.
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Satellite Analysis of Tropical Cyclones
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Solution
• Global product that combine satellite-based winds or Multiplatform Tropical Cyclone -Surface Wind Analysis (MTC-SWA)
– Storm relative winds (12-h window)
– Account for the shortcomings
• Quality control
• Variational data analysis at flight-level
– Data weights
– Previous analysis as first guess
– Cylindrical analysis grid
– Adjust flight-level winds to the surface
• Simple rules
• Account for land/sea differences
– Produce diagnostics every 6 hours & globally
• Wind radii
• MSLP
Real-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/
and
http://www.ssd.noaa.gov/PS/TROP/mtcswa.html
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Satellite Analysis of Tropical Cyclones
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Input Data
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AMSU – derived balanced winds
Scatterometry
Cloud and feature track winds
IR – based analogs of flight-level (850-700
hPa) winds (i.e., aircraft-based wind
analogs)
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Satellite Analysis of Tropical Cyclones
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Input: AMSU-Based Balanced Winds
Bessho et al. (2006)
• These are created as part of a NCEP operational tropical
cyclone intensity and structure products
• AMSU antenna temperatures are used to estimate
temperature retrievals and cloud liquid water (Goldenberg
1999)
• Cloud liquid water and horizontal temperature anomalies are
used to correct temperature retrievals (Demuth et al. 2004,
2006)
• The corrected temperatures are then analyzed on standard
pressure levels (using GFS boundary conditions).
• Using the resulting height field the non-linear balance
equation is solved to estimate the 2-dimensional wind field
(Bessho et al. 2006)
• Because of the resolution of AMSU, the winds in the core of
TCs are not resolved using this method.
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Satellite Analysis of Tropical Cyclones
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Input: AMSU-Based Balanced Winds
Bessho et al. (2006)
Advanced Microwave Sounding
Unit (AMSU) – Based, byproduct of an operational
intensity estimation algorithm
• Polar orbit (NOAA-15, 16 & 18)
• Analysis of temperature
retrievals provide a height field
• Non-linear balance
approximation provides wind
estimates at flight-level (700
hPa)
Shortcomings
• Resolution, too weak near the
2 km resolution
center
Hurricane
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Workshop on Paloma 7 Nov 2008 2225 UTC
• Too asymmetric
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Satellite Analysis of Tropical Cyclones
Input: Surface Scatterometry
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Active radar method (k-band, c-band)
Accurate low level winds
Attenuates in high winds (i.e., > ~50 kt)
Is adversely affected by heavy
precipitation
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Satellite Analysis of Tropical Cyclones
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Input: Surface Scatterometry
A-SCAT on MetOp
Surface wind vectors from ASCAT
and QuikSCAT scatterometers
• Polar orbit
• 10-m wind vectors
• ASCAT is c-band
– 25km resolution
– Less affected by
precipitation
• QuikSCAT is k-band
– N/A
Shortcomings
• Saturation in high winds
2 km resolution
• Attenuation/contamination in
Hurricane
heavy rain
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Workshop on Paloma 8 Nov 2008 0545 UTC
Satellite Analysis of Tropical Cyclones
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Input: Cloud/Feature Tracked Winds
• Routinely available
• Accurate
• But low-level winds are often not available
near the core of TCs
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Satellite Analysis of Tropical Cyclones
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Input: Cloud/Feature Track winds
various methods from operational centers
GOES Cloud/Feature Track
Winds – Operational Product
at NESDIS, JMA, EUMETSAT
• Track clouds or water vapor
features
• Assign a pressure level
• Available 3 hourly
Shortcoming
• Coverage near the center
4 km resolution
Hurricane
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Workshop on Paloma 8 Nov 2008 0545 UTC
Satellite Analysis of Tropical Cyclones
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Input: IR Flight-Level Analog Winds
Mueller et al. (2006)
• Relatively new development
• Provides representative winds near the
core of the TC
• Makes a surface analysis possible
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Satellite Analysis of Tropical Cyclones
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Input: IR Flight-Level Analog Winds
Mueller et al. (2006)
• IR imagery (typically 3)
– Analysis of the azimuthal mean
brightness temperatures
– Scales TC size
• Intensity estimate (advisories)
• Latitude (advisories)
• Storm motion (advisories)
Output
• 2-D flight-level (700 hPa) wind
estimate
Shortcomings
• Too symmetric
• Cases of small radius of
maximum winds or multiple
wind maxima
1 km resolution
Hurricane
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Workshop on Paloma 8 Nov 2008 0600 UTC
Satellite Analysis of Tropical Cyclones
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Output: Quality Controlled Inputs
(Hurricane Paloma 8 Nov 06UTC)
Scatterometry
Cloud / Feature winds
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Satellite Analysis of Tropical Cyclones
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Output: Quality Controlled Inputs
(Hurricane Paloma 8 Nov 00UTC)
AMSU Balanced Winds
IR flight-level analog
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Satellite Analysis of Tropical Cyclones
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Sample Analysis of
Hurricane Paloma 8
Nov 2008 06UTC
Analysis:
R34 75 70 60 65
R50 55 55 55 55
R64 45 45 40 40
RMW 16
MSLP 950 hPa
NHC Best track:
R34 120 80 60 80
R50 60 45 35 45
R64 25 25 25 25
RMW 10
MSLP 951 hPa
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Satellite Analysis of Tropical Cyclones
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Real-Time Products
Graphical Products
Text Products (ftp)
• Surface Wind Analysis
Input:
• Input assumptions
• Raw Input data (ascii)
• 600km environmental pressure
Products
• Fix file (ATCF formatted)
• Surface Winds (ascii)
– 10 degree
– 4 degree
• Inputs reduced/turned to the
surface
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AMSU
SCAT
CDFT
IRWD
Time series of Vmax & Central
pressure (CP)
• Kinetic Energy (ftp)
• IR image
– Polar grid
– Azimuthal average
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•
•
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Analysis level Winds (ascii)
GrADS binaries & .ctl files
Kinetic Energy
Vmax and CP
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Satellite Analysis of Tropical Cyclones
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http://www.ssd.noaa.gov/PS/TROP/mtcswa.html
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Satellite Analysis of Tropical Cyclones
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Satellite Analysis of Tropical Cyclones
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Satellite Analysis of Tropical Cyclones
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Real-World Example
Northern Hemisphere/Sheared TC
Hurricane
Kyle
2008
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Satellite Analysis of Tropical Cyclones
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Satellite Analysis of Tropical Cyclones
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Verification (2008-2009, Atlantic)
Are These Any Good?
Ground Truth
1. H*Wind Analyses
2. NHC best track of wind radii (when
aircraft reconnaissance ± 2 hours)
3. NHC best track of central pressure (when
aircraft reconnaissance ± 2 hours)
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Satellite Analysis of Tropical Cyclones
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Vs. H*Wind (all cases)
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Satellite Analysis of Tropical Cyclones
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Vs. H*Wind (> 64 kt cases)
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Satellite Analysis of Tropical Cyclones
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Vs. H*Wind (≤ 64 kt)
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Satellite Analysis of Tropical Cyclones
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Satellite Analysis of Tropical Cyclones
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Do the size extimates
correlate with the
observations?
Answer: Yes
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Satellite Analysis of Tropical Cyclones
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Pressure Estimation
MTCSWA
Climatology
(Dvorak 1975)
Bias
0.5
2.4
MAE
6.8
7.0
RMSE
9.5
9.2
R2 [%]
84
82
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Satellite Analysis of Tropical Cyclones
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Interpreting the Verification
Strengths
Weaknesses
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• 64-kt winds too large, which
causes central pressure
estimates to be too low for
the most intense systems.
• 34-kt winds a little too small
• Negative biases in SE (NE)
quadrant in the N.
Hemisphere (Southern
Hemisphere)
• Most of the inner core
errors are associated with
poorly estimating the radii of
maximum winds
Always available
Global
Available every 6 hours
Wind radii well correlated
with storm radii
• Errors are generally lower
than climatology (Knaff et
al. 2007), except in the SE
quadrant.
• Central pressure estimates,
particularly for the Vmax <
100 kt.
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Satellite Analysis of Tropical Cyclones
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Review of the
purpose
Develop a product that
uses existing TC surface
and near-surface wind
information to construct
an analysis of the 2dimensional structure of
the surface wind around
TC.
•Uses existing satellite
inputs
•Combines their strengths
•Produces and analysis
with lower errors than any
of the inputs.
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Satellite Analysis of Tropical Cyclones
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Questions?
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Satellite Analysis of Tropical Cyclones
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Additional information/reading
Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson and M. G. Seybold,
2011: An automated, objective, multi-satellite platform tropical cyclone
surface wind analysis. Submitted to J. Appl. Meteorol.
Knaff, J. A., C. R. Sampson, M. DeMaria, T. P. Marchok, J. M. Gross, and C. J.
McAdie, 2007: Statistical Tropical Cyclone Wind Radii Prediction Using
Climatology and Persistence, Wea. Forecasting, 22:4, 781–791.
Mueller, K.J., M. DeMaria, J.A. Knaff, J.P. Kossin, T.H. Vonder Haar: 2006:
Objective Estimation of Tropical Cyclone Wind Structure from Infrared
Satellite Data. Wea. Forecasting, 21:6, 990–1005.
Bessho, K., M. DeMaria, J.A. Knaff , 2006: Tropical Cyclone Wind Retrievals
from the Advanced Microwave Sounder Unit (AMSU): Application to Surface
Wind Analysis. J. of Applied Meteorology. 45:3, 399 - 415.
Demuth, J., M. DeMaria, and J.A. Knaff, 2006: Improvement of Advanced
Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation
Algorithms, J. Appl. Meteor. Clim., 45:11, 1573–1581.
Demuth, J. L., M. DeMaria, J. A. Knaff, and T. H. Vonder Haar, 2004: Validation
of an advanced microwave sounder unit (AMSU) tropical cyclone intensity
and size estimation algorithm, J. App. Met., 43, 282-296.
Real-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/
and
WMO International Workshop on
Satellite
Analysis of Tropical Cyclones
http://www.ssd.noaa.gov/PS/TROP/mtcswa.html
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