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A Multi-platform (i.e, Satellite) Tropical Cyclone Surface Wind Analysis
John Knaff, NOAA/NESDIS/StAR, RAMMB, Fort Collins, CO, USAMark 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, USAMatthew Seybold, NOAA/NESDIS/OSDPD, Suitland, MD, USA
Graciously Presented byAndrew 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– 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.
2WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Solution• Global product that combine satellite-based winds or Multi-
platform 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
3
Real-time cases available at http://rammb.cira.colostate.edu/products/tc_realtime/andhttp://www.ssd.noaa.gov/PS/TROP/mtcswa.html
WMO International Workshop on Satellite Analysis of Tropical Cyclones
Input Data
• 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)
4WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: AMSU-Based Balanced WindsBessho 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.
5WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: AMSU-Based Balanced Winds Bessho et al. (2006)
Advanced Microwave Sounding Unit (AMSU) – Based, by-product 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
center• Too asymmetric
Hurricane Paloma 7 Nov 2008 2225 UTC2 km resolution
6WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: Surface Scatterometry
• 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
7WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: Surface ScatterometryA-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• Attenuation/contamination in
heavy rain 2 km resolutionHurricane Paloma 8 Nov 2008 0545 UTC
8WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: Cloud/Feature Tracked Winds
• Routinely available
• Accurate
• But low-level winds are often not available near the core of TCs
9WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: Cloud/Feature Track windsvarious 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
Hurricane Paloma 8 Nov 2008 0545 UTC4 km resolution
10WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: IR Flight-Level Analog WindsMueller et al. (2006)
• Relatively new development
• Provides representative winds near the core of the TC
• Makes a surface analysis possible
11WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Input: IR Flight-Level Analog WindsMueller 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
estimateShortcomings• Too symmetric• Cases of small radius of
maximum winds or multiple wind maxima
Hurricane Paloma 8 Nov 2008 0600 UTC1 km resolution
12WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 06UTC)
Scatterometry Cloud / Feature winds
13WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 00UTC)
AMSU Balanced Winds IR flight-level analog
14WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Sample Analysis of Hurricane Paloma 8 Nov 2008 06UTCAnalysis:
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
15WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Real-Time Products
Graphical Products• Surface Wind Analysis
– 10 degree– 4 degree
• Inputs reduced/turned to the surface– AMSU– SCAT– CDFT– IRWD
• Time series of Vmax & Central pressure (CP)
• Kinetic Energy (ftp)• IR image
Text Products (ftp)Input:• Input assumptions• Raw Input data (ascii)• 600km environmental pressure Products• Fix file (ATCF formatted)• Surface Winds (ascii)
– Polar grid– Azimuthal average
• Analysis level Winds (ascii)• GrADS binaries & .ctl files• Kinetic Energy• Vmax and CP
16WMO International Workshop on
Satellite Analysis of Tropical Cyclones
http://www.ssd.noaa.gov/PS/TROP/mtcswa.html
17WMO International Workshop on
Satellite Analysis of Tropical Cyclones
18WMO International Workshop on
Satellite Analysis of Tropical Cyclones
19WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Real-World ExampleNorthern Hemisphere/Sheared TC
HurricaneKyle2008
20WMO International Workshop on
Satellite Analysis of Tropical Cyclones
21WMO International Workshop on
Satellite Analysis of Tropical Cyclones
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)
22WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Vs. H*Wind (all cases)
23WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Vs. H*Wind (> 64 kt cases)
24WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Vs. H*Wind (≤ 64 kt)
25WMO International Workshop on
Satellite Analysis of Tropical Cyclones
26WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Do the size extimates correlate with the observations?
Answer: Yes
27WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Pressure Estimation
MTCSWA Climatology
(Dvorak 1975)
Bias 0.5 2.4
MAE 6.8 7.0
RMSE 9.5 9.2
R2 [%] 84 82
28WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Interpreting the Verification
Strengths• 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.
Weaknesses• 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
29WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Review of the purposeDevelop a product that uses existing TC surface and near-surface wind information to construct an analysis of the 2-dimensional 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.
30WMO International Workshop on
Satellite Analysis of Tropical Cyclones
Questions?
31WMO International Workshop on
Satellite Analysis of Tropical Cyclones
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/andhttp://www.ssd.noaa.gov/PS/TROP/mtcswa.html
32WMO International Workshop on
Satellite Analysis of Tropical Cyclones
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