a multi-platform (i.e, satellite) tropical cyclone surface wind analysis john knaff,...

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)



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.



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



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



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



Input: Cloud/Feature Tracked Winds

• Routinely available

• Accurate

• But low-level winds are often not available near the core of TCs



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




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



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




Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 06UTC)

Scatterometry Cloud / Feature winds



Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 00UTC)

AMSU Balanced Winds IR flight-level analog



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



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



http://www.ssd.noaa.gov/PS/TROP/mtcswa.html



Real-World ExampleNorthern Hemisphere/Sheared TC

HurricaneKyle2008



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)



Vs. H*Wind (all cases)



Vs. H*Wind (> 64 kt cases)



Vs. H*Wind (≤ 64 kt)



Do the size extimates correlate with the observations?

Answer: Yes



Pressure Estimation

MTCSWA Climatology

(Dvorak 1975)

Bias 0.5 2.4

MAE 6.8 7.0

RMSE 9.5 9.2

R2 [%] 84 82



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



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.



Questions?



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



a multi-platform (i.e, satellite) tropical cyclone surface wind analysis john knaff,...

Documents

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hpa winds

representative winds

wmo international workshop

surface analysis possible

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balanced winds bessho