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 –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. 2 WMO 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 and 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 ( hPa) winds (i.e., aircraft-based wind analogs) 4 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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. 5 WMO 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 UTC 2 km resolution 6 WMO 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 7 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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 Attenuation/contamination in heavy rain 2 km resolution Hurricane Paloma 8 Nov UTC 8 WMO 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 9 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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 Hurricane Paloma 8 Nov UTC 4 km resolution 10 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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 11 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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 Hurricane Paloma 8 Nov UTC 1 km resolution 12 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 06UTC) ScatterometryCloud / Feature winds 13 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Output: Quality Controlled Inputs (Hurricane Paloma 8 Nov 00UTC) AMSU Balanced WindsIR flight-level analog 14 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Sample Analysis of Hurricane Paloma 8 Nov UTC Analysis: R R R RMW 16 MSLP 950 hPa NHC Best track: R R R RMW 10 MSLP 951 hPa 15 WMO 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 V max & 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 V max and CP 16 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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Real-World Example Northern Hemisphere/Sheared TC Hurricane Kyle WMO International Workshop on Satellite Analysis of Tropical Cyclones

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Verification ( , 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) 22 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Vs. H*Wind (all cases) 23 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Vs. H*Wind (> 64 kt cases) 24 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Vs. H*Wind ( 64 kt) 25 WMO International Workshop on Satellite Analysis of Tropical Cyclones

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Do the size extimates correlate with the observations? Answer: Yes 27 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Pressure Estimation MTCSWA Climatology (Dvorak 1975) Bias MAE RMSE R 2 [%] WMO 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 29 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Review of the purpose Develop 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. 30 WMO International Workshop on Satellite Analysis of Tropical Cyclones

Questions? 31 WMO 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, 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, Real-time cases available at and 32 WMO International Workshop on Satellite Analysis of Tropical Cyclones