Hurricane Wind Retrieval Algorithm Development for the

Imaging Wind and Rain Airborne Profiler (IWRAP)

MS Thesis ProjectSanthosh Vasudevan

End of Semester MeetingDecember 10, 2005

Central Florida Remote Sensing Lab

Thesis Objective

• Develop a hurricane wind vector retrieval algorithm for the UMass dual frequency (C-SCAT and Ku-SCAT) Imaging Wind and Rain Airborne Profiler (IWRAP)

• Provide a real time simulation of hurricane wind vector retrieval

2 286 meters

IWRAP Overview

Ocean Surface Sigma-0 Measurements

•Collected during 360 deg conical scan

• Data are averaged into 32 az sectors (11.25° bins)

• Grouped into wind vector cells (WVC)

• WVC’s are chosen to be 1 km x 1 km

• Swath comprises 4 WVC’s ( 2 on either side of sub-track)

3836 m

4000 m

2640 mWind vector cells, 1Km by 1 Km

Scan Geometry and Sigma-0 Collocation

Example–WVC 4c

• WVC 4C is populated by 6 az-bins at outer(40deg) and 8 az bins at inner(30deg) beam.

• Total of 14 az bins available for both beams

WVC 4c

Effect of A/C Attitude Variations on Sigma-0 Grouping

• Typical aircraft attitude variations are ± 2 deg in roll & pitch

• Attitude changes cause the scan geometry to change which can effect the collocation (grouping) of sigma-0’s for wind retrieval

• Effects are presented next

-2 Deg Roll,-2 Deg Pitch

Contour changed by attitude change

Actual scan contour

Result of Attitude Variability Study

• Changes in scan geometry, with typical A/C attitude changes, is negligible for WVC sig-0 collocation

• No attitude correction required for the wind vector retrieval algorithm

High Wind Speed Geophysical Model Function (GMF)

U A0 U 1 a1 U cos rel a2 U cos 2

NCRS

Inc angle

az look direction

rel up

a1 U c0 U c1 U U c2 U U2

a2 U d0 U d1 U U d2 U tanhU

d3

U

GMF - High Speed Adjustment

• C & Ku band high wind speed GMF’s are developed from experimental airborne scatterometer data obtained over 10 years of HRD flights through hurricanes (UMASS)

• GMF exhibits a slow roll-off in the power law wind exponent and causes the sig-0 to saturate with wind speed (Usat )

C-Band V-pol GMF Plot @ 30° inc

C-Band H-pol GMF Plot @ 30° inc

Wind Retrieval

• Method of Maximum Likelihood Estimator (MLE) was adopted to retrieve wind speed and direction from measured sigma-0’s

• MLE

1

n

i

meas mod 2

2

n is the no. of independent measurements

2 is the varience

Wind Vector Retrieval - 1st Results

• Wind retrieval was tested using a compass simulation– Constant wind speed & direction– Gaussian noise corrupted sig-0’s– Monte Carlo simulation 100 trials

• For case of 25m/s @ 65° constant wind-field ,the following results were obtained

C-Band Wind vector cell#1, retrieved speed

C-Band Wind vector cell#1, retrieved direction

Ku-Band Wind vector cell#1 retrieved speed

Ku-Band Wind vector cell#1 retrieved direction

Hurricane Simulation

• A simulated hurricane wind field based on hurricane Floyd used

• Resolution set to 100m by interpolation

• Noise added to the wind field

Simulated Hurricane Wind field -Magnitude

Simulated Hurricane Wind field -Direction

IWRAP scan simulation

• Using IWRAP Radar geometry ,flight altitude and speed- scan pattern generated

• The scan pattern flown over simulated wind field to generate hurricane sigma-0 measurements

IWRAP Scan pattern

Simulated flight over the hurricane

Simulated IWRAP Wind retrieval

• Data generated in stream to simulate real scenario • The streaming sig-0 measurements at 100m

resolution from the simulated flight is co-located into 1 Km WVC

• Co-located sigma-0’s grouped and averaged: magnitude and direction retrieved for 1 Km WVC using the wind retrieval algorithm

Preliminary Results: Retrieved wind magnitude

from several flights m/s

Preliminary Results: Retrieved wind magnitude from several flights

deg

Future Work

• Perform multiple retrievals .• Compare retrieved parameters with true

values to validate measurements • Add a rain flag to measurement