Transcript Multiple Scatterometer Hurricane Winds

Assessment of Neural Network Retrieval of Outer
Core Surface Wind Structure and Intensity from
QuikSCAT's Tropical Cyclone Overpasses
Rick Danielson1 Mike Brennan2 Bryan Stiles3
Lee Poulsen3 and Svetla Hristova-Veleva3
1UCAR visiting scientist at
2NOAA/NWS/NCEP National Hurricane Center
3NASA/Caltech Jet Propulsion Laboratory
• In the battle to win the hurricane season (Mayfield
2005) what happens between seasons (launches)
can define success (in a satellite mission), e.g.:
• design of QuikSCAT did not envision quantitative
passive obs being used (Ahmad et al. 2005)
• H*Wind analyses used to re-train WindSAT
passive retrieval (Meissner and Wentz 2009)
Scatterometer Platforms
ASCAT
Metop A: 2006-
OSCAT
Oceansat-2: 2009-
QuikSCAT
1999 - 2009
Frequency
5.3 GHz (C)
13.5 GHz (Ku)
13.4 GHz (Ku)
Geometry
6 Fan Beams
2 Rotating Pencil
2 Rotating Pencil
Polarization
VV
HH inner / VV outer
HH inner / VV outer
Altitude
817 km
720 km
803 km
Sun-Sync Orbit / Repeat
101 min / 29 days
99 min / 2 days
101 min / 4 days
Footprint
900 km2 and 2500 km2
950 km2 / 1600 km2
580 km2 / 740 km2
Incidence Angles
25o – 53o 34o – 64o
49o / 57o
46o / 54o
Swath
550km 700km-gap 550km
1400 km / 1836 km
1400 km / 1800 km
Continuity
Metop B: 2012
Oceansat-3: 2015
RapidSCAT: 2014
SMAP: 2014
1.3 GHz (L)
(only Gulf of
Mexico?)
Retrieval Training
• Operational QuikSCAT winds
required an active-sensing
backscatter-wind relationship:
“JPL-V1” (“V2” too strong)
Isaac at 2300 UTC
Aug 27
• Meissner and Wentz (2009)
45 kt
re-trained passive-sensing (NHC ~60kt)
WindSAT on H*Wind; was
impetus for an update to V2:
“Ku-2011” (used in JPL-V3)
• Neural network passes V3
winds, except in rain where
only speed is modified (assuming rain attenuation and/or
backscatter is not completely dominant)
NRCS scatterplots for 20,30, and 40 m/s H*WIND
(QuikSCAT/H*WIND matched within 2 hours, Clear conditions)
• In rainfree conditions (rain impact quantity <= 2.5), QuikSCAT HH pol 46 degree
incidence NRCS values are sensitive to wind speed and direction in the 20-40 m/s
range.
• QuikSCAT VV 54 degree incidence values have less sensitivity.
(Blue, Green, Red) = (20,30,40) m/s + or -10% H*WIND
HH pol, 46° Inc.
2011/05/09, B. W. Stiles, JPL
VV pol, 54° Inc.
NRCS scatterplots for 20,30, and 40 m/s H*WIND
(QuikSCAT/H*WIND matched within 2 hours, Rainy conditions)
• In rainy conditions (rain impact quantity > 2.5), the wind sensitivity of both
polarizations are reduced especially for VV pol, but still apparent at least for
moderate rain.
(Blue, Green, Red) = (20,30,40) m/s +or -10% H*WIND
HH pol, 46° Inc.
2011/05/09, B. W. Stiles, JPL
VV pol, 54° Inc.
2011/05/09, B. W. Stiles, JPL
2011/05/09, B. W. Stiles, JPL
Neural Network Structure
FINAL
QRAD rain rate
Rain Impact
MLE speed
Speed Net 2
OUTPUT
SPEED
Version 3 speed
Rain Corrected
Speed Network 1
Trained on
AMSR/SeaWinds
CTD
CTD
QuikSCAT MLE speed
Hurricane Speed Net 1
Trained on 72
QuikSCAT/H*WIND
scenes from 2005
Backscatter values
QRAD rain rate
Rainfree NRCS vs Wind Speed
QuikSCAT
(co-pol)
50-60 m/s
sensitivity limit
(Fernandez
et al. 2006)
OSCAT (co-pol; by
histogram matching)
Cross-pol sensitivity limit?
(Vachon and Wolfe 2011)
Global Comparisons
• comparing 21600 cyclone overpasses of Oct 1999 – Nov 2009
(see http://tropicalcyclone.jpl.nasa.gov) to IBTrACS yields:
• 6903 NHC-JTWC scenes (or 5952 WMO scenes) with neural
net coverage of the center and 73 neural net training scenes
QuikSCAT vs H*Wind (Atlantic)
• collocations between 50 and 500 km
• NNet bin averages are closer together, but suggest positive bias
• JPL-V2,V3 weaker winds, bin averages farther apart, and negatively biased
QuikSCAT vs SFMR and GPS drops
• NNet has slight positive bias at < 40m/s (H*Wind also positively biased)
• DiNapoli et al. (JAOT 2012) suggest poor rain flagged QuikSCAT winds
in H*Wind analyses, but maybe V2 (over-)retrievals are an issue too(?)
QuikSCAT–NHC Best Track
34-kt Radii (km)
Max. Wind (m/s)
N
MBE
MAE
HWind
521
-2.7
56.9
44%
N.Net
1587
25.2
63.8
38%
5.8
36%
JPL v3
1587
-3.2
60.6
36%
8.9
20%
JPL v2
1587
44.4
83.6
26%
N
MBE
MAE
HWind
155
-3.1
4.2
N.Net
1274
1.4
5.1
JPL v3
1274
-0.5
JPL v2
1274
5.4
FSP
50-kt Radii (km)
N
MBE
MAE
HWind
422
-6.0
30.0
N.Net
1094
6.2
36.7
JPL v3
1094
-11.1
JPL v2
1094
73.0
FSP
64-kt Radii (km)
FSP
N
MBE
MAE
HWind
313
4.1
20.7
41%
N.Net
697
8.0
32.4
40%
40.7
37%
JPL v3
697
-20.2
34.4
36%
91.9
22%
JPL v2
697
56.7
81.3
24%
Green denotes superior QuikSCAT retrieval
FSP
QuikSCAT vs NHC+WMO Max Wind
QuikSCAT vs NHC+JTWC Max Wind
34-kt Radii (km) Difference (H*Wind/QScat – NHC)
Atlantic
H*Wind
NNet
JPL-V3
East Pac
NNet
JPL-V3
Hur (534)
+17 (285)
+16
-16
Hur (420)
-11
-36
TS (1053)
-27
+30
+4
TS (1341)
+24
+13
Aug 24
2006
NHC
125 kt
(236)
QuikSCAT Wavenumber-1 Asymmetry
QuikSCAT Wavenumber-1 Asymmetry
Ueno and Kunii (JMSJ 2008)
TC motion > Shear
Shear > TC motion
QuikSCAT–NHC Best Track
Asymmetry of a scene
is sum over all valid
34/50/64-kt radii of
Quad radius – Quad Avg
Quad Avg
Summary
• QuikSCAT neural net active+passive+geometric
wind retrieval seems to mitigate rain impacts in TC
outer core. By comparison with H*Wind, SFMR, GPS
drops, IBTrACs
a) neural net seems to have a slight high bias, but
b) rain contamination is reduced at low winds
c) more extreme wind speeds can be retrieved
• Small TCs, more common in the East Pacific, are
expected to be difficult to resolve (by most satellites)
• Outer core asymmetry (from 34/50/64-kt radii) is
slightly greater than that of operational best estimates
and H*Wind analyses, but is its shear and precipitation
dependence really synonymous (Ueno and Bessho 2011)?
Acknowledgements : UCAR, NOAA, and NASA’s Ocean Vector Winds program
OSCAT Neural Net Experiment
• OceanSAT-2 flyover of Hurricane Irene,
Aug 27, 2011
• Radar backscatter produced by ISRO
using initial calibration
• Winds retrieved by NASA JPL at 25 km
resolution
• OceanSAT-2 flyover of Cyclone Thane,
Dec 28, 2011
• Radar backscatter produced by ISRO
using updated calibration
• Winds retrieved by NASA JPL at 12.5 km
resolution, making use of improved
calibration of high resolution “slice”
backscatter measurements
OSCAT Neural Net Experiment
• OceanSAT-2 flyover of Hurricane Irene,
Aug 27, 2011.
• Updated Calibration
• 12.5 km resolution
• OceanSAT-2 flyover of Cyclone Thane,
Dec 28, 2011.
• Updated Calibration
• 12.5 km resolution
• Neural Network wind speeds retrieved using histogram
matching of brightness temperature and NRCS inputs
References
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Ahmad, K. A., W. L. Jones, T. Kasparis, S. W. Vergara, I. S. Adams, and J. D. Park (2005), Oceanic rain
rate estimates from the QuikSCAT Radiometer: A Global Precipitation Mission pathfinder, J.
Geophys. Res., 110, D11101, 26 pages, 2005.
Chan, Kelvin T. F., Johnny C. L. Chan, “Size and Strength of Tropical Cyclones as Inferred from
QuikSCAT Data”. Monthly. Weather. Review., 140, 811–824. 2012.
Chavas, D. R. and K. A. Emanuel, “A QuikSCAT climatology of tropical cyclone size,”Geophysical
Research Letters, VOL. 37, L18816, doi:10.1029/2010GL044558, 2010.
Donelan M. A., Haus B. K., Reul N., Plant W. J., Stiassnie M., Graber H. C., Brown O. B., Saltzman E.S
(2004). “On the limiting aerodynamic roughness of the ocean in very strong winds”. Geophysical
Research Letters, 31(18), -. Publisher's official version: http://dx.doi.org/10.1029/2004GL019460,
Open Access version : http://archimer.ifremer.fr/doc/00000/10873/
Fernandez, D. Esteban; Carswell, J. R.; Frasier, S.; Chang, P. S.; Black, P. G.; Marks, F. D. “Dualpolarized C- and Ku-band ocean backscatter response to hurricane-force winds,” J. Geophys. Res.,
Vol. 111, No. C8, 2006.
Congling Nie; David G. Long; , "A C-Band Wind/Rain Backscatter Model," Geoscience and Remote
Sensing, IEEE Transactions on , vol.45, no.3, pp.621-631, March 2007, doi:
10.1109/TGRS.2006.888457
Stiles, B.W.; Dunbar, R.S.; , "A Neural Network Technique for Improving the Accuracy of
Scatterometer Winds in Rainy Conditions," Geoscience and Remote Sensing, IEEE Transactions on ,
vol.48, no.8, pp.3114-3122, Aug. 2010
Stiles, B. W., and Yueh, S. H., “Impact of rain on Spaceborne Ku-Band Wind Scatterometer Data,”
IEEE Transactions on Geoscience and Remote Sensing, Vol 40, No 9, pp 1973-1983, 2002.
Uhlhorn, E. W., and P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007:
“Hurricane surface wind measurements from an operational Stepped Frequency Microwave
Radiometer”. Mon. Wea. Rev., 135, 1370–1385.
Neural Network Configuration
• Proof of concept:
where Ku attenuation
is incomplete, a neural
network can distinguish
rain and wind info locally
using only multiple views
• Stiles, Hristova-Veleva,
et al. (2010) simulated
two-way path-integrated
extinction & backscatter
from WRF hydrometeors
QuikSCAT
wind speed
Rita
Sept 21
2005
Examples Maximum Speed Tracks – Ivan 2004
Wind Speed Neural Network
• Training:
72 H*Wind Analyses (from
2005) by HRD (<10h shift)
• Validation:
1329 Atlantic overpasses
with 38 (non-2005)
H*Wind analyses by UNC
Rain /
No Rain
Nets
Rain
Impact
Net
Multiple
Speed
Nets
• single layer networks with
up to 50 nodes and
O[1000] weights
10 QuikSCAT inputs:
• active JPL-V3 wind retrieval
• passive noise channel rain rate (Ahmad et al. 2005)
• active backscatter: 2 looks (fore and aft), 2 polarizations
(H and V), and 2 scales (12.5 km and 87.5 km)
Wind Speed Neural Network
Rain /
No Rain
Nets
Rain
Impact
Net
Multiple
Speed
Nets
• single layer networks with
up to 50 nodes and
O[1000] weights
10 QuikSCAT inputs:
• active JPL-V3 wind retrieval
• passive noise channel rain rate (Ahmad et al. 2005)
• active backscatter: 2 looks (fore and aft), 2 polarizations
(H and V), and 2 scales (12.5 km and 87.5 km)
Recommended Use of AMSU Radii
(NHC study by Montalvo and Landsea)
Given the skill (substantial improvement over stratified
climatology) in the AMSU size estimates and the frequent
lack of other tools, its use is recommended in the absence
of aircraft reconnaissance
Considerations in its use:
 34 kt radii too small, 64 kt too large – can adjust by ~20%
 AMSU is too round – make right front quad larger by ~20%, make
left rear quad smaller by ~20%
 AMSU method does not have enough sensitivity – smallest TCs
aren’t small enough, largest TCs aren’t big enough
 Can weight the AMSU 34 kt and 50 kt radii more for hurricanes
than tropical storms
 Can weight the AMSU 64 kt radii more for major hurricanes than
Category 1 and 2 hurricanes
Synopsis of Technique
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Using a simple neural network (e.g. Stiles and Dunbar, 2010), we fit a nonlinear
mapping
– From 9 scatterometer measurements and one geometry indicator
– To wind speed.
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Inputs are:
–
8 sets of backscatter values
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•
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2 different azimuths,
2 different polarizations,
2 different spatial scales (12.5 and 87.5 km)
– a rain rate from the scatterometer noise channel [Ahmad et al, 2005].
– cross track distance as a proxy for viewing geometry
– Information from latest of version (3) of QuikSCAT global wind retrieval product
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Speed corrected for rain
Maximum likelihood speed (no correction for rain)
Rain Impact quantity
Ground truth speeds are from H*WIND data from 2005 Atlantic hurricanes.
Structure employs a set of sub-networks to simplify the mappings needed.
Attempt to correct wind direction in rain is left for future work.
– Nominal direction retrievals from JPL QuikSCAT L2B products are maintained.
9/12/12
National Hurricane Center Visitor Seminar
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