Radiometric Characteristic of Cassini RADAR Imagery Bryan

Global Tropical Cyclone Winds from the QuikSCAT and OceanSAT-2 Scatterometers
Bryan W. Stiles1, Rick Danielson2, W. Lee Poulsen1, Alexander Fore1, Michael J. Brennan3, Tsae-Pyng J. Shen1, and Svetla M. Hristova-Veleva1 (Email contact [email protected])
1.Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr. Pasadena CA, USA, 91109, 2. University Corporation for Atmospheric Research, PO Box 3000, Boulder CO, 80307 3. NOAA/NWS/NCEP/National Hurricane Center, 11691 SW 17th St, Miami FL, USA, 33165
© 2012. All rights reserved.
Synopsis of Technique
Using a simple neural network (e.g. [1]), we fit a nonlinear mapping from
scatterometer data to wind speed.
Inputs are:
Climatological Distributions of winds from
1999-2009 in the North Atlantic Basin
Nominal QuikSCAT (version 2) Winds:
Hurricane Ivan 23:37 UTC 11 Sept. 2004
8 sets of backscatter (Normalized Radar Cross-Section) values
2 different azimuths,
2 different polarizations (and incidence angles),
2 different spatial scales (12.5 and 87.5 km)
– a rain rate from the scatterometer noise channel [3].
– cross track distance as a proxy for viewing geometry
– Information from version 3 of QuikSCAT global wind retrieval product
Each sub-network is a weighted sum of tanh functions of 10-20 weighted sums of the inputs.
Weights are constants that are set during a preliminary training procedure.
Ground truth speeds are from H*WIND data from 2005 Atlantic hurricanes.
Speed corrected for rain
Maximum likelihood speed (no correction for rain)
Rain Impact quantity
Structure employs a set of sub-networks to simplify the mappings needed.
Used for fitting network weights.
Attempt to correct wind direction in rain is left for future work.
– Nominal direction retrievals from JPL QuikSCAT L2B products are maintained.
Neural Network Structure
Speed Net 2
Trained on 2005
QRAD rain rate[3]
Rain Impact Quantity
MLE speed
Neural Net Winds:
Hurricane Ivan 23:37 UTC 11 Sept. 2004
CAT 1-3 by Quadrant
Version 3 speed
Rain Corrected
Speed Network 1
Trained on
AMSR/ SeaWinds Data
QuikSCAT MLE speed
Hurricane Speed Net 1
Trained on 2005
Backscatter values
Hurricane Katrina (2005) as observed by QuikSCAT just before
landfall; Same scale as Sandy above; 300 mile diameter TS winds
CAT 4-5 by Quadrant
QRAD rain rate
QuikSCAT 1999-2009 Data Set
QuikSCAT vs H*Wind (Atlantic)
OceanSAT-2 Tropical Cyclone Winds
Examples Maximum Speed Tracks – Ivan 2004
Validation of QuikSCAT Winds. Vs. Best Track Max Speeds
• 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
Comparison between maximum best-track intensity (x-axis) and maximum
QuikSCAT winds (y-axis) for all storm overpasses from 1999-2009. Different
panels (a-f) correspond to different basins. Compared to neural net winds,
version 2 winds are biased high in low intensity storms (due to rain effects)
and saturate quickly above 30 m/s. Version 3 winds saturate quickly at
higher winds but perform well below 30 m/s.
OceanSAT-2 is a 13.4 GHz ocean wind
scatterometer operated by the Indian Space
Research Organization (ISRO).
For the past two years ISRO has been
collaborating with NASA/JPL and NOAA to
refine the calibration of the OceanSAT-2
backscatter data.
The goal of the collaboration is to extend the
Ku-band scatterometer wind data record
initiated by QuikSCAT.
A crucial element of this effort has been the
repointing of the QuikSCAT instrument to
match the OceanSAT-2 viewing geometry
(higher incidence angle).
Although QuikSCAT ceased nominal
operations in November 2009, its precisely
calibrated backscatter measurements remain
useful for cross-platform calibration.
•OceanSAT-2 flyover of Cyclone
Thane, Dec 28, 2011.
•12.5 km MLE retrieval
•Similar to QuikSCAT v2
The different colors represent different QuikSCAT 12.5-km wind
retrievals: Neural Network wind speed (red), nominal version 2 JPL
L2B12 HDF product wind speed (blue) and version 3 JPL wind speed
(green). Neural Net winds are optimized for high wind (>20 m/s)
conditions with and without rain. Version 2 QuikSCAT data is
optimized for speeds < 20 m/s and rain free conditions. Version 3 is
optimized for < 20 m/s winds with and without rain. All grid cells
with retrieved winds are presumed valid for purposes of comparison.
No rain flagging is utilized.
Dashed lines are binned by ground truth speeds (x-axis). Solids lines
are binned by QuikSCAT speeds (y-axis). Number of samples in each
bin are reported on the plots. Thin red solid and dashed lines indicate
bin locations. For statistical reasons, random errors in ground truth
winds lead to biases in the retrieved speed vs. truth curve. The sign
of the bias differs depending upon which binning is used. A curve
bisecting the area between the dashed and solid lines of the same
color is a good proxy for QuikSCAT speed as a function of the true
speed. Saturation results in truncation of the solid lines.
[1] B. W. Stiles and R.S. Dunbar, “A Neural Network Technique For Improving The
Accuracy Of Scatterometer Winds In Rainy Conditions.” IEEE TGARS, Vol 48 ,
No. 8, P 3114-3122, 2010.
[2] B. W Stiles, S. Hristova-Veleva, et al, “Obtaining Accurate Ocean Surface Winds
In Hurricane Conditions: A Dual Frequency Scatterometry Approach,” IEEE
TGARS, Vol 48 , No. 8, P 3101-3113, 2010.
[3] Ahmad, K. A., W. L. Jones, T. Kasparis, S. W. Vergara, I. S. Adams, and J. D. Park,
“Oceanic rain rate estimates from the QuikSCAT Radiometer: A Global Precipitation
Mission pathfinder”, J. Geophys. Res., 110, D11101, 26 pages, 2005.
Hurricane Sandy (2012) as observed by OceanSAT-2 1 day before
landfall as a tropical storm, 720 mile diameter of TS force winds
Mean wind speed as a
function of km from
storm center (y-axis)
and days (x-axis)
from maximum
Top = Cat 1-3 storms
Bottom = Cat 4-5
Storm Movement Direction
We have produced a comprehensive set of tropical cyclone
storm wind retrieval scenes for all ten years of the QuikSCAT
mission and a sample set of storms observed by OceanSAT-2.
The wind speeds were corrected for rain and optimized to avoid
saturation at high winds using an artificial neural network
method similar to that in [1] and [2]. The QuikSCAT wind
imagery and the quantitative speed, direction, and backscatter
data can be obtained at The
QuikSCAT wind speeds have been validated against best track
intensity (i.e., maximum wind speeds), H*WIND tropical
cyclone (TC) wind model analysis fields, and wind speeds from
aircraft over flights (GPS drop wind sondes and step frequency
microwave radiometer (SFMR) wind measurements). Storms
from all basins are included for a total of 21600 scenes over the
ten years of nominal QuikSCAT operations. Of these, 11435
scenes include the best track center of the cyclone in the
retrieved wind field. Among these, 3295 were of tropical storms
and 788, 367, 330, 289, and 55 were of category 1, 2, 3, 4 and 5
hurricanes, respectively, on the Saffir-Simpson Hurricane Wind
In addition to the QuikSCAT TC winds, we have also
processed wind fields from the Indian Space Research
organization (ISRO) OceanSAT-2 satellite. OceanSAT-2
employs a scanning pencil beam Ku-band scatterometer with a
design similar to QuikSCAT. JPL and NOAA have been
working extensively with ISRO to aid in cross calibration
between OceanSAT-2 and QuikSCAT. Toward this end the
QuikSCAT instrument has been repointed in order to acquire
data at the OceanSAT-2 incidence angles, and several meetings
in India between the teams have taken place. The neural
network that was trained on QuikSCAT data was used to
retrieve OceanSAT-2 winds. The backscatter inputs to the
network were transformed to match the histograms of the
corresponding values in the QuikSCAT data set.
To date the ISRO/NASA/NOAA
collaboration has resulted in:
– More robust wind retrieval in low wind
– Improved wind accuracy as compared to
numerical wind products and buoys.
– Ongoing monitoring of calibration drift
by comparison between QuikSCAT and
OceanSAT-2 backscatter values.
The fruitfulness of the collaboration is
further illustrated by the tropical
cyclone winds on the following
– The OceanSAT-2 operational wind
product is binned at 50 km with a
conservative land mask employed to
insure accurate winds.
– JPL has retrieved winds at higher (12.5
km) resolutions and closer to the coast.
– QuikSCAT neural network processing
has been applied to OceanSAT-2 data.
• The work reported here was performed at the Jet Propulsion
Laboratory, California Institute of Technology, and at the National
Hurricane Center under contract with the National Aeronautics and
Space Administration.
• We would like to thank the Indian Space Research Organization for
providing the excellent OceanSAT-2 scatterometer data set.
• The work described here is funded by NASA’s Ocean Vector Winds
• The website portal used to distribute the data set is funded by
NASA’s Hurricane Science Research program.
Acronyms: MAE = Mean Absolute Error, MBE = Mean
Bias Error, FSP=Frequency of Superior Performance, TC= tropical
cyclone, GPS=Global Positioning System, SFMR=Step Frequency
Microwave Radiometer, H*WIND=Hurricane Research Division
Real-Time Wind Analysis System Wind Fields, NHC = National
Hurricane Center, JTWC=Joint Typhoon Warning Center,
TS=Tropical Storm, ANN=Artificial Neural Network, CTD =Cross
Track Distance, MLE = Maximum Likelihood Estimation
• Wind tropical cyclones speed fields have been
Examples Maximum Speed Tracks – Isabel 2003
Here blue
Here blue
• NNet has slight positive bias at < 40m/s (H*Wind also positively biased)
Green denotes superior QuikSCAT retrieval
Max. Wind (m/s)
34-kt Radii (km)
•OceanSAT-2 flyover of Cyclone
Thane, Dec 28, 2011.
•Neural Network wind speeds
using histogram matching of
– Optimized for accuracy
– Produced for all ten years of the QuikSCAT mission including over
5,000 observations of tropical storms and above.
– Validated vs. H*WIND, SFMR, GPS drops, and best track wind
– Made available online to the community at large in a browsable data
• Ancillary data such as backscatter imagery and co-located rain information are
also included.
• We now begin the scientific exploitation of the QuikSCAT TC
– Initial investigations will include:
• Distribution of max winds as a function of storm movement and geographic
• Correlation of storm features with rapid intensification and de-intensification
• Storm size estimation and trend analysis
50-kt Radii (km)
64-kt Radii (km)
• We also plan to produce similar datasets for OceanSAT-2 and
– Sample OceanSAT-2 fields have been produced and we are currently
validating and optimizing them.
– ASCAT fields are planned for the following year.
• A paper describing the production and validation of the QuikSCAT
data set is in preparation.

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