Augmentation of Early Intensity Forecasting in Tropical Cyclones Teams: UA: Elizabeth A. Ritchie, J. Scott Tyo , Kim Wood, Oscar Rodriguez, Wiley Black, Kelly Ryan, Miguel F. Piñeros , G. Valliere -Kelley, Ivan Dario Hernandez, Brian LaCasse
NOPP Review March 2012
Summary: The DAV technique characterizes the axisymmetry of the system and correlates this with storm intensity to develop a parametric relationship.
DAV is robust and importantly gives good results at low intensities
Eastern North Pacific:
Infrared Imagery (Stitched GOES-E /GOES-W)
Atlantic and Gulf of Mexico:
Infrared Imagery (GOES-E)
Western North Pacific:
Infrared Imagery (MTSAT)
2011 saw the successful adaptation of the DAV-T to the West Pac Basin for both Intensity and Genesis Applications and in the East Pac for Intensity (Genesis Ongoing)
The deviation angle θfrom a perfect radial is calculated for every pixel within 350 km of the reference point.
A histogram of angles is plotted. The variance of the histogram is calculated.
Deviation Angle Variance
09/18/2005 08:15 UTC 25kt
09/19/2005 14:15 UTC 55 kt
09/21/2005 14:15 UTC 130 kt
Hurricane Rita (2005)
DAV Time Series
Hurricane Rita (2005)
2D Histogram: DAV – Intensity (Atlantic)
20 deg2 x 5 kt bins
Hurricane Jeanne (2004)
2D Histogram: DAV – Intensity
Results of testing
Training: 70% of 2004-2009
Testing: 30% of 2004-2009,
Early low departures in the DAV value are robust indicators of TC genesis
Hurricane Rita (2005)
True positives are named systems that were detected at a given DAV threshold
False positives are systems fell below a given DAV threshold but did not develop
ROC curve for cyclogenesis detections during 2009 and 2010 for various deviation-angle variance threshold values in the Western North Pacific and for the Atlantic during 2004 and 2005.
Mean and median time of detection of tropical cyclones (relative to operational TD designation) during 2090 and 2010 in the Western North Pacific basin and for the Atlantic during 2004 and 2005.
Root Mean Square Error
Training: 2004-2008 Testing: 2009, RMSE: 24.8kt
An option to “fix” this kind of problem (when the real center of circulation is exposed and away from the cloud center of mass):
Use the “operational center fix” as the center pixel for the DAV calculation and only calculate the DAV within a small area of that center.
Use of Expert Centers (Gen Kelley)
Use “best track centers”
Erika RMSE reduced from 58 kt to 6 kt
Ana RMSE reduced from 29 kt to 19 kt
2009 – 2010 Estimate Improvements
We are now using subjectively fixed centers in all of our analyses, but the ability to compute both still exists and will likely be reported in any real-time studies
3-D Surface Intensity Estimation
Example of a 3-D parametric surface for all samples (2007-2009) using a combination of the two “best” radii – 250 km and 500 km for the western North Pacific.
RMSE: 12.5 kt
(all storms train-test)
DAV 250 km
DAV 500 km
Microwave Imager TC Intensity (NRL)
Microwave imager data provides structural characteristics not always found in typical Vis/IR imagery.
SSM/I 85 GHz
Leave-One(TC)-Out Cross Validation
Training and Testing
Image feature extraction
Feature selection to reduce redundant and irrelevant features
Atlantic Basin Data Set
**319 samples from 60 TC’s**
RMSE: 11.9 kts
Segmented 85 GHz
Courtesy: Rich Bankert
TC intensity – Microwave Imager
Adding 37 GHz Data
SSM/I 85 GHz channel detects convective precipitation due to high scattering from ice particles that produce very low brightness temperatures (TB). The 37 GHz channel provides a representation of the cloud liquid water as well as detection of the lower rain bands.
85 GHz Channel (H) 37 GHz Channel (H)
Brightness Temperature TB (note different color scales)
Microwave Imager TC Intensity
#1 Microwave TB feature related to TC intensity
Idealized Case: Low deviation angle variance – High axisymmetry
Example: Significant convection leads to an over-estimation of intensity. With addition of the gradient axisymmetry feature (high deviation angle variance), the disorganization of the convection is now represented for this weak TC (40 kt).
Western North Pacific Basin (Darios, Rodriguez)
Example of 10-km resolution image from the JMA Geostationary Meteorological Satellite (MT-SAT)
Data Pre-Processed by NRL and made available to UA for download.
2007 – 2009 have been processed for intensity (2010 in progress)
2009 – 2010 have been processed for genesis
2011 Data is beginning to be processed
Two-dimensional histogram of the 250-km filtered DAV samples and best-track intensity estimates using 20-deg x 5-kt bins for 40 tropical cyclones of 2007 and 2008. The black line corresponds to the best-fit sigmoid curve for the median of the samples.
Intensity estimates and best-track intensities for 2009, using 2007 to 2008 to calculate the parametric curve. The RMSE is 15.7 kt (8.07 m/s).
Estimate radius is 250 km
Eastern North Pacific Basin (Kim Wood)
Example of 10-km resolution image stitched together from GOES-EAST and GOES-WEST IR satellite data. Approximate bounds of image: 170 W to 79 W, 3 S to 39 N.
GOES-WEST: 1430 UTC 22 June 2010
GOES-EAST: 1445 UTC 22 June 2010
Hurricane Celia is a minimal category 2 hurricane at this time.
Eastern Pacific Training Data
Sigmoid from training
all 80 storms from
2005 to 2010
Best RMSE at 250 km
R = 250 km
wind speed (kt)
2005 RMSE: 13.3 kt at 200 km
wind speed (kt)
13.4 kt RMS Error for 2005 (training on 2006 – 2010)
14.9 kt RMS Error for entire 2005 – 2010 data set
What about the low wind speeds?? (Kelly Ryan)
What about the low wind speeds??
Hurricane Rita (2005)
We recently published an interactive, web-based tool that allows access to portions of the DAV database. System is currently configured in “detect” mode for genesis, with access to the Atlantic database and a subset of the WNP data.
Ongoing and Future Work
Piñeros, M. F., E. A. Ritchie, and J. S. Tyo 2008: Objective measures of tropical cyclone structure and intensity change from remotely-sensed infrared image data. IEEE Trans. Geosciences and remote sensing.46, 3574 – 3580.
Piñeros, M. F., E. A. Ritchie, and J. S. Tyo 2010: Detecting tropical cyclone genesis from remotely-sensed infrared image data. IEEE Trans. Geosciences and Remote SensingLetters, 7:826 – 830.
Piñeros, M. F., E. A. Ritchie, and J. S. Tyo 2011: Estimating tropical cyclone intensity from infrared image data. Wea. Forecasting, 26:690 – 698
Ritchie, E. A., G. Valiere-Kelly, M. F. Piñeros, and J. S. Tyo, “Tropical cyclone intensity estimation in the North Atlantic basin using an improved deviation angle variance technique,” Submitted to Weather & Forecasting, December 2011
Piñeros, M. F., I. Darios Hernandez, E. A. Ritchie, and J. S. Tyo, “Deviation Angle Variance Technique for Tropical Cyclones Intensity and Genesis in the Western North Pacific,” in preparation for submission to Weather & Forecasting
Wood, K. W., M. F. Piñeros, E. A. Ritchie, and J. S. Tyo, “Estimating Tropical Cyclone Intensity in the Eastern North Pacific from GOES-E/GOES-W Infrared Data,” in preparation for submission to Weather & Forecasting
Upcoming Talks & Posters
Elizabeth A. Ritchie, Wiley Black, J. Scott Tyo, M. F. Pineros, Kimberly M.Wood, Oscar Rodriguez-Herrera, Matthew E. Kucas, and James W. E. Darlow, “A Web-based Interactive Interface for Researching and Forecasting Tropical Cyclone Genesis and Intensity using the Deviation Angle Variance Technique,” submitted to the 2012 Interdepartmental Hurricane Conference, Charleston, SC, March 2012 – MONDAY EVENING POSTER SESSION
E. A. Ritchie, M. F. Piñeros, J. Scott Tyo, Kimberly M. Wood, Genevieve Valliere-Kelley, Wiley Black, Oscar Rodriguez-Herrera, and Ivan Arias Hernández, “Tropical Cyclone Intensity Estimation and Formation Detection using the Deviation Angle Variance Technique,” submitted to the 2012 Interdepartmental Hurricane Conference, Charleston, SC, March 2012 – TUESDAY 3:45 PM
Miguel F. Piñeros, Elizabeth A. Ritchie, J. Scott Tyo, Kim M. Wood, Genevieve Valliere-Kelley, Ivan Arias Hernández, Wiley Black, and Oscar Dominguez , “Tropical Cyclone Intensity Estimation and Formation Detection using the Deviation Angle Variance Technique,” submitted to the American Meteorological Socitey 30th Conference on Hurricanes and Tropical Meteorology, April 15-20, Ponte Vedra Beach, FL, USA.
Atlantic and Western N. Pacific Supported
Navigate to Atlantic storm 2004 Matthew and go back
When we selected Matthew it took us to it, and checked
the storm as displayed. Here we see the color key for
the storm track.
We can see the storm track for Matthew displayed as we
selected. Notice the small red circle – it shows that DAV recently
detected a storm center at that spot.
We step forward one hour. We see a storm icon appear,
indicating that DAV has detected a potential storm formation
here. Also stepping forward an hour put us after the start of the
best track, and we can see a circle indicating the current best track
Pointing our mouse over the storm icon gives us
numerical details about the detection. We can also point
at the best track to find out when the storm crossed a point.
We can click on “Animate…” to see a popup menu appear.
animation takes a while to generate, so we use the
default of 6 frame. Animations up to 60 frames can be
generated, but take a little while.
After we’re done with animations, we click Unanimate.
We can manage clutter on the main display and look at
different overlays with the “Display Options…” popup. Let’s
turn on the Variance Overlay from here.
The Variance Overlay appears in red. Here, the IR (cloud)
overlay is still present, as well as the lat/long grid and
detection markers. Note the small marker on the
colorbar – this indicates the current threshold setting
for identifying a potential storm system.