Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes Matthew Sitkowski CIMSS – University of Wisconsin Jim Kossin.

Slides:



Advertisements
Similar presentations
Improvements to Statistical Intensity Forecasts John A. Knaff, NOAA/NESDIS/STAR, Fort Collins, Colorado, Mark DeMaria, NOAA/NESDIS/STAR, Fort Collins,
Advertisements

J. Kossin, 65th IHC, Mar 2011 Jim Kossin NOAAs National Climatic Data Center, Asheville, NC CIMSS/University of Wisconsin, Madison, WI
A Blended, Multi-Platform Tropical Cyclone Rapid Intensification Index
Hurricanes and Climate Change: What do the Observations Show? Hurricanes and Climate Change: What do the Observations Show? 25 April 2012 Chris Landsea,
Hurricane center-fixing with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) method Tony Wimmers, Chris Velden University of Wisconsin.
By: Andrew Lee. Kaplan and Demaria 2003 Paper Findings of Previous Studies Ocean’s impact on tropical cyclone (TC) intensity: Upwelling and vertical.
Geostationary Lightning Mapper (GLM) 1 Near uniform spatial resolution of approximately 10 km. Coverage up to 52 deg latitude % flash detection day.
August- September NSF NOAA NRL NCAR UW UM AGU, San Francisco, 12 December 2006.
Participation: Mark DeMaria (NESDIS), John Knaff (CSIRA/CSU), Buck Sampson (NRL/JTWC), Michelle Mainelli (NHC), Isaac Ginis (URI), Lynn Shan (RSMAS). Work.
Advanced Applications of the Monte Carlo Wind Probability Model: A Year 1 Joint Hurricane Testbed Project Update Mark DeMaria 1, Stan Kidder 2, Robert.
Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA,
Hurricanes Earth Science Mr. Doe. Hurricane Season  Hurricane season in the Atlantic Ocean officially runs from June 1 st to November 30 th.  Every.
OPERATIONAL IMPLEMENTATION OF AN OBJECTIVE ANNULAR HURRICANE INDEX ANDREA B. SCHUMACHER 1, JOHN A. KNAFF 2, THOMAS A. CRAM 1, MARK DEMARIA 2, JAMES P.
Tropical Cyclone Applications of GOES-R Mark DeMaria and Ray Zehr NESDIS/ORA, Fort Collins, CO John Knaff CIRA/CSU, Fort Collins, CO Applications of Advanced.
The Impact of Satellite Data on Real Time Statistical Tropical Cyclone Intensity Forecasts Joint Hurricane Testbed Project Mark DeMaria, NOAA/NESDIS/ORA,
Improved Statistical Intensity Forecast Models: A Joint Hurricane Testbed Project Update Mark DeMaria, NOAA/NESDIS, Fort Collins, CO John A. Knaff, CIRA/CSU,
Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report Mark DeMaria NOAA/NESDIS/ORA,
Guidance on Intensity Guidance Kieran Bhatia, David Nolan, Mark DeMaria, Andrea Schumacher IHC Presentation This project is supported by the.
Continued Development of Tropical Cyclone Wind Probability Products John A. Knaff – Presenting CIRA/Colorado State University and Mark DeMaria NOAA/NESDIS.
A Preliminary Verification of the National Hurricane Center’s Tropical Cyclone Wind Probability Forecast Product Jackie Shafer Scitor Corporation Florida.
An Improved Wind Probability Program: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, CO Stan Kidder,
An Improved Wind Probability Program: A Joint Hurricane Testbed Project Update Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, CO Stan Kidder,
J. Kossin, 67th IHC, Mar 2013 James Kossin 1,2, William Lewis 2, Matthew Sitkowski 2, and Christopher Rozoff 2 1 NOAA’s National Climatic Data Center,
Statistical Evaluation of the Response of Intensity to Large-Scale Forcing in the 2008 HWRF model Mark DeMaria, NOAA/NESDIS/RAMMB Fort Collins, CO Brian.
Update on 2011 National Hurricane Center Proving Ground Mark DeMaria, NESDIS/STAR PG All Hands Conference Call July 22,
Tropical Cyclone Intensity Forecasting National Hurricane Center.
Improving SHIPS Rapid Intensification (RI) Index Using 37 GHz Microwave Ring Pattern around the Center of Tropical Cyclones 65 th Interdepartmental Hurricane.
Improvements to the SHIPS Rapid Intensification Index: A Year-2 JHT Project Update This NOAA JHT project is being funded by the USWRP in NOAA/OAR’s Office.
61 st IHC, New Orleans, LA Verification of the Monte Carlo Tropical Cyclone Wind Speed Probabilities: A Joint Hurricane Testbed Project Update John A.
The ARCHER automated TC center-fixing algorithm: Updates on real-time operations, accuracy and capabilities Anthony Wimmers and Christopher Velden Cooperative.
John Kaplan (NOAA/HRD), J. Cione (NOAA/HRD), M. DeMaria (NOAA/NESDIS), J. Knaff (NOAA/NESDIS), J. Dunion (U. of Miami/HRD), J. Solbrig (NRL), J. Hawkins(NRL),
Developing an Objective Identification Algorithm for Tropical Cloud Clusters from Geostationary Satellite Data By Chip Helms Faculty Advisor: Dr. Chris.
Review of NOAA Intensity Forecasting Experiment (IFEX) 2008 Accomplishments and Plans for 2009 Eric Uhlhorn, Frank Marks, John Gamache, Sim Aberson, Jason.
Development of Probabilistic Forecast Guidance at CIRA Andrea Schumacher (CIRA) Mark DeMaria and John Knaff (NOAA/NESDIS/ORA) Workshop on AWIPS Tools for.
World-Wide Lightning-Location Network: WWLLN & Tropical-Cyclone Monitoring Natalia Solorzano Jeremy Thomas Robert Holzworth Reported by: Abram R. (“Abe”)
1 Joint Hurricane Testbed (JHT) 2011 Update Transition from Research to Operations Jiann-Gwo Jiing JHT Director NHC Chris Landsea NHC Chris Landsea NHC.
The Impact of Lightning Density Input on Tropical Cyclone Rapid Intensity Change Forecasts Mark DeMaria, John Knaff and Debra Molenar, NOAA/NESDIS, Fort.
CIMSS/NESDIS-USAF/NRL Experimental AMSU TC Intensity Estimation: Storm position corresponds to AMSU-A FOV 8 [1 30] Raw Ch8 (~150 hPa) Tb Anomaly: 5.36.
New Tropical Cyclone Intensity Forecast Tools for the Western North Pacific 1 John Knaff and Mark DeMaria, NOAA/NESDIS/STAR Fort Collins, CO Joint Typhoon.
Atlantic Simplified Track Model Verification 4-year Sample ( ) OFCL shown for comparison Forecast Skill Mean Absolute Error.
Upgrades to the Rapid intensification index (RII ) John Kaplan (NOAA/HRD), Christopher Rozoff (CIMSS), Charles Sampson (NRL), James Kossin (NOAA/NCDC),
Improvements to Statistical Forecast Models and the Satellite Proving Ground for 2013 Mark DeMaria, John Knaff, NOAA/NESDIS/STAR John Kaplan, Jason Dunion,
Tropical Cyclone Rapid Intensity Change Forecasting Using Lightning Data during the 2010 GOES-R Proving Ground at the National Hurricane Center Mark DeMaria.
1 1. FY09 GIMPAP Project Proposal Title Page Revised: June 17, 2008  Title: Tropical Cyclone Forecast Product Improvement with GOES  Project Type: Product.
Operational Uses for an Objective Overshooting Top Algorithm Sarah A. Monette* #, Wayne Feltz*, Chris Velden*, and Kristopher Bedka^ Cooperative Institute.
John Kaplan (NOAA/HRD), J. Cione (NOAA/HRD), M. DeMaria (NOAA/NESDIS), J. Knaff (NOAA/NESDIS), J. Dunion (U. of Miami/HRD), J. Solbrig (NRL), J. Hawkins(NRL),
Development of a Rapid Intensification Index for the Eastern Pacific Basin John Kaplan NOAA/AOML Hurricane Research Division Miami, FL and Mark DeMaria.
Improved Statistical Intensity Forecast Models: A Joint Hurricane Testbed Year 2 Project Update Mark DeMaria, NOAA/NESDIS, Fort Collins, CO John A. Knaff,
AODT The Advanced Objective Dvorak Technique JHT Progress Report - Latest Advancements Timothy Olander, Christopher Velden, James Kossin, Anthony Wimmers,
Enhancement of SHIPS RI Index Using Satellite 37 GHz Microwave Ring Pattern: A Year-2 Update 67 th IHC/Tropical Cyclone Research Forum March 5-7, 2013.
Enhancement of SHIPS Using Passive Microwave Imager Data—2005 Testing Dr. Daniel J. Cecil Dr. Thomas A. Jones University of Alabama in Huntsville
Improvement to the Satellite-based 37 GHz Ring Rapid Intensification Index – A Year-2 Update 69 th IHC/2015 Tropical Cyclone Research Forum March 2-5,
Development and Implementation of NHC/JHT Products in ATCF Charles R. Sampson NRL (PI) Contributors: Ann Schrader, Mark DeMaria, John Knaff, Chris Sisko,
The National Hurricane Center GOES-R Proving Ground Mark DeMaria NOAA/NESDIS, Fort Collins, CO GLM Science Meeting, Huntsville, AL September 26,
2012 NHC Proving Ground Products Hurricane Intensity Estimate (HIE) Chris Velden and Tim Olander 1.
New Tropical Cyclone Intensity Forecast Tools for the Western North Pacific Mark DeMaria and John Knaff NOAA/NESDIS/RAMMB Andrea Schumacher, CIRA/CSU.
J. P. Kossin, 62 nd IHC, Charleston, SC An Objective Tool for Identifying Hurricane Secondary Eyewall Formation Jim Kossin and Matt Sitkowski Cooperative.
1 Current and planned research with data collected during the IFEX/RAINEX missions Robert Rogers NOAA/AOML/Hurricane Research Division.
Shuyi S. Chen Rosenstial School of Marine and Atmospheric Science University of Miami Overview of RAINEX Modeling of 2005 Hurricanes In the eye of Katrina.
EXTREME WINDS AND PRECIPITATION FROM SPACE
Rosenstial School of Marine and Atmospheric Science
Mark DeMaria and John A. Knaff - NOAA/NESDIS/RAMMB, Fort Collins, CO
Accounting for Variations in TC Size
A Guide to Tropical Cyclone Guidance
Derek Ortt1 and Shuyi S. Chen, RSMAS/University of Miami
Part of the ASAP program
Statistical Evaluation of the Response of Intensity
Hurricane Rita Model Results
Hurricane Rita Model Results
Hurricane Rita Model Results
Presentation transcript:

Performance of an Objective Model for Identifying Secondary Eyewall Formation in Hurricanes Matthew Sitkowski CIMSS – University of Wisconsin Jim Kossin NOAA/NESDIS/National Climatic Data Center

Acknowledgments Thank$ –Office of Naval Research –NOAA GOES-R Risk Reduction Jeff Hawkins –NRL Webpage Mark DeMaria –SHIPS Dataset Dave Nolan, Chris Rozoff, John Knaff, Howard Berger, & Chris Velden

Marked changes of the inner core structure Broaden wind field – increase storm surge Linked with rapid intensity changes Landfall blessing/curse “SO SOME ADDITIONAL STRENGTHENING IS POSSIBLE... IF AN EYEWALL REPLACEMENT CYCLE DOES NOT INHIBIT THE INTENSIFICATION PROCESS.” “... AND WE HAVE NO SKILL IN FORECASTING EYEWALL REPLACEMENT CYCLES BEYOND ABOUT 6-12 HOURS...AT BEST Rita (2005) NHC Discussion #18 (Stewart)

Secondary Eyewall Formation (SEF) Objective Model Development  Algorithm requires the knowledge of prior SEF events  Developed an SEF climatology from  Uses environmental features from SHIPS –Shear, MPI, relative humidity …  GOES IR features –Improve probability of detection from 22% to 30%

SEF Algorithm 2 classes (Cat 1+ over water) –SEF occurs within the next 12 hours –SEF does not occur within next 12 hours The algorithm is based on the Bayes probabilistic model  P (C yes | F) estimates the probability of imminent secondary eyewall formation, given the set F of observed features.  P (C yes ) is the climatological probability (~12% in the North Atlantic). “Leave-one-season-out” cross validated

2008 Season  8 Hurricanes  Bertha  Dolly  Gustav  Hanna  Ike  Kyle  Omar  Paloma  3 SEF Storms Algorithm ran smoothly –Missing key IR features New forecast every 6 hours –t=0,6,12,18, & 24 hr Greatest probability for non- SEF storm was 6% (excluding Gustav) Ike had 18 hr forecast of 65% that verified Sept UTCSept UTC

Hurricane Bertha First major hurricane Never had probability >0% Classic eyewall replacement Features never acted in accord to produce a high probability

Hurricane Bertha Some features were favorable at times –Shear somewhat high –Low MPI for SEF storm

Probability Intensity CubaLA

TXCuba Probability Intensity SEF

Cuba Probability Intensity SEF

2008 Beta Version Run with key features missing – Improvement expected with the addition of the missing IR features No major glitches –Automated - updates every 6 hr –Available online: ftp://ftp.ssec.wisc.edu/pub/matts/ A climatology of recon-based intensity changes associated with secondary eyewall formation would be of great benefit to forecasters