Advanced Dvorak Technique

Slides:



Advertisements
Similar presentations
Operational Use of the Dvorak Technique at the NHC
Advertisements

1 GOES-R Hurricane Intensity Estimation (HIE) Validation Tool Development Winds Application Team Tim Olander (CIMSS) Jaime Daniels (STAR)
RAMMT/CIRA Tropical Cyclone Overview THE DVORAK TECHNIQUE Introduction Visible Technique IR Technique Strengths and Weaknesses Lab Exercise: Visible Pattern.
A Blended, Multi-Platform Tropical Cyclone Rapid Intensification Index
Future Plans  Refine Machine Learning:  Investigate optimal pressure level to use as input  Investigate use of neural network  Add additional input.
Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion.
Future Plans  Refine Machine Learning:  Investigate optimal pressure level to use as input  Investigate use of neural network  Add additional input.
Acknowledgments: ONR NOPP program HFIP program ONR Marine Meteorology Program Elizabeth A. Ritchie Miguel F. Piñeros J. Scott Tyo Scott Galvin Gen Valliere-Kelley.
Hurricane center-fixing with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) method Tony Wimmers, Chris Velden University of Wisconsin.
Microwave Imagery and Tropical Cyclones Satellite remote sensing important resource for monitoring TCs, especially in data sparse regions Passive microwave.
CORP Symposium Fort Collins, CO August 16, 2006 Session 3: NPOESS AND GOES-R Applications Tropical Cyclone Applications Ray Zehr, NESDIS / RAMM.
Diagnosing Tropical Cyclone Structure Presented by John Knaff with input and efforts from A. Schumacher, R. DeMaria, G. Chirokova, C. Slocum.
Analysis of High Resolution Infrared Images of Hurricanes from Polar Satellites as a Proxy for GOES-R INTRODUCTION GOES-R will include the Advanced Baseline.
Application of the Computer Vision Hough Transform for Automated Tropical Cyclone Center-Fixing from Satellite Data Mark DeMaria, NOAA/NCEP/NHC Robert.
Update on the UW-CIMSS Advanced Dvorak Technique (ADT) Tim Olander, Chris Velden and Tony Wimmers University of Wisconsin – Madison Cooperative Institute.
Advanced Applications of the Monte Carlo Wind Probability Model: A Year 1 Joint Hurricane Testbed Project Update Mark DeMaria 1, Stan Kidder 2, Robert.
A. Schumacher, CIRA/Colorado State University NHC Points of Contact: M. DeMaria, D. Brown, M. Brennan, R. Berg, C. Ogden, C. Mattocks, and C. Landsea Joint.
Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA,
Bias Corrections of Storm Counts from Best Track Data Chris Landsea, National Hurricane Center, Miami, USA Gabe Vecchi, Geophysical Fluid Dynamics Lab,
The Impact of Satellite Data on Real Time Statistical Tropical Cyclone Intensity Forecasts Joint Hurricane Testbed Project Mark DeMaria, NOAA/NESDIS/ORA,
New and Updated Operational Tropical Cyclone Wind Products John A. Knaff – NESDIS/StAR - RAMMB, Fort Collins, CO Alison Krautkramer – NCEP/TPC - NHC, Miami,
TC Intensity Estimation: SATellite CONsensus (SATCON) Derrick Herndon, Chris Velden, Tony Wimmers, Tim Olander International Workshop on Tropical Cyclone.
Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report Mark DeMaria NOAA/NESDIS/ORA,
JTWC SATOPS Challenges Capt Kathryn Payne 28 April 2009.
April nd IBTrACS Workshop 1 Operational Procedures How can we build consistent, homogeneous, well- documented climate quality data?
UW-CIMSS Tropical Cyclone Research : Current Progress and Developments Tim Olander, Chris Velden, Jim Kossin, and Derrick Herndon 2004 Meteorological Satellite.
CIMSS TC Intensity Satellite Consensus (SATCON) Derrick Herndon and Chris Velden Meteorological Satellite (METSAT) Conference Ford Island Conference Center.
A Brief Digression: Waterspouts Szilagyi (2005, 2009) Waterspout Nomogram 850 hPa T: ~3-7°C SST: ~19-21°C.
STATISTICAL ANALYSIS OF ORGANIZED CLOUD CLUSTERS ON WESTERN NORTH PACIFIC AND THEIR WARM CORE STRUCTURE KOTARO BESSHO* 1 Tetsuo Nakazawa 1 Shuji Nishimura.
Possible impacts of improved GOES-R temporal resolution on tropical cyclone intensity estimates INTRODUCTION The Advanced Baseline imager (ABI) on GOES-R.
NHC Activities, Plans, and Needs HFIP Diagnostics Workshop August 10, 2012 NHC Team: David Zelinsky, James Franklin, Wallace Hogsett, Ed Rappaport, Richard.
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.
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),
NHC/JHT Products in ATCF Buck Sampson (NRL, Monterey) and Ann Schrader (SAIC, Monterey) IHC 2007 Other Contributors: Chris Sisko, James Franklin, James.
A. FY12-13 GIMPAP Project Proposal Title Page Title: Improvements to the Advanced Dvorak Technique Status: New – but continuing work from GIMPAP FY07-09.
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.
1 2 nd GOES-R AWG Validation Workshop Winds Application Team Topic: Hurricane Intensity Estimation (HIE) Algorithm Chris Velden (CIMSS) Tim Olander (CIMSS)
Upgrades to the Rapid intensification index (RII ) John Kaplan (NOAA/HRD), Christopher Rozoff (CIMSS), Charles Sampson (NRL), James Kossin (NOAA/NCDC),
Can Dvorak Intensity Estimates be Calibrated? John A. Knaff NOAA/NESDIS Fort Collins, CO.
Tropical Cyclone Rapid Intensity Change Forecasting Using Lightning Data during the 2010 GOES-R Proving Ground at the National Hurricane Center Mark DeMaria.
Improving Intensity Estimates Using Operational Information John Knaff NOAA/NESDIS Regional and Mesoscale Meteorology Branch Fort Collins, CO.
Forecasting Storm Tracks Before Formation and Wind Speed per Platform Scott Morris Earth Science Associates Long Beach, CA November 5, 2015 User Conference.
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,
GOES-R Hurricane Intensity Estimation (HIE) Winds-HIE Application Team Chris Velden & Tim Olander (CIMSS) Jaime Daniels (STAR)
Second IBTrACS Workshop, April 2011, Honolulu, Hawaii 1 ESCAP/WMO Typhoon Committee Best Track Consolidation Meeting, Dec 2010 Hong Kong Summary.
Enrica Bellone, Jessica Turner, Alessandro Bonazzi 2 nd IBTrACS Workshop.
Overview of CIRA and NESDIS Global TC Services Presented by John Knaff NOAA/NESDIS Regional and Mesoscale Meteorology Branch Fort Collins, CO USA For The.
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.
1 Current and planned research with data collected during the IFEX/RAINEX missions Robert Rogers NOAA/AOML/Hurricane Research Division.
Satellite Analysis Branch 2009 Year in Review Anthony Salemi with Michael Turk NOAA Hurricane Conference December 1, 2009.
TOWARD AN OBJECTIVE SATELLITE-BASED ALGORITHM TO PROVIDE REAL-TIME ESTIMATES OF TC INTENSITY USING INTEGRATED MULTISPECTRAL (IR AND MW) OBSERVATIONS Jeff.
Preparing for GOES-R and JPSS: New Tropical Cyclone Tools Based on 30 Years of Continuous GOES IR Imagery John A. Knaff1, Robert T. DeMaria2, Scott P.
Tropical Cyclone Forecasting and Monitoring
Andrea Schumacher1, M. DeMaria2, and R. DeMaria1
A consensus approach to operationally estimate and forecast tropical cyclone wind radii John Knaff NOAA Center for Satellite Applications.
Training Session: Satellite Applications on Tropical Cyclones
Accounting for Variations in TC Size
BFB: 9/29/2015 Tropical Weather
GOES-R Risk Reduction Research on Satellite-Derived Overshooting Tops
Training Session: Satellite Applications on Tropical Cyclones
TC Intensity Estimation: SATellite CONsensus (SATCON)
Objective Dvorak Technique (ODT) AFWA/XOGM
Michael J. Brennan National Hurricane Center
Validation of CIRA Tropical Cyclone Algorithms
Presentation transcript:

Advanced Dvorak Technique Latest Upgrades Currently running experimental ADT-Version 8.3.1 at CIMSS in parallel with ‘operational’ ADT-Version 8.2.1 CIMSS: http://tropic.ssec.wisc.edu/real-time/adt831 NESDIS/SAB running same version 8.2.1 Primary ADT-V8.3.1 upgrades Extratropical Transition intensity estimate adjustment Analysis of Sub-Tropical systems with modifications ARCHER (V2.8) objective algorithm for auto center fix SFC wind radii estimates (4 quadrants, based on Knaff et al) Extreme TC (CI=>7.0) intensity adjustments implemented Modifications to allow for more frequent image sampling Handling of MW information (no longer a ‘HOLD’ status) Optional ADT using RSMC short-term track forecasts for first guess positions in SH TCs (testing w/ ADTV8.2.1)

Advanced Dvorak Technique V8.3.1 Modifications – Extratropical Transition Extratropical Transition adjustment Research collaboration with Clark Evans at Univ. of Wisconsin – Milwaukee Primary finding: ADT too weak in ET phase Regression-based adjustments (Atlantic/non-Atlantic) are applied to the ADT CI# based on real and simulated ET cases ET cases with decent Vmax verification used ET is identified (and triggered in the ADT) by FSU phase space “beta” parameter Current and previous (6hr) beta values must be >10, and latitude > 20N/S 50% of previous 12-h period ADT scene types must be ‘shear’ or ‘curved band’ Once ET is triggered, the intensity adjustment is applied to the ADT CI# for the duration of the storm (and back-blended 12-h from start of ET implementation).

Advanced Dvorak Technique V8.3.1 Modifications -- Extratropical Transition --- ADT-V8.3.1 --- ADT-V8.2.1 08W 09L 10L

Advanced Dvorak Technique V8.3.1 Modifications – Subtropical Cyclones Analysis of Sub-Tropical systems Finding: ADT always too weak is ST systems using regular TC methodology Added new ADT keyword to identify when a storm is classified as Subtropical, as designated with ”SD” or “SS” indicator in JTWC or NHC ATCF Best Track file ADT ST intensity estimates based on modified Curved Band (CB) analysis Examines additional (warmer) cloud top temperature range between -10C and -30C Result: T#s increased to partially account for the ADT weak bias Additional +5 knot adjustment applied (based on remaining statistical bias) Does not allow ‘Shear’ scene type, only ‘CB’ and ‘Irregular CDO’

Advanced Dvorak Technique V8.3.1 Modifications – Subtropical Cyclones Blue shaded area represents new ST BD curve range -10C to -30C Increases spatial size and coherency of curved band scene being examined

Advanced Dvorak Technique V8.3.1 Modifications – Subtropical Cyclones Statistical Analysis (Development sample) Atlantic Basin cases between 2000 and 2015 ADT CI# intensity estimates vs Best Track w/in 30 minutes (with aircraft recon observation w/in 3 hours of NHC Best Track) 66 homogeneous cases Dvorak: operational Dvorak estimates from TAFB/SAB (averaged when coincident) ADT T/CI# converted to Vmax using Dvorak relationship Corrective measures ameliorate most of the ADT ST weak bias Vmax (kts) Bias |Mean| StDv Baseline    -12.1      12.1      6.5 Experimental    -1.4       5.4       6.4 Dvorak        -1.5        4.7     6.1

Advanced Dvorak Technique V8.3.1 Modifications – Subtropical Cyclones 2015 Tropical Storm Ana - 01L Subtropical Extratropical (no ET adjustment applied) 2002 Hurricane Gustav – 08L

Advanced Dvorak Technique V8.3.1 Modifications – ARCHER V2.8 Implementation of ARCHER (V2.8) objective TC center fix algorithm ARCHER V2.8 significantly upgraded from original algorithm used in ADT Code rewritten in C to match current ADT code Allows ADT ARCHER code to be ported more easily to remote sites (e.g. SAB) Obj. TC center posits derived hourly using multi-spectral/multi-platform imagery Geostationary: IR, SWIR, and Visible imagery from GOES, Meteosat, Himawari PMW: 37GHz and 85-92GHz imagery from SSMIS, GMI, AMSR2 Scatterometer data available but not currently employed (future investigations) Position extrapolated to current ADT analysis time from ARCHER history file Confidence indicators for each available fix determines the best position estimate and whether it will be used by the concurrent ADT analysis Currently testing two confidence threshold values

Advanced Dvorak Technique V8.3.1 Modifications – ARCHER V2.8 ARCHER analysis webpage products

Advanced Dvorak Technique V8.3.1 Modifications – SFC Wind Radii estimates SFC wind radii estimates Based upon Knaff et al. 2016 Uses current ADT CI# and RMW estimates (or climatological value if not available), and storm latitude/speed to determine 34/50/64 kt wind radii in four storm-relative quadrants Current analysis and graphical displays of wind radii values available, along with a past history listing

Advanced Dvorak Technique V8.3.1 – Other Modifications Extreme TC (CI=> 7.0) intensity adjustments implemented Based upon analysis of “historical extreme TC events” study (Velden et al., 2016) Adjustments of 0.1 (Atlantic/East Pacific) to 0.3 (West Pacific) T# applied to initial eye scene type regression equations to T# => 7.0 Modifications led to implementation of three regression equations for eye scenes Atlantic/East Pacific, West Pacific, Other Modifications to allow for more frequent image sampling Motivation: Imagery now available for analysis at 5/10 min. intervals from GOES-16/Himawari-8 Impact study conducted to investigate impact of higher temporal data on ADT Minimal impact in vast majority of cases using higher temporal data Occasional improvement in eye scene detection, but also more short-term “noise” CIMSS will continue to use 30-minute imagery New ADT option: Use RSMC short-term track forecasts for first guess positions in SH TCs (testing w/ ADTV8.2.1) Similar to current NESDIS/SAB operational ADT analyses