Tie Yuan and Haiyan Jiang Department of Earth & Environment, FIU, Miami, Florida Margie Kieper Private Consultant 65 th Interdepartmental Hurricane Conference.

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

A Blended, Multi-Platform Tropical Cyclone Rapid Intensification Index
Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion.
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.
Further Development of a Statistical Ensemble for Tropical Cyclone Intensity Prediction Kate D. Musgrave 1 Mark DeMaria 2 Brian D. McNoldy 3 Yi Jin 4 Michael.
Benjamin A. Schenkel and Robert E. AMS Tropical Conference 2012 Department of Earth, Ocean, and Atmospheric Science.
HFIP Ensemble Products Subgroup Sept 2, 2011 Conference Call 1.
Examination of the Dominant Spatial Patterns of the Extratropical Transition of Tropical Cyclones from the 2004 Atlantic and Northwest Pacific Seasons.
Genesis Potential Index and ENSO Suzana J. Camargo.
+ Effects of Climate Change on Ocean Storms Chloe Mawer.
Benjamin A. Schenkel 1 Lance F. Bosart 1, Daniel Keyser 1, and Robert E. Hart 2 1 University at Albany,
Application of the Computer Vision Hough Transform for Automated Tropical Cyclone Center-Fixing from Satellite Data Mark DeMaria, NOAA/NCEP/NHC Robert.
Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA,
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.
Andrea Schumacher 1, Mark DeMaria 2, John Knaff 3, Liqun Ma 4 and Hazari Syed 5 1 CIRA, Colorado State University, Fort Collins, Colorado 2 NOAA/NWS/NHC,
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,
Andrea Schumacher 1, Mark DeMaria 2 and John Knaff 2 1. CIRA/CSU, Fort Collins, CO 2. NOAA/NESDIS/StAR, Fort Collins, CO.
Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report Mark DeMaria NOAA/NESDIS/ORA,
Benjamin A. Schenkel Lance F. Bosart, and Daniel Keyser University at Albany, State University of New York.
Measuring gaps in tropical cyclone rainbands using Level II radar reflectivity data Corene Matyas Department of Geography, University of Florida Funding:
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,
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.
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.
Statistical Typhoon Intensity Prediction Scheme John Knaff CIRA/Colorado State Univerisity In Partnership with Mark DeMaria NOAA/NESDIS.
PREDICTABILITY OF WESTERN NORTH PACIFIC TROPICAL CYCLONE EVENTS ON INTRASEASONAL TIMESCALES WITH THE ECMWF MONTHLY FORECAST MODEL Russell L. Elsberry and.
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),
2/10/03F.Marks1 Development of a Tropical Cyclone Rain Forecasting Tool Frank D. Marks NOAA/AOML, Hurricane Research Division, Miami, FL QPE Techniques.
The Impact of Lightning Density Input on Tropical Cyclone Rapid Intensity Change Forecasts Mark DeMaria, John Knaff and Debra Molenar, NOAA/NESDIS, Fort.
TropicalCyclone Tropical Cyclone Studies by Microwave Sensors Chandra Mohan Kishtawal ASDMOG ASD/MOG Space Applications Centre ISRO/MOP/SM-2.1.
The Potential for Improved Short-term Atlantic Hurricane Intensity Forecasts Using Recon-based Core Measurements Andrew Murray, Robert Hart,
Application of a Hybrid Dynamical-Statistical Model for Week 3 and 4 Forecast of Atlantic/Pacific Tropical Storm and Hurricane Activity Jae-Kyung E. Schemm.
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),
An Updated Baseline for Track Forecast Skill Through Five Days for the Atlantic and Northeastern and Northwestern Pacific Basins Sim Aberson NOAA/AOML/Hurricane.
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.
Operational Uses for an Objective Overshooting Top Algorithm Sarah A. Monette* #, Wayne Feltz*, Chris Velden*, and Kristopher Bedka^ Cooperative Institute.
Dynamic Hurricane Season Prediction Experiment with the NCEP CFS Jae-Kyung E. Schemm January 21, 2009 COLA CTB Seminar Acknowledgements: Lindsey Long Suru.
THE NESDIS TROPICAL CYCLONE FORMATION PROBABILITY PRODUCT: PAST PERFORMANCE AND FUTURE PLANS Andrea B. Schumacher, CIRA Mark DeMaria, NESDIS/StAR John.
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),
Exploring the Possibility of Using Tropical Cyclone Numbers to Project Taiwan Summer Precipitation Patterns Mong-Ming Lu and Ru-Jun May Research and Development.
Tropical cyclone activity in the Minerva T1279 seasonal forecasts. Preliminary analysis Julia Manganello 1, Kevin Hodges 2 1 COLA, USA 2 NERC Centre for.
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,
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
Benjamin A. Schenkel University at Albany, State University of New York, and Robert E. Hart, The Florida State University 4 th.
TC Projects Joint Hurricane Testbed, Surface winds GOES-R, TC structure – TC Size TPW & TC size (Jack Dostalek) IR climatology – RMW/wind profile Proving.
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,
Developing an Inner-Core SST Cooling Parameter for use in SHIPS Principal Investigator: Joseph J. Cione NOAA’s Hurricane Research Division Co-Investigators:
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.
The National Hurricane Center GOES-R Proving Ground Mark DeMaria NOAA/NESDIS, Fort Collins, CO GLM Science Meeting, Huntsville, AL September 26,
Analysis of Typhoon Tropical Cyclogenesis in an Atmospheric General Circulation Model Suzana J. Camargo and Adam H. Sobel.
Description of the IRI Experimental Seasonal Typhoon Activity Forecasts Suzana J. Camargo, Anthony G. Barnston and Stephen E.Zebiak.
New Tropical Cyclone Intensity Forecast Tools for the Western North Pacific Mark DeMaria and John Knaff NOAA/NESDIS/RAMMB Andrea Schumacher, CIRA/CSU.
Andrea Schumacher, CIRA/CSU, Fort Collins, CO Mark DeMaria and John Knaff, NOAA/NESDIS/StAR, Fort Collins, CO NCAR/NOAA/CSU Tropical Cyclone Workshop 16.
A proposition on Seasonal Prediction of TC Activity over western North Pacific H. Joe Kwon Kongju National University, KOREA.
Revisiting the 26.5°C Sea Surface Temperature Threshold for Tropical Cyclone Development McTaggart-Cowan et al. (2015) Revisiting the 26.5°C Sea Surface.
Andrea Schumacher1, M. DeMaria2, and R. DeMaria1
Training Session: Satellite Applications on Tropical Cyclones
Mark DeMaria and John A. Knaff - NOAA/NESDIS/RAMMB, Fort Collins, CO
Accounting for Variations in TC Size
McTaggart-Cowan et al. (2015)
Development and applications of a new genesis potential index
Development and applications of an index for tropical cyclone genesis
Quantifying Environmental Control on Tropical Cyclone Intensity Change
Presentation transcript:

Tie Yuan and Haiyan Jiang Department of Earth & Environment, FIU, Miami, Florida Margie Kieper Private Consultant 65 th Interdepartmental Hurricane Conference 1

2  The prediction of rapid intensification (RI) of tropical cyclones (TCs) has always been a great challenge in tropical weather forecasting.  Compared with the progresses in RI forecast in the Atlantic and Eastern North Pacific, there are few works in RI forecast in the North Western Pacific (NWP).  Margie Kieper’s subjective forecast method (2009) using the 37 GHz microwave can predict the onset of RI over Atlantic and Eastern North Pacific, will this method be equally valuable for NWP?

37 GHz ring pattern RI index (Ring-RII) Environment RI index (Envi-RII) Combined 37 GHz ring pattern RI index and environment RI index (Comb-RII) 3

4 Data period: ( ) JTWC best-track data (6-h interval) TRMM TMI 37 GHz data from FIU/UU Tropical Cyclone Precipitation Feature (TCPF) database (16 km×9 km) ERA-Interim reanalysis (1.5°×1.5°, 6-h interval) Reynolds Daily SST analysis (V2, 0.25°×0.25°) Samples include cases south of 30°N and over water during 24-h period

Cumulative frequency distribution of the overwater 24-h intensity change RI definition over NWP  RI of TCs is usually defined as the 95th percentile of all 24-h over-water intensity changes.  RI threshold of 30 kt is employed for the North Atlantic Basin in Kaplan and DeMaria’s study (2003). 5  In this study, 30, 35, and 40 kt RI thresholds are used, which represent the 93th, 95th, and 97th percentiles

37 GHz ring pattern RI index  Must have a ring (follow Kieper 2009)  Vmax is between 35 and 100 kt  Over water during 24-h period 6

7 TRMM 37 GHz microwave imagery Best-track center corrected center bright cyan: pink Automatic ring detection method: Case 1 TMI 37GHz Ring:  Color bright cyan: 37 GHz PCT >=270K and 37 GHz Vertical >= 265K or pink: 37 GHz PCT <= 270K  Filled area >= 60 %  Max radius <= 160 km  Ring thickness must be >= 50% of the diameter of the outer edge (different with ATL)

TRMM 37 GHz microwave imagery 8 Best-track center Corrected center TC center correction :  Automatic Find the pixels with PCT at 37 GHz >= 280 K within 60 km of best- track center, then calculate the mean of latitude and longitude, which is the corrected center  Manual Interactive selection Able to process  missing data  partial scan Automatic ring detection method: Case 2

VariableUnitsDefinition VMXm s -1 Maximum sustained surface wind speed LAT˚NLatitude LON˚ELongitude SPDm s -1 Storm speed of motion DVMXm s -1 Intensity change during the previous 12 h SST℃Sea surface temperature POTm s -1 Maximum potential intensity (MPI) - VMX SHRDm s –200 hPa vertical shear averaged from R=200–800 km USHRDm s –200 hPa zonal wind shear averaged from R=200–800 km U200ms hPa u component of wind averaged from R=200–800 km T200℃200 hPa temperature averaged from R=200–800 km RHLO%850–700 hPa relative humidity averaged from R=200–800 km RHHI%500–300 hPa relative humidity averaged from R=200–800 km 9 Environment RI index

VariableUnits RI (35 kt) (N=407 N e =208) Non-RI (N=4863 N e =1590) Diff= RI-Non-RI Significance level LAT ˚N˚N LON ˚E˚E VMAXm s e-005 SHRDm s e-014 SST ℃ e-012 RHLO% RHHI% SPDm s T200 ℃ USHRDm s U200m s DVMXm s e-027 POTm s N e in RI and non-RI sample are the effective sample size after serial correlation following Aberson and DeMaria (1994). Selected RI predictors: DVMX, SHRD,SST, VMAX,T200

The probability of RI when the selected RI predictors were satisfied for the RI and non-RI samples. 11 Previous 12-h intensity change (DVMX) has the highest probability. The followings are vertical wind shear (SHRD), and sea surface temperature (SST), and Maximum sustained wind speed (VMAX) 30 kt RI 40 kt RI 35 kt RI

 The probabilities are provided as a function of the total number of the five RI predictor (DVMX, SHRD, SST, VMAX, and T200)thresholds satisfied  The probabilities of RI are close to the sample mean value when two thresholds were satisfied  The highest probability of RI are 52%, 40%, 38% for 30, 35 and 40 kt RI threshold, respectively. The composite probability of Envi-RII kt RI 40 kt RI 35 kt RI Total sample size is % 14.1% 10.6%

13 The Brier score of Envi-RII This result is similar to the skill score of the SHIPS RII (Kaplan et al. 2010) for the Atlantic basin.

14 The verification of Envi-RII for independent forecasts 35 kt RI Good years Not so good years

15 Probability Of Detection  The performance of Environment RII is better than that of the ring RII  The combined index is better than both.  In North Western Pacific, the internal process is not as important as the environmental factors as in the Atlantic and East Pacific basins Evaluation of the three RI indices Perice Skill Score False Alarm Ratio

16 I.The 37 GHz ring pattern in the North Western Pacific is more common than in ATL, therefore we have to use a higher constrain for the ring thickness. II.Environment RI index including five predictors is constructed. This index is shown to be skillful relative to climatology and through the verification of independent forecasts. III. The 37 GHz ring index has a smaller contribution to the combined index, which suggests that the large-scale environment maybe play a more important role for the RI of tropical cyclones in the North Western Pacific.

17  Refine the ring definition, such as color range, thickness.  Further investigate the effects of environmental condition on RI of TCs in NWP, and find out their inherent physical mechanism.