Objective and Automated Assessment of Operational Global Forecast Model Predictions of Tropical Cyclone Formation Patrick A. Harr Naval Postgraduate School.

Slides:

Advertisements

Similar presentations

Introduction to Hurricane Forecasting John P. Cangialosi Hurricane Specialist National Hurricane Center HSS Webinar 13 March 2012 John P. Cangialosi Hurricane.

Advertisements

Acknowledgments: ONR NOPP program HFIP program ONR Marine Meteorology Program Elizabeth A. Ritchie Miguel F. Piñeros J. Scott Tyo Scott Galvin Gen Valliere-Kelley.

Relationships Between Eye Size and Intensity Changes of a N. Atlantic Hurricane Author: Stephen A. Kearney Mentor: Dr. Matthew Eastin, Central College.

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.

Applications of Ensemble Tropical Cyclone Products to National Hurricane Center Forecasts and Warnings Mark DeMaria, NOAA/NESDIS/STAR, Ft. Collins, CO.

Analysis of Precipitation Distributions Associated with Two Cool-Season Cutoff Cyclones Melissa Payer, Lance F. Bosart, Daniel Keyser Department of Atmospheric.

HFIP Ensemble Products Subgroup Sept 2, 2011 Conference Call 1.

Tropical Cyclone Formation and Extratropical Transition IWTC – V Recommendations There is a strong need for a consistent definition of tropical cyclone.

Examination of the Dominant Spatial Patterns of the Extratropical Transition of Tropical Cyclones from the 2004 Atlantic and Northwest Pacific Seasons.

The Contribution of Eastern North Pacific Tropical Cyclones to the Warm Season Rainfall Climatology of the Southwest US Kristen L. Corbosiero (UCLA) Michael.

WEST AFRICAN STORM TRACKS AND THEIR RELATIONSHIP WITH ATLANTIC TROPICAL CYCLONES Susanna Hopsch Department of Earth and Atmospheric Sciences University.

A Diagnostic Analysis of a Difficult- to-Forecast Cutoff Cyclone from the 2008 Warm Season Matthew A. Scalora, Lance F. Bosart, Daniel Keyser Department.

Use of the Nondivergent Wind for Diagnosing Banded Precipitation Systems Thomas J. Galarneau, Jr., and Daniel Keyser Department of Earth and Atmospheric.

Impact of the 4D-Var Assimilation of Airborne Doppler Radar Data on Numerical Simulations of the Genesis of Typhoon Nuri (2008) Zhan Li and Zhaoxia Pu.

Genesis Potential Index and ENSO Suzana J. Camargo.

CLIQR Rainfall analog David Roth & Kyle Griffin NOAA Hydrometeorological Prediction Center Camp Springs, MD Last Updated: June 4, 2009.

Evaluation of Potential Impacts of Doppler Lidar Wind Measurements on High-impact Weather Forecasting: A Regional OSSE Study Zhaoxia Pu and Lei Zhang University.

1 26 April 2013 Future WorkResultsMethodologyMotivation Chip HelmsComposite Analyses of Tropical Convective Systems Composite Analyses of Tropical Convective.

Observing Strategy and Observation Targeting for Tropical Cyclones Using Ensemble-Based Sensitivity Analysis and Data Assimilation Chen, Deng-Shun 3 Dec,

Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA,

1 22 July 2013 Future WorkResultsMethodologyMotivation Chip HelmsComposite Analyses of Tropical Convective Systems Composite Analyses of Tropical Convective.

1 Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model Presented by Hiep Van Nguyen Main contributors: Mai Van Khiem, Tran Thuc,

Towards an object-oriented assessment of high resolution precipitation forecasts Janice L. Bytheway CIRA Council and Fellows Meeting May 6, 2015.

Forecast Pressure. Pressure Observations ASOS is the best…the gold standard Ships generally the worst.

Sensitivity of Tropical Cyclone Inner-Core Size and Intensity to the Radial Distribution of Surface Entropy Flux Wang, Y., and Xu, 2010: Sensitivity of.

An Improved Wind Probability Program: A Joint Hurricane Testbed Project Update Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, CO Stan Kidder,

A Comparison of Two Microwave Retrieval Schemes in the Vicinity of Tropical Storms Jack Dostalek Cooperative Institute for Research in the Atmosphere,

NHC Activities, Plans, and Needs HFIP Diagnostics Workshop August 10, 2012 NHC Team: David Zelinsky, James Franklin, Wallace Hogsett, Ed Rappaport, Richard.

Current and Potential Thesis Topics: - Forecasting of tropical cyclone formation and intensity - Predictability associated with the extratropical transition.

Modification of GFS Land Surface Model Parameters to Mitigate the Near- Surface Cold and Wet Bias in the Midwest CONUS: Analysis of Parallel Test Results.

61 st IHC, New Orleans, LA Verification of the Monte Carlo Tropical Cyclone Wind Speed Probabilities: A Joint Hurricane Testbed Project Update John A.

Ensemble-based prediction and diagnostics during the PREDICT field experiment Sharan Majumdar (RSMAS / U. Miami) Ryan Torn (SUNY at Albany) Fuqing Zhang.

Upgraded Russian Radiosonde Network for IPY U.S. (NOAA) Winter NOAA G-4 and Air Force C-130s JapanPalau Typhoon Landfall U.S.(NSF/ONR), EU, Japan, Korea,

27 Sept Future WorkResultsMethodologyMotivation Chip HelmsComposite Analyses of Tropical Convective Systems1 Composite Analyses of Tropical Convective.

USING THE ROSSBY RADIUS OF DEFORMATION AS A FORECASTING TOOL FOR TROPICAL CYCLOGENESIS USING THE ROSSBY RADIUS OF DEFORMATION AS A FORECASTING TOOL FOR.

PREDICTABILITY OF WESTERN NORTH PACIFIC TROPICAL CYCLONE EVENTS ON INTRASEASONAL TIMESCALES WITH THE ECMWF MONTHLY FORECAST MODEL Russell L. Elsberry and.

Prediction of Tropical Cyclones Chapter 9. Tropical weather data from traditional sources (surface and radiosonde) is scarce, so remote sensing via other.

CLIQR database development David Roth NOAA Weather Prediction Center (Formerly Hydrometeorological Prediction Center) College Park, MD Last Updated: February.

Hurricane Forecast Improvement Project (HFIP): Where do we stand after 3 years? Bob Gall – HFIP Development Manager Fred Toepfer—HFIP Project manager Frank.

Munehiko Yamaguchi Typhoon Research Department, Meteorological Research Institute of the Japan Meteorological Agency 9:00 – 12: (Thr) Topic.

POTENTIAL THESIS TOPICS Professor Russell L. Elsberry January 26, 2006 Graduate School of Engineering and Applied Sciences Department of Meteorology, Naval.

Shuyi S. Chen Joseph Tenerelli Rosenstiel School of Marine and Atmospheric Science University of Miami Effects of Environmental Flow and Initial Vortex.

1 James D. Doyle 1, Hao Jin 2, Clark Amerault 1, and Carolyn Reynolds 1 1 Naval Research Laboratory, Monterey, CA 2 SAIC, Monterey, CA James D. Doyle 1,

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.

Possible North Atlantic extratropical cyclone activity in a warmer climate Lanli Guo William Perrie Zhenxia Long Montreal 2012 Bedford Institute of Oceanography,

Exploitation of Ensemble Prediction System in support of Atlantic Tropical Cyclogenesis Prediction Chris Thorncroft Department of Atmospheric and Environmental.

1 Developing Assimilation Techniques For Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline.

Dynamic Hurricane Season Prediction Experiment with the NCEP CFS CGCM Lindsey Long and Jae Schemm Climate Prediction Center / Wyle IS NOAA/NWS/NCEP October.

An Examination Of Interesting Properties Regarding A Physics Ensemble 2012 WRF Users’ Workshop Nick P. Bassill June 28 th, 2012.

NCEP Vision: First Choice – First Alert – Preferred Partner NOAA Hydrometeorological TestBed at the NCEP Hydrometeorological Prediction Center (HPC) Faye.

Application of T382 CFS Forecasts for Dynamic Hurricane Season Prediction J. Schemm, L. Long, S. Saha and S. Moorthi NOAA/NWS/NCEP October 21, 2008 The.

Dynamic Hurricane Season Prediction Experiment with the NCEP CFS Jae-Kyung E. Schemm January 21, 2009 COLA CTB Seminar Acknowledgements: Lindsey Long Suru.

The Role of Tropical Waves in Tropical Cyclogenesis Frank, W. M., and P. E. Roundy 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea.

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,

TC Projects Joint Hurricane Testbed, Surface winds GOES-R, TC structure – TC Size TPW & TC size (Jack Dostalek) IR climatology – RMW/wind profile Proving.

An Investigation of Model-Simulated Band Placement and Evolution in the 25 December 2002 Northeast U.S. Banded Snowstorm David Novak NOAA/ NWS Eastern.

Analysis of Typhoon Tropical Cyclogenesis in an Atmospheric General Circulation Model Suzana J. Camargo and Adam H. Sobel.

New Tropical Cyclone Intensity Forecast Tools for the Western North Pacific Mark DeMaria and John Knaff NOAA/NESDIS/RAMMB Andrea Schumacher, CIRA/CSU.

Improving the Validation and Prediction of Tropical Cyclone Rainfall Robert Rogers NOAA/AOML/HRD Tim Marchok NOAA/GFDL Bob Tuleya NCEP/EMC/SAIC Funded.

Satellite + Aircraft Tropical Cyclone Surface Wind Analysis Joint Hurricane Testbed.

Developing a tropical cyclone genesis forecast tool: Preliminary results from 2014 quasi- operational testing Daniel J. Halperin 1, Robert E. Hart 1, Henry.

Shuyi S. Chen, Robert A. Houze Bradley Smull, David Nolan, Wen-Chau Lee Frank Marks, and Robert Rogers Observational and Modeling Study of Hurricane Rainbands.

The African Monsoon Region and the Tropical Atlantic

An Analysis of Large Track Error North Atlantic Tropical Cyclones.

The Impact of TY Sinlaku on the Northern Hemisphere Midlatitudes During T-PARC Elizabeth Sanabia & Patrick Harr Naval Postgraduate School Acknowledgments:

The predictability of Tropical Storm Alma 2008

University of Wisconsin - Madison

Western North Pacific Tropical Cyclone Formation and Structure Change

Presentation transcript:

Objective and Automated Assessment of Operational Global Forecast Model Predictions of Tropical Cyclone Formation Patrick A. Harr Naval Postgraduate School Monterey, CA Acknowledgment: NOAA, Joint Hurricane Testbed Project

Synthesize model representations of 850 hPa vorticity centers Data are gathered on all tropical vortices Before formation of tropical cyclone Target circulations may be waves Not intended to replace operational trackers associated with existing tropical cyclones Not applicable to regional models that are only run when a tropical cyclone exists OBJECTIVES

Summarize a large number of parameters associated with each center throughout its life cycle. Two levels of interface (1) Interface during operations to examine current vortices via NMAP2 allow hurricane specialists to monitor: - vortices in the current analysis and their forecasts -vortices forecast to form in the current modelrun (2) Interface to examine general summaries of forecast performance with respect to tropical vortices that may become a tropical cyclone conditioned on season, location, intensity, etc. via web-interface with the database (MySQL/PHP) Identification of false alarms - forecast to become a tropical cyclone but does not verify -declaration of a model tropical cyclone based on several criteria (warm core, etc.) OBJECTIVES (continued)

Catalog in a dynamic database –Database designed to answer user-generated queries for statistical summaries associated with model performance based on Time period area Forecast parameter (850 hPa vorticity, SLP, rainfall, warm core, etc). Parameter thresholds (e.g., vorticity above/below a specified value) Model OBJECTIVES (continued) Identify factors that discriminate between likely model traits with respect to tropical cyclone formation. [ Objective, feature based, dynamic]

Forecast Model Analyses and Forecast Fields Tropical Cyclone Vortex Tracking Program(TCVTP) Tropical Cyclone Vortex Tracking Program(TCVTP) Forecast Data Base Circulations forecast to occur Forecast Data Base Circulations forecast to occur Analyzed Data Base Current tropical circulations Analyzed Data Base Current tropical circulations Current circulation summaries/ verifications Final Circulation Catalog summaries/verifications conditioned on Season, location, intensity,etc. Final Circulation Catalog summaries/verifications conditioned on Season, location, intensity,etc. Catalog of False Alarms Catalog of False Alarms Overall System Design GFS NOGAPS UKMET Interface during Operations via NMAP2 Database Interface

With respect to the each tracked vortex, the following parameters are cataloged: 850  maximum value SLP average, minimum value hPa wind shear hPa wind shear height thickness 500 hPa vertical motion hPa moisture model-derived accumulated precipitation temperature difference between area inside ellipse and outside ellipse at 500 hPa

Interface to monitor forecasts of current vortices and vortices forecast to form Interface is provided via NMAP2 Choose Data Source (model) Choose Data Window (time)

Choose from track list that identifies vortices in the current analysis Display 850 hPa vorticity and track for vortex of interest

Choose from track list that identifies vortices forecast to form in the current model run Vortices in the current analysis and their forecasts (red) Vortices forecast to form and their forecast tracks labeled by the time at which they appear in the forecast sequence (green)

Interface 2 Summaries of overall model traits with respect to tropical vortices Use of database design to allow user-defined queries for the catalog of analyzed and forecast parameters with respect to tropical vortices. Examine vortices within specific model runs Examine general summaries of model performance

Choose region, time, and model Choose to display parameters for a vortex defined in a list of current vortices Choose from a list of previous model runs Choose from a list of previous vortices

Overall model performance: Can be defined via user specification of important operational factors (i.e., location, intensity, time, parameter) Define conditions on which to base statistical analysis of model traits Choose the parameter to summarize Choose summary format: Table, Plot of absolute error and bias error, Box and whisker plot

Output available in tabular and graphical formats. Output available in various statistical representations to identify likely sources of forecast errors.

Objectively identify and track analyzed and forecast vorticity centers in operational global forecast models Summarize a comprehensive set of physical parameters associated with analyzed and forecast vortices. Catalog parameters in a dynamic data base Provide daily and other summaries of model forecast performance associated with developing and non-developing tropical vortices. Use summaries to discriminate between potentially accurate/inaccurate forecasts of TC formation. Summary