Large Ensemble Tropical Cyclone Forecasting K. Emanuel 1 and Ross N. Hoffman 2, S. Hopsch 2, D. Gombos 2, and T. Nehrkorn 2 1 Massachusetts Institute of.

Slides:

Advertisements

Similar presentations

Joint Typhoon Warning Center Forward, Ready, Responsive Decision Superiority UNCLASSIFIED An Overview of Joint Typhoon Warning Center Tropical Cyclone.

Advertisements

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

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 GORDON and the NWP models near the Iberian Peninsula.

Suzana J. Camargo Lamont-Doherty Earth Observatory Columbia University ANALYSIS OF 20 TH CENTURY ATLANTIC HURRICANE POTENTIAL INTENSITY AND TROPICAL CYCLONE.

Figure 1.1 Area of responsibility of the RSMC Tokyo - Typhoon Center.

Tropical Cyclone Forecasts Dr. Richard J. Murnane Risk Prediction Initiative Bermuda Biological Station for Research, Inc.

Probabilistic Verification of Ensemble Forecasts of Tropical Cyclogenesis Sharanya J. Majumdar RSMAS / University of Miami Ryan D. Torn SUNY at Albany.

Validation of the Ensemble Tropical Rainfall Potential (eTRaP) for Landfalling Tropical Cyclones Elizabeth E. Ebert Centre for Australian Weather and Climate.

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

Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter April 2006, EnKF Wildflower Meeting Greg Hakim & Ryan Torn University of Washington.

Exploiting Satellite Observations of Tropospheric Trace Gases Ross N. Hoffman, Thomas Nehrkorn, Mark Cerniglia Atmospheric and Environmental Research,

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

The Relative Contribution of Atmospheric and Oceanic Uncertainty in TC Intensity Forecasts Ryan D. Torn University at Albany, SUNY World Weather Open Science.

Background In deriving basic understanding of atmospheric phenomena, the analysis often revolves around discovering and exploiting relationships between.

Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.

Genesis Potential Index and ENSO Suzana J. Camargo.

The Impact of GPS Radio Occultation Data on the Analysis and Prediction of Tropical Cyclones Bill Kuo UCAR.

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,

Using Physics to Generate Tropical Cyclone Event Catalogs Kerry Emanuel and Sai Ravela Massachusetts Institute of Technology.

Update on Storm Surge at NCEP Dr. Rick Knabb, Director, National Hurricane Center and representing numerous partners 21 January 2014.

Tropical Cyclones and Climate Change: An Assessment WMO Expert Team on Climate Change Impacts on Tropical Cyclones February 2010 World Weather Research.

Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report Mark DeMaria NOAA/NESDIS/ORA,

Three Lectures on Tropical Cyclones

The MJO signal in tropical cyclone activity A study using a genesis potential index Suzana J. Camargo Lamont-Doherty Earth Observatory, Columbia University,

30 November December International Workshop on Advancement of Typhoon Track Forecast Technique 11 Observing system experiments using the operational.

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,

Verification Approaches for Ensemble Forecasts of Tropical Cyclones Eric Gilleland, Barbara Brown, and Paul Kucera Joint Numerical Testbed, NCAR, USA

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

Computing Deep-Tropospheric Vertical Wind Shear Analyses for TC Applications: Does the Methodology Matter? Christopher Velden and John Sears Univ. Wisconsin.

Page 1© Crown copyright 2006 Matt Huddleston With thanks to: Frederic Vitart (ECMWF), Ruth McDonald & Met Office Seasonal forecasting team 14 th March.

KIT – University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association Variability in forecasts of the track.

2/10/03F.Marks1 Development of a Tropical Cyclone Rain Forecasting Tool Frank D. Marks NOAA/AOML, Hurricane Research Division, Miami, FL QPE Techniques.

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

OFCM-Sponsored Working Group for Tropical Cyclone Research 1 WG/TCR 2009 IHC: WG/TCR Workshop Wrap-up Workshop: Identifying Tropical Cyclone Research Needs,

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

Adaptive Observation Techniques ENSEMBLE TRANSFORM KALMAN FILTER SINGULAR VECTORS Sensitive areas for adaptive sampling include both the hurricane core.

The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.

Improved Statistical Intensity Forecast Models: A Joint Hurricane Testbed Year 2 Project Update Mark DeMaria, NOAA/NESDIS, Fort Collins, CO John A. Knaff,

Doppler Lidar Winds & Tropical Cyclones Frank D. Marks AOML/Hurricane Research Division 7 February 2007.

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

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

THORPEX THORPEX (THeObserving system Research and Predictability Experiment) was established in 2003 by the Fourteenth World Meteorological Congress. THORPEX.

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

Jennifer Catto Supervisors: Len Shaffrey and Kevin Hodges Extra-tropical cyclones and Storm Tracks.

Forecasting Oceanic Cyclones at the NOAA Ocean Prediction Center Joseph M. Sienkiewicz, D. Scott Prosise, and Anthony Crutch NOAA/NWS/NCEP/Ocean Prediction.

1 Current and planned research with data collected during the IFEX/RAINEX missions Robert Rogers NOAA/AOML/Hurricane Research Division.

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

Munehiko Yamaguchi 12, Takuya Komori 1, Takemasa Miyoshi 13, Masashi Nagata 1 and Tetsuo Nakazawa 4 ( ) 1.Numerical Prediction.

Fuzzy Cluster Analysis Investigating Wavebreaking in the Tropics Philippe P. Papin Team Torn Meeting – April 15, 2015 Department of Atmospheric and Environmental.

NOAA Hurricane Forecast Improvement Project Development Fred Toepfer, HFIP Manager Bob Gall, HFIP Development Manager.

Tropical Cyclone Forecasting and Monitoring

Predictability and forecast evaluation of ensemble simulations of long-lived Hurricane Nadine (2012)

Kerry Emanuel Lorenz Center Massachusetts Institute of Technology

Massachusetts Institute of Technology

By Nicholas Leonardo Advisor: Brian Colle

Impact of North Atlantic hurricanes on episodes of intense rainfall over the Mediterranean Florian Pantillon1,2 Jean-Pierre Chaboureau1 and Evelyne.

Predictability of Tropical Cyclone Intensity

A Guide to Tropical Cyclone Guidance

A Simple, Fast Tropical Cyclone Intensity Algorithm for Risk Models

Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR)

An Analysis of Large Track Error North Atlantic Tropical Cyclones.

Nonlinear high-dimensional data assimilation

Vector Curl Analysis (Hurricane Harvey)

Ensemble tropical cyclone and windstorm forecast applications

University of Wisconsin - Madison

Verification of Tropical Cyclone Forecasts

MOGREPS developments and TIGGE

The Impact of Moist Singular Vectors and Horizontal Resolution on Short-Range Limited-Area Ensemble Forecasts for Extreme Weather Events A. Walser1) M.

Presentation transcript:

Large Ensemble Tropical Cyclone Forecasting K. Emanuel 1 and Ross N. Hoffman 2, S. Hopsch 2, D. Gombos 2, and T. Nehrkorn 2 1 Massachusetts Institute of Technology 2 Atmospheric and Environmental Research, Inc. Tuesday March 1 st, 2011 Kerry A. Emanuel Massachusetts Institute of Technology

Technique Begin with ECMWF global 51-member ensemble Calculate ensemble mean TC track velocity vectors and covariances among them Calculate mean and covariances among global wind components at 250 and 850 hPa Synthesize track velocity vectors, using track velocity vectors at early lead times giving way to beta-and- advection model at long lead times Run CHIPS model along each track Easy and fast to generate thousands of tracks in real time

Data ECMWF Deterministic and Ensemble forecasts (51 ensemble members at 00 and 12 UTC) Track data from all ensemble members Spatial resolution: 2° latitude/longitude grid 17 vertical levels from deterministic forecast 850 and 250 hPa winds from the ensemble forecasts Temporal resolution: 12 hourly time steps Filter ECMWF wind fields to remove model TCs

unfiltered relative vorticity Julia (AL 12) Igor (AL 11) Relative Vorticity Igor (AL11), GMT

After vorticity surgery filtered relative vorticity

Example: Hurricane Igor, GMT

With Best Track

With NHC Forecast

With ECMWF Track Ensembles

Wind field for one (very good) sample track (T+36 h) NHC Forecast & Best track NHC official forecast Best track

Gray = downscaled ensemble based on 100 tracks NHC official forecast final best track Boxplot based on 1000 tracks Intensity forecast

Sample size: 1000 tracks Observed at airport (TXKF): 59kts (81kts gusts) Wind exceedence probabilities for Bermuda (32.4N, 64.7W)

50 % peak wind exceedence (knots) NHC official forecast Best track

75 % peak wind exceedence (knots) NHC official forecast Best track

90 % peak wind exceedence (knots) NHC official forecast Best track

Discussion Capability to generate hundreds or thousands of TC intensity forecasts for individual storms. Must develop efficient methods to communicate the results for: –Ease of understanding, and –For use in decision-making. Problem in communicating uncertainty in many dimensions; both the –Probabilistic forecasts, and the –Skill metrics of these forecasts. Many potential approaches. –Methods shown are just a start, and were restricted to non- interactive images or animations.