Stream 1.5 Runs of SPICE Kate D. Musgrave 1, Mark DeMaria 2, Brian D. McNoldy 1,3, and Scott Longmore 1 1 CIRA/CSU, Fort Collins, CO 2 NOAA/NESDIS/StAR,

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

Improvements in Statistical Tropical Cyclone Forecast Models: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria 1, Andrea Schumacher 2, John.

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

National Hurricane Center 2008 Forecast Verification James L. Franklin Branch Chief, Hurricane Specialists Unit National Hurricane Center 2009 Interdepartmental.

Operational Hurricane Model Diagnostics at EMC Hurricane Diagnostics and Verification Workshop NHC, Miami, FL 4 May 2009 – 6 May 2009 Vijay Tallapragada,

Frank Marks NOAA/AOML/Hurricane Research Division 11 February 2011 Frank Marks NOAA/AOML/Hurricane Research Division 11 February 2011 Hurricane Research.

Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009 Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009.

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.

Creation of a Statistical Ensemble for Tropical Cyclone Intensity Prediction Kate D. Musgrave 1, Brian D. McNoldy 1,3, and Mark DeMaria 2 1 CIRA/CSU, Fort.

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

Ensemble Forecasting of Hurricane Intensity based on Biased and non-Gaussian Samples Zhan Zhang, Vijay Tallapragada, Robert Tuleya HFIP Regional Ensemble.

Demonstration Testbed for the Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement.

Predictability of Tropical Cyclone Intensity Forecasting Mark DeMaria NOAA/NESDIS/StAR, Fort Collins, CO CoRP Science Symposium Fort Collins, CO August.

HFIP Ensemble Subgroup Mark DeMaria Nov 21, 2011 Conference Call 1.

Hurricane Diagnostics and Verification Workshop Opening Remarks Mark DeMaria May 4-6, 2009 National Hurricane Center Miami, Florida.

Advanced Applications of the Monte Carlo Wind Probability Model: A Year 1 Joint Hurricane Testbed Project Update Mark DeMaria 1, Stan Kidder 2, Robert.

Advanced Applications of the Monte Carlo Wind Probability Model: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria 1, Robert DeMaria 2, Andrea.

USE OF A MODIFIED SHIPS ALGORITHM FOR HURRICANE INTENSITY FORECASTS

ATMS 373C.C. Hennon, UNC Asheville Tropical Cyclone Forecasting Where is it going and how strong will it be when it gets there.

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.

Applications of ATMS/CrIS to Tropical Cyclone Analysis and Forecasting Mark DeMaria and John A. Knaff NOAA/NESDIS/STAR Fort Collins, CO Andrea Schumacher,

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.

The Impact of Satellite Data on Real Time Statistical Tropical Cyclone Intensity Forecasts Joint Hurricane Testbed Project Mark DeMaria, NOAA/NESDIS/ORA,

Diagnostics and Verification of the Tropical Cyclone Environment in Regional Models Brian McNoldy 1*, Kate Musgrave 1, Mark DeMaria 2 1 CIRA, Colorado.

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.

Guidance on Intensity Guidance Kieran Bhatia, David Nolan, Mark DeMaria, Andrea Schumacher IHC Presentation This project is supported by the.

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,

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.

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

A. FY12-13 GIMPAP Project Proposal Title Page version 25 October 2011 Title: Combining Probabilistic and Deterministic Statistical Tropical Cyclone Intensity.

On the ability of global Ensemble Prediction Systems to predict tropical cyclone track probabilities Sharanya J. Majumdar and Peter M. Finocchio RSMAS.

Upgrades to the GFDL/GFDN Operational Hurricane Models Planned for 2015 (A JHT Funded Project) Morris A. Bender, Matthew Morin, and Timothy Marchok (GFDL/NOAA)

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.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity.

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

Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI): Results from 2006 Daniel J. Cecil University of Alabama/Huntsville.

Ligia Bernardet, S. Bao, C. Harrop, D. Stark, T. Brown, and L. Carson Technology Transfer in Tropical Cyclone Numerical Modeling – The Role of the DTC.

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

A Global Tropical Cyclone Formation Probability Product Andrea Schumacher, CIRA/CSU Mark DeMaria and John Knaff, NOAA/NESDIS/StAR Daniel Brown, NHC 64.

Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement Project (HFIP) Barbara G. Brown,

The Impact of Lightning Density Input on Tropical Cyclone Rapid Intensity Change Forecasts Mark DeMaria, John Knaff and Debra Molenar, NOAA/NESDIS, Fort.

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.

National Hurricane Center 2010 Forecast Verification James L. Franklin and John Cangialosi Hurricane Specialist Unit National Hurricane Center 2011 Interdepartmental.

Improvements to Statistical Forecast Models and the Satellite Proving Ground for 2013 Mark DeMaria, John Knaff, NOAA/NESDIS/STAR John Kaplan, Jason Dunion,

An Updated Baseline for Track Forecast Skill Through Five Days for the Atlantic and Northeastern and Northwestern Pacific Basins Sim Aberson NOAA/AOML/Hurricane.

Tropical Cyclone Rapid Intensity Change Forecasting Using Lightning Data during the 2010 GOES-R Proving Ground at the National Hurricane Center Mark DeMaria.

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,

2006 NHC Verification Report Interdepartmental Hurricane Conference 5 March 2007 James L. Franklin NHC/TPC.

Impact of New Global Models and Ensemble Prediction Systems on Consensus TC Track Forecasts James S. Goerss NRL Monterey March 3, 2010.

2015 Production Suite Review: Report from NHC 2015 Production Suite Review: Report from NHC Eric S. Blake, Richard J. Pasch, Andrew Penny NCEP Production.

M. Fiorino :: IHC 61 st NOLA Performance of the ECMWF High- Resolution Global Model during the 2006 Northern Hemisphere Season and Impact on CONsensus.

National Hurricane Center 2009 Forecast Verification James L. Franklin Branch Chief, Hurricane Specialist Unit National Hurricane Center 2009 NOAA Hurricane.

National Hurricane Center 2010 Forecast Verification James L. Franklin Branch Chief, Hurricane Specialist Unit National Hurricane Center 2010 NOAA Hurricane.

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

National Hurricane Center 2007 Forecast Verification Interdepartmental Hurricane Conference 3 March 2008 James L. Franklin NHC/TPC.

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.

M. Fiorino :: 63 rd IHC St. Petersburg, FL Recent trends in dynamical medium- range tropical cyclone track prediction and the role of resolution.

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

Hurricane Joaquin Frank Marks AOML/Hurricane Research Division 10 May 2016 Frank Marks AOML/Hurricane Research Division 10 May 2016 Research to Improve.

Kate Musgrave1, Christopher Slocum2, and Andrea Schumacher1

Mark DeMaria and John A. Knaff - NOAA/NESDIS/RAMMB, Fort Collins, CO

A Guide to Tropical Cyclone Guidance

HFIP Ensemble Product Subgroup Update on Prototype Products

Statistical Evaluation of the Response of Intensity

Presentation transcript:

Stream 1.5 Runs of SPICE Kate D. Musgrave 1, Mark DeMaria 2, Brian D. McNoldy 1,3, and Scott Longmore 1 1 CIRA/CSU, Fort Collins, CO 2 NOAA/NESDIS/StAR, Fort Collins, CO 3 Current Affiliation: RSMAS, University of Miami, Miami, FL Acknowledgements: Yi Jin, Naval Research Lab Michael Fiorino, Jeffrey Whitaker, Philip Pegion, NOAA/ESRL Vijay Tallapragada, NOAA/NWS/NCEP/EMC

Outline SPICE Overview 2012 Verification – NHC Delivery 2012 Verification – Full Season Global SPICE Diagnostic Code Updates Outlier Analysis Summary 2

SPICE (Statistical Prediction of Intensity from a Consensus Ensemble) Model Configuration for Consensus SPICE forecasts TC intensity using a combination of parameters from: – Current TC intensity and trend – Current TC GOES IR – TC track and large-scale environment from GFS, GFDL, and HWRF models These parameters are used to run DSHP and LGEM based off each dynamical model 3 HFIP Telecon, 10/24/2012 The forecasts are combined into two unweighted consensus forecasts, one each for DSHP and LGEM The two consensus are combined into the weighted SPC3 forecast DSHP and LGEM Weights for Consensus

SPICE Verification Compare with Parent Models LGEM, DSHP, GHMI, HWFI 4 HFIP Telecon, 10/24/2012 Use working best track [ through AL17 (Rafael), and EP16 (Paul) ] NHC verification rules – Tropical, subtropical only Must also have OFCL forecast Stream 1.5 sample – Only those delivered to NHC Combined sample – Add cases run at CIRA but not sent to NHC

5 Mean Absolute Errors – Atlantic HFIP Telecon, 10/24/2012

Skill Relative to SHF5 - Atlantic 6 HFIP Telecon, 10/24/2012

7 Mean Absolute Errors – East Pacific HFIP Telecon, 10/24/2012

Skill Relative to SHF5 – East Pacific 8 HFIP Telecon, 10/24/2012

Global SPICE 9 HFIP Telecon, 10/24/2012

Diagnostic Code Updates 10 HFIP Telecon, 10/24/2012

Outlier Analysis 11 HFIP Telecon, 10/24/2012

Improvements to SPICE through Outlier Analysis Develop single parameter for intensity error for a given forecast case – Normalize error at each forecast time by standard deviation of intensity changes from best track over that time interval – Time averaged normalized intensity errors (TANIE) – Require verification out to at least 36 h Identify outliers – Look for common characteristics – Use as guidance for statistical model improvements – Adjust weights in SPICE if errors are systematic 12 HFIP Telecon, 10/24/2012

TANIE for 2012 LGEM Forecasts 13 HFIP Telecon, 10/24/2012

Top 10 TANIE Values for 2012 Models 14 LGEMDSHPHWFIGHMI 1.Michael Kirk Michael Michael Kirk Michael Michael Michael Michael Michael Michael Michael Michael Gordon Florence Kirk Kirk Michael Ernesto Michael Leslie Ernesto Michael Leslie Ernesto Kirk Kirk Alberto Leslie Michael Alberto Kirk Leslie Michael Michael Isaac Michael Nadine Blue = Low Bias Red = High Bias

Possible SPICE Improvements Low bias for RI cases – Use RII as predictor in SHIPS/LGEM High bias for nonlinear combination of dry air and shear (Kirk and several east Pacific cases) – New predictor for SHIPS/LGEM or modified MPI Shear direction relative to motion vector may be important (Ernesto, Gordon) – Modify shear direction predictor Larger uncertainty for low V(0), high SST cases Serial correlation of errors – SPICE weight adjustments 15 HFIP Telecon, 10/24/2012

Summary SPICE was run for most cases during 2012 Hurricane Season Atlantic SPICE errors smaller than parent models for 24 thorough 72 hr East Pacific SPICE errors larger than some parent models at all forecast times Global Ensemble SPICE under development CIRA diagonstic code to be upgraded for 2013 Outlier analysis may lead to SPICE improvements for HFIP Telecon, 10/24/2012

Back-Up Slides 17 HFIP Telecon, 10/24/2012

Motivation for Statistical Ensemble The Logistic Growth Equation Model (LGEM) and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) model are two statistical-dynamical intensity guidance models SHIPS and LGEM are competitive with dynamical models Both SHIPS and LGEM use model fields from the Global Forecast System (GFS) to determine the large-scale environment Runs extremely fast (under 1 minute), using model fields from previous 6 hr run to produce ‘early’ guidance Atlantic Operational Intensity Model Errors JTWC experience with a similar statistical model shows improvements with multiple inputs We focus on using Decay-SHIPS (DSHP) and LGEM, initialized with model fields from GFS, the Hurricane Weather Research and Forecasting (HWRF) model, and the Geophysical Fluid Dynamics Laboratory (GFDL) model to create an ensemble HFIP Telecon, 10/24/2012