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.

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.
A Blended, Multi-Platform Tropical Cyclone Rapid Intensification Index
By: Andrew Lee. Kaplan and Demaria 2003 Paper Findings of Previous Studies Ocean’s impact on tropical cyclone (TC) intensity: Upwelling and vertical.
Climate Change and Hurricanes
Relationships Between Eye Size and Intensity Changes of a N. Atlantic Hurricane Author: Stephen A. Kearney Mentor: Dr. Matthew Eastin, Central College.
First Law of Thermodynamics The internal energy dU changes when: 1.heat dQ is exchanged between a parcel and its environment 2.work is done by a parcel.
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.
Tropical Cyclone Forecasts Dr. Richard J. Murnane Risk Prediction Initiative Bermuda Biological Station for Research, Inc.
Geostationary Lightning Mapper (GLM) 1 Near uniform spatial resolution of approximately 10 km. Coverage up to 52 deg latitude % flash detection day.
A Simplified Dynamical System for Understanding the Intensity-Dependence of Intensification Rate of a Tropical Cyclone Yuqing Wang International Pacific.
Tropical Cyclones and Climate Change – PhD Project Results from HiGEM High Resolution Climate Model Ray Bell Supervisors – Prof. Pier Luigi Vidale, Dr.
Tropical storms and the First Law of Thermodynamics ATMO 435.
Section 6: Tropical Cyclones 6.4 Maximum Potential Intensity How intense can a tropical cyclone get? Resources: Emanuel 1991 “The theory of hurricanes”,
Air-Sea Interaction in Hurricanes Kerry Emanuel Massachusetts Institute of Technology.
+ Effects of Climate Change on Ocean Storms Chloe Mawer.
USE OF A MODIFIED SHIPS ALGORITHM FOR HURRICANE INTENSITY FORECASTS
Applications of ATMS/CrIS to Tropical Cyclone Analysis and Forecasting Mark DeMaria and John A. Knaff NOAA/NESDIS/STAR Fort Collins, CO Andrea Schumacher,
Team Lead: Mark DeMaria NOAA/NESDIS/STAR Fort Collins, CO
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.
Tropical Cyclone Applications of GOES-R Mark DeMaria and Ray Zehr NESDIS/ORA, Fort Collins, CO John Knaff CIRA/CSU, Fort Collins, CO Applications of Advanced.
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.
New and Updated Operational Tropical Cyclone Wind Products John A. Knaff – NESDIS/StAR - RAMMB, Fort Collins, CO Alison Krautkramer – NCEP/TPC - NHC, Miami,
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,
Prediction of Atlantic Tropical Cyclones with the Advanced Hurricane WRF (AHW) Model Jimy Dudhia Wei Wang James Done Chris Davis MMM Division, NCAR Jimy.
Guidance on Intensity Guidance Kieran Bhatia, David Nolan, Mark DeMaria, Andrea Schumacher IHC Presentation This project is supported by the.
Tropical cyclone intensification Roger Smith Ludwig-Maximilians University of Munich Collaborators: Michael Montgomery, Naval Postgraduate School, Monterey,
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,
1 DEATH and DESTRUCTION from HURRICANES in the 21 st CENTURY Hugh Willoughby, FIU E&E National Tropical Weather Conference 9 April 2015.
A. FY12-13 GIMPAP Project Proposal Title Page version 25 October 2011 Title: Combining Probabilistic and Deterministic Statistical Tropical Cyclone Intensity.
Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,
Energy Production, Frictional Dissipation, and Maximum Intensity of a Numerically Simulated Tropical Cyclone 4/ 蘇炯瑞 Wang, Y., and J. Xu, 2010: Energy.
C20C Workshop, ICTP Trieste 2004 The impact of stratospheric ozone depletion and CO 2 on tropical cyclone behaviour in the Australian region Syktus J.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity.
Ocean Surface Winds Research Summary: Meteorological Applications Mark DeMaria, NOAA/NESDIS, Fort Collins, CO Ocean Surface Winds Workshop NCEP/Tropical.
Statistical Typhoon Intensity Prediction Scheme John Knaff CIRA/Colorado State Univerisity In Partnership with Mark DeMaria NOAA/NESDIS.
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),
Three Lectures on Tropical Cyclones Kerry Emanuel Massachusetts Institute of Technology Spring School on Fluid Mechanics of Environmental Hazards.
A Global Tropical Cyclone Formation Probability Product Andrea Schumacher, CIRA/CSU Mark DeMaria and John Knaff, NOAA/NESDIS/StAR Daniel Brown, NHC 64.
Tropical Cyclones and Climate Kerry Emanuel Massachusetts Institute of Technology.
The Impact of Lightning Density Input on Tropical Cyclone Rapid Intensity Change Forecasts Mark DeMaria, John Knaff and Debra Molenar, NOAA/NESDIS, Fort.
The Potential for Improved Short-term Atlantic Hurricane Intensity Forecasts Using Recon-based Core Measurements Andrew Murray, Robert Hart,
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.
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,
Tie Yuan and Haiyan Jiang Department of Earth & Environment, FIU, Miami, Florida Margie Kieper Private Consultant 65 th Interdepartmental Hurricane Conference.
Tropical Cyclone Rapid Intensity Change Forecasting Using Lightning Data during the 2010 GOES-R Proving Ground at the National Hurricane Center Mark DeMaria.
Sensitivity of Tropical Cyclone Intensity to Ventilation in an Axisymmetric Model Brian Tang, and Kerry Emanuel J. Atmos. Sci., 69, 2394–2413.
“It's tough to make predictions, especially about the future” HO fail all WEVER… HO fail all WEVER… “You can see a lot by looking” Yogi Berra High Pressure.
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,
Enrica Bellone, Jessica Turner, Alessandro Bonazzi 2 nd IBTrACS Workshop.
Andrea Schumacher, CIRA/CSU Mark DeMaria and John Knaff, NOAA/NESDIS/StAR.
Developing an Inner-Core SST Cooling Parameter for use in SHIPS Principal Investigator: Joseph J. Cione NOAA’s Hurricane Research Division Co-Investigators:
Hurricanes and Global Warming Kerry Emanuel Massachusetts Institute of Technology.
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.
Andrea Schumacher1, M. DeMaria2, and R. DeMaria1
Accounting for Variations in TC Size
A Simple, Fast Tropical Cyclone Intensity Algorithm for Risk Models
Development and applications of a new genesis potential index
Development and applications of an index for tropical cyclone genesis
Statistical Evaluation of the Response of Intensity
Impacts of Air-Sea Interaction on Tropical Cyclone Track and Intensity
Validation of CIRA Tropical Cyclone Algorithms
Presentation transcript:

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 November 2011 CIRA, Fort Collins, CO

 Maximum potential intensity (MPI)  Upper bound to TC intensity  Important quantity to know when predicting TC intensity  Approaches  Empirical  Theory  Modeling (not discussed here)

 Atlantic (DeMaria and Kaplan 1994)  Clim SST/Vmax sample from  Fit upper bound of Vmax (least squares, exponential)  A = 28.2, B = 55.8, C = ,T 0 = 30.0 °C  T in °C  V given in ms-1  E. Pacific (Whitney and Hobgood 1997)  Clim SST/Vmax sample from  Fit upper bound of Vmax (least squares, linear)  C 0 = ms -1, C 1 = 5.36 ms -1  SST in °C  EPMPI given in ms -1

 Two main thermodynamic theories  Carnot heat engine (Emanuel 1986; 1991) ▪ Acquire enthalpy from the ocean ▪ Convert to wind energy ▪ Function of sea surface and storm outflow temperature ▪ Calculates MPI as a maximum surface wind speed  Eye subsidence (Miller 1958 and Holland 1996) ▪ Requires SST, atmospheric sounding and surface pressure ▪ Calculates MPI as a minimum central pressure

 Current version of Statistical Hurricane Intensity Prediction Scheme (SHIPS) uses empirical MPI as a predictor  Pros: ▪ Availability (function of SST only)  Cons: ▪ Small sample set – 31 years ▪ Doesn’t include atmospheric variables ▪ Only uses SST – no ocean structure (e.g., OHC) ▪ Doesn’t always serve as true “upper bound” to Vmax…

 Sometimes, Vmax > theoretical MPI  Example: Hurricane Celia 2010

 Re-examine empirical formulas using SHIPS sample set – valid?  Examine Emanuel’s theoretical MPI (1995) over the SHIPS dataset  Identify when valid / not valid  Create a combined theoretical / empirical formula for MPI in SHIPS

10653 cases total Vmax > MPI for 30 cases (0.3%)  Good upper bound for sample cases total Vmax > MPI for 8 cases (0.1%)  Good upper bound for sample

First, try a few simple improvements... Vmax > MPI 12% of cases Vmax > MPI 16% of cases *Note: Atmospheric sounding comes from GFS model.

 Vmax includes asymmetric adjustment, MPI does not  For proper comparison, remove asymmetric component from storm motion (Schewerdt): Vmax(adjusted) = Vmax – 1.5*(TC speed) 0.63 TC Motion Symmetric Vortex Asymmetric Vortex

Vmax > MPI 10% of cases Vmax > MPI 14% of cases

 Emanuel MPI assumes 700-mb winds reduced by factor of 0.8 at surface (friction)  Franklin et al. (2002) used dropwinsonde data to show reduction factor is closer to 0.9  Multiplied Emanuel MPI by 0.9/0.8

Vmax > MPI 9% of cases Vmax > MPI 11% of cases

Observed Vmax Exceeds Theoretical MPI. Why? When?

“Violators” have…  Higher intensity, weakening  Lower MPI, decreasing  Lower SST, decreasing  Larger low-level temperature gradient  Lower OHC  Higher latitude Average (t=0)Avg 12-hr Change V > MPIMPI > VV > MPIMPI > V VMAX MPI EMPI RSST TGRD TADV T SHDC RHCN EPOS V V V20C TSPD TSPY LAT

 Empirical MPI provides good upper bound for intensity  SHIPS parameter based on Emanuel theoretical MPI improved by  Adding asymmetry adjustment due to storm motion  Using more appropriate reduction factor when converting 700-mb winds to surface

 Objectively identifying conditions where Emanuel MPI should be adjusted or replaced with empirical  Fast-moving TCs moving over cooler SST/OHC, lack of equilibrium  Extratropical cases (Atlantic)  Annular cases (E Pacific)  Creating combination empirical/theoretical MPI for testing in SHIPS  Parameterizing SST change under inner core  Decrease MPI