UAH 28 Sept 2008R. Boldi NSSTC/UAH 1 Hazardous Cell Tracking Robert Boldi 29 September 2008 NSSTC/UAH.

Slides:

Advertisements

Similar presentations

Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.

Advertisements

Cell Tracking Algorithm (R3) Dan Cecil, UAH

DYnamical and Microphysical Evolution of Convective Storms Thorwald Stein, Robin Hogan, John Nicol DYMECS.

Thunderstorm Nowcasting at NOAA-CREST Presented by Brian Vant-Hull, Robert Rabin CREST team: Arnold Gruber, Shayesteh Mahani, Reza Khanbilvardi CREST Students:

GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.

Inter-comparison of Lightning Trends from Ground-based Networks during Severe Weather: Applications toward GLM Lawrence D. Carey 1*, Chris J. Schultz 1,

PROVIDING DISTRIBUTED FORECASTS OF PRECIPITATION USING A STATISTICAL NOWCAST SCHEME Neil I. Fox and Chris K. Wikle University of Missouri- Columbia.

Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields Yoav Yair 1, Barry Lynn 1, Colin Price 2,

GOES-R Proving Ground NOAA’s Hazardous Weather Testbed Chris Siewert GOES-R Proving Ground Liaison OU-CIMMS / Storm Prediction Center.

FAA Tactical Weather Forecasting In The

RECENT PROGRESS IN CONVECTIVE PHENOMENA MONITORING AND FORECASTING AT THE INM F. Martín, F. Elizaga, I. San Ambrosio and J. M. Fernández Servicio de Técnicas.

VALLIAPPA LAKSHMANAN NATIONAL SEVERE STORMS LABORATORY / UNIVERSITY OF OKLAHOMA SEP, 2009 An Algorithm to Identify and Track Objects on.

The 5.06 APPLICATIONS OF THE MCGILL ALGORITHM FOR PRECIPITATION NOWCASTING USING SEMI-LAGRANGIAN EXTRAPOLATION WITHIN THE ARPAV HYDROMET DECISION SUPPORT.

On the relationship of in-cloud convective turbulence and total lightning Wiebke Deierling, John Williams, Sarah Al-Momar, Bob Sharman, Matthias Steiner.

September 2005WSN05, Toulouse, France Applications of the McGill Algorithm for Precipitation Nowcasing Using Semi- Lagrangian Extrapolation (MAPLE) within.

SCAN SCAN System for Convection Analysis and Nowcasting Operational Use Refresher Tom Filiaggi & Lingyan Xin

Data Integration: Assessing the Value and Significance of New Observations and Products John Williams, NCAR Haig Iskenderian, MIT LL NASA Applied Sciences.

Chapter 9: Weather Forecasting

Warn on Forecast Briefing September 2014 Warn on Forecast Brief for NCEP planning NSSL and GSD September 2014.

Prakash Chockalingam Clemson University Non-Rigid Multi-Modal Object Tracking Using Gaussian Mixture Models Committee Members Dr Stan Birchfield (chair)

Principles of Flash Flood Development: An Introduction to Hydrometeorology Anthony Phillips GEOG 490/590 Ball State University  Hazards associated with.

Nowcasting thunderstorms in complex cases using radar data Alessandro Hering* Stéphane Sénési # Paolo Ambrosetti* Isabelle Bernard-Bouissières # *MeteoSwiss.

THE GOES-R GLM LIGHTNING JUMP ALGORITHM (LJA): RESEARCH TO OPERATIONAL ALGORITHM Elise V. Schultz 1, C. J. Schultz 1,2, L. D. Carey 1, D. J. Cecil 2, G.

Development of an object- oriented verification technique for QPF Michael Baldwin 1 Matthew Wandishin 2, S. Lakshmivarahan 3 1 Cooperative Institute for.

GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.

The NWS/NCAR “Forecaster Over the Loop” Fort Worth Operational Demonstration Human Enhancement of a Thunderstorm Nowcasting System Eric Nelson, Rita Roberts,

Small scale magnetic energy release driven by supergranular flows Hugh Potts, Joe Khan and Declan Diver How to automatically detect and analyse supergranular.

A. FY12-13 GIMPAP Project Proposal Title Page version 27 October 2011 Title: Probabilistic Nearcasting of Severe Convection Status: New Duration: 2 years.

USE AND EVALUATION OF TOTAL LIGHTNING DATA IN THE GOES-R PROVING GROUND AND EXPERIMENTAL WARNING PROGRAM Kristin Kuhlman (CIMMS/NSSL) Geoffrey Stano (NASA/SPORT)

TITAN - identifying and tracking convective storms as objects 1 Heuristic Probabilistic Forecasting Workshop Munich, Germany August 2014 Mike Dixon.

Update on NCAR Auto-Nowcaster Juneau, AK. The Auto-Nowcaster System An expert system which produces short-term (0-1 hr) forecasts of thunderstorm initiation,

NCAR Auto-Nowcaster Convective Weather Group NCAR/RAL.

Forward-Scan Sonar Tomographic Reconstruction PHD Filter Multiple Target Tracking Bayesian Multiple Target Tracking in Forward Scan Sonar.

Nowcasting Thunderstorms with SIGnificant weather Object Oriented Nowcasting System Pascal Brovelli, Stéphane Sénési, Etienne Arbogast, Philippe Cau, Sandrine.

RDT – Rapidly Developing Thunderstorms Satellite Application Facilities (SAFs) are dedicated centres for processing satellite data, achieved by utilizing.

Weather Predicting Weather forecasting is a prediction of what the weather will be like in an hour, tomorrow, or next week. Weather forecasting involves.

The Benefit of Improved GOES Products in the NWS Forecast Offices Greg Mandt National Weather Service Director of the Office of Climate, Water, and Weather.

Doppler Weather Radar Algorithms

Chapter 9: Weather Forecasting Surface weather maps 500mb weather maps Satellite Images Radar Images.

1 National Severe Storms Laboratory & University of Oklahoma Nowcasting.

Nowcasting Trends Past and Future By Jim Wilson NCAR 8 Feb 2011 Geneva Switzerland.

+ Storm Tracking and Lightning Cell Clustering using GLM for Data Assimilation and Forecast Applications Principal Investigators: Kristin Kuhlman and Don.

Storm tracking & typing for lightning observations Kristin Calhoun, Don MacGorman, Ben Herzog.

Titelfoto auf dem Titelmaster einfügen Deutscher Wetterdienst Dr. Paul James, German Weather Service, ECAM/EMS Conference, Reading, 9. Sept NowCastMIX.

The Rapid Developing Thunderstorm (RDT) product CDOP to CDOP2

An Object-Based Approach for Identifying and Evaluating Convective Initiation Forecast Impact and Quality Assessment Section, NOAA/ESRL/GSD.

NOAA-MDL Seminar 7 May 2008 Bob Rabin NOAA/National Severe Storms Lab Norman. OK CIMSS University of Wisconsin-Madison Challenges in Remote Sensing to.

Travis Smith Hazardous Weather Forecasts & Warnings Nowcasting Applications.

CI VERIFICATION METHODOLOGY & PRELIMINARY RESULTS

Recent and Planned Updates to the NCAR Auto-Nowcast (ANC) System Thomas Saxen, Rita Roberts, Huaqing Cai, Eric Nelson, Dan Breed National Center for Atmospheric.

Presented by: Idan Aharoni

Developers: John Walker, Chris Jewett, John Mecikalski, Lori Schultz Convective Initiation (CI) GOES-R Proxy Algorithm University of Alabama in Huntsville.

COMPARISONS OF NOWCASTING TECHNIQUES FOR OCEANIC CONVECTION Huaqing Cai, Cathy Kessinger, Nancy Rehak, Daniel Megenhardt and Matthias Steiner National.

Instructor : Dr. Powsiri Klinkhachorn

Lightning and Severe Weather: Process in the context of satellite applications “Measurements from the vantage point of space to: Investigate the formation.

Machine Vision. Image Acquisition > Resolution Ability of a scanning system to distinguish between 2 closely separated points. > Contrast Ability to detect.

VISITview Teletraining Nearcasting Convection using GOES Sounder Data 1 ROBERT M. AUNE AND RALPH PETERSEN NOAA/ASPB/STAR JORDAN GERTH AND SCOTT LINDSTROM.

 Mentor : Prof. Amitabha Mukerjee Learning to Detect Salient Objects Team Members - Avinash Koyya Diwakar Chauhan.

ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson.

4:40 pm EST Wednesday November 16th

Paper Review Jennie Bukowski ATS APR-2017

NCAR Research on Thunderstorm Analysis & Nowcasting

Nic Wilson’s M.S.P.M. Research

DIAGNOSTICS Sensing DSP and Data Fusion Failure Feature Extraction

Dr. Chang Shu COMP 4900C Winter 2008

A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning

Multiscale Data Assimilation

Marilyn Wolfson (MIT/LL),

Aiding Severe Weather Forecasting

CSSE463: Image Recognition Day 30

Presentation transcript:

UAH 28 Sept 2008R. Boldi NSSTC/UAH 1 Hazardous Cell Tracking Robert Boldi 29 September 2008 NSSTC/UAH

UAH 28 Sept 2008R. Boldi NSSTC/UAH 2 What is Hazardous Cell Tracking? Hazardous –Severe Lightning, Winds, Hail –Tornadoes Cell –Compact region embedded within a larger- scale convective system Tracking –Compute time-rate-of-change of Cell’s attributes.

UAH 28 Sept 2008R. Boldi NSSTC/UAH 3 Methodology Construct an “Interest Image”, each pixel of which represents the likelihood that a hazardous cell is present [ ] Track this image using Multi-Scale XCT Locate “Cells” by thresholding this image Construct Lagrangian history of properties of the cells Predict future location of cells ( and properties )

UAH 28 Sept 2008R. Boldi NSSTC/UAH 4 Constructing “Interest” Image Functional Template Correlation –A template (small image), convolved against a larger input image, producing an “interest image”, each pixel of which is the probability that the feature represented by the template is present at the given pixel. –Templates map onto simple features ( lines, circular cells, low variance regions, etc. Fuzzy Logic –Groups of “Images” are combined to produce meteorologically-understandable phenomena, e.g. Stratiform, Convective, Decaying, Growing … –These phenomena are combined to produce the final “Interest Image”

UAH 28 Sept 2008R. Boldi NSSTC/UAH 5 Cell Detection and Tracking Lightning Radar Data Satellite Data Interest Image Generator FTC  WxTypes Fuzzy Logic  Fusion Multi-Scale XCT Tracker Cell-Scale Motions Storm-Scale Motions Cell Identification

UAH 28 Sept 2008R. Boldi NSSTC/UAH 6 Cell Detection and Tracking Lightning Events Radar Data Interest Image 01:48Z

UAH 28 Sept 2008R. Boldi NSSTC/UAH 7 Cell Detection

UAH 28 Sept 2008R. Boldi NSSTC/UAH 8 Multi-Scale Tracking Interest Image 01:48Z Envelope Motion Cell Motion

UAH 28 Sept 2008R. Boldi NSSTC/UAH 9 Multi-Scale Tracking

UAH 28 Sept 2008R. Boldi NSSTC/UAH 10 Cell Identification = Thresholding Interest Image Interest Image 01:48Z Cell ID’s 01:48Z

UAH 28 Sept 2008R. Boldi NSSTC/UAH 11 Cell ID’s NOT persistent ( movie )

UAH 28 Sept 2008R. Boldi NSSTC/UAH 12 Lagrangian History Re- Construction Multi-Scale Vectors T(0) T(-  t) T(-2  t) ….. Input Fields -Lightning -Radar -Satellite T(0) T(-  t) T(-2  t) ….. Current Cell Map T(0) Past Data Advector Cell Integrator Cell 1 History Cell 2 History Cell 3 History :

UAH 28 Sept 2008R. Boldi NSSTC/UAH 13 Advector: Forward Advection of Inputs to Current Time In the absence of convergence, divergence, growth or decay, the alignment would be perfect. Current Time Past Time Advected to Current Time Past Time  =

UAH 28 Sept 2008R. Boldi NSSTC/UAH 14 Advector: Forward Advection of Inputs to Current Time

UAH 28 Sept 2008R. Boldi NSSTC/UAH 15 Lagrangian History Re- Construction

UAH 28 Sept 2008R. Boldi NSSTC/UAH 16 Lagrangian History Re- Construction

UAH 28 Sept 2008R. Boldi NSSTC/UAH 17 Lagrangian History Re- Construction

UAH 28 Sept 2008R. Boldi NSSTC/UAH 18 Cell Position Prediction Multi-Scale Vectors T(0) T(-  t) T(-2  t) ….. Current Cell Map T(0) Advector Future Cell Map T(+  t) Future Cell Map T(+2.  t) :

UAH 28 Sept 2008R. Boldi NSSTC/UAH 19 Input Field Prediction Multi-Scale Vectors T(0) T(-  t) T(-2  t) Current Input Field T(0) Advector Forecast Input Field T(+  t) Forecast Input Field T(+2.  t) : Trends, WxType

UAH 28 Sept 2008R. Boldi NSSTC/UAH 20 Summary Tracker running Creating Histories for Lightning Jump ALG Creating Predicted Cell Positions Platform for Lightning Prediction Work In Progress Obtaining proxy ABI data for : –WxType –Storm intensity image

UAH 28 Sept 2008R. Boldi NSSTC/UAH 21 THE END