ounding nalog etrieval ystem Ryan Jewell Storm Prediction Center Norman, OK SARS Sounding Analog Retrieval System.

Slides:



Advertisements
Similar presentations
Chapter 13 – Weather Analysis and Forecasting
Advertisements

Matthew Vaughan, Brian Tang, and Lance Bosart Department of Atmospheric and Environmental Sciences University at Albany/SUNY Albany, NY NROW XV Nano-scale.
Ounding nalog etrieval ystem Ryan Jewell Storm Prediction Center Norman, OK SARS Sounding Analog Retrieval System.
A Forecasting Success A negatively tilting mid level trough approaching North Carolina, combined with strong instability and increasing deep layer shear,
Details for Today: DATE:3 rd February 2005 BY:Mark Cresswell FOLLOWED BY:Assignment 2 briefing Evaluation of Model Performance 69EG3137 – Impacts & Models.
Aspects of 6 June 2007: A Null “Moderate Risk” of Severe Weather Jonathan Kurtz Department of Geosciences University of Nebraska at Lincoln NOAA/NWS Omaha/Valley,
My grandparents’ farm or so The farm NW of Sac City near Nemaha.
An Investigation of Null-Event Severe Convective Watches in the WFO Sterling Forecast Area Lee Picard Student Volunteer, WFO LWX University of Miami, Coral.
An Overview of Environmental Conditions and Forecast Implications of the 3 May 1999 Tornado Outbreak Richard L. Thompson and Roger Edwards Presentation.
Paul Fajman NOAA/NWS/MDL September 7,  NDFD ugly string  NDFD Forecasts and encoding  Observations  Assumptions  Output, Scores and Display.
Thunderstorm Forecasting in NYC Christina Speciale, Rutgers University Dr. Steven Decker, Rutgers University Brandon Hertell, Consolidated Edison.
S. Hunter Coleman*, Michael Cammarata, Anthony Petrolito NOAA/National Weather Service WFO Columbia, SC * A Significant Hail.
Upper-level Mesoscale Disturbances on the Periphery of Closed Anticyclones Thomas J. Galarneau, Jr. and Lance F. Bosart University at Albany, State University.
Warm-Season Lake-/Sea-Breeze Severe Weather in the Northeast Patrick H. Wilson, Lance F. Bosart, and Daniel Keyser Department of Earth and Atmospheric.
Using Ensemble Probability Forecasts and High Resolution Models To Identify Severe Weather Threats Josh Korotky NOAA/NWS, Pittsburgh, PA and Richard H.
6/26/2015 RUC Convective Parameters and Upscale Events in Southern Ontario Mike Leduc Environment Canada.
Operational Use of SPC’s Storm Scale Ensemble of Opportunity Bill Martin November 2014.
* Reading Assignments:
Hail Large hail is not a killer, but does considerable damage.
Determining Favorable Days for Summertime Severe Convection in the Deep South Chad Entremont NWS Jackson, MS.
Corfidi, et al – convection where air parcels originate from a moist absolutely unstable layer above the PBL. Can produce severe hail, damaging.
© Craig Setzer and Al Pietrycha Supercell (mesocyclone) tornadoes: Supercell tornado environments Developed by Jon Davies – Private Meteorologist – Wichita,
Use of TAMDAR Data in a Convective Weather Event Saturday, May 21, 2005.
Verification has been undertaken for the 3 month Summer period (30/05/12 – 06/09/12) using forecasts and observations at all 205 UK civil and defence aerodromes.
Horizontal Convective Rolls MPO 551 Paper Presentation Dan Stern Horizontal Convective Rolls : Determining the Environmental Conditions Supporting their.
Ensemble Numerical Prediction of the 4 May 2007 Greensburg, Kansas Tornadic Supercell using EnKF Radar Data Assimilation Dr. Daniel T. Dawson II NRC Postdoc,
The diagram shows weather instruments A and B.
 Mentoring: A productive volunteer experience.  To explore environments that effect cloud top cooling detections.  To explore thunderstorm behavior/impacts.
Lecture 2a Severe Thunderstorm Primer Synoptic Laboratory II – Mesoscale Professor Tripoli.
Mike Evans NWS / WFO BGM. CSTAR V – Severe convection in scenarios with low-predictive skill SUNY Albany researchers are examining SPC forecasts and associated.
COMET HYDROMET Enhancements to PPS Build 10 (Nov. 1998) –Terrain Following Hybrid Scan –Graphical Hybrid Scan –Adaptable parameters appended to.
A Study on the Environments Associated with Significant Tornadoes Occurring Within the Warm Sector versus Those Occurring Along Boundaries Jonathan Garner.
Composite Analysis of Environmental Conditions Favorable for Significant Tornadoes across Eastern Kansas Joshua M. Boustead, and Barbara E. Mayes NOAA/NWS.
Research Update 10 February 2012 Updated 15 February 2012.
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis Continued.
19 July 2006 Derecho: A Meteorological Perspective and Lessons Learned from this Event Ron W. Przybylinski, James E. Sieveking, Benjamin D. Sipprell NOAA.
Forecast Parameters. CAPE Convective Available Potential Energy – obviously, positive buoyancy is helpful for producing convection –100 mb mixed layer.
The Ingredients Based Tornado Parameter Matt Onderlinde.
Severe Weather? What Happened… Jim Connolly NWS New York, NY April 2009.
The Similar Soundings Technique For Incorporating Pattern Recognition Into The Forecast Process at WFO BGM Mike Evans Ron Murphy.
Mike Evans NWS Binghamton, NY. Outline The checklist Example – April 28, 2011 Verification Summary / Conclusion.
SPC Ensemble Applications: Current Status, Recent Developments, and Future Plans David Bright Storm Prediction Center Science Support Branch Norman, OK.
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis.
Phillip Bothwell Southern Thunder 2011 Workshop July 13, 2011 Multi-Model Lightning Prediction.
I n t e g r i t y - S e r v i c e - E x c e l l e n c e Air Force Weather Agency Probabilistic Lightning Forecasts Using Deterministic Data Evan Kuchera.
40 th Annual Meeting, National Weather Association, Broadcast Meteorology Workshop Oct. 18, 2015 Understanding SPC’s Outlooks or Everything you wanted.
Title Climatology of High Lapse Rates and Associated Synoptic-Scale Flow Patterns over North America and the Northeast US(1974  2007) Jason M. Cordeira*,
Spatial Verification Methods for Ensemble Forecasts of Low-Level Rotation in Supercells Patrick S. Skinner 1, Louis J. Wicker 1, Dustan M. Wheatley 1,2,
Summer Tornadoes – NWA 2015 Statistical Severe Convective Risk Assessment Model (SSCRAM) (Hart & Cohen, 2015) SPC Mesoanalysis Data Every hour from
CI VERIFICATION METHODOLOGY & PRELIMINARY RESULTS
Tornado Warning Skill as a Function of Environment National Weather Service Sub-Regional Workshop Binghamton, New York September 23, 2015 Yvette Richardson.
Statistical Severe Convective Risk Assessment Model (SSCRAM) SPC Mesoanalysis Data every hour from (Bothwell et al. 2002) + CG NLDN Lightning.
WSR-88D PRECIPITATION ESTIMATION FOR HYDROLOGIC APPLICATIONS DENNIS A. MILLER.
A Case Study of Two Left-Moving Mesoanticyclonic Supercells on 24 April 2006 Chris Bowman National Weather Service – Wichita, KS.
A Rare Severe Weather and Tornado Event in Central New York and Northeast Pennsylvania: July 8, 2014 Presented by Mike Evans 1.
Upper Level Jet and Large Hail in Summer Jonathan D. Finch.
Gridded WAFS Icing Verification System Matt Strahan WAFC Washintgon.
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis Continued.
Environmental Features Discriminating Between High Shear/Low CAPE Severe Convection and Null Events Keith Sherburn Matthew Parker North Carolina State.
How to forecast the likelihood of thunderstorms!!!
Estimating Rainfall in Arizona - A Brief Overview of the WSR-88D Precipitation Processing Subsystem Jonathan J. Gourley National Severe Storms Laboratory.
The 22 May 2014 Duanesburg, NY, Tornadic Supercell
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis Ooohhhh!!!!!!!!!!! Aaaahhhhhhhh!!!!!! Look at the pretty picture!
Michael K. Tippett1,2, Adam H. Sobel3,4 and Suzana J. Camargo4
Thermodynamic Diagrams and Severe Weather
Ingredients approach for severe weather
Severe Weather and Storm Chasing
Comparison of Observed Conditions with Stability Indices
Differences Between High Shear / Low CAPE Environments in the Northeast US Favoring Straight-Line Damaging Winds vs Tornadoes Michael E. Main, Ross A.
Supercell tornado environments
Presentation transcript:

ounding nalog etrieval ystem Ryan Jewell Storm Prediction Center Norman, OK SARS Sounding Analog Retrieval System

What is SARS? SARS is a forecast system based on sounding analogs. The algorithm matches forecast soundings to a large database of proximity soundings associated with severe weather. SARS finds matches using a small number of parameters and parameter ranges determined by a calibration process.

Name inspired by MARS – Map Analog Retrieval System Greg Carbin – Safe to use! Used experimentally at the SPC. Integrated into NSHARP (Sounding displays) RUC and NAM plan view display (Model Grids) What is SARS?

Two types: Hail and Supercell/Tornado Hail: 1148 Severe hail proximity soundings (Observed) Supercell Tornado: 938 Supercell proximity soundings (RUC) (Under Development) Types of SARS Hail SARS can forecast: 1) Probability of SIG (≥ 2.00”) hail. 2) Maximum expected hail size (≥ 0.75”).

Matching Sounding Database Includes 1148 observed hail soundings Within 100 nm and +/- 2.5 hrs either side of 2330Z (21-02). Had to be in same air mass as storm. Modified for surface conditions (if needed). Thrown out if contaminated by outflow, etc. Expansion of dataset used in Jewell and Brimelow (WAF 2009). Severe Hail Proximity Soundings

Matching Sounding Database Assume dataset is “representative.” Spans all seasons 18 years of data All regions of the CONUS

A function of climatology and quality of soundings.

SARS Calibration Method 1Matching Parameters – Relevant parameters associated with severe storms (various measures of instability and shear associated with hail). 2Define initial ranges for each parameter to be used in search. (Example +/- 500 CAPE) 3Test each sounding independently against the database, analyze matches. 4Adjust parameters and ranges until the desired result is received. Desired Result = The majority of matches agree on a particular type and magnitude of severe weather, and it verifies. If a sounding is associated with 3.00” hail, most of the SARS matches should be very large hail. Determine matching parameters and ranges

Example – Calibration for hail SARS. Remove 1 sounding…test against remaining soundings (1147). Calculate skill scores for parameter set #1 and range combination # 1... Test various combinations of parameters and parameter ranges. 8 different parameters with 5 ranges each = 5 8 or 390,625 combinations.

SARS Matching Parameters Most Unstable (MU) CAPE Mixing Ratio of MU Parcel mb Lapse Rate 500 mb Temperature 0-6 km Bulk Shear Final list of matching parameters (out of about 20) Notably showed little or no skill: Freezing Level Wet Bulb Zero Heights 0-3 Storm Relative Helicity (SRH)

SARS Parameters Ranges Significant Hail Parameter Ranges (Resulted in best skill scores) MUCAPE +/- 40% Mixing Ratio of MU Parcel +/- 2.0 g/kg mb Lapse Rate +/- 1.5 C/km 500 mb Temperature +/- 7 C 0-6 km Bulk Shear +/- 9 m/s Large ranges, but all 5 must overlap.

Performance SARS SIG Hail Algorithm

SARS Skill Scores Significant Hail (≥ 2.0”) Forecast Total Soundings = 1148 HitMiss False Alarm Correct Null No Matches Found * 5 CSITSSPODFAR NOTE: Highest skill score AND highest % with matches * 1 Tie

SARS Skill Scores Significant Hail Forecast - Filtered Remove Golf Ball (1.75”) and 2.00” reports (near 2” threshold) Total Soundings = 889 CSITSSPODFAR HitMiss False Alarm Correct Null No Matches Found * 4 NOTE: Highest skill score AND highest % with matches * 1 Tie

Performance SARS SIZE Algorithm

Mean value of SARS binned by report size – Observed vs. Forecast MEAN STDEV: 0.43” Correlation (All): 0.68 r 2 = 0.47 Correlation (Filtered): 0.75 r 2 = 0.56

SARS MATCHING EXAMPLES (one HAIL of a year!)

National Record - July 23, 2010 – Vivian, SD

Contours = # Matches Color Fill = % that are SIG (≥ 2.00”) RUC Model

GRIDDED SARS EXAMPLE Mean SARS Hail Size (inches) RUC Model

KS Record (dia) - Sep 15, 2010 – Wichita, KS

7.75” Hail

2.75” Hail

4.25” Hail

Contours = # Matches Color Fill = % that are SIG (≥ 2.00”) RUC Model CIN

RUC Model Mean SARS Hail Size (inches) CIN

RARE EVENTS

Put AZ Hail Case HERE Contours = # Matches Color Fill = % that are SIG (≥ 2.00”) RUC Model

Put AZ Hail Case HERE Mean SARS Hail Size (inches) RUC Model

Contours = # Matches Color Fill = % that are SIG (≥ 2.00”) RUC Model

SUPERCELL SARS Forecast Soundings

F5 Tornado

F4 Tornado

F5 Tornado

SARS Summary The SARS method can be applied to various types of severe weather (hail, tornado, wind). SARS forecasts storm REPORTS! Local biases in reporting WILL be reflected in SARS! SARS may miss rare events if they have not been accounted for in the database, but may also find rare events and heighten awareness. SARS is conditional…cannot predict whether storms will form (capping, forcing issues). And oh, by the way…accuracy of SARS heavily depends upon the forecasts models.

This slide intentionally left blank.

ND Record - Jul 14, 2010 – Sioux County, ND

1.75” Hail