Forecasting Storm Duration Neil I. Fox David Jankowski, Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of.

Slides:



Advertisements
Similar presentations
JMA Takayuki MATSUMURA (Forecast Department, JMA) C Asia Air Survey co., ltd New Forecast Technologies for Disaster Prevention and Mitigation 1.
Advertisements

1 Ground Based Meteorological Radars Presented By: David Franc NOAAs National Weather Service September 2005.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
Quantification of Spatially Distributed Errors of Precipitation Rates and Types from the TRMM Precipitation Radar 2A25 (the latest successive V6 and V7)
Great Lakes Weather and Climate. Introduction Standing in for a speaker from Environment Canada that was unable to attend at the last minute Until yesterday.
Terry Schuur Weather Radar Research Meteorological Observations in Support of Dual Polarization Research.
National Weather Association 31 st Annual Meeting 18 October 2006 Cleveland, Ohio Kevin Scharfenberg University of Oklahoma Cooperative Institute for Mesoscale.
Louisville, KY August 4, 2009 Flash Flood Frank Pereira NOAA/NWS/NCEP/Hydrometeorological Prediction Center.
The Areal Mean Basin Estimated Rainfall (AMBER) Program A Tool to Assist in Flash Flood Forecasting Ami Arthur Cooperative Institute for Mesoscale Meteorological.
Precise Remapping Conclusion Introduction Weather Remapping of radar-rainfall estimates onto a two-dimensional Cartesian grid is commonly done using one.
Chapter 9: Weather Forecasting Acquisition of weather information Acquisition of weather information Weather forecasting tools Weather forecasting tools.
PROVIDING DISTRIBUTED FORECASTS OF PRECIPITATION USING A STATISTICAL NOWCAST SCHEME Neil I. Fox and Chris K. Wikle University of Missouri- Columbia.
Watching your rear Neil I. Fox with help from Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of Missouri.
Predicting lightning density in Mediterranean storms based on the WRF model dynamic and microphysical fields Yoav Yair 1, Barry Lynn 1, Colin Price 2,
Where (not) to measure rainfall Neil I. Fox University of Missouri - Columbia.
WHAT IS Z?  Radar reflectivity (dBZ)  Microwave energy reflects off objects (e.g. hydrometeors) and the return is reflectivity WHAT IS R?  Rainfall.
Flooding is deceptively deadly, especially Flash Flooding.
Chapter 24 Section 4 Handout
Direct observations and measurements, weather maps, satellites, and radar 6.4.6: Predict weather conditions and patterns based upon weather data collected.
September 2005WSN05, Toulouse, France Applications of the McGill Algorithm for Precipitation Nowcasing Using Semi- Lagrangian Extrapolation (MAPLE) within.
March 14, 2006Intl FFF Workshop, Costa Rica Weather Decision Technologies, Inc. Hydro-Meteorological Decision Support System Bill Conway, Vice President.
49 COMET Hydrometeorology 00-1 Matt Kelsch Tuesday, 19 October 1999 Radar-Derived Precipitation Part 3 I.Radar Representation of.
Integration of Multiple Precipitation Estimates for Flash Flood Forecasting Reggina Cabrera NOAA/National Weather Service.
Rainfall Interpolation Methods Evaluation Alejandra Rojas, Ph.D. Student Dept. of Civil Engineering, UPRM Eric Harmsen, Associate Prof. Dept. of Ag. and.
The 2014 Flash Flood and Intense Rainfall Experiment Faye E. Barthold 1,2, Thomas E. Workoff 1,3, Wallace A. Hogsett 1*, J.J. Gourley 4, and David R. Novak.
Name, Surname, Position Logo(s) Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project Vassiliki Kotroni (1), Konstantinos.
FLASH FLOOD PREDICTION James McDonald 4/29/08. Introduction - Relevance  90% of all national disasters are weather and flood related  Central Texas.
Application of a rule-based system for flash flood forecasting taking into account climate change scenarios in the Llobregat basin EGU 2012, Vienna Session.
Wayne Faas Chief, NOAA National Climatic Data Center Data Operations Division December 3, 2003.
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.
ROFFG Romania Flash Flood Guidance System. The Romania Flash Flood Guidance System is an adaptation of the HRC Flash Flood Guidance System used in various.
Towards an object-oriented assessment of high resolution precipitation forecasts Janice L. Bytheway CIRA Council and Fellows Meeting May 6, 2015.
STEPS: An empirical treatment of forecast uncertainty Alan Seed BMRC Weather Forecasting Group.
National Weather Service - Southeast River Forecast Center Southeast River Forecast Center North Florida Visit July 17-20, 2006 Southeast River Forecast.
COMET HYDROMET Enhancements to PPS Build 10 (Nov. 1998) –Terrain Following Hybrid Scan –Graphical Hybrid Scan –Adaptable parameters appended to.
Flash Flood Forecasting on a Tropical Small Island towards Disaster Preparedness – Trinidad Glendell De Souza Science & Technology Officer Caribbean Meteorological.
The IEM-KCCI-NWS Partnership: Working Together to Save Lives and Increase Weather Data Distribution.
2 SMAP Applications in NOAA - Numerical Weather & Seasonal Climate Forecasting 24-Hours Ahead Atmospheric Model Forecasts Observed Rainfall 0000Z to 0400Z.
An Examination of the Climatology and Environmental Characteristics of Flash Flooding in the Binghamton, New York County Warning Area Stephen Jessup M.S.
1 Flash Floods in the South-Central U.S.: What are They and How Can we Forecast Them? Matt Kelsch Thursday, 30 March 2000 UCAR Cooperative Program for.
Flash Flood Monitoring and Prediction (FFMP) John Ferree Warning Decision Training Branch Norman, OK John Ferree Warning Decision Training Branch Norman,
1 Heavy/Intense Precipitation Precipitation Intensity Precipitation Efficiency Precipitation Duration The precipitation part to the flood/flash flood problem.
Flash flood forecasting in Slovakia Michal Hazlinger Slovak Hydrometeorological Institute Ljubljana
Flash Flood A rapid and extreme flow of high water into a normally dry area, or a rapid water level rise in a stream or creek above a predetermined flood.
Project Atmosphere American Meteorological Society Weather series: Thunderstorms.
Edward Mansell National Severe Storms Laboratory Donald MacGorman and Conrad Ziegler National Severe Storms Laboratory, Norman, OK Funding sources in the.
FLOODS.
Automated Flash Flood Forecasting Systems ¿Fact or Fantasy? International Workshop on Flash Flood Forecasting San Jose, Costa Rica, March 2006 Session.
Flash Flood Monitoring and Prediction Current Operational Capabilities, Issues and Perspectives Britt Westergard, Service Hydrologist WFO Jackson, KY Q2.
National S&T Center for Disaster Reduction Rainfall estimation by BMRC C-Pol radar ICMCS-V Lei FengBen Jong-Dao Jou 1 Lei Feng and 1,2 Ben Jong-Dao.
Weather Related Data Products for Emergency Management Practitioners Christina McCullough Indiana National Guard
Introduction to Urban Hydrology
2 009 W ater S upply F orecasting William B. Reed Senior Hydrologist Colorado Basin RFC September 18, W ater S eminar “Dust in the Wind and.
A Study of In-Cloud and Cloud-to-Ground Lightning in Tornado-Bearing Supercells in the Midwest Ben Herzog and Patrick S. Market Dept. of Soil, Environmental.
AN INDEX FOR ANTICIPATING EXCESSIVE PRECIPITATION WITH ELEVATED THUNDERSTORMS Alzina Foscato and Patrick Market Dept. of Soil, Environmental & Atmospheric.
Sarah Callaghan British Atmospheric Data Centre, UK, The effects of climate change on rain The consensus in the climate change.
Flood Mapping: Hydrology and Communities 2013 Boulder Floods Presented by: Sandra Sweat Fall 2015 Professor: Dr. David Maidment.
NOAA, National Weather Service Middle Atlantic River Forecast Center Briefing 1:00PM February 13, 2016 Peter Ahnert
ENVI 412 Hydrologic Losses and Radar Measurement Dr. Philip B. Bedient Rice University.
Edward “Ted” Mansell NSSL/WRDD Thunderstorm Electrification and Lightning Simulation.
1 Application of MET for the Verification of the NWP Cloud and Precipitation Products using A-Train Satellite Observations Paul A. Kucera, Courtney Weeks,
11 Short-Range QPF for Flash Flood Prediction and Small Basin Forecasts Prediction Forecasts David Kitzmiller, Yu Zhang, Wanru Wu, Shaorong Wu, Feng Ding.
Michael L. Jurewicz, Sr. NOAA/NWS, Binghamton, NY ER Flash Flood Workshop, Wilkes-Barre, PA June 3, 2010.
National Weather Service
An overview by: Thomas Jones December 2, 2002
TOWARDS HIGH-RESOLUTION GLOBAL SATELLITE PRECIPITATION ESTIMATION
You Can Avoid the Rain! Weather Tips for Biking
Dept. of Earth and Atmospheric Sciences
Project Atmosphere American Meteorological Society
Presentation transcript:

Forecasting Storm Duration Neil I. Fox David Jankowski, Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of Missouri - Columbia

Considering rear edge propagation velocity in flash flood forecasting  Why worry about your rear  How your rear moves compared to your middle  Using knowledge of your rear to forecast rainfall totals  Stop the rear jokes

Why worry about your rear?  Current nowcasting tools (e.g. SCIT tracks) concentrate on arrival time  Excellent for Severe Weather warning  Flash flood forecasting: Interested in total duration of precipitation  Event management / Emergency services like to know end time

This study looked at  The use of three measures of storm velocity as indicators of flash flood potential 1/v c1/v c 1/v r1/v r (v c -v r )/v c v r(v c -v r )/v c v r  The last of these is defined as the ‘Storm Duration Factor’

Storm duration factor (SDF) Duration (D) over a point at distance x : Rainfall accumulation (R a ) at x assuming steady-state rainfall rate R:

Data  Initially data was taken from a number of cases where (flash) flooding occurred  A range of storm types, locations and situations  Not all storm cells observed caused flooding  Then more data for more cases

Analysis  Centroid velocities found using the NSSL algorithms (SCIT)  Rear edge velocities found by locating position from tracing centroid vector backward until Z falls below threshold

Analysis  The three measures were plotted against rainfall accumulations for the subsequent 60 minutesfor the subsequent 60 minutes 0 km and 25 km ahead of storm center location0 km and 25 km ahead of storm center location Greater distances saw very little rain (storms don’t move that fast or dissipate within the distance)Greater distances saw very little rain (storms don’t move that fast or dissipate within the distance)

Comparison of v c & v r

1/v c & precip accumulation

1/v r & precip accumulation

SDF & precip accumulation

Results  All correlation coefficients are horrible  If you squint you can kind of see what you want to see  More work required!!

Next  This could be because We don’t consider development/dissipationWe don’t consider development/dissipation We don’t consider size of stormWe don’t consider size of storm We don’t look at sensible distances or have good rainfall dataWe don’t look at sensible distances or have good rainfall data

Accounting for storm size Tried a “pure” measure Unsuccessful – so try measure based on velocity and storm size

Test  Rainfall accumulation versus duration based on centroid velocity (x+Δx/v c )centroid velocity (x+Δx/v c ) rear edge velocity (x+Δx/v r )rear edge velocity (x+Δx/v r ) Both (reduces to the others for x = 0)Both (reduces to the others for x = 0)

Rainfall total vs v c /Δx

Rainfall total vs v r /Δx

Problems  Radar rainfall accumulations use gaugeuse gauge  Rear edge velocity determination automate – make robustautomate – make robust  Mixture of storm types stratifystratify

Thanks  Parts of this work have been funded by the COMET Partners Program and The University of Missouri Research Council