Phillip Bothwell and Patrick Marsh-Storm Prediction Center Lindsey Richardson –CIMMS Dry Thunderstorm Forecasting Using Perfect Prog(nosis) Forecast results.

Slides:



Advertisements
Similar presentations
WEATHER Fronts and Mapping
Advertisements

Mei Xu, Jamie Wolff and Michelle Harrold National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL) and Developmental Testbed.
Jess Charba Fred Samplatsky Phil Shafer Meteorological Development Laboratory National Weather Service, NOAA Updated September 06, 2013 LAMP Convection.
CANSAC Products tour From the perspective of an operational fire-weather Meteorologist.
Louisville, KY August 4, 2009 Flash Flood Frank Pereira NOAA/NWS/NCEP/Hydrometeorological Prediction Center.
This is. Jeopardy Earth Science Air Masses FrontsStorms Tornadoes and Hurricanes Weather Forecasting Capture the Chapter r Jeopardy.
Analysis of Rare Northeast Flow Events By Joshua Beilman and Stephanie Acito.
Thunderstorms.
Part 5. Human Activities Chapter 13 Weather Forecasting and Analysis.
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,
Improving Excessive Rainfall Forecasts at HPC by using the “Neighborhood - Spatial Density“ Approach to High Res Models Michael Eckert, David Novak, and.
Characteristics of Isolated Convective Storms
For the Lesson: Eta Characteristics, Biases, and Usage December 1998 ETA-32 MODEL CHARACTERISTICS.
Severe Weather Kim Penney September 30,2010 Science Fair Open House All are Welcome October 20, 2010 Gymnasium Fremont Elementary Waupaca, WI Watches.
WORLD CLIMATE PATTERNS
AIR MASSES A large body of air (thousands of miles) Changes in weather are caused by movements of air masses As an air mass moves away, temp & humidity.
Microbursts Hazards of air mass thunderstorms. Today Mature phase Downdraft.
An Examination of the Tropical System – Induced Flooding in Central New York and Northeast Pennsylvania in 2004.
FACTORS INFLUENCING CLIMATE
Ch. 16: Weather (#1-2).
Weather.
The March 01/02 Non-Winter Weather Event: Part 1 Michael W. Cammarata Anthony W. Petrolito.
Outlook Winter/Spring 2014 John Pendergrast National Weather Service Melbourne, FL
1 Localized Aviation Model Output Statistics Program (LAMP): Improvements to convective forecasts in response to user feedback Judy E. Ghirardelli National.
Using Short Range Ensemble Model Data in National Fire Weather Outlooks Sarah J. Taylor David Bright, Greg Carbin, Phillip Bothwell NWS/Storm Prediction.
Climate and Climate Change
WORLD CLIMATE PATTERNS
Eastern Great Basin July – October, 2014 Fire Potential Outlook Shelby Law EGBCC Predictive Services Meteorologist.
NOAA, National Weather Service Middle Atlantic River Forecast Center Briefing 5:00 pm June 26, 2015.
Phillip Bothwell Senior Development Meteorologist-Storm Prediction Center 3 rd Annual GOES-R GLM Science Meeting Dec. 1, 2010 Applying the Perfect Prog.
Chapter 19.  Result of intense convection  Associated with heating Earth’s surface ◦ During spring, summer, and fall  Three-stage life cycle: ◦ Beginning.
June 19, 2007 GRIDDED MOS STARTS WITH POINT (STATION) MOS STARTS WITH POINT (STATION) MOS –Essentially the same MOS that is in text bulletins –Number and.
By John Metz Warning Coordination Meteorologist WFO Corpus Christi.
Abnormal Weather II October 24, Heat Waves Can occur anywhere in the tropics and temperate zones Common in urban areas, steppes, prairies, and deserts.
Water and Weather Chapter Six: Weather and Climate 6.1 Introduction to Weather 6.2 Weather Patterns 6.3 Climates and Biomes.
Weather Forecasting Chapter 9 Dr. Craig Clements SJSU Met 10.
FACTORS INFLUENCING CLIMATE The factors that influence climate can be identified by using the following anagram: J. BLOWER J. = Jet Stream B = Bodies of.
a large body of air that has the same temperature and humidity throughout classified according to where they originate during the time the air mass.
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis Continued.
The Rapid Evolution of Convection Approaching the New York City Metropolitan Region Brian A. Colle and Michael Charles Institute for Terrestrial and Planetary.
Soundings and Adiabatic Diagrams for Severe Weather Prediction and Analysis.
CC Hennon ATMS 350 UNC Asheville Model Output Statistics Transforming model output into useful forecast parameters.
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.
Tropical Severe Local Storms Nicole Hartford. How do thunderstorms form?  Thunderstorms result from moist warm air that rises due to being less dense.
Transitioning Unique NASA Data and Research Technologies to Operations The Utility of the Real-Time NASA Land Information System for Drought Monitoring.
SPC National Fire Weather Outlooks March 4, 2005 Dr. Phillip Bothwell.
by Brent Rivenbark and Rosalind Byrd
Trials of a 1km Version of the Unified Model for Short Range Forecasting of Convective Events Humphrey Lean, Susan Ballard, Peter Clark, Mark Dixon, Zhihong.
a large body of air that has the same temperature and humidity throughout classified according to where they originate during the time the air mass.
Chapter 8 Section 3-5 (section 1-2 info is in your Water Cycle Presentation)
Brian Freitag 1 Udaysankar Nair 1 Yuling Wu – University of Alabama in Huntsville.
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Developing GFS-based MOS Thunderstorm Guidance for Alaska Phillip E. Shafer* and Kathryn Gilbert Meteorological Development Laboratory, NWS, NOAA
Estimating Rainfall in Arizona - A Brief Overview of the WSR-88D Precipitation Processing Subsystem Jonathan J. Gourley National Severe Storms Laboratory.
Weather & Climate. Weather & Climate Definitions Weather- “the state of the atmosphere with respect to heat or cold, wetness or dryness, calm or storm,
Heavy Rain Climatology of Upper Michigan Jonathan Banitt National Weather Service Marquette MI.
Dr. Phillip Bothwell Storm Prediction Center October 1, 2007NOAA SAB - Fire Weather Research Working Group Experimental Probabilistic Forecasts of Lightning.
Meteorology and Weather Forecasting
EASC 11 Forecasting, Weather Maps, and Severe Storms Forecasting
The Study of the Weather and Climate
Characteristics of Isolated Convective Storms
New Unit! Climate Change.
West Virginia Floods June 2016 NROW 2016 Albany NY
Unit 5 Section 1 Thunderstorms
Severe Weather Weather describes the conditions in the atmosphere in an area over a short period of time. Weather that is extreme and outside of normal.
Visible Satellite, Radar Precipitation, and Cloud-to-Ground Lightning
Brian McInerney Hydrologist National Weather Service
Potential Snow Sunday evening into Monday morning (March 1-2, 2009)
Northwest Connecticut Snowburst of 19 February 2009
Presentation transcript:

Phillip Bothwell and Patrick Marsh-Storm Prediction Center Lindsey Richardson –CIMMS Dry Thunderstorm Forecasting Using Perfect Prog(nosis) Forecast results from Summer 2013 and Experimental Web Page for 2014

New for 2014 New interactive experimental GFS-based Dry Thunderstorm Web page ( no longer using JAVA applet…works on any PC or SMART PHONE) Explicit Dry Thunderstorm Forecasts DRYTH1 (probability of lightning with less than 0.10 inch of precipitation); also for less than 0.25 inch (DRYTH2). Using GFS input…3 hourly grid forecasts out to 180 hours (available from 00, 06, 12, 18 UTC runs)…using new equations-derived using 12 years NARR and lightning data. 40 km (grid) for lower 48 states…10 km (grid) for Alaska. Explicit probability forecasts for precipitation amounts (example: precipitation > 0.10 inch, > 0.25 inch). Atmospheric dryness forecasts. Experimental web page (for PC or smart

Forecasting Dry Thunderstorms The simple concept of heavy precipitation...attributed to veteran forecaster and researcher C.F. (Charlie) Chappell. “The heaviest precipitation occurs where the rainfall is the highest for the longest time.” So: What about the opposite…a dry thunderstorm*? T he least precipitation with lightning ( or less*) occurs: 1)where the rainfall is the lowest, 2)falling through the driest air, 3)from the highest cloud bases, 4)for the shortest period of time, yet still producing lightning

Dry Thunderstorm probability Low or very low probability of 0.10” or greater precipitation Dry lower- level humidity Areas that could be most favorable for dry thunderstorms are where the fuels are dry to very dry and the intersection of: 1) dry thunderstorm probability 2) dry air mass (low relative humidity) 3) a low (or zero) probability of 0.10 inch precipitation or greater.

The “range” of Dry thunderstorms (or…one-size DOES NOT fit all) Thunderstorms with no precipitation reaching the ground. Thunderstorms with less than 0.10 (or 0.25) inch reaching the ground. Could be single flash event or large numbers of flashes. Can range in scale from isolated event to large geographical areas. *Also depends amount/type and dryness of fuels. Lightning can and does start wildfires virtually anywhere outside the western U.S. from what would not normally be considered a “dry storm”. Totally DRY (lightning and NO precipitation) Totally WET (lightning and heavy precipitation/flooding) Lightning with any amount of precipitation (also need to consider : amount, type and dryness of fuels*)

The “range” of Dry thunderstorms (or…one-size DOES NOT fit all)-continued Actual precipitation could range from 0 up to generally about 0.25 inch (storms with greater than 0.25 inch tend to producing wetting rains). Fire starts also depend on weather before and after the event (prolonged wet/dry before/after) and/or long term drought. Lightning outside the main rain shaft can and does start wildfires from both wet and dry storms.

Box-and-Wisker plot of Average sub-cloud humidity at 21 UTC (from summer of 2003 study). “DRY”“Transition” “WET >” Sample Size (n)-> For grid cells with lightning and “binned” by precipitation amounts (X-axis) 50% Importance of a dry sub-cloud layer

A measure of sub-cloud humidity In the past, the Dry Thunderstorm Potential Index (DTPI) has been used along with the NAM perfect prog forecasts. (DTPI is a combined measure of height of cloud base and sub-cloud humidity). At times (especially overnight), depending the parcel and parcel level selected, values were not reflective of how dry the air mass was (also required a significant amount of computer time to calculate correctly). Relative humidity value from.94 sigma to.72 sigma level (approximately…uniform 8000 feet…terrain following) now used from GFS to better identify dry lower levels…especially in the overnight hours and across all terrain (readily available from GFS output).

Reliability diagram for new lightning forecast equations using GFS (left) and old equations using NAM (right). Probability of one or more CG flashes for full US 40 km grid. June-July-August (JJA) 2013 results using 2013 equations. Full US -All GFS Cycles (0-180 hours-every 3 hours) June-July-August (JJA) 2013 results using 2003 equations. Full US-All NAM Cycles (0-84 hours-every 3 hours) 2013 PPF equations shown an improvement in lightning prediction (1 or more CG flashes) compared to earlier 2003 PPF equations

All GFS cycles and forecasts (0-180 hours-every 3 hours), June, July, August 2013, but for Western US* (West of 102 longitude) <- main area for dry thunderstorms Probability of 1 or more CG flashes Probability of 1 or more CG flashes and < 0.10 inch Probability of 1 or more CG flashes and < 0.25 inch *

Example of web page for CONUS

Example of web page for Alaska

Web page (continued with animation controls, Parameter Overlays and Geopolitical Borders) Animation control buttons -> <-Overlay control (selecting a higher number than the other images will place image as top overlay). Sector -> NEW-GACC boundaries NEW order for overlays Loads by default

Example of dProg/dT for May UTC Forecast for 0.10 inch or greater is dominant overlay 3 hr fcst from 18 UTC GFS cycle15 hr fcst from 06 UTC GFS cycle

3 hr fcst from 18 UTC GFS cycle15 hr fcst from 06 UTC GFS cycle Example of dProg/dT for May UTC Forecast for DRYTH1 is dominant overlay

Use of the SPC Experimental Dry Thunderstorm Web page June 2014 cases (CONUS and Alaska) Each forecast is valid for a 3 hour time period and the times are the start times

Oak Fire 340 PM (MST) 6/17/14 and 24 hour lightning ending 12 UTC 6/18/2014 (1,3,10,30,100,300 CG flashes) 3 hour forecast valid 21 UTC (2 PM MST) 45 hour forecast valid 21 UTC (2 PM MST) 21 hour forecast valid 21 UTC (2 PM MST)

Oak Fire 340 PM (MST) 6/17/14 and 24 hour lightning ending 12 UTC 6/18/2014 (1,3,10,30,100,300 CG flashes) 69 hour forecast valid 21 UTC (2 PM MST) 93 hour forecast valid 21 UTC (2 PM MST) 117 hour forecast valid 21 UTC (2 PM MST)

24 hour lightning-color coded by 3 hr time period ending 12 UTC 06/20/ hour forecast valid 18 UTC 06/19/ hour forecast valid 21 UTC 06/19/ hour forecast valid 00 UTC 06/19/2014

15 hour forecast valid 03 UTC 06/19/ hour forecast valid 06 UTC 06/19/ hour forecast valid 09 UTC 06/19/ hour lightning-color coded by 3 hr time period ending 12 UTC 06/20/21014