Depicting LAMP probabilities and uncertainty in best category forecast New LAMP web page product Judy E. Ghirardelli Scott Scallion National Weather Service.

Slides:



Advertisements
Similar presentations
Slide 1ECMWF forecast products users meeting – Reading, June 2005 Verification of weather parameters Anna Ghelli, ECMWF.
Advertisements

A Brief Guide to MDL's SREF Winter Guidance (SWinG) Version 1.0 January 2013.
Accessing and Using GFS LAMP Products National Weather Service Meteorological Development Laboratory Mesoscale Prediction Branch Scott D. Scallion, March.
What is a Synoptic Weather Map?
1 of Introduction to Forecasts and Verification.
What is Probability? The study of probability helps us figure out the likelihood of something happening. In math we call this “something happening” or.
What is Probability? The study of probability helps us figure out the likelihood of something happening. In math we call this “something happening” or.
Use of Probablistic Weather Forecasts by Utilities AMS - “Enhancing Weather Information with Probability Forecasts” January 2002.
Seasonal Predictability in East Asian Region Targeted Training Activity: Seasonal Predictability in Tropical Regions: Research and Applications 『 East.
GreenCig/Vis Categories match Pale Green Situational awareness Orange 2 categories off, Multiple impacts Yellow 1 category off, Singular impact Red 3 categories.
Paul Fajman NOAA/NWS/MDL September 7,  NDFD ugly string  NDFD Forecasts and encoding  Observations  Assumptions  Output, Scores and Display.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Quantitative precipitation forecasts in the Alps – first.
New Local Climate Outlook Products from the NWS Jay Breidenbach NOAA/National Weather Service.
New Local Climate Outlook Products from the NWS Andrea Bair NOAA/NWS Western Region Headquarters Climate Services Program Manager.
Chapter 25 Weather Section 4 Forecasting Weather Notes 25-6.
A Process-Oriented Observational Study of Snowfall Potential in the Central United States Chad M Gravelle Saint Louis University Charles E Graves Saint.
The effect of uncertainty information and graphic design on decision-making Dr. Kim Klockow AMS/UCAR Congressional Science Fellow American Association.
Juan Ruiz 1,2, Celeste Saulo 1,2, Soledad Cardazzo 1, Eugenia Kalnay 3 1 Departamento de Cs. de la Atmósfera y los Océanos (FCEyN-UBA), 2 Centro de Investigaciones.
Probability.
1 Localized Aviation Model Output Statistics Program (LAMP): Improvements to convective forecasts in response to user feedback Judy E. Ghirardelli National.
5.5 Counting Techniques. More Challenging Stuff  The classical method, when all outcomes are equally likely, involves counting the number of ways something.
Department Store A department store is divided into two sections, electronics and furniture. Each section offers a discount rate; items in the same section.
COLLECTING QUANTITATIVE DATA: Sampling and Data collection
Met Alert Tool (MAT). Introduction What is MAT? –Met Alert Tool (MAT) monitors and alerts the user to weather conditions exceeding thresholds (for example,
Weather forecasting began in the mid 1800’s when basic tools, like the thermometer and barometer, were invented Global Weather Reporting Weather observations.
4IWVM - Tutorial Session - June 2009 Verification of categorical predictands Anna Ghelli ECMWF.
Verification of the Cooperative Institute for Precipitation Systems‘ Analog Guidance Probabilistic Products Chad M. Gravelle and Dr. Charles E. Graves.
The Utilization of the Graphic Forecast Generator (GFE) to Locally Apply CPC’s Week Two Forecast.
You will use Game Board Version P for this Practice Round of the Photon Game.
Introductory Statistics
Web Colors. Web Colors: Up until now, we have been using only pre- defined color names, such as "orange" and "lightblue". As web designers, we need the.
Pablo Santos WFO Miami, FL Mark DeMaria NOAA/NESDIS David Sharp WFO Melbourne, FL rd IHC St Petersburg, FL PS/DS “HURRICANE CONDITIONS EXPECTED.”
Chapter 13 Weather Forecasting and Analysis. Weather forecasting by the U.S. government began in the 1870s when Congress established a National Weather.
Utilizing Localized Aviation MOS Program (LAMP) for Improved Forecasting Judy E. Ghirardelli National Weather Service Meteorological Development Laboratory.
World Meteorological Organization Working together in weather, climate and water Enhanced User and Forecaster Oriented TAF Quality Assessment CAeM-XIV.
7.4 Probability of Independent Events 4/17/ What is the number of unique 4-digit ATM PIN codes if the first number cannot be 0? The numbers to.
Priority project Advanced interpretation COSMO General Meeting, 18. September 2006 Pierre Eckert.
Section 3.2 Notes Conditional Probability. Conditional probability is the probability of an event occurring, given that another event has already occurred.
Collaborative Convective Forecast Product (CCFP).
1 Gridded Localized Aviation MOS Program (LAMP) Guidance for Aviation Forecasting Judy E. Ghirardelli and Bob Glahn National Weather Service Meteorological.
The Viper Main Interface Layout and interpretation.
OPERATIONAL 2-H THUNDERSTORM GUIDANCE FCSTS TO 24 HRS ON 20-KM GRID JESS CHARBA FRED SAMPLATSKY METEOROLOGICAL DEVELOPMENT LABORATORY OST / NWS / NOAA.
The LAMP/HRRR MELD FOR AVIATION FORECASTING Bob Glahn, Judy Ghirardelli, Jung-Sun Im, Adam Schnapp, Gordana Rancic, and Chenjie Huang Meteorological Development.
Probabilistic Forecasts of Extreme Precipitation Events for the U.S. Hazards Assessment Kenneth Pelman 32 nd Climate Diagnostics Workshop Tallahassee,
Short Range Ensemble Prediction System Verification over Greece Petroula Louka, Flora Gofa Hellenic National Meteorological Service.
Nathalie Voisin 1, Florian Pappenberger 2, Dennis Lettenmaier 1, Roberto Buizza 2, and John Schaake 3 1 University of Washington 2 ECMWF 3 National Weather.
Localized Aviation MOS Program (LAMP) Judy E. Ghirardelli National Weather Service Meteorological Development Laboratory February 05, 2009.
Improved Gridded Localized Aviation MOS Program (LAMP) Ceiling and Visibility Guidance using HRRR model output Presenting: Judy E. Ghirardelli/Adam Schnapp.
Station Models and Converting Station Pressure into Millibars.
What is the purposes of this slide? This slide is used to confirm the effects of applying the moving average method and showing the difference of choosing.
Aviation Products from the Localized Aviation MOS Program (LAMP) Judy E. Ghirardelli National Weather Service Meteorological Development Laboratory Presented.
Estimating Rainfall in Arizona - A Brief Overview of the WSR-88D Precipitation Processing Subsystem Jonathan J. Gourley National Severe Storms Laboratory.
Favorite Color Mrs. Baird May 21, Favorite Color Survey Results BlueGreenRedYellowOther
UERRA User Workshop Toulouse, 4 th Feb 2016 Questions to the users.
LAMP improvements for ceiling height and visibility guidance*
LEPS VERIFICATION ON MAP CASES
Forecast Capability for Early Warning:
Unified Modeling Language
Weather Forecast Verification Using
ART HISTORY QUIZ #1 FRIDAY, NOVEMBER 6th.
Event Chain Methodology
Probabilities and Proportions
Probabilistic forecasts
Register.
What forecast users might expect: an issue of forecast performance
Perceptual Learning and Decision-Making in Human Medial Frontal Cortex
Orbitofrontal Cortex Uses Distinct Codes for Different Choice Attributes in Decisions Motivated by Curiosity  Tommy C. Blanchard, Benjamin Y. Hayden,
Seasonal Forecasting Using the Climate Predictability Tool
Probability of Independent Event
Orbitofrontal Cortex Uses Distinct Codes for Different Choice Attributes in Decisions Motivated by Curiosity  Tommy C. Blanchard, Benjamin Y. Hayden,
Presentation transcript:

Depicting LAMP probabilities and uncertainty in best category forecast New LAMP web page product Judy E. Ghirardelli Scott Scallion National Weather Service Meteorological Development Laboratory

How we make our “best category” selection in LAMP Development: –Thresholds are statistically developed by one of two techniques, either: ♦Targeting unit bias (forecast the event as often as it occurs), or ♦Maximizing the threat score within a bias range –Thresholds vary by region, start time, projection, element, category, etc. Implementation: –LAMP compares the probability of the category to the threshold for the category –LAMP starts with the rarest category and goes to the most common category –The first category whose probability exceeds its threshold is the chosen category

LAMP Categorical Forecast Selection Process Probability (%) Does the probability equal or exceed the threshold? The probability of “few” exceeds the threshold value for “few” – therefore LAMP categorical forecast is “few” Category 1Category 4Category 3Category 2Category 5 Does the probability equal or exceed the threshold?

Depicting Probabilistic Information Take the “simple” example of Probability of Precipitation, which is a yes/no decision. If the probability exceeds the threshold, then the category chosen is “yes.” If not, the category chosen is “no.”

Depicting Probabilistic Information Purpose: indicate to user the uncertainty associated with the Best Category forecasts given the probabilistic information Threshold = dashed black line Probability < thres = green line Probability ≥ thres = red line San Francisco – very small chance of precip St. Louis – slight chance of precip Chicago – slight chance yes and slight chance no precip St. Cloud – high chance of precip

Depicting Probabilistic Information Now take the more complicated example of visibility, which has multiple categories to choose.

The probability of vis < 1 mile (solid line) does not exceed the threshold (dashed line). Look to next rarest category. LAMP Probabilities and Thresholds for Flight Categories Threshold Plot Tab Look at rarest of these categories first.

LAMP Probabilities and Thresholds for Flight Categories Threshold Plot Tab Look at next rarest of these categories. The probability of vis < 3 miles (solid line) exceeds the threshold (dashed line) only for the last hour; therefore this condition is indicated for only that hour. Look to next rarest category to determine the conditions for other hours.

LAMP Probabilities and Thresholds for Flight Categories Threshold Plot Tab Look at next rarest of these categories. The probability of vis ≤ 5 miles (solid line) DOES exceed the threshold (dashed line) at times; therefore this condition is indicated for those times. Note that vis ≤ 5 is not chosen for the last hour, because a rarer condition (vis < 3 miles) was already indicated.

LAMP Probabilities and Thresholds for Flight Categories Threshold Plot Tab Show all together. Looking at these categories, look at the rarest first, then the next rarest, etc. The condition indicated is the rarest probability which exceeds its threshold. This is indicated by the red probability line.

Depicting Probabilistic Information In addition to the previous kinds of plots, we are also offering a web page that shows one category’s probability and threshold at a time, and color codes the confidence in choosing that category by indicating how close the probability was to the threshold. One would have more confidence in a chosen category if the probability exceeded the threshold by a large amount, compared to the probability just barely exceeding the threshold.

Red=Yes Probability exceeds threshold by more than 10% Orange=Likely Probability exceeds threshold but NOT by more than 10% Yellow = Chance Probability is less than threshold but within 10% Cyan = No Probability is less than threshold by more than 10% LAMP Probabilities and Thresholds for Flight Categories Uncertainty Plot Tab – looking at vis ≤ 5 miles Note that this shows you one condition (e.g., vis ≤ 5 miles). To determine the most likely condition, you should consider the rarest conditions first.

Depicting Probabilistic Information New LAMP web pages coming soon: –Uncertainty plots - station selection page: ♦ ♦Accessible from the main GFS-LAMP Realtime Products page: ■ –Uncertainty plots - direct access: ♦ –Threshold plots - direct access: ♦

Depicting Probabilistic Information Documentation on the new page: – For questions or comments, contact: (LAMP Task (LAMP Developer and Web