PPT-Communicating Uncertainty via Probabilistic Forecasts for the January 2016 Blizzard in

Frank M Nocera Stephanie L Dunten amp Kevin J Cadima NOAANational Weather Service Boston MA 20142015 Probabilistic Snow Method WPC 90 th Percentile What QPF is driving WPC Percentile Forecasts. Natalie . Harvey, Helen . Dacre. (Reading. ) . Helen Webster, David Thomson, Mike Cooke (Met . Office). Nathan . Huntley (Durham). Impact on aircraft. 2. Volcanic ash is hard and abrasive. Volcanic ash can cause engine failure. by: . chabrail. Jones, Sophie Ilunga. Definition of a blizzard. A . blizzard. is a severe snowstorm characterized by strong sustained winds of at least 56 km/h (35 mph) and lasting for a prolonged period of time — typically three hours or more.. EFSA work. JRC, 29/6-1/7/2016. Training course on Uncertainty. L7/. 1. Rift Valley Fever. Affects cattle, sheep, goats and camels. Virus transmitted by mosquitoes. Endemic in East and West Africa. AHAW were asked to assess risk of entry into North Africa. Uncertainty. Karen . Akerlof. , PhD. Research Assistant Professor. Center for Climate Change Communication. George Mason University. What are the social science fields that study decision-making under conditions of uncertainty?. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Pérez. Nicolás. . Suárez. CRIDA A.I.E.. COmbining. Probable . TRAjectories. — COPTRA. Brussels 5. th. of October . 2016. COmbining. Probable . TRAjectories. — COPTRA. 2. Introduction. COPTRA . A Systematic Review of the Literature. Cindy Jardine. University of Alberta. S. Michelle Driedger. University of Manitoba. Study Objectives. To evaluate empirical studies of communicating uncertainty . Anastasiya. . Stoycheva. Head of “Meteorological forecasts“. Forecaster on duty. anastassia.stoycheva@meteo.bg. Our duties and products;. Our main stakeholders;. How the stakeholders get the information from NIMH in . Chapter 2: . Data . Uncertainty Model. 2. Objectives. In this chapter, you will:. Learn the formal definition of uncertain data. Explore different granularities of data uncertainty. Become familiar with different representations of uncertain data. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Uncertainty. Irreducible uncertainty . is inherent to a system. Epistemic uncertainty . is caused by the subjective lack of knowledge by the algorithm designer. In optimization problems, uncertainty can be represented by a vector of random variables . Assessing risk, . considering chances and uncertainties.. What is Probability?. “A strong likelihood or chance of something” (dictionary.com). “The likelihood of something occurring or the chance of something happening” (yourdictionary.com). CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access). Understanding Probabilistic Weather Information. September 12, 2014. SAS 2014 Spring Open Recommendation. Finding: . Information about the likelihood of predicted weather events has the potential to lead to better operational decisions by airline...