The “User Metric” Concept

http://cfab.eas.gatech.edu/usermetric.html

Contents

1. Introduction 1. Introduction

2. Concept 2. Concept

3. Problem 3. Problem

4. Providers 4. Providers

Concept

User metric concept

Combining probabilistic forecasts with knowledge from the user community to provide a metric for optimal choice.

Problem

Issues to address:

A user community is faced with making an absolute decision (yes or no).

Forecasts are in the form of probabilities. How can we use probabilistic forecasts to

provide the best information to the user community?

Can only occur with incorporation of user information.

Providers

1. Forecaster provider 1. Forecaster provider

2. User community provider 2. User community provider

Forecaster provides:

Probabilities of particular event occurring at a particular time at a particular intensity

ECMWF: Sept. 9-13, 2002ECMWF: Sept. 9-13, 2002

Discharge Probabilities as Computed in April, 1998

Discharge Probabilities as Computed in April, 1998

Do you have anything to say here?

ECMWF: Sept. 9-13, 2002ECMWF: Sept. 9-13, 2002

Discharge Probabilities as Computed in April, 1998

Discharge Probabilities as Computed in April, 1998

User community provides:

An assessment of the cost if a certain phenomena were to occur with a particular intensity.

This may be quantified to provide the cost of occurrence of a phenomena.

Community input provides an assessment of the cost of a strategy if an event were to occur.

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The user metric . . .

Takes the probabilistic forecast and the statistics provided by the user community to produce….

A statistical assessment of the aggregate risk of taking a particular action so that….

The user community can choose an optimal strategy given all of the available information.

User Metric Flowchart

ForecasterForecasterUser CommunityUser Community

User Metric User Metric

Flow of input

User Metric Flowchart

Scenarios

1. Simple example 1. Simple example

2. Typical situation 2. Typical situation

3. Scenario A: 3. Scenario A:

4. Scenario B: 4. Scenario B:

5. Scenario C: 5. Scenario C:

Scenarios

1. Simple example . . . 1. Simple example . . .

What is the best strategy for harvesting crops for a particular forecast?Are there some strategies better than others?We assume that the farming community may harvest all crops early with reduced yield, harvest a certain % early and the rest later, or all later - noting that the closer to maturation, the greater the crop yield.What is the risk in making a particular decision?

What is the best strategy for harvesting crops for a particular forecast?Are there some strategies better than others?We assume that the farming community may harvest all crops early with reduced yield, harvest a certain % early and the rest later, or all later - noting that the closer to maturation, the greater the crop yield.What is the risk in making a particular decision?

Scenarios

2. Typical situation may be . . . 2. Typical situation may be . . .

Over successive periods forecasts of rainfall indicate:

Initially decreases (A) Increases significantly (B) Decreases slightly (C)

Over successive periods forecasts of rainfall indicate:

Initially decreases (A) Increases significantly (B) Decreases slightly (C)

Scenarios A, B and C

AB C

Scenarios

Scenario A: Scenario A:

Rainfall probabilities suggest that there will be significant decreases in rainfall amounts

Rainfall probabilities suggest that there will be significant decreases in rainfall amounts

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Scenario A

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Yield Based on Aggregate Risk

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Scenarios

Scenario B: Scenario B:

Rainfall forecast probabilities suggest that rainfall will increase dramatically

Rainfall forecast probabilities suggest that rainfall will increase dramatically

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Scenario B

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Yield Based on Aggregate Risk

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Scenario C: Scenario C:

Forecast probabilities indicate that rainfall will decrease moderately

Forecast probabilities indicate that rainfall will decrease moderately

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Dissemination Process

Forecaster

Interpretation

Dissemination

End User

ModellerData

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