Post on 31-Dec-2015
description
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.
100-0 75-2550-50 25-75
10-900-100
High Flood
Med. Flood
Light Flood
No Flood
0
10
20
30
40
50
60
70
80
90
100
High Flood
Med. Flood
Light Flood
No Flood
Do you have anything to say here?
100-0 75-2550-50 25-75
10-900-100
High Flood
Med. Flood
Light Flood
No Flood
0
10
20
30
40
50
60
70
80
90
100
High Flood
Med. Flood
Light Flood
No Flood
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
A
Scenario A
65%
25%
8% 2%
No Rain
Light rain
Mod. Rain
Heavy Rain
Scenario A
100-0 75-25 50-50 25-75 10-90 0-100
Heavy Rain
Light Rain0
10
20
30
40
50
60
70
80
90
100
Heavy Rain
Mod. Rain
Light Rain
No Rain
Scenario A
Yield Based on Aggregate Risk
74
76
78
80
82
84
86
88
100-0 75-25 50-50 25-75 10-90 0-100
Action (now-later)
Perc
enta
ge C
rop Y
ield
Scenarios
Scenario B: Scenario B:
Rainfall forecast probabilities suggest that rainfall will increase dramatically
Rainfall forecast probabilities suggest that rainfall will increase dramatically
B
Scenario B
5%
15%
50%
30%
No Rain
Light rain
Mod. Rain
Heavy Rain
Scenario B
Yield Based on Aggregate Risk
0102030405060708090
100-0 75-25 50-50 25-75 10-90 0-100
Action (now-later)
Perc
enta
ge C
rop Y
ield
Scenarios
Scenario C: Scenario C:
Forecast probabilities indicate that rainfall will decrease moderately
Forecast probabilities indicate that rainfall will decrease moderately
C
Scenario C
15%
55%
25%
5%
No Rain
Light rain
Mod. Rain
Heavy Rain
Scenario C
100-0 75-25 50-50 25-75 10-90 0-100
Heavy Rain
Light Rain0
10
20
30
40
50
60
70
80
90
100
Heavy Rain
Mod. Rain
Light Rain
No Rain
Scenario C
Yield Based on Aggregate Risk
0102030405060708090
100-0 75-25 50-50 25-75 10-90 0-100
Action (now-later)
Perc
enta
ge C
rop Y
ield
Click to edit title
Title1 Title2 Title3 Title4
A
B
C
D
Click to edit title
Text 1Text 1 Text 2Text 2 Text 3Text 3
Click to edit title
TextText
TextText
TextText
TextTextTextText
Add Title
Click to edit title
Text 1 Text 2 Text 3 Text 4
Chart Documents
12
34
5
8830
12
1030
30
2030
90
6523
15
2533
65
0
20
40
60
80
100
120
140
A B C
100-0 75-2550-50 25-75
10-900-100
High Flood
Med. Flood
Light Flood
No Flood
0
10
20
30
40
50
60
70
80
90
100
High Flood
Med. Flood
Light Flood
No Flood
Dissemination Process
Forecaster
Interpretation
Dissemination
End User
ModellerData
Your Company Slogan