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Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1 23 rd April 2012 The support of Munich Re is gratefully acknowledged Centre for the Analysis of Time Series, London School of Economics www2.lse.ac.uk/CATS

Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

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Page 1: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Misleading estimates of forecast quality: quantifying skill with

sequential forecastsAlex Jarman and Leonard A. Smith

EGU 2012 Conference – Session NP4.1

23rd April 2012

The support of Munich Re is gratefully acknowledged

Centre for the Analysis of Time Series, London School of Economics

www2.lse.ac.uk/CATS

Page 2: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Serial Dependence in Data and Statistical Inference

Background

Geophysical forecasting applications are numerous and have potentially significant beneficial economic impacts...

Sources: NASA, interestingengineering.com, SOHO/EIT, Torsten Blackwood/AFP/Getty

...hence, the potentially adverse impacts of misleading forecast skill estimation are also significant!

Page 3: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Serial Dependence in Data and Statistical Inference

Background

Serial dependence is common in geophysical data time series – even onlong-term timescales; a well documented and well understood problem forstatistical inference

Source: Wilks, Statistical Methods for the Atmospheric Sciences, 2011

However, the effect on forecast skill has not been so well documented...

Page 4: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Serial Dependence in Data and Statistical Inference

Statistical Inference of Forecast Skill

Source: Wilks, Sampling Distributions of the Brier Score and Brier Skill Score under serial dependence, QJRMS, 2010

Serial correlation can lead to sample variance inflation and overconfidence in skill, BUT not always…

Page 5: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Overview of Research

3 Cases of Serial Correlation Effect/Non-effect on Forecast Skill Inference

1. Linear serial correlation in data

2. Linear serial correlation in data

3. Non-linear serial correlation in data

misleading estimate of forecast skill

non-misleading estimate of forecast skill

misleading estimate of forecast skill

Page 6: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Linear Serial Correlation in Data Misleading Estimate of Skill

Lorenz63 System: Univariate Time Series

Exhibits chaotic behaviour but has strong degree of serial correlation: r1 ~ 0.95

Page 7: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Lorenz63 System: Skill Score Statistics

Skill score serial correlation…

r

1 ~ 0.55

Linear Serial Correlation in Data Misleading Estimate of Skill

Page 8: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Linear Serial Correlation in Data Misleading Estimate of Skill

Lorenz63 System: Skill Score Statistics

Estimates of forecast skill are unreliable…

…but is dependent on location on the attractor and lead time

Page 9: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Overview of Research

3 Cases of Serial Correlation Effect/Non-effect on Forecast Skill Inference

1. Linear serial correlation in data

2. Linear serial correlation in data

3. Non-linear serial correlation in data

misleading estimate of forecast skill (e.g. Lorenz63)

non-misleading estimate of forecast skill

misleading estimate of forecast skill

Page 10: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Linear Serial Correlation in Data Non-misleading Estimate of Skill

AR(1) Process: Time Series

Highly autocorrelated time series (φ=0.9) ...

… but is the serial correlation propagated into the skill score statistics??

Page 11: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Linear Serial Correlation in Data Non-misleading estimate of skill

AR(1) Process: Skill Score Statistics

No serial correlation in skill score: r1 ~ 0

…so forecast skill estimates are reliable, even though the data is serially correlated

Page 12: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Linear Serial Correlation in Data Non-misleading Estimate of Skill

AR(1) Process: Skill Score Statistics

…but the skill estimate is unreliable with a perfect climatological forecast

Page 13: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Overview of Research

3 Cases of Serial Correlation Effect/Non-effect on Forecast Skill Inference

1. Linear serial correlation in data

2. Linear serial correlation in data

3. Non-linear serial correlation in data

misleading estimate of forecast skill (e.g. Lorenz63)

non-misleading estimate of forecast skill (e.g. AR(1) Process)

misleading estimate of forecast skill

Page 14: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Non-linear Serial Correlation in Data Misleading Estimate of Skill

Chaotic Map: Delay Plots

CAUTION FOR DECISION-MAKERS: sample size may be important for accurate skill inference

Truth (Data) Skill Score

Page 15: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Overview of Research

3 Cases of Serial Correlation Effect/Non-effect on Forecast Skill Inference

1. Linear serial correlation in data

2. Linear serial correlation in data

3. Non-linear serial correlation in data

misleading estimate of forecast skill (e.g. Lorenz63)

non-misleading estimate of forecast skill (e.g. AR(1) Process)

misleading estimate of forecast skill (e.g. Chaotic Map)

Page 16: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Misleading Estimates of Forecast Quality: Quantifying Skill with Sequential Forecasts

Summary

1. Serial dependence in verification data can lead to erroneous estimates of forecast skill (in some cases)

• Effect of serial correlation on forecast skill inference outlined by Wilks (2010), who showed:1. the effect is increased for skilful forecasts and rarer events2. sample size becomes important

2. Relationship between serial dependence in data and serial dependence in forecast skill is varied

• 3 identified cases of serial correlation in data, and effect or non-effect on statistical inference of skill e.g. a linearly correlated AR(1) process can result in un-correlated forecast skill

• The degree of serial correlation can be dependent on phase and forecast lead time in dynamical systems e.g. Lorenz63 attractor

• Wilks’s findings are not applicable in all cases e.g an unskilful forecast can lead to skill score sample variance inflation

3. Decision-makers should be aware of the complexities of the relationship, and recognise when the effects of serial dependence may lead to unrealistic expectations of forecast skill!

Page 17: Misleading estimates of forecast quality: quantifying skill with sequential forecasts Alex Jarman and Leonard A. Smith EGU 2012 Conference – Session NP4.1

Additional Information

Contact:

Alex Jarman London School of Economics

Email: [email protected]

EGU Poster:

“Distinguishing between Skill and Value in Hurricane Forecasting”Poster Programme: HS4.3Location: Hall A, #A252