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NPE - Cross-cutting research
on verification techniques
Presentation Session Code:
SCI-PS153.03
Verifying modelled currents using a
threshold exceedance approach
Dr Ray Mahdon
An exploration of the Gerrity Skill Score
© Crown copyright Met Office
Verifying modelled currents using a threshold
exceedance approach
An exploration of the Gerrity Skill Score
Table of Contents
• Introduction
• Data Source & Locations
• Differing Current Regimes
• Time Series, Continuous Statistics & Simple Cat. Metrics
• Neighbourhood Methods
• Bias Removal Questions
• Multi-Cat. Metric – Gerrity Skill Score & Ocean Currents
• Threshold Choices
© Crown copyright Met Office
Introduction
• Surface currents forecasts important for commercial
or defence “weather-windows”
• e.g. Current speed below 1kt for 12 hours.
• e.g. Does not exceed 1kt more than x times
• Good for site-specific & threshold based analysis
• Some questions we are trying to answer…..
• Does the model capture extreme events or
“weather-windows”?
• In which locations or time of year do the models
have the best performance; is there a
significant difference in regime, time or area?
© Crown copyright Met Office
Data Source & Locations
Donostia
62083
62025
6201030
Shelf Circulation
62024
Wind & Tidal
Matxitxako
Currents
61280
Eddies
61281
General Ocean
Circulation
61417
62085
26-56N,19W-5E
© Crown copyright Met Office
61198
Slope Current
MyOcean Puertos
Del Estado
61430
Data, Time Series & Continuous
Statistics
• Hourly frequency, Jan 2012 – Jun 2014 (30 months)
• Collocated model & In-Situ moored observation surface currents
• Continuous statistics are helpful to describe overall behaviour
• e.g. q-q & histogram plots describe climatology
• Timeseries can show seasonal patterns or significant events
• Do not quantify the performance of a system when exceeding
thresholds is of interest
• We focus on surface currents
•
validation is relatively sparse for this parameter
• → Categorical Metric Assessment
• Simple 2x2 (binary) contingency table per chosen threshold
© Crown copyright Met Office
Neighbourhood Sampling
Spatial Neighbourhoods
Neighbourhoods:
1x1, 3x3, 5x5,..,NxN
Combinations
spatial & temporal
neighbourhoods
trialled
Temporal Neighbourhoods
T+1
T+0
T-1
© Crown copyright Met Office
Time averaging
& shifting
Simple Categorical Metrics
Improvements from temporal averaging
hour-hour assessment not good
as CSI → ETS
says model mostly correct by chance!
CORR. REJ.
CSI
ETS
F. ALARMS
HITS
MISSES
CSI & ETS require un-biased
input data
Over what period should a
tidally dominated field be
normalised:–
1 tidal cycle;
spring-neap cycle;
astronomical cycle?
How to handle –ve currents?
© Crown copyright Met Office
Multi-Categorical Metric Method
The Gerrity Skill Score
© Crown copyright Met Office
Gerrity* Skill Score (GSS)
• Refinement of binary categorical methods
• Does not depend on the forecast distribution
• Rewards/penalises for rare(extreme)/disparate events
• does not reward conservative forecasting
• Large choice of threshold divisions
• Good observation (sample) climatology required
• Contingency table distribution leads to scoring matrix
OBS
• EquitableC≤T1
(i.e., random
constant forecasts score a value of 0)
T1<C≤T2 &C≥T3
GSS=0.38
C≤T1
46
21
4
MODEL
T1<C≤T2
26
55
5
C≥T3
5
4
8
© Crown copyright Met Office
×
0.56
-0.45
-1.00
-0.45
0.55
-0.01
-1.00
-0.01
5.08
* Gerrity, J.P., (1992), Monthly Weather Review, 120, 2709-2712.
GSS - Threshold Choices
1 year rolling data per point, captured from 2 ½ years
(365 × 24 = 8760 pts. – a good climatology!)
Skewed Thresholds
Equal Frequency Distribution
[0.10,0.25,0.45,0.7]
[20,40,60,80] percentiles
Variability in skill versus thresholds, neighbourhood & time
Clues in events from time series & data captured
© Crown copyright Met Office
GSS - Threshold Choices Cont.
Equal Frequency Distribution = [0.07 , 0.12 , 0.18 , 0.25]
Skewed Thresholds = [0.10 , 0.25 , 0.45 , 0.70]
Daily Max/Min Current Speed - 62024
Mean error = -0.03 ms-1
© Crown copyright Met Office
RMSE = 0.11 ms-1
GSS - Threshold Choices Cont.
Equal Frequency Distribution = [0.05 , 0.1 , 0.15 , 0.2]
Skewed Thresholds = [0.1 , 0.25 , 0.45 , 0.7]
Daily Max/Min Current Speed - 62024
Mean error = -0.03 ms-1
© Crown copyright Met Office
RMSE = 0.11 ms-1
GSS - Threshold Choices Cont.
1 year’s data captured from 2 ½ years
(365 × 24 = 8760 pts. – a good climatology )
Equal Frequency Distribution
Regular Thresholds
OBS
OBS
C<=0.25 0.25<C<=0.5
FC
C<=0.25
272
6
FC
0.25<C<=0.5
16
19
GSS=0.7
×
0.09
-1.00
-1.00
11.52
Equal Frequency Distribution = [0.07 , 0.12 , 0.18 , 0.25]
Regular Thresholds = [0.25 , 0.5 , 0.75 , 1.0]
© Crown copyright Met Office
Other trials & results
• Various spatial & temporal neighbourhoods
• Report similar results
• Preliminary results on other model systems show similar
skill scores
• Met Office FOAM-Shelf system
• Maximum skill versus neighbourhood size
• Other binning thresholds
• No firm a priori binning remains a deficiency
• Decoupling tidal cycle & residual current from raw signal
to highlight skill partitioning
• Doodson sea surface height decoupler trialled
• Separation of potentially non-parallel (orthogonal) fields not
addressed
© Crown copyright Met Office
Conclusions
© Crown copyright Met Office
Conclusions
• Hourly frequency currents, Jan 2012 – Jun 2014 (30 months)
• Threshold based assessment
• Continuous statistics are helpful to describe overall behaviour
• Timeseries can show seasonal patterns
• Does not quantify spatial or temporally coordinated
model/obs values
• → Categorical Metric Assessment
• Gerrity Skill Score – attractive attributes for rewards/penalties
© Crown copyright Met Office
Conclusions cont.
• Choice of thresholds important
• Model CAN CAPTURE EXTREME EVENTS – Threshold dependent !
• Equal Frequency Distribution appears to be the fairest a priori
• Can be personalised to a particular regime or current distribution
• Timeseries needed alongside Gerrity
• Missing data can skew results
• Similar locations/regimes appear to give broadly similar
Gerrity Skill Scores
• Winter months tend to show better skill – more extreme events
• Multi-category methods on surface ocean current speed are
relatively new, so expectation of skill level is unknown
© Crown copyright Met Office
Future Work
• Now concept established, apply to forecast data
• Include other regional models which have long-term observation
record
• Bootstrapping Gerrity Skill Score
• Error estimation around each score
• Return to bias removal issue
• Scaled currents, rather than constant removal?
• Assess wind speed with Gerrity Skill Score & compare to
surface currents
• Potentially highlights efficiency of wind speed transmission to surface
currents in Ocean:Atmosphere boundary
© Crown copyright Met Office
Acknowledgement
• Thank you to MyOcean for
funding towards this work
© Crown copyright Met Office
THANK YOU FOR YOUR ATTENTION
Any Questions (& answers)?
© Crown copyright Met Office