Robin%20Hogan

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Verifying cloud forecastsWhat is the
half-life of a cloud forecast?Is the Equitable
Threat Score really equitable?

• Robin Hogan
• Ewan OConnor, Anthony Illingworth
• University of Reading, UK
• Chris Ferro, Ian Jolliffe, David Stephenson
• University of Exeter, UK

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How skillful is a forecast?
ECMWF 500-hPa geopotential anomaly correlation

• Most model evaluations of clouds test the cloud
  climatology
• What about individual forecasts?
• Standard measure shows ECMWF forecast half-life
  of 6 days in 1980 and 9 days in 2000
• But virtually insensitive to clouds!

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Overview

• The Cloudnet processing of ground-based radar
  and lidar observations
• Continuous evaluation of the climatology of
  clouds in models
• Evaluation of the diurnal cycle of boundary-layer
  clouds
• Desirable properties of verification measures
  (skill scores)
• Usefulness for rare events the Symmetric Extreme
  Dependency Score
• Equitability is the Equitable Threat Score
  equitable?
• Testing the skill of cloud forecasts from seven
  models
• Skill versus cloud fraction, height, scale,
  forecast lead time, season...
• Estimating the forecast half life
• Testing the skill of cloud forecasts from space
• Evaluation of ECMWF model with ICESat/GLAS lidar
• Most results taken from these papers
• Hogan, OConnor Illingworth (QJ 2009)
• Hogan, Ferro, Jolliffe Stephenson (WAF, in
  press)

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Project

• Aim to retrieve and evaluate the crucial cloud
  variables in forecast and climate models
• 8 models global, mesoscale and high-resolution
  forecast models
• Variables cloud fraction, LWC, IWC, plus a
  number of others
• Sites 4 across Europe plus worldwide ARM sites
• Period several years to avoid unrepresentative
  case studies
• Current status
• Funded by US Department of Energy Climate Change
  Prediction Program to apply to ARM data worldwide

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Level 1b

• Minimum instrument requirements at each site
• Cloud radar, lidar, microwave radiometer, rain
  gauge, model or sondes

• Radar
• Lidar

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Level 1c

• Instrument Synergy product
• Example of target classification and data quality
  fields

Ice
Liquid
Rain
Aerosol
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Level 2a/2b

• Cloud products on (L2a) observational and (L2b)
  model grid
• Water content and cloud fraction

L2a IWC on radar/lidar grid L2b Cloud fraction
on model grid
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Cloud fraction
Chilbolton Observations Met Office Mesoscale
Model ECMWF Global Model Meteo-France ARPEGE
Model KNMI RACMO Model Swedish RCA model
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Cloud fraction in 7 models

• Mean PDF for 2004 for Chilbolton, Paris and
  Cabauw

0-7 km
Illingworth et al. (BAMS 2007)
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Diurnal cycle composite of clouds
Radar and lidar provide cloud boundaries and
cloud properties above site

• Barrett, Hogan OConnor (GRL 2009)

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Joint PDFs of cloud fraction

• Raw (1 hr) resolution
• 1 year from Murgtal
• DWD COSMO model

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Contingency tables
Observed cloud Observed clear-sky
a 7194 b 4098
c 4502 d 41062
DWD model, Murgtal DWD model, Murgtal
a Cloud hit b False alarm
c Miss d Clear-sky hit

• Model cloud
• Model clear-sky

For given set of observed events, only 2 degrees
of freedom in all possible forecasts (e.g. a
b), because 2 quantities fixed - Number of
events that occurred n a b c d - Base
rate (observed frequency of occurrence) p (a
c)/n
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Skill-Bias diagrams
Reality (n16, p1/4) Forecast

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
-
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5 desirable properties of verification measures

• Equitable all random forecasts receive
  expected score zero
• Constant forecasts of occurrence or
  non-occurrence also score zero
• Note that forecasting the right cloud climatology
  versus height but with no other skill should also
  score zero
• Difficult to hedge
• Some measures reward under- or over-prediction
• Useful for rare events
• Almost all measures are degenerate in that they
  asymptote to 0 or 1 for vanishingly rare events
• Dependence on full joint PDF, not just 2x2
  contingency table
• Difference between cloud fraction of 0.9 and 1 is
  as important for radiation as a difference
  between 0 and 0.1
• Difficult to achieve with other desirable
  properties wont be studied much today...
• Linear so that can fit an inverse exponential
  for half-life
• Some measures (e.g. Odds Ratio Skill Score) are
  very non-linear

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HedgingIssuing a forecast that differs from
your true belief in order to improve your score
(e.g. Jolliffe 2008)

• Hit rate Ha/(ac)
• Fraction of events correctly forecast
• Easily hedged by randomly changing some forecasts
  of non-occurrence to occurrence

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Equitability

• Defined by Gandin and Murphy (1992)
• Requirement 1 An equitable verification measure
  awards all random forecasting systems, including
  those that always forecast the same value, the
  same expected score
• Inequitable measures rank some random forecasts
  above skillful ones
• Requirement 2 An equitable verification measure
  S must be expressible as the linear weighted sum
  of the elements of the contingency table, i.e. S
  (Saa Sbb Scc Sdd) / n
• This can safely be discarded it is incompatible
  with other desirable properties, e.g. usefulness
  for rare events
• Gandin and Murphy reported that only the Peirce
  Skill Score and linear transforms of it is
  equitable by their requirements
• PSS Hit Rate minus False Alarm Rate a/(ac)
  b/(bd)
• What about all the other measures reported to be
  equitable?

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Some reportedly equitable measures
HSS x-E(x) / n-E(x) x ad ETS
a-E(a) / abc-E(a)
E(a) (ab)(ac)/n is the expected value of a
for an unbiased random forecasting system
LOR lnad/bc ORSS ad/bc 1 / ad/bc 1
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Skill versus cloud-fraction threshold

• Consider 7 models evaluated over 3 European sites
  in 2003-2004

LOR
HSS
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Extreme dependency score

• Stephenson et al. (2008) explained this behavior
• Almost all scores have a meaningless limit as
  base rate p ? 0
• HSS tends to zero and LOR tends to infinity
• They proposed the Extreme Dependency Score
• where n a b c d
• It can be shown that this score tends to a
  meaningful limit
• Rewrite in terms of hit rate H a/(a c) and base
  rate p (a c)/n
• Then assume a power-law dependence of H on p as p
  ? 0
• In the limit p ? 0 we find
• This is useful because random forecasts have Hit
  rate converging to zero at the same rate as base
  rate d1 so EDS0
• Perfect forecasts have constant Hit rate with
  base rate d0 so EDS1

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Symmetric extreme dependency score

• EDS problems
• Easy to hedge (unless calibrated)
• Not equitable
• Solved by defining a symmetric version
• All the benefits of EDS, none of the drawbacks!

Hogan, OConnor and Illingworth (2009 QJRMS)
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Skill versus cloud-fraction threshold
SEDS
LOR
HSS
SEDS has much flatter behaviour for all models
(except for Met Office which underestimates high
cloud occurrence significantly)
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Skill versus height

• Most scores not reliable near the tropopause
  because cloud fraction tends to zero

LBSS
EDS
LOR
HSS
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A surprise?

• Is mid-level cloud well forecast???
• Frequency of occurrence of these clouds is
  commonly too low (e.g. from Cloudnet Illingworth
  et al. 2007)
• Specification of cloud phase cited as a problem
• Higher skill could be because large-scale ascent
  has largest amplitude here, so cloud response to
  large-scale dynamics most clear at mid levels
• Higher skill for Met Office models (global and
  mesoscale) because they have the arguably most
  sophisticated microphysics, with separate liquid
  and ice water content (Wilson and Ballard 1999)?
• Low skill for boundary-layer cloud is not a
  surprise!
• Well known problem for forecasting (Martin et al.
  2000)
• Occurrence and height a subtle function of
  subsidence rate, stability, free-troposphere
  humidity, surface fluxes, entrainment rate...

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Key properties for estimating ½ life

• We wish to model the score S versus forecast lead
  time t as
• where t1/2 is forecast half-life
• We need linearity
• Some measures saturate at high skill end
  (e.g. Yules Q / ORSS)
• Leads to misleadingly long half-life
• ...and equitability
• The formula above assumes that score tends to
  zero for very long forecasts, which will only
  occur if the measure is equitable

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Which measures are equitable?

• Expected values of ad for a random forecasting
  system may score zero
• SE(a), E(b), E(c), E(d) 0
• But expected score may not be zero!
• ES(a,b,c,d) S P(a,b,c,d)S(a,b,c,d)
• Width of random probability distribution
  decreases for larger sample size n
• A measure is only equitable if positive and
  negative scores cancel

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Asyptotic equitability

• Consider first unbiased forecasts of events that
  occur with probability p ½

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What about rarer events?

• Equitable Threat Score still virtually
  equitable for n gt 30
• ORSS, EDS and SEDS approach zero much more slowly
  with n
• For events that occur 2 of the time (e.g.
  Finleys tornado forecasts), need n gt 25,000
  before magnitude of expected score is less than
  0.01
• But these measures are supposed to be useful for
  rare events!

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Possible solutions

• Ensure n is large enough that E(a) gt 10
• Inequitable scores can be scaled to make them
  equitable
• This opens the way to a new class of non-linear
  equitable measures

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What is the origin of the term ETS?

• First use of Equitable Threat Score Mesinger
  Black (1992)
• A modification of the Threat Score a/(abc)
• They cited Gandin and Murphys equitability
  requirement that constant forecasts score zero
  (which ETS does) although it doesnt satisfy
  requirement that non-constant random forecasts
  have expected score 0
• ETS now one of most widely used verification
  measures in meteorology
• An example of rediscovery
• Gilbert (1884) discussed a/(abc) as a possible
  verification measure in the context of Finleys
  (1884) tornado forecasts
• Gilbert noted deficiencies of this and also
  proposed exactly the same formula as ETS, 108
  years before!
• Suggest that ETS is referred to as the Gilbert
  Skill Score (GSS)
• Or use the Heidke Skill Score, which is
  unconditionally equitable and is uniquely related
  to ETS HSS / (2 HSS)

Hogan, Ferro, Jolliffe and Stephenson (WAF, in
press)
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Properties of various measures
Measure Equitable Useful for rare events Linear
Peirce Skill Score, PSS Heidke Skill Score, HSS Y N Y
Equitably Transformed SEDS Y Y
Symmetric Extreme Dependency Score, SEDS Y
Log of Odds Ratio, LOR
Odds Ratio Skill Score, ORSS (also known as Yules Q) N
Gilbert Skill Score, GSS (formerly ETS) N N
Extreme Dependency Score, EDS N Y
Hit rate, H False alarm rate, FAR N N Y
Critical Success Index, CSI N N N

• Truly equitable
• Asymptotically equitable
• Not equitable

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Skill versus lead time
2004
2007

• Only possible for UK Met Office 12-km model and
  German DWD 7-km model
• Steady decrease of skill with lead time
• Both models appear to improve between 2004 and
  2007
• Generally, UK model best over UK, German best
  over Germany
• An exception is Murgtal in 2007 (Met Office model
  wins)

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Forecast half life
2004
2007

• Fit an inverse-exponential
• S0 is the initial score and t1/2 is the half-life
• Noticeably longer half-life fitted after 36 hours
• Same thing found for Met Office rainfall forecast
  (Roberts 2008)
• First timescale due to data assimilation and
  convective events
• Second due to more predictable large-scale
  weather systems

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Why is half-life less for clouds than pressure?

• Different spatial scales? Convection?
• Average temporally before calculating skill
  scores
• Absolute score and half-life increase with number
  of hours averaged

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Geopotential height anomaly Vertical velocity

• Cloud is noisier than geopotential height Z
  because it is separated by around two orders of
  differentiation
• Cloud vertical wind relative vorticity
  ?2streamfunction ?2pressure
• Suggests cloud observations should be used
  routinely to evaluate models

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Satellite observations IceSAT

• Cloud observations from IceSAT 0.5-micron lidar
  (first data Feb 2004)
• Global coverage but lidar attenuated by thick
  clouds direct model comparison difficult

Lidar apparent backscatter coefficient (m-1 sr-1)
Latitude
Optically thick liquid cloud obscures view of any
clouds beneath
Solution forward-model the measurements
(including attenuation) using the ECMWF variables
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Global cloud fraction comparison
ECMWF raw cloud fraction
ECMWF processed cloud fraction

• Results for October 2003
• Tropical convection peaks too high
• Too much polar cloud
• Elsewhere agreement is good
• Results can be ambiguous
• An apparent low cloud underestimate could be a
  real error, or could be due to high cloud above
  being too thick

IceSAT cloud fraction
Wilkinson, Hogan, Illingworth and Benedetti (MWR
2008)
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Testing the model skill from space
Unreliable region

• Clearly need to apply SEDS to cloud estimated
  from lidar radar!

Wilkinson, Hogan, Illingworth and Benedetti (MWR
2008)
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CCPP project

• US Dept of Energy Climate Change Prediction
  Program recently funded 5-year consortium project
  centred at Brookhaven, NY
• Implement updated Cloudnet processing system at
  Atmospheric Radiation Measurement (ARM)
  radar-lidar sites worldwide
• Ingests ARMs cloud boundary diagnosis, but uses
  Cloudnet for stats
• New diagnostics being tested
• Testing of NWP models
• NCEP, ECMWF, Met Office, Meteo-France...
• Over a decade of data at several sites have
  cloud forecasts improved over this time?
• Single-column model testbed
• SCM versions of many GCMs will be run over ARM
  sites by Roel Neggers
• Different parameterization schemes tested
• Verification measures can be used to judge
  improvements

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US Southern Great Plains 2004
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Winter2004
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Summer2004
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Summary and outlook

• Model comparisons reveal
• Half-life of a cloud forecast is between 2.5 and
  4 days, much less than 9 days for ECMWF 500-hPa
  geopotential height forecast
• In Europe, higher skill for mid-level cloud and
  lower for boundary-layer cloud, but larger
  seasonal contrast in Southern US
• Findings applicable to other verification
  problems
• Symmetric Extreme Dependency Score is a
  reliable measure of skill for both common and
  rare events (given we have large enough sample)
• Many measures regarded as equitable are only so
  for very large samples, including the Equitable
  Threat Score, but they can be rescaled
• Future work (in addition to CCPP)
• CloudSat Calipso what is the skill of cloud
  forecasts globally?
• What is half-life of ECMWF cloud forecasts? (Need
  more data!)
• Near-real-time evaluation for rapid feedback to
  NWP centres?
• Dept of Meteorology Lunchtime Seminar, 1pm
  Tuesday 3rd Nov Faster and more accurate
  representation of clouds and gases in GCM
  radiation schemes

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Monthly skill versus time

• Measure of the skill of forecasting cloud
  fractiongt0.05
• Comparing models using similar forecast lead time
• Compared with the persistence forecast
  (yesterdays measurements)
• Lower skill in summer convective events

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Statistics from AMF

• Murgtal, Germany, 2007
• 140-day comparison with Met Office 12-km model
• Dataset released to the COPS community
• Includes German DWD model at multiple resolutions
  and forecast lead times

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Possible skill scores
Contingency table Observed cloud Observed clear sky
Modeled cloud a hit b false alarm
Modeled clear sky c miss d correct negative

• Cloud deemed to occur when cloud fraction f is
  larger than some threshold fthresh

• To ensure equitability and linearity, we can use
  the concept of the generalized skill score
  (x-xrandom)/(xperfect-xrandom)
• Where x is any number derived from the joint
  PDF
• Resulting scores vary linearly from random0 to
  perfect1
• Simplest example Heidke skill score (HSS) uses
  xad
• We will use this as a reference to test other
  scores

DWD model DWD model
a 7194 b 4098
c 4502 d 41062
Perfect forecast Perfect forecast
ap 11696 bp 0
cp 0 dp 45160
Random forecast Random forecast
ar 2581 br 8711
cr 9115 dr 36449

• Brier skill score uses xmean squared
  cloud-fraction difference, Linear Brier skill
  score (LBSS) uses xmean absolute difference
• Sensitive to errors in model for all values of
  cloud fraction

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Alternative approach

• How valid is it to estimate 3D cloud fraction
  from 2D slice?
• Henderson and Pincus (2009) imply that it is
  reasonable, although presumably not in convective
  conditions
• Alternative treat cloud fraction as a
  probability forecast
• Each time the model forecasts a particular cloud
  fraction, calculate the fraction of time that
  cloud was observed instantaneously over the site
• Leads to a Reliability Diagram

Perfect
No resolution
No skill
Jakob et al. (2004)
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ECMWF raw cloud fraction

• Simulate lidar backscatter
• Create subcolumns with max-rand overlap
• Forward-model lidar backscatter from ECMWF water
  content particle size
• Remove signals below lidar sensitivity

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Testing the model climatology