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On the use of radar data to verify mesoscale model precipitation forecasts. Martin Goeber and Sean Milton Model Diagnostics and Validation group N umerical W eather P rediction Division Met Office, Bracknell, U.K. contributions from Clive Wilson, Dawn Harrison, Dave Futyan and Glen Harris. - PowerPoint PPT Presentation
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On the use of radar data to verify mesoscale model precipitation forecasts
Martin Goeber and Sean MiltonModel Diagnostics and Validation group
Numerical Weather Prediction Division
Met Office, Bracknell, U.K.contributions from Clive Wilson, Dawn Harrison, Dave Futyan
and Glen Harris
2
Outline of talk
Importance of precipitation verification Verifying observations and ‘wet’ model Statistical methods and interpretation Examples from operational mesoscale model
forecasts from the wet autumn 2000
3
12 km12
km
5kmgauge
Model
5km
Nimrod
Rain gauge representative of an area of about 20 km2
on mesoscale timescales(Kitchen and Blackall 1992)
# observations per model grid box 0.1:1 5:1
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Characteristics of the Nimrod data Ground clutter removal fixed Z-R conversion attenuation correction removal of corrupt images removal of anaprop accounting for variations in the vertical
reflectivity profile gauge adjustment
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‘Wet’ Forecast system characteristics
3D-Var latent heat nudging, 3D-cloud from MOPS cloud microphysics with ice and explicit
calculation of transfer between phases prognostic cloud liquid water and ice penetrative convection scheme based on an
ensemble of buoyant entraining plumes with a treatment of downdraughts
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Categorical statisticsCount concurrent event/no-event, e.g.
precipitation > 2 mm / 3 hours
Observations
Yes No
Yes Hits
aFalse Alarms
b
Fore
cast
s
No Misses
cCorrect rejections
d
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Categorical measures (1)
( ¦ )
( ¦ )
p f o
p f o
aH
a c
bF
b d
Hit rate
False alarm rate
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Which measures for categorical statistics ?
complete description of 2*2 contingency table description of different aspects of relationship
between forecasts and observations, e.g. independence from marginal distributions
confidence intervals interpretation relationship to value
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Categorical measures (2)( )
( )_
p f
p o
a bfrequency bias
a c
2
( ¦ ) ( ¦ ) 1
( ) ( ) 1
( , )
( )( )O
p f o p f o
HKS accuracy event accuracy non event
Cov O F ad bcHKS H F
s a c b d
HKS
Frequency bias
Hansen-Kuipers score
1
-1
( ) / ( ) /(1 )
( ¦ ) ( ¦ )
( ¦ ) ( ¦ )
1 1
odds p x p x p p
p f o p f o
p f o p f o
H F ad
H F bc
Odds ratio
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Categorical measures (3)
Confidence intervals
2 22 4( )( )( )
4 ( )( )
n a b c d HKSs HKS
n a b c d
/ 2 / 2
1 1 1 1(log )
log (log ) log log (log )
sa b c d
z s z s
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Autumn 2000 Orography Accumulation
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Accumulation autumn (SON) 200012km resolution, 18-24 h forecasts Model Nimrod-obs Bias
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Rain/no-rain (>0.4 mm/6hrs)12km resolution, 12-18 h forecasts Bias HKS Odds
ratio
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Heavy precipitation (>4mm/6hrs)12km resolution, 12-18 h forecasts Bias HKS
Odds ratio
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Heavy precipitation (>4mm/6hrs)36km resolution, 12-18 h forecasts Bias HKS
Odds ratio
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Estimates of confidence intervals
Minimum(a,b,c,d) Error(HKS) Error(log(OR))
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Frequency bias
Hansen-Kuipers score
Odds ratio
Summary (6h)
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Frequency bias
Hansen-Kuipers score
Odds ratio
Summary (3h)
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Regionally integrated statistics
a) observed area, b) hourly accumulationc) wet area, d) maximum
A) Obs b) 2-3hrc) 8-9hr d) 14-15e) 20-21 f) 26-27
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Regionally integrated statistics
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Regionally integrated statistics
Probability for rain in one hour
Nimrod (obs) 6-12hrs. forecast 18-24 hrs. forecast
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Regionally integrated statistics
Brier skill score for p(rain in one hour)
6-12 hrs. forecasts 18-24 hrs. forecasts
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Summary
Nimrod (radar) data are relatively great for verifying mesoscale quantitative precipitation forecasts, because of their spatial and temporal resolution and near real time availability.
Last autumn’s extreme precipitation in England and Wales was relatively well forecasted.
Time series of regionally integrated statistics and categorical data analysis provide scientifically based, yet customer friendly, measures to verify quantitative precipitation forecasts.
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Future developments
Application and development of tests on significance of difference between two samples (e.g. convective vs. frontal precipitation, orographic enhancement, spinup, dependency on resolution, new model formulations)
Extension of investigations on catchment scale Comparison of spatio-temporal spectral
characteristics of model and observations Lagrangian (event based) statistics