priority project « advanced interpretation and verification of very high resolution models »
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Priority project « Advanced interpretation and verification of very high resolution models ». Topics. Advanced postprocessing of weather parameters Verification of very high resolution models, incl. fuzzy verification methods Hydrological applications. 3. Hydrological applications. - PowerPoint PPT PresentationTRANSCRIPT
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Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss
Priority project « Advanced interpretation and verification
of very high resolution models »
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2 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Topics
1. Advanced postprocessing of weather parameters
2. Verification of very high resolution models, incl. fuzzy verification methods
3. Hydrological applications
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3 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
3. Hydrological applications
Hydrology (precipitation adaptation):
Presentation by A. Mazur
Snow parametrisation:
Presentation by E. Machulskaya
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4 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
1. Recognition of weather elements
• Done last year: recognition of thunderstorms with the boosting algorithm:
• Choice of predictors
Perler, Kohli, Walser
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5 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
1. Kalman filtering of COSMO LEPS
V. Stauch, poster outside
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6 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
2. Verification of very high resolution models
Goals
• 1-3 km scale (VHR)• Focus on precipitation• Is VHR (~2km) better than HR (~7km)?• Model intercomparison• Generate products related to the verification• Way to define the scores could depend on the
application (value)• Use synop, (high resolution rainguage network),
radar, evt. composition of all (gridded observations)
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7 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Which rain forecast would you rather use?
Mesoscale model (5 km) 21 Mar 2004
Sydney
Global model (100 km) 21 Mar 2004
Sydney
Motivation
Observed 24h rain
RMS=13.0 RMS=4.6
B. Ebert
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8 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Motivation: precipitation pattern
7km
2km
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9 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Fuzzy Verification F. Ament
Verification on coarser scales than model scale: “Do not require a point wise match!“
X XX X
X Xx XXX
x
Method Raw Data Fuzzyfication Score Example result
Upscaling
Average
Equitable threat score
Fraction Skill
Score (Roberts and Lean, 2005)
Fractional coverage
Skill score with reference to
worst forecast
X XX X
X Xx XXX
x
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10 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Expected behaviour of scores
From Nigel Roberts (2005)
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11 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Application of scores to a perfect forecast
All scores should equal !
But, in fact, 5 out of 12 do not!
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12 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Requested theoretical properties of scores
Avoid « leaking » scores Use illustrative and understandable scores Score should give a real information of the forecast
quality on the different scales Monotonic behavior concerning
• scale (best values for large scales)• frequency of occurrence (best values for high
frequencies of occurrence) Represent some significant characteristics of the PDF
(obs and forecast)
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13 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Requested practical properties of scores
Agreement between subjective and objective judgment
Possible help in decision making Correspond to the needs of the users Should be able to provide a comparison between 2km
and 7 km models (also global models) Should not use a matching between prediction and
observation because it would not allow the generation of univocal products
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14 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Chosen scores
Our best candidates:
Upscaling and Fraction skill score
Corresponding products
• Upscaling mean around a point / station• Fraction skill score probability to exceed some
threshold in a neighbourhood
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15 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Sp
atia
l sca
le (
km)
Sp
atia
l sca
le (
km)
Fuzzy Verification: COSMO-DE – COSMO-EU
90
58
33
20
7
90
58
33
20
7
goodbad
Threshold (mm/3h) Threshold (mm/3h) Threshold (mm/3h)
- =
Fra
ctio
n sk
ill s
core
Ups
calin
g
=-COSMO-EU (7km)COSMO-DE (2.8km) Difference
COSMO-EU better COSMO-DE better
JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations
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16 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Sp
atia
l sca
le (
km)
Sp
atia
l sca
le (
km)
Fuzzy Verification COSMO-2 – COSMO-7
90
58
33
20
7
90
58
33
20
7
goodbad
Threshold (mm/3h) Threshold (mm/3h) Threshold (mm/3h)
- =
Fra
ctio
n sk
ill s
core
Ups
calin
g
=-COSMO-7 (7km)COSMO-2 (2.2km) Difference
COSMO-7 better COSMO-2 better
JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations
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17 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Monthly dependencycut-off 03h, accumulation 03h
COSMO-DE -
COSMO-EU
June
COSMO-2-
COSMO-7
July
August
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18 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Quarterly summaries of „Fuzzy“-scores
FSS Autumn 2007
U. Damrath
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19 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Monthly summaries of „Fuzzy“-scores
FSS July 2007
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2020
Analysis of precipitation in boxesAnalysis of precipitation in boxes
Average number of stations in each area
( SON 2007)
X
We devised a verification methodology by aggregating observed and predicted precipitation in boxes of 1°x 1°(labelled boxes in the map)
The choice of the size and position of the areas has been performed according to different rules:
• boxes have to be enough large in order to contain a high number of observation points (ranging from 20 to over 100, depending on location and period of time considered)
• boxes have to be homogeneous as much as possible in terms of geographic-territorial characteristics
M.-S. TesiniM.-S. TesiniC. CacciamaniC. Cacciamani
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2121
Box 2 aut2007Box 2 aut2007
25 mm/24
23 mm/24
19 mm/24
90th percentile of “climatological” pdf
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2222
Consideration on “day-by-day” Consideration on “day-by-day” behaviour behaviour
• COSMO-I7 seems to be more realistic than ECMWF in reproducing the intra-box variability.
• However, COSMO-I7 presents both a large number of false alarms and high “spikes”. On the other hand, ECMWF presents a greater number of missed alarms, especially for high thresholds.
• According to most standard verification measures, COSMO-I7 forecast would have poor quality, but it might be very valuable to the forecaster since it provides information on the distribution and variability of the rain field over the considered region.
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23 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Neighbourhood method P. Kaufmann
• Cylindrical neighbourhood with fading zone
• Settings at MeteoSwiss: • COSMO-7 (6.6 km):
rxy= 5, rf= 5, rt=3
• COSMO-2 (2.2 km): rxy=10, rf=10, rt=1
• Effective radius:• COSMO-7: ~50 km• COSMO-2: ~35 km
x
y
t
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24 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
12 July: high probabilities match well with precipitation pattern
24 h sum 06 – 06 UTC next day
Probability of 12 h sum above 35 mm
06 – 18 UTC
18 – 06 UTC
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25 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
15 August: high probabilities match well precipitation pattern
24 h sum 06 – 06 UTC next day
Probability of 12 h sum above 35 mm
06 – 18 UTC
18 – 06 UTC
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26 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
17 July: completely missed event
24 h sum 06 – 06 UTC next day
Probability of 12 h sum above 35 mm
06 – 18 UTC
18 – 06 UTC
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27 COSMO General meeting ¦ Cracow, September 2008Pierre.Eckert[at]meteoswiss.ch
Conclusionson verification of very high resoution models
• Results of Upscaling and Fraction skill score are reasonable.• Scores increase with box size, but it is difficult to extract
optimal size by looking at one single model.• Overall better results for very high-res models• This benefits of very high-res models is rather to see in
situations where precipitation variability is large: convection , orography, summer…
• …and at scales of 30 to 50 km• Products can be generated
• Regional means (not new)• Probability to exceed threshold in neighborhood• Or possibly the whole pdf?