Recent progress in global weather modelling A tale about signal

Transcription

Recent progress in global weather modelling
A tale about signal, noise, error and value
zThe quest for perfect forecasts
zExtreme Events – could we be warned earlier?
zDecision making in chaotic environments
François Lalaurette, European Centre for Mediun-Range Weather Forecasts
IABM conference, Forum 2004, Barcelona, 3 June 2004
Recent Progress in NWP
F. Lalaurette, ECMWF
ECMWF
ECMWF Member States
Belgium
Denmark
Germany
Spain
France
Greece
Ireland
Italy
Luxembourg
The Netherlands
Norway
Austria
Portugal
Switzerland
Finland
Sweden
Turkey
United Kingdom
Co-operation agreements or working arrangements with:
Czech Republic
Croatia
Iceland
Hungary
Romania
Serbia & Montenegro
Slovenia
ACMAD
EUMETSAT
WMO
JRC
CTBTO
ECMWF
Recent progress
500hPa
GEOPOTENTIAL
ANOMALY CORRELATION
N.HEM
9
FORECAST
SCORE REACHES 60.00
SCORE REACHES 60.00 MA
LAT 20.000 TO 90.000 LON -180.000 TO 180.000
Forecast Day
MA = 12 Month Moving Average
8.5
8
7.5
7
6.5
6
5.5
5
4.5
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
ECMWF
No Sat
Recent progress: a
NH tribute to
1) meteorological
No RS
satellites
2) advanced methods
for data assimilation
(4D-var)
SH
No RS
Satellite data are now the
main source of information
even in the NH
No Sat
ECMWF
Number of observations used by ECMWF
10
3.6 millions
1
6h 3D
6h 4D
millions
12h 4D
25r4/26r1
0.1
AIRS
0.01
1997 1998 1999 2000 2001 2002 2003
ECMWF
Recent progress: Rainfall events distribution
Distribution of daily precipitation events
Northern Extratropics (>20N) Dec.-Jan.-Feb., 1500 stations
100
2002-03 SYNOP reports
10
CDF
4%
1
Of SYNOP
reports
exceed
0.1
0.01
1
10
10mm/day
100
1000
Daily rainfall (mm)
ECMWF
Recent progress: Rainfall events distribution
100
1999-2000: Too many light rain…
18-42h forecasts (DJF 1999-00)
CDF
10
1
… too few heavy rain events
0.1
0.01
1
10
100
1000
Daily rainfall (mm)
ECMWF
Recent progress forecasting rainfall: a tribute to better
physical representations and increased resolution
100
2002-2003: less light rain…
18-42h forecast (DJF2002-03)
18-42h forecasts (DJF 1999-00)
CDF
10
1
… and more heavy rain events
0.1
0.01
1
10
100
1000
Daily rainfall (mm)
ECMWF
Resolution changes
T319
T106
T511
(2000)
(1987)
(2001)
T63
T213
(1993)
ECMWF
Summer 2003 European heat wave: a 40 years
perspective
2mT monthly daily means averaged over Europe: land points only
24
wJJA2003 (Ops)
23
C
22
21
20
19
18
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
YEAR
1996
1999
2002
ECMWF
European Heat wave from a local point of view
2m-Temperature
o
Chartres (near Paris), France, August 2003
40
SYNOP 3-hourly reports
D-2 forecast (48-69h range, 3-hourly)
D-5 forecast (120-141h range, 6-hourly)
o
o
30
o
2m temperature ( C)
35
w9 days with
Tx>35C and
o
25
Tn>20C
o
20
o
15
o
10
07-Aug 00UTC
14-Aug 00UTC
ECMWF
19-21 March
2004 Storms:
Satellite and
gust reports
90-108km/h
• 108-126km/h
• >126km/h
ECMWF
19 March 2004 storm: Max Wind gusts
Reports
25 - 30
•90-108km/h
20°W
Forecast (12-36h range)
30 - 35
35 - 99• >126km/h
• 108-126km/h
0°
Thursday 18 March 2004 12UTC ECMWF Forecast t+(33-36) VT: Saturday 20 March 2004 00UTC Surface: **wind gust at 10m
20°E
0°
20°W
20°E
6.
15.
4.
24.
60°N
60°N
20°E
20°E
13.
27.
17.
30.
33.
17.
50°N
50°N
20.
23.
20°W
2.
18.
10.
16.
5.
0°
0°
ECMWF
Recent progress in global weather modelling
z Satellite observations and the use of advanced (4D-var)
assimilation techniques have resulted in much improved
forecasts over the last 5-10 years
z Physical processes are now handled in a way that makes it
possible to compare model and observed values for rainfall, wind
and temperatures
Î There are however still severe limitations (local effects, impact
of convective downdrafts on wind gusts)
z … but not all model improvements result into better forecasts
ECMWF
Error or Chaos?
?
“Why is it so difficult for meteorologists to forecast the weather with some
success? (…) We see that perturbations usually happen to be where the
atmosphere is in a state of unstable equilibrium. The meteorologist sees very
well that the equilibrium is unstable, that a cyclone will be formed somewhere,
but exactly where they are not in a position to say; a tenth of a degree more
or less at any given point, and the cyclone will burst here and not there,
and extend its ravages over districts it would otherwise have spared. If
they had been aware of this tenth of a degree, they could have known it
beforehand, but the observations were neither sufficiently comprehensive nor
sufficiently precise, and that is the reason why it all seems due to the
intervention of chance.”
ECMWF
Erreur ou Chaos?
« Pourquoi les météorologistes ont-ils tant de peine à prédire le temps
avec quelque certitude ? (…) Nous voyons que les grandes perturbations se
produisent généralement dans les régions où l’atmosphère est en équilibre
instable. Les météorologistes voient bien que cet équilibre est instable, qu’un
cyclone va naître quelque part ; mais où, ils sont hors d’état de le dire ; un
dixième de degré en plus ou en moins en un point quelconque, le
cyclone éclate ici et non pas là, et il étend ses ravages sur des contrées
qu’il aurait épargnées. Si on avait connu ce dixième de degré, on aurait pu le
savoir d’avance, mais les observations n’étaient ni assez serrées, ni assez
précises, et c’est pour cela que tout semble dû à l’intervention du hasard. »
Henri Poincaré « Science et Méthodes » (1908) Chap IV: Le Hasard; section II
Quoted by E. Lorenz (The Essence of Chaos, 1993, p. 119)
ECMWF
Verifying Analysis
Modelling
the chaos: ECMWF ensemble
H
OPER T511
Cntr T255
OPER T511
Cluster 1
H
H
L
0
0
L
10
10
1 01
L
1 01
L
L
H
Day 4 forecast valid
20 March 2004
L
L
10
10
H
H
Member 2
Cluster 1
L
Member 7
Cluster 1
Member 9
Cluster 1
Cluster 1
L
Member 10
Cluster 1
H
L
1 01
L
L
L
L
1 01
10
10
5
10
10
L
Member 8
Cluster 1
H
L
L
L
L
L
L
L
L
L
L
L
L
5
L
1 01
5
L
H
Member 6
Cluster 1
H
9 90
5
L
Member 5
Cluster 1
H
H
1 01
L
Member 4
Cluster 1
0
L
Member 3
Cluster 1
1 01
H
H
H
L
ECMWF ENSEMBLE FORECASTS
Tuesday 16 March 2004 12UTC ECMWF Forecast t+96 VT: Saturday 20 March 2004 12UTC Surface: mean sea level pressure
MSLP (countour every 5hPa) and Temperature at 850hPa (only -6 and16 isolines are plotted)
L
H
Member 1
Cluster 1
Verifying Analysis
Cluster 1
L
L
1 01 5
H
H
L
H
L
H
1 02 0
H
H
L
10
10
L
H
L
L
L
L
L
L
L
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Cluster 1
H
1015
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15
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Cluster 1 Member 30
Cluster 1 Member 29
H
L
1 01
9 85
10
10
L
5
L
L
H
101 5
5
L
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H
Cluster 1 Member 28
Cluster 1 Member 27
Cluster 1 Member 26
Cluster 1 Member 25
10
10
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5
1 01
1010
1 01
L
L
H
H
H
H
1 01
H
L
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H
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Cluster 1 Member 24
10
10
L
H
H
Cluster 1 Member 23
Cluster 1 Member 22
H
H
H
L
H
L
L
H
L
H
Member 21
Cluster 1
L
L
L
L
H
H
H
L
1015
Member 3
L
10
10
Cluster
L
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L
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H
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10
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1 0 01 0
1
1 01
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1 01 1 0
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L
H
L
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Cluster 1
H
L
L
H
Cluster 1 Member 20
L
H
L
H
Cluster 1 Member 19
Cluster 1 Member 18
Cluster 1 Member 17
H
H
H
H
Cluster 1 Member 16
Cluster 1 Member 15
Cluster 1 Member 14
H
L
L
H
H
Cluster 1 Member 13
Cluster 1 Member 12
Member 11
H L
H
H
H
H
H
H
H
H
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H
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1 0101 0
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H
H
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10
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H
L
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H
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Cluster 1
H
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H
L
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H
H
H
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Cluster 1 Member 50
H
L
L
1015
L
H
H
H
L
L
H
H
Cluster 1 Member 49
Cluster 1 Member 48
H
L
H
H
H
H
1 01 0
H
L
L
10
10
L
H
1000
5
L
L
H
Cluster 1 Member 47
H
H
1 01
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L
1015
H
H
L
H
H
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Cluster 1 Member 46
Cluster 1 Member 45
Cluster 1 Member 44
1 01
L
5
H
H
L
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1 01 5
H
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Cluster 1 Member 43
Cluster 1 Member 42
Member 41
H
H
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10
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L
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L
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Cluster 1 Member 40
Cluster 1 Member 39
Cluster 1 Member 38
Cluster 1 Member 37
H
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Cluster 1 Member 36
Cluster 1 Member 35
10
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Cluster 1 Member 33
H
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Member 31
H
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H
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H
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H
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ECMWF
Making decisions based on a balance of
probabilities is nothing new
z Traditionally, probabilities have been used (implicitely) to make
decisions in uncertain environments
Î I cannot forecast the temperature in Barcelona in a year’s time,
but I know that it is likely to be warmer than in London
Î Even if I know that by betting for an outsider I am most likely to
lose, I may be likely to make a profit if I have some piece of
information unknown from the bookmaker (his odds are too high)
Î In a meteorological context, you may think of the bookmaker as
the one making his decision on a climatology basis, and the
informed bet-taker as the one with a good knowledge of the
meteorological forecast
z What is important is by how much the new piece of information (e.g.
the meteorological forecast) is shifting the probabilities
ECMWF
20 March 2004 storm: Max Wind gusts
Reports
25 - 30
•90-108km/h
20°W
Forecast (12-36h range)
- 35
35 - 99• >126km/h
•30
108-126km/h
0°
Friday 19 March 2004 12UTC ECMWF Forecast t+(33-36) VT: Sunday 21 March 2004 00UTC Surf ace: **wind gust at 10m
20°W
20°E
0°
20°E
9
13.
6.
15.
4.
60°N
60°N
20°E
20°E
30.
3
26.
21.
37.
50°N
50°N
20°W
3
21.
3.
24.
5.
8.
0°
0°
ECMWF
2
20 Mars 2004 storm
(Extreme Forecast Index, Day 4 Forecast)
ECMWF
Rhone & Marseille floods, 1-5 December 2003
(Le Provencal, 5/12)
ECMWF
1-3 December observed rainfall (courtesy Meteo-France)
Hauteurs des précipitations en 3 jour
YS S
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1 4 4
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Is ère
M1 7
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© Mé té o-Fra nc e Dire ction In te rré giona le Sud-Es t
Ex ploita tion Te c hnique Opé ra tionne lle / CLIMATOLOGIE
2 Bd Châ te a u double
13098 AIX EN PROVENC E c e de x 02
te l : 04 42 95 90 00
Fa x : 04 42 95 90 29
S ARTENE
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ECMWF
Extreme Forecast Index Chart Base 28/11 12UTC for rain
accumulation from 29/11 06UTC up to 4/12 06UTC
Precipitation accumulated over last 120h EPS Extreme Forecast Index 0
Base 28 November 2003 12UTC, VT: Thursday 4 December 2003 06UTC
40°W
60°W
15
01
20
0°
20°E
40°E
60°E
LEGEND
01
60°W
30
10
01
40
20°W
1
100
40
40
501
30
70°N
60°E
30
501
30
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30
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1
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50
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60
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°N
30
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1
5 01
40
30
20°W
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30
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N
0°
0°
50
20°E
ECMWF
Tropical Cyclones:
Isabel, 18/9/2003
1555UTC
wMODIS on Terra,
http://terra.ssec.wisc.edu
ECMWF
EPS forecast (Probability that Isabel will
strike within 120km in the next 5 days)
80°W
60°W
40°W
100
90
Deterministic landfall forecast
40°N
40°N
80
70
Observed landfall
60
30°N
30°N
50
0
40
-12
-12
-24
-36 -48 -60
-72 -84
20°N
-96
-108
20°N
-120
30
-132
-144
-156
-168
20
-192
-180-192
10°N
10°N
10
FC base time: 14/9/2003 12UTC
5
80°W
60°W
40°W
ECMWF
35
2m Tem perature Reduced to T511 Orography (deg C)
73M (T511) 79M (T255)
30
25
20
European
Heat wave:
a local
perspective
(Chartres,
30/7 and 3/8
EPSgrams)
15
10
5
40
THU FRI SAT SUN MON TUE WED THU FRI SAT
31
1
2
3
4
5
6
7
8
9
2m Tem perature Reduced to T511 Orography (deg C)
73M (T511) 79M (T255)
35
30
25
20
15
10
MON TUE WED THU FRI SAT SUN MON TUE WED
4
5
6
7
8
9
10
11
12
13
max
AUGUST
75%
2003
median
25%
TL255 CTRL
min
IABM conference, Forum 2004,
Barcelona, 3 June 2004
TL511 OPS
ECMWF
Economic Value of Probabilistic Forecasts
z Using a simple cost/loss ratio (C/L) decision model, one can
compute the expense associated with different decision-making
strategies:
1. taking preventive action (with cost C) on a systematic basis;
2. never taking action (and therefore facing loss L when the event
occurs);
3. taking action based on the meteorological forecast;
4. taking action based on a perfect forecast (wishful thinking)
(Richardson, QJRMS 2000; Murphy, MWR 1977)
ECMWF
Economic Value
Jun03-Aug03 t + 144 Europe an
T850 anomaly greater than
8K
1
VALUE (0=climate 1=perfect forecast
0.9
0.8
Better always take action
(the forecast misses too many events)
Better never take action
(the forecast has too many false alarms)
0.7
0.6
0.5
0.4
0.3
CONTROL
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
COST/LOSS RATIO
No value added by the meteorological forecast
ECMWF
Economic Value: Adding Probabilities
Applications that
are sensitive to
missed events
may benefit from
acting even when
the probability is
low
Jun03-Aug03 t + 144 Europe an If probabilities are available,
T850 anomaly greater than
8K
applications that are
1
sensitive to false alarms
may benefit from waiting for
a high probability
VALUE (0=climate 1=perfect forecast
0.9
0.8
0.7
0.6
0.5
0.4
0.3
CONTROL
0.2
EPS
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
COST/LOSS RATIO
Extra value added by the probabilistic forecast
ECMWF
Probabilities: a way to adjust the products
z Decreasing the forecast probability threshold from which an action is
taken goes with increasing rates of detection AND of false alarms); Ex:
Probability of rain amount at Day4 >10mm
Action
Threshold
Percentage of
Detection
False Alarm
Rate
60%
8%
0%
30%
37%
3%
20%
52%
6%
10%
71%
13%
2%
97%
51%
(Verification data are Europe SYNOP reports in Winter
2002-2003
(DJF); average frequency of occurrence=6%)
IABM conference, Forum 2004, Barcelona, 3 June 2004 Recent Progress in NWP
ECMWF
Summary / discussion
z Numerical methods for modelling the weather have improved a lot
over the last 5-10 years – increased computer power, new
satellites, new, advanced methods for data assimilation
z Large scale weather systems are now forecast with the same
accuracy 8 days in advance than 5-6 days 25 years ago
z The chaotic behaviour of some aspects of atmospheric dynamics
makes it however still almost impossible to accurately forecast
severe weather a few days in advance
z New methods (ensembles) are providing critical information on
the occurrence of severe weather in a probabilistic way
Î The new challenge is to bring this information to the public
Î Maybe one way is to stress not only how likely an extreme
event is, but how these (dynamical) probabilities compare with
those derived from past records (frequency of occurrence)
z Meteorologists (and broadcasters) only reduce the value of their
forecasts by ignoring their margin of error
ECMWF

Recent progress in global weather modelling A tale about signal

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