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Operational forecasting at ECMWF: Science, Components and Products. With contributions from : Renate Hagedorn, David Richardson, Antonio Garcia Mendez, Gerald van der Grijn, Lars Isaksen and others. Ervin Zsoter ECMWF, Meteorological Operations Section [email protected]. Outline. - PowerPoint PPT Presentation
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Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 1 / 95
Operational forecasting at ECMWF: Science, Components and Products
Ervin Zsoter
ECMWF, Meteorological Operations Section
15.0m/s
20°S 20°S
10°S10°S
40°E
40°E
With contributions from:
Renate Hagedorn, David
Richardson, Antonio Garcia
Mendez, Gerald van der Grijn,
Lars Isaksen and others
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 2 / 95
Outline
ECMWF as an operational and research centre
EMOS – ECMWF Meteorological Observational System
Quality control at ECMWF
Important characteristics of the ECMWF’s operational analysis
and forecasting system
ECMWF 4D-VAR data assimilation system
Model computational characteristics
Model performance
Some applications
Different forecast products
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 3 / 95
ECMWF as an organisation
ECMWF is an independent international organization, supported by 18 member states and 8 co-operating states
Co-operating states:
Iceland
CzechRepublic
Slovenia
Romania
Serbia andMontenegro
Hungary
Croatia
Estonia
Convention establishing ECMWF entered into force on 1st Nov 1975
Co-operating organisations:
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 4 / 95
ECMWF Budget 2006
Main Revenue 2006Member States’contributions £27,460,600
Co-operating States’contributions £425,100
Other Revenue £1,454,600
Total £29,340,300
Main Expenditure 2006Staff £12,961,900
Leaving Allowances& Pensions £1,807,500
ComputerExpenditure £11,785,900
Buildings £1,858,000
Supplies £927,000
Total £29,340,300
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 5 / 95
Objectives of the centre
Development of global models and data assimilation systems for the dynamics, thermodynamics and composition of the Earth’s fluid envelope and interacting parts of the Earth-system
Preparation and distribution of medium-range weather forecasts
Scientific and technical research directed towards improving the quality of these forecasts
Collection and storage of appropriate meteorological data
Make available research results and data to Member States
Provision of supercomputer resources to Member States
Assistance to WMO programmes
Advanced NWP training
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 6 / 95
Principal Goal
Maintain the current, rapid rate of improvement of its global, medium-range weather forecasting products, with particular effort on early warnings of severe weather events.
Impressive improvement in the quality of the NWP
2-3 days over 15-20 years
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 7 / 95
Operational activities at ECMWF
Observations
– Acquisition / Pre-processing / Quality control / Bias correction
Data assimilation
– Dynamical fit to observations
Forecasts
Product dissemination and archiving
Verification
– Operational / pre-operational validation
Data Monitoring
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 8 / 95
Data sources for the ECMWF Meteorological Operational System (EMOS)
Number of observed data assimilated in 24 hours 13th February 2006
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 9 / 95
Conventional observations used
MSL Pressure, 10m-wind, 2m-Rel.Hum.
BUOY: MSL Pressure, Wind-10m
Wind, Temperature, Spec. Humidity
PILOT/Profilers: Wind
Aircraft: Wind, Temperature
SYNOP/METAR/SHIP:
TEMP: Land - ASAP - Dropsonde
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 10 / 95
J1994
FMAMJJASONDJ1995
FMAMJJASONDJ1996
FMAMJJASONDJ1997
FMAMJJASONDJ1998
FMAMJJASONDJ1999
FMAMJJASONDJ2000
FMAMJJASONDJ2001
FMAMJJASONDJ2002
FMAMJJASONDJ2003
FMAMJJASONDJ2004
FMAMJJASONDJ2005
FMAMJJASONDJ2006
FMAMJJ01
23
45
6
78
910
1112
1314
15
1617
1819
Fre
qu
ency
*100
0
Temperature 500 hPa - GLOBALMonthly counts of Radiosondes received at ECMWF
00 UTC 12 UTC 06 UTC 18 UTC
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 11 / 95
J1994
FMAMJJASONDJ1995
FMAMJJASONDJ1996
FMAMJJASONDJ1997
FMAMJJASONDJ1998
FMAMJJASONDJ1999
FMAMJJASONDJ2000
FMAMJJASONDJ2001
FMAMJJASONDJ2002
FMAMJJASONDJ2003
FMAMJJASONDJ2004
FMAMJJASONDJ2005
FMAMJJASONDJ2006
FMAMJJ0
1
2
3
4
5
6
7
Fre
qu
ency
*100
0
Temperature 10 hPa - GLOBALMonthly counts of Radiosondes received at ECMWF
00 UTC 12 UTC 06 UTC 18 UTC
Positive trend in the number of Radiosondes reaching the upper Startosphere
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 12 / 95
20AUG
2006
21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18SEP
2006
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
Fre
qu
en
cy*
100
AircraftsAircraftsAircraftsAIRCRAFT AIREP AIRCRAFT AMDAR AIRCRAFT ACARS
Manual obs Automatic obs
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 13 / 95
NOAA AMSUA/B HIRS, AQUA AIRS DMSP SSM/I
SCATTEROMETERS GEOS
TERRA / AQUA MODIS OZONE
28 satellite data sources used in the operational ECMWF analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 14 / 95
Satellite data important
Satellite measurements are increasingly important:
– Global coverage (often only source of observations over ocean and remote land)– High spatial and temporal resolution– Decrease in conventional observing networks (fewer radiosonde stations)
But satellite data are not easy to use:
– Satellites do not measure the model variables (temperature, wind, humidity)– They measure radiances, so
• either use derived products (e.g. cloud motion and scatterometer winds)• or calculate ‘model radiances’ and compare with observations
Recent developments in data assimilation are designed to improve the use of satellite data
– Variational data assimilation: can use radiance data directly– Added model levels in upper stratosphere allow use of additional satellite data– 4D-Var: use observations at appropriate time– Increased resolution – more in agreement with resolution of measurements
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 15 / 95
Example: Tropical cyclone Bonnie near Florida satellite data complement conventional data
L. Isaksen ‘Assimilation of ERS-1 and ERS-2 scatterometer winds in ERA-40’ ECMWF ERA-40 proceedings 2002
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 16 / 95
Large increase in number of observations used
Especially number of satellite data increases
A scientific and technical challenge
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 17 / 95
Observations for one 12h 4D-Var cycle 0900-2100UTC 26 March 2006
Synop: 389.000 (0.49%)
Aircraft: 362.000 (0.46%)
Dribu: 20.000 (0.03%)
Temp: 135.000 (0.17%)
Pilot: 108.000 (0.14%)
AMV’s: 2.811.000 (3.56%)
Radiance data: 74.825.000 (94.81%)
Scat: 269.000 (0.34%)
TOTAL: 78.918.000 (100.00%)
Synop: 60.000 (1.84%)
Aircraft: 179.000 (5.50%)
Dribu: 5.600 (0.17%)
Temp: 67.000 (2.06%)
Pilot: 48.000 (1.48%)
AMV’s: 127.000 (3.90%)
Radiance data: 2.646.000 (81.34%)
Scat: 122.000 (3.75%)
TOTAL: 3.253.000 (100.00%)
Screened Assimilated
99% of screened data is from satellites 86% of assimilated data from satellites
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 18 / 95
Data extraction
Thinning
• Skipped data to avoid Over sampling
• Even so departures from FG and ANA are generated and usage flags also
Blacklist
• Data skipped due to systematic bad performance or due to different considerations (e.g. data being assessed in passive mode)
• Departures and flags available for further assessment
4DVAR QC
• Rejections
• Used data increments
• Check out duplicate reports
• Ship tracks check
• Hydrostatic check
ANALYSIS
Observations – Quality control - Analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 19 / 95
Data input Data assimilationOI
3DVAR
4DVAR
• Feedback files (BUFR)
• ODB
• Raw observation
• Departures (FG & AN)
• Flags (data used, thinned, rejected)
Monthly BUFR files for different Obs types Long term statistics
Observations – Quality control - Analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 20 / 95
Data Monitoring (Procedures)
The basic information is included in the feedback files or ODB
(feedback from the assimilation scheme)
The statistics are normally computed by comparing the
observations with a FG (6 or 12 hours forecast)
– Model independent statistics should be used also Co-locations
But the quality of those forecasts is not the same everywhere
no fixed criteria should be applied when assessing data quality
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 21 / 95
Blacklists
The idea behind the blacklist usage is to remove from the system
observations with a systematic bad performance. A blacklisted
observation is considered as passive data in the data assimilation
The blacklist at ECMWF is flexible enough to consider partial
blacklisting depending on
– Parameters, areas, atmospheric layers, time cycles
– And of course different observation types…….
– MetOps Data Monitoring elaborates a proposal to update the
blacklist which then is discussed with HMOS and HDA. In cases
with heavy changes sensitivity experiments are carried out before
implementing the new blacklist
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 22 / 95
140 stationsRS blacklisted for temperature SEP - 2006
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
170°W
170°W
150°W
150°W
130°W
130°W
110°W
110°W
90°W
90°W
70°W
70°W
50°W
50°W
30°W
30°W
10°W
10°W 0°
0° 10°E
10°E 20°E
20°E 30°E
30°E 40°E
40°E 50°E
50°E 60°E
60°E 70°E
70°E 80°E
80°E 90°E
90°E
110°E
110°E
130°E
130°E
150°E
150°E
170°E
170°E
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
170°W
170°W
150°W
150°W
130°W
130°W
110°W
110°W
90°W
90°W
70°W
70°W
50°W
50°W
30°W
30°W
10°W
10°W 0°
0° 10°E
10°E 20°E
20°E 30°E
30°E 40°E
40°E 50°E
50°E 60°E
60°E 70°E
70°E 80°E
80°E 90°E
90°E
110°E
110°E
130°E
130°E
150°E
150°E
170°E
170°E
80 All levels 3 Below 700 2 Above 700 3 Above 300 6 Above 200
14 Above 150 19 Above 100 9 Above 70 2 Above 50 2 Above 30
Quality problems in Asia & Russia
Blacklists
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 23 / 95
76 stationsRS blacklisted for wind SEP - 2006
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
170°W
170°W
150°W
150°W
130°W
130°W
110°W
110°W
90°W
90°W
70°W
70°W
50°W
50°W
30°W
30°W
10°W
10°W 0°
0° 10°E
10°E 20°E
20°E 30°E
30°E 40°E
40°E 50°E
50°E 60°E
60°E 70°E
70°E 80°E
80°E 90°E
90°E
110°E
110°E
130°E
130°E
150°E
150°E
170°E
170°E
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
170°W
170°W
150°W
150°W
130°W
130°W
110°W
110°W
90°W
90°W
70°W
70°W
50°W
50°W
30°W
30°W
10°W
10°W 0°
0° 10°E
10°E 20°E
20°E 30°E
30°E 40°E
40°E 50°E
50°E 60°E
60°E 70°E
70°E 80°E
80°E 90°E
90°E
110°E
110°E
130°E
130°E
150°E
150°E
170°E
170°E
73 All levels 0 Below 700 0 Above 700 0 Above 300 1 Above 200
1 Above 150 1 Above 100 0 Above 70 0 Above 50 0 Above 30
Quality problems in Africa and southern Asia
Blacklists
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 24 / 95
NH
All data
Used data:
Blacklist and 4DVAR QC appliedReduced
random deviation
Ob-FGOb-AN
What’s the benefit of using a blacklist?
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 25 / 95
All data considered
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
FG (M/S)
05
1015202530354045505560657075
OB
S (
M/S
)
WINDSPEEDAREA: (20N ,180W) - (90N , 40W)
1 - 31 JAN 2006PROFILER
USED: 71 %NO.: 941910 (271475 REJTD) BIAS: .1 STD: 4.1 (.0/ 2.2)
PROFILERS USA ALL DATA
2 - 7
7 - 20
20 - 54
54 - 148
148 - 403
403 - 1096
1096 - 2980
2980 - 8103
What’s the benefit of using a blacklist?
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 26 / 95
Blacklist applied
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
FG (M/S)
05
1015202530354045505560657075
OB
S (
M/S
)
WINDSPEEDAREA: (20N ,180W) - (90N , 40W)
1 - 31 JAN 2006PROFILER
USED: 99 %NO.: 674127 (3692 REJTD) BIAS: .0 STD: 3.4 (.0/ 2.1)
PROFILERS USA NOT BLACKLISTED
2 - 6
6 - 14
14 - 36
36 - 90
90 - 221
221 - 544
544 - 1339
1339 - 3294
3294 - 8103
What’s the benefit of using a blacklist?
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 27 / 95
Blacklist plus 4DVAR Quality Control applied
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
FG (M/S)
05
1015202530354045505560657075
OB
S (
M/S
)
WINDSPEEDAREA: (20N ,180W) - (90N , 40W)
1 - 31 JAN 2006PROFILER
USED: 100 %NO.: 670435 (0 REJTD) BIAS: .0 STD: 2.7 (.0/ 2.0)
PROFILERS USA USED DATA
2 - 6
6 - 14
14 - 36
36 - 90
90 - 221
221 - 544
544 - 1339
1339 - 3294
3294 - 8103
What’s the benefit of using a blacklist?
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 28 / 95
J2000
F M A M J J A S O N D J2001
F M A M J J A S O N D J2002
F M A-40.0-30.0-20.0-10.0
0.010.020.030.040.0
DE
GR
EE
S
35 38 31 32 28 25 13 28 28 16 23 22 24 27 27 27 24 19 24 28 30 26 26 23 22 28 27 27 29 29 26 16 14 7 19 20 18 31 28 25 30 25 19 15 16 14 22 25 21 28 27 25 29 26 24 27 26 25 12 8 9 19 11 20 28 25 23 26 25 15 17 7 13 15 21 18 28 26 22 29 22
OBS-FG WIND DIRECTION: BIAS and STD 12UTCStation 72248(52) (32N, 094W) Elevation: 84 m
200 hPa 200 hPa 500 hPa 500 hPa 700 hPa 700 hPa
J2000
F M A M J J A S O N D J2001
F M A M J J A S O N D J2002
F M A-40.0-30.0-20.0-10.0
0.010.020.030.040.0
DE
GR
EE
S
25 36 34 29 23 27 13 29 31 20 21 26 24 26 25 25 23 19 21 30 29 29 28 18 26 28 26 24 30 27 27 14 14 7 23 23 22 26 31 27 29 25 25 14 14 15 20 26 21 29 23 27 29 23 25 29 25 27 18 10 5 20 12 22 26 29 25 26 25 19 19 11 15 12 25 20 28 25 24 29 23
OBS-FG WIND DIRECTION: BIAS and STD 0UTCStation 72248(52) (32N, 094W) Elevation: 84 m
200 hPa 200 hPa 500 hPa 500 hPa 700 hPa 700 hPa
Site’s directional setting changed by 6.5 degrees
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 29 / 95
6182 stationsSYNOP pressure bias OBS-FG (hPa) APR/2006
-13.3
-12
-11.9
-10.8
-10.5
-10.3
-10.1
-9.8
-9.4
-8.8
-8.7
-8.5
-8.2
-8.1
-7.9
-7.5
-7.4
-7.2
-7.1
-6.7
-4.7-6.7
-6.6
-6.5
-6.2
-6.1
-6.1
-5.6
-5.5
-5.5-5.1
-5.3
-5.2
-5.2
-5.2
-5.2
-4.8-4.9
-5
-4.9
-4.7
-4.7
-4.8 -4.4
-4.6
-4.6
-4.5
-4.4
-4.2
-4.2
-4.2
-3.9-3.9-3.5
-3.9
-3.7-4
-3.1
-3.8
-3.9
-3.9
-3.7
-3.8
-3.8
-3.7
-3.7
-3.6
-3.6
-3.5
-3.1
-3.5
-3.4
-3.1
-3.1
-3.4
-3.2
-3.4
-3.4-3.3
-3.3
-3.1
-3.2
-3.1
-3.1-3.1-3.1
-3.1
-2.9
-3-2.5
-2.9
-3
-3
-3
-2.6-3
-2.9
-3
-2.9
-2.9
-3
-2.9
-2.9 -2.9-2.7
-2.9
-2.1
-2.8
-2.8
-2.5-2.3
-2.6
-2.3
-2.4
-2.7
-2.8
-2.5
-2.7-2.8
-2.5
-2.6
-2.5
-2.2
-2.4
-2.5
-2.6
-2.5 -2.5
-2.1-2.4
-2.5
-2.2
-2.1
-2.5
-2.2
-2.1
-2.4
-2.1
-2.4-2.4
-2.4 -2.3
-2.1
-2.3-2.2
-2.3
-2.3
-2.2
-2.1
-2.3
-2.3
-2.3
-2.3
-2.3
-2.1
-2.1
-2.2
-2.1
-2.1
-2.1-1.9
-1.8
-1.5
-1.5
-1.3
-1.1-1.4-1.7
-1.4
-1.3
-1.4
-1.9
-2
-2
-1.1
-1.8
-1.5
-2
-1.6
-1.9
-1.9
-1.8
-2
-1.4
-1.5
-1.9
-1.9
-1.9
-1.9-1.9
-2 -1.2
-1.9
-2
-1.5
-1.7
-1.2
-1.7
-2
-1.9
-1.8
-1.1
-1.6
-1.5
-1.3
-2
-2
-1.9
-1.9
-1.5
-1.7
-2
-1.8
-2
-1.9
-1.7
-1.5
-1.9
-1.6
-1.5
-1.7
-1.9
-1.9
-1.7
-1.4
-1.9
-1.9-1.9
-1.9
-1.5
-1.8
-1.9
-1.7
-1.4
-1.8
-1.4
-1.7
-1.2
-1.7
-1.7
-1.3
-1.8-1.8
-1.7
-1.1
-1.6
-1.5
-1.7-1.6
-1.4
-1.7
-1.7
-1.5
-1.5
-1.6-1.4
-1.5
-1.5
-1.8
-1.7-1.5
-1.5
-1.7
-1.6
-1.5-1.4
-1.4
-1.2
-1.5
-1.4-1.4
-1.7
-1.6
-1.6
-1.5
-1.6
-1.7
-1.1
-1.2
-1.4
-1.4
-1.1
-1.4 -1.5
-1.5
-1.4
-1.5
-1.2
-1.3
-1.2
-1.5
-1.2
-1.4
-1.5
-1.5
-1.5
-1.3
-1.4-1.4-1.3
-1.4
-1.1
-1.4
-1.3
-1.3-1.5-1.3
-1.3
-1.3
-1.5
-1.5
-1.3
-1.1-1.3
-1.1-1.4
-1.2 -1.1-1.3
-1.4
-1.1-1.5
-1.3
-1.1
-1.2
-1.3
-1.3-1.2
-1.2
-1.2
-1.2
-1.2
-1.4
-1.3
-1.3
-1.3
-1.1
-1.3
-1.3
-1.3
-1.1
-1.3
-1.1
-1.4-1.3
-1.3
-1.2
-1.2
-1.2
-1.1
-1.1
-1.2
-1.2
-1.3
-1.1
-1.2
-1.3
-1.1
-1.1
-1.1-1.2
-1.3
-1.2
-1.1
-1.1
-1.2
-1.3
-1.1
-1.1
-1.2
-1.1
-1.1
-1.1
-1.3
-1.1
-1.1
-1.1
-1.2
-1.2
-1.2
-1.1
-1.1
-1.2
-1.1
-1.2
-1.1
-1.1
-1.2
-1.1
-1.1
-1.1-1.1
-1.1
-1.1
-1.1
-1.1
-1.1
-1.1
-1.1-1.1
-1
-0.8
-0.4
-0.2
-0.8
-0.4
-0.3
-0.4
-0.1
-0.2
-0.3
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0.4
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0
0
0.8
0.3
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0.4
0.5
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0.2
0.3
0.5
0.10.5
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0
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0.9
0.3
0.2
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0.3
0.7 0.7 0.70.8
0.3
0.5
0.1
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0.6
0.3
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0.7 0.30.80.9
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0 0.6
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0.9
0.5
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0
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0.4
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0.6
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0.5
0.7
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0.5
0.4
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0.3
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0.6
0.4
0
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0
0.8
0.3
0
0.3
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0.9
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0.5
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0
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0.8
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0
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0
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0
0
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0.4
0.5
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1.6
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1.8
1.7
1.9
1.2
1.9
1.7
1.7
1.6
1.61.6
1.1
1.1
1.2
1.9
1.5
1.71.6
1.8 1.8
1.1
1.2
1.6
1.61.8
1.5
1.8
1.1
1.9
1.9
1.3
1.1
1.7
1.6
1.9
1.21.4
1.7
1
1.8
1.6
1.51.5
1.3
1.8
1.3
1.6
1.4
1.1
1.71
1.6
1.6
1
1.8
1.8 1.5
1.8
1.6
1.4
1.7
1.7
1.51
1.7 1.6 1.8
1.3
1.31.6
1.4
1.5
1.7
1.5 1.4
1.7
1.5
1.7
1.2
1.7
1.5
1.4
1.4
1.5
1.21.5
1.1
1.3
1.5
1.5
1.4
1.4
1.5
1
1.7
1.6 1.4
1.1
1.3
1.5
1.6
1.1
1.41.3
1.2
1.41.1
1.6
1.4
1.6
1.5
1.41.21.51.1
1
1.4
1.5
1.3
1.1
1.4
1.41.3
1.4
1.61.5
1.6
1.1
1.3
1.3
1.4
1.2
1.1
1.5
1.4
1.4
1.4
1.3
1.5 1.21.2
1.3
1.3
11.4
1.3
1.3
1.5
1
1.2
1.5 1.31.5
1.4
11.4
1
1 1.3
1.51.41.2
1.4
1
1.4
1.2
1.31.2
1.3
1.4
1.1
1.3
1
11.3
1.1
1
1.4
1.1
1.4
1.3
1
1.31.2
1
1.3
1.3
1
1.11.31.3
1.3
1
1.2
1.3
1.2
1.4
1.2
1.3
1.3
1.3
1.21
1.2
1
1.1
1.3
1
1
1.1
1.3
1.3
1
1.1
1.3 1.1
1.1
11.2
1
1.1
1.3
1.21.3
1.3
1.3
1.21.2
1.2
1.1
1.3
1.1
1.1
1.3
1
1.2
1.2
1.2
1.1
1
1.2
1
1
1.1
1.1
1.2 1.1
1.1
1.2
1
1.1
1.11.1
1.1
1.1
1.1
1.1
1.1
1.2
1.2
1.1
1.1
1
1
1
1
1.2
1.1
1
1.2
1.1
1
11.2
1 1
1
1.1 1.1
1
1.1
1.2
1.1
1.1
1.1
1
1
1
1.1
11
1
1
1 1.1
1
1
1
1.1
1
1
1
1
1
1
11
1
1
1
1 1.1
11
1
11
11
11
1
1
11
1
11
1
2.2
2.1
2.9
2.9
2.8
2.7
2.9
2.9 2.8
2.9
2.8
2.7
2.7
2.6
2.8
2
2.8
2.7
2.7
2.6
2.7
2.7
2.7
2.7
2.72.5
2.6
2.5
2.6 2.1
2
2.5
2.6
2.5
2.4
2.5
2.6
2.4
2.52.3
22.3 2.1
2.1
2.42.4
2.3
2.2
2.42.3
2.1
2
2.1
2.2
2.3
2.2
2.2
2.1
2.3
2.3
2 2.1
2.2
2
2.22.2
2
2.2
2.2
2
2.1
2
2.1
2
2
2.1 2
2
2
2
2
2 3.6
3.7
3.9
3.9
3.9
3.9
3.8
3.93.6
3.7
3.5
3.6
3.6
3.5
3.1
3.4
3.4
3.3
3.13.3
3.3
3.2
3.3
3.3
3.1
3.2
3
3.1
3.1
314.3
14.2
14.2
14.1
13.9
13.8
13.313
12.7
11.8
11.4
11.1
10.2
9.8
9.79.5
9.1
8.8
8.4
7.5
7.16.8
6.7
6.5
6.4
6
6
5.85.8
5.4
5.85.7
5.7
5.6
5.6
5.4
4.8
5.4
5.1
5.2
4.9
5
5
4.7
4.7
4.7
4.5
4.5
4.3
4.4
4.34.2
4.1
4.2
4.2
4.24
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
hPa
-20
-4
-3
-2
-1
0
1
2
3
4
20
Strong biases related to wrong station heights in the catalogue
Example for data monitoring – SYNOP pressure bias correction
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 30 / 95
6211 stationsSYNOP pressure bias OBS-FG (hPa) APR/2006
-10.3-8.8
-8.1
-7.9
-7.5
-7.2
-6.7
-6.6
-6.5
-6.2
-6.1
-6.1
-5.5
-5.3-5.2
-5
-4.7
-4.7
-4.4
-4.6
-4.5
-4.2
-3.9
-3.9
-3.7-3.1
-3.8
-3.6
-3.4
-3.1
-3.2
-3.3
-3.1-3.1-3.1
-3-2.5
-2.9
-2.2
-3
-2.6-3
-2.9
-2.9
-2.9
-2.9 -2.9
-2.9
-2.1-2.5-2.3
-2.4
-2.5
-2.5
-2.6
-2.5 -2.5
-2.2
-2.1
-2.2
-2.3-2.2-2.3
-2.1
-2.1
-2.1-1.9
-1.8
-1.5
-1.1-1.3
-1.4
-1.3
-1.4
-1.9
-2
-1.1
-1.4
-1.6
-1.9-1.2
-1.8
-1.5
-1.7
-1.9
-1.8
-1.1
-2
-1.1
-1.9
-2-1.9
-1.7
-1.5
-1.6
-1.4
-1.9
-1.5
-1.8
-1.4-1.4
-1.7
-1.2
-1.7
-1.7
-1.8
-1.7
-1.7-1.6
-1.7
-1.5
-1.4
-1.5-1.7-1.5
-1.5
-1.5
-1.1
-1.2
-1.4
-1.4
-1.5
-1.4
-1.5
-1.2
-1.5
-1.4
-1.3
-1.1
-1.3
-1.5
-1.3
-1.1-1.4
-1.3-1.1 -1.1
-1.3
-1.4
-1.3
-1.1
-1.3
-1.3
-1.3
-1.3
-1.2-1.2
-1.3
-1.2
-1.1-1.1
-1.3
-1.2
-1.1
-1.1
-1.2
-1.2
-1.1
-1.1
-1.1
-1
-0.8
-0.4
-0.1
-0.8
-0.4
-0.5
-0.9
-0.2
-0.4
-0.2
-1
-0.6
-0.6
-0.2
-0.9
-0.9
-0.5
-0.2
-0.5
-0.9-0.6-0.2-0.9
-0.4
-0.4-0.4-0.8
-0.9
-0.2
-0.4
-0.4
-0.2
-0.4
-0.8
-0.8
-0.6-0.5
-0.6
-0.4
-0.4
-0.3
-0.3
-0.6
-0.1
-0.5
-0.2
-0.8
-0.8
-0.3
-0.9-0.2
-0.4-1-0.7
-0.9
-0.1 -0.4
-0.8-0.8
-0.6
-0.8
-0.5-0.1
-0.9
-0.3
-0.2
-0.2
-0.1
-0.1
-0.8
-0.3
-0.2
-0.4
-0.3-0.1
-0.1
-0.3
-0.7
-1
-0.3-0.3
-0.1
-0.8
-0.3
-0.1
-0.7
-0.5
-0.4
-0.1
-0.2
-0.4
-0.1
-0.6
-0.8
-0.1
-0.5
-0.2
-0.3
-0.5
-0.1
-0.3
-0.3-0.5
-0.8
-0.9
-0.1-0.1
-0.4
-0.4
-0.1
-0.5
-0.1
-1
-0.8-0.9
-0.4
-0.2
-0.3
-0.3
-0.6
-0.3
-0.4-0.6
-0.4
-0.4
-0.7
-0.4
-0.4
-0.1
-0.7
-0.3
-1
-0.6
-0.1
-0.8
-0.7
-0.8-0.5
-0.4
-1
-0.5-0.1
-0.4
-0.2
-0.4-0.5
-0.5
-0.8
-1
-0.6
-0.6
-0.8
-0.5
-0.7
-0.4
-0.6
-0.9
-0.8
-0.3
-0.3
-0.8 -1
-0.2
-0.1
-0.7 -1
-0.2
-0.4
-0.7
-0.1-0.4
-1
-0.1
-0.7
-0.7
-0.8
-0.6
-0.3
-0.6
-0.4
-0.7
-0.9
-0.8
-0.8
-0.5
-0.3
-0.2
-0.6
-0.8
-0.2
-0.7
-0.1
-0.5
-0.4
-0.5
-0.1
-0.6
-0.6
-1
-0.6
-1
-0.4-0.4
-0.6
-0.3
-0.8-0.9
-0.7
-0.3-0.6
-1
-0.1
-1
-0.5
-0.3
-0.3
-0.3
-0.2
-1
-0.3
-0.8
-0.5-0.5
-0.6
-0.8
-0.9
-0.2
-0.3
-1
-0.3
-0.2
-0.1
-0.7
-0.5-0.3
-1
-1
-0.6
-0.9
-0.2
-0.8
-0.2
-0.1
-0.2
-0.7
-0.3
-0.2
-0.4
-0.3
-0.4
-0.6
-0.1
-0.3
-0.3
-0.6
-0.8
-0.4
-0.1
-0.5
-0.3
-0.3
-0.1
-0.3
-0.9
-0.8
-0.7
-0.2
-0.3
-0.2
-0.2
-0.3
-0.7
-0.4
-0.1
-0.1
-0.5
-0.9
-0.9
-0.3
-0.4
-0.3
-0.3
-0.6
-0.3
-0.4-0.4
-0.4
-0.9
-0.4 -0.4
-0.3-0.2
-0.1
-0.4
-0.5-0.2
-0.6
-0.6
-0.4
-0.4
-1-0.6
-0.4
-0.4
-0.7
-0.1
-0.2
-0.5-0.1
-0.5
-0.1
-0.3
-0.8
-0.6
-0.5
-0.6
-0.2
-0.6
-0.8-0.4
-0.8
-0.3
-0.3
-0.2
-1-0.7
-0.3-0.1
-0.6
-0.4
-0.4
-0.4
-0.4
-0.7
-0.2
-0.3
-0.6
-0.3
-0.5
-0.4
-0.5
-1
-0.7
-0.6
-0.2
-0.6-0.5 -0.1
-0.2
-0.1
-0.1
-0.5
-0.4
-0.2
-0.2-0.1
-1
-0.5
-0.5
-0.5
-0.1 -0.2
-0.3
-0.4
-0.6
-0.8
-0.2
-0.1
-1
-0.2
-0.3
-0.1-0.6
-0.5
-0.7
-0.3
-0.1
-0.9-0.9
-0.3
-0.1
-0.7-0.6
-0.5
-0.3
-0.7
-0.1
-0.4
-0.2
-0.2
-0.1
-0.5
-0.6
-0.3
-0.2
-0.4
-0.4
-0.5
-0.5
-0.4
-0.4
-0.3
-0.5
-0.2
-0.6
-0.7
-0.3
-0.2
-0.4-0.2
-0.3
-0.5
-0.7
-0.6-0.6
-0.3
-0.1
-0.7
-0.5
-0.5
-0.2-0.4
-0.6
-0.6
-0.2
-0.6
-0.4
-0.3
-0.4
-0.3
-0.4
-0.5
-0.9
-0.5
-0.5
-0.6
-0.3
-0.4
-0.4
-0.1-0.2
-0.8
-0.9
-0.3
-0.1
-0.2
-0.1-0.2
-0.2
-0.3
-0.8
-0.2
-0.4
-0.3-0.1
-0.5
-0.3
-0.3
-0.6
-0.5
-0.8
-0.5
-0.5 -0.6
-0.8
-0.8
-0.1
-0.6
-0.3
-0.7
-0.3
-0.7
-0.5
-0.7
-0.5
-0.8
-0.6
-0.1
-0.1
-0.3
-0.7
-0.8
-0.6
-0.3
-0.3
-0.1
-0.4
-0.4
-0.3
-0.5
-1
-0.8-0.9
-0.6
-0.1
-0.4
-0.7
-0.4-0.8
-0.4
-0.4
-0.4
-0.1
-0.6
-0.4
-0.7
-0.1
-0.2-0.3
-0.3
-0.1-0.2
-0.6
-0.8
-0.2-0.3
-0.3
-0.5
-0.1
-0.7
-0.4
-0.4-0.2
-0.1
-0.5
-0.3
-0.6-0.6
-0.4
-0.4
-0.3
-0.2
-0.5
-0.9
-0.4
-0.3
-0.4
-0.4
-0.2
-0.8
-0.5
-0.5
-0.1
-0.3
-0.6
-0.2
-0.8
-0.7
-0.3
-0.7
-0.4
-0.6
-0.4
-0.1
-0.2
-0.6-0.4
-0.4
-0.8
-0.2
-0.1
-0.6
-0.9 -0.4-0.8
-0.5
-0.5
-0.1 -0.4
-0.5-0.2
-0.4
-0.3
-0.7-0.5
-0.1
-0.4
-0.2-0.1-0.6
-0.6-0.6 -0.5-0.5
-0.3
-0.3-0.6
-0.1
-0.3
-0.2
-0.1
-0.3
-0.5-0.4
-0.1
-0.8
-0.1
-0.7-0.5
-0.4
-0.6
-0.6
-0.6
-0.4
-0.5
-0.5
-0.1-0.6-0.7
-0.5
-0.4
-0.7
-0.7
-0.4
-0.5
-0.2
-0.1
-0.1
-0.7-0.5
-0.5
-0.2
-0.3
-0.3
-0.6
-0.1
-0.3
-0.4
-0.5
-0.6
-0.5
-0.1
-0.5
-0.2
-0.5-0.5
-0.1-0.6
-0.9
-0.3
-0.1-0.6
-0.4-0.6
-0.4
-0.8
-0.1
-0.2
-0.1
-0.3
-0.6
-0.5
-0.4
-0.4
-0.3
-0.3
-0.3
-0.4
-0.4
-0.4
-0.6
-0.5-0.2
-0.1
-0.5
-0.6
-0.2-0.3
-0.1
-0.3-0.8
-0.3
-0.6
-0.1
-0.2
-0.2
-0.6-0.6
-0.3
-0.2
-0.7
-0.6
-0.5
-0.3
-0.2
-0.4
-0.3
-0.7
-0.3
-0.1-0.2
-0.5
-0.7
-0.1
-0.2
-0.5-0.1
-0.3
-0.7
-0.3
-0.6
-0.1
-0.5-0.1
-0.5
-0.2
-0.2-0.4
-0.7
-0.7
-0.6
-0.5
-0.5
-0.3
-0.4
-0.2
-0.4 -0.6
-0.4
-0.5
-0.3
-0.1
-0.7
-0.5 -0.8
-0.3
-0.8
-0.7
-0.4
-0.2
-0.2-0.5
-0.7
-0.1
-0.4
-0.1
-0.5
-0.4
-0.4
-0.3
-0.1-0.5
-0.3 -0.4
-0.4
-0.5
-0.1-0.3
-0.7 -0.7
-0.1
-0.1
-0.1
-0.3
-0.1
-0.3
-0.1
-0.3
-0.3
-0.6
-0.2
-0.5-0.6
-0.1
-0.7
-0.8-0.5
-0.5
-0.2
-0.4-0.4-0.1
-0.5
-0.8
-0.1
-0.5
-0.7
-0.1
-0.1
-0.1
-0.7-0.7
-0.3
-0.3
-0.3
-0.2
-0.4
-0.6
-0.6
-0.5
-0.5
-0.4
-0.8
-0.4
-0.1
-0.3
-0.7
-0.6
-0.2
-0.6-0.4
-0.4
-0.1
-0.1
-0.4
-0.1
-0.6-0.2
-0.1-0.6
-0.6 -0.5
-0.7-0.4
-0.1
-0.8-0.4
-0.3
-0.1-0.2
-0.1
-0.8
-0.3
-0.2
-0.8
-0.5
-0.5
-0.4
-0.5
-0.6-0.1
-0.6
-0.5
-0.7
-0.5
-0.1
-0.1
-0.5
-0.5
-0.5
-0.6 -0.8
-0.8
-0.7-0.1
-0.1
-0.6
-0.2
-0.3
-0.1
-0.1
-0.4
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0
0.40.3
0.2
0.4
0
0.5
0.5
0.5
0.3
0
0.3
0.3
0.5
0.3
0.2
0.20.2
0.30
0.2
0
0.3
0.3
0
0
0.1
0.1
0.3
0
0.4
0.4
0.1
0.3
0 0
00.3
0
0.1
0.1
0.4
0.1
0.5
0
0.3
0.4
0
0.4
0.2
0.5
0.4
0.50.5
0.1
0
0
0.2
0.30
0
0.50
0.2
0.4
0.3
0.20.10.2
0.3
0.2
0
0
0
0.3
0.3
0
0
0 0
0.2
0.10.3
0.3
0.30.2
0.4
0.10.10.10.100
0
0.1
0.1
0
0.1
0
0.6
0.3
0.5
0.6
0.40.1
0.3
0.3
0.2
0.4
0.4
0
0.3
0.3
0.2
0
0.1
0.2 0
0.2
0.2 0.4
0.3
0
0.3
0.10.4
0.50.4
0.4
0.4
0.2
0.1
0.20.3
0.20.3
0.5
0
0.2
0.2
0
0
0
0.4
0.1
0.3
0
0.4
0.4
0
0.1
0.3
0.10.1
0.4
0.1
0.1
0.1
0.4
0
0
0.2
0
0
0.3 00.5
0
0.3
0.4
0.3
0.2
0
0.30.300.1
0.1
0
0.50.5
0.2
0.4
00.4 0
0.4
0.3
0.3
0.4
0.30.2
00.5
0
0
0.20.3
0.3
0.2
0.4
0.1
0
0.1
0.5
0.3
00.20.3
0.1
0.30.1
0
0
0.5
0 0.5 0.4
0.2
0.3 0.4
0
0.1
0
0.2
0
0.1
0.30.1
0.5
0.4
0.2
0.40
0.3
0.4
0.5
0.1
0.1
0.3
00.2
0
0.50
00
0
0.1
0.2
0
0.2
0.1
0.3
0.5 0.40.2
0.1
0.1
0
0.20.4
0.4
0
0.3
0.2
0.5
0
0.1
0
0.4
0.20.2
0.40.3
0.2
0.4
0.5
0.2
0.2
00.30 0
0.1
0
0.4
0.5
0.1
0.2
0.5
0.20.3
0.5
0.4
0.3
0.1
0
0.3
0
0.2
0.4
0.4
0.3
0.4
0.2
0.3
0.4
0.10.4
0.4
0.4
0
0.3 00
0.4
0
0.20
0
0.2
0.40.2
00.3
0.2
0.1
0.10
0.4
0.1
0
0.3
00.4
0.4
0
0.5
0
0.1
0.1
0
0.2
0.2
0
0.3
0
0.2
0
0
0
0
0.1
0.3
0.3
0.4
0.2
0.3
0.1
0
0.5
0
00.1
0.10.3
0.1
0.3
0
0.4
0.4
0.1 0.5
0.3
0
0.4
0.4
0.3
0.5
0.3
0.1
0.2
0.1
0.4
0.3
0.2
0.40
0.2
0.3
0.4
0.2
0.4
0.2
0.5
0.1
0.2
0.3
0.10.30
0.2
0.2
0
0.2
0
0.3
0
0.1
0.4
0.4
0.5
0.2
0.2
0.3
0.40.3
0.2
0.3 0.30.4
0.1
0.3
0.1
0.3
0.20
0.3
0.5
0.3
0.2
0.3
0.3
0.3
0.1
0.10.2
0.1
00.1
00.2
0.3
0.1
0.1
0.3
0.1
0
0.40.3
0.4
0.3
0.20.2
0.2
0
0
0.2
0.1
0.1
0
0.4
0.1
0.30
0.10.3
0.2
0.3
0.1
0
0
0.10.2
0.1
0.20.3
0.3
0.2
0.20
0
0.3
0.2
0.3
0.2
0
0
0.4
0.3
0
0
0.1
0.20.3
0.10.1
0.1
0
0.1
0
0.1
0.1
0.1
000.1
00.2
0.3
0.2
0.4
0.4
0.2
0.2
0.4
0.2
0
0.3
0.2
0.2 0
0
0.5
0.3
0.1
0.4
0
0.3
0.1
0.40
0.2
0.1
0.30.2
0.3
0.2
0.3
0.3
0
0.3
0.1
0.1
0.2
0.3
0.2
0.3
0.1
0.3 0.3
0.4
00.10.1
0.3
0.30
0.3
0.3
0.30.4
0.2
0.2
0.1
0.1
0
0.1
00.30.3
0.4
0.4
0.3
0
0.1
0
0.3
0.4
0
0.2
0.4
0
0.30
0
0
0.2
0.2
0
0
0.2
0.3
0.2
0.3
0.1
0
0.3
0.4
0.1
0
0
0.4
0.2
0
0.1
0.3
0.4
0.4 0.2
0.3
0.4
0.3
0
0
0.4
0.4
0.3
0.2
0.3
0.2
0.3
0.20.1
0
0.1
0
0.1
0.1
0.3
0.4
0
0.20
0.2
00.4
0.2
0.4
0.2
0.1
0.1
0.3
00.4
0.3
0.1
0.1
0.1
0.3
0.3
0
0
0
00
0.2
0.1
0.2 0.30
0.2
0.3
0.3
0.1
0.3 0.2
00.3
0
0.1
0.30.1
00.2
0
0.2
0.3
0.10.2
0
0.10.2
0.3
00.1
0
0.2
0.3
0.1
0.3
0
0.3
0
0.3
0
0.10.1
0.1
0
0.1
0
0.2
0.20.4
0
0.40
0.3
0
0
0.1
00.3
0.2
0.4
0.3
0.2
0
0.1
0
0
0.40.2
00.1
0.3
0
0.1
0.2
0
0.2
0
0.3
0.3
0
0.4
0.2
0.2
0.1
0.20
0.1
0
0
0
0.2
0.2
0.3
0.1
0.4
0.2
0.40
0.1
0.3
0.10.1
0.2
0.3
0.2
0
0.1
0.2
0.4
0.3
0.20.1
0.3
0.10.30.10.3
0.3
0.3
0.1
0
0.30.2
0
0.4
0.2
0.1
0.10.4
0
0.4
0.1
0.2
0
0
0.3
0.3
0.30.2
0.2
0.10
0
0.3
0.2
0.10
0.2
0.2
0
0.2
0.3
0.1
0
0.2
0
0.2
0.3
0.40
0.3
0.10
0.1
0.2
0.3
0
0.1
0.10.1
0
0.1
0
0
0.1
0.2
0.3
0.10.1
0
0.1
0.20.1
0.1
0.2
0.10
0.1
0.1
0.1
0.2
0.3
0 0.2
0.1
0.3
0
00.1
0
0.2
0.2
0.30.1
0.1
0
0.1
0.2
0
0.1
0
0.30.30
0.1
0
0.10
0
0.3
0.1
0.2
0
0
0.1
0.2
0
0
0.30.1
0.1
0.40.1
0.1
0
0
0.1
0 0.3
0.1
0.2
0.2
0
0.2
0
0.3
0 0
0.1
0.2
0.2
0.2
0.2
0.1
0.3
0.3
0
0.1
0.10.3
0
0.2
000.2
0.1
0.2
0.3
0
0.1
0.10.3
0.2
0.1
0.10.1
0
0.1
0.2
0.3
0.1
00.20.1
0.30.1
0.1
0.2
0.2
0.1
0.2
0
0.2
0.3
0.20
0.30.1
0.2
0.2
0
0.10.3
0.3
0.20.3
0.2
0.1
0.1
0.4
0.2
0.20.1
0.3 0
0.3
00.1 0.3
0.2
0.3
0.2
0.4 0.2
0.3
0.2
0
0
0.20
0.3
0.3
0
0.2
0.3
0
0.1
0.10.3
0.2
0.1
0.2
0
0.30.1
0.1
0.1
0.3
0
0.3
0.1
0
0.1
0.1
0
0
0.1
0.3
0.2
0.2
0.2
0.2
0.30.4 0.2
0.1
0
0.3
0
0.1
0.1
0.3
0.1 0
0.1
0.2
0
0.1
0
00.1
0
0
0.2
0.200.3
0.1
0.2
0.2
0.10.2
0.2
0.20
0.10.2
0
0.1
0.10.1
0
0.2
0.2
0.2
0.2
0.10
0
0.1
0
0.1
0.2
00.2 0
0.1
0.1
0.10.3
0.2
0.2
0.30.1
0.10
0
0
0
0
0.20
0.3
0
0.3
00.1
0.1
0.3
0.30
0.2
0.10.2
0.30
0.2
0
0
0.20
0
0.1
0.10.3
0.2
0.1
0.2
0.1
0.10
0.2
0.3
0.2
0.2
0.3
0.3
0.1
0.1
0
0.2
0.1
0.1
0.1
0.2
0.2
0
0.2 0
00.3
0
0.20.3
0
0.2
0.1
0.2
00.2
0.10.20.2 0.1
0
0.2
0.2
0.2
0
0.1
0.1
0
0.20.10.3
0.20.1
0.2
0.20.2
0
0.2
0.1
0.3
0.1
0
0.3
0.1
0
0.1
0
0.2
0.1
0.1
0.2
0.2
0.1
0.3
0.20.1
0.2
0.1
0.2
0
0.20
0.3
0.20.10.2
0
0.1
0
00.1
0.1
0.1
000.3
0.1
0.30.2
0.1
0
0.3
0.2
0
0.10
0
00.1
0.1
0
0.2
00.2
00.3
0.30.1
0
0.20.2
0.1
0.2
0
0.3
0
0
0.1
0.20.2
0
0.2
0
0.1
0.3
0
0.3
0
0
0
0.1
0
0.1
0.2
0.2 0.10
0.2
0.1
0.1
0.2
0.1
0.20.2 0
0.1
0.1
0.1
0
0
0.2
0.3
0.1
0.200.2
00.3
00.1
0.1
0.1 0.10
00
0
0.20.3
0.1
0.10
0.1
0.2
0.2
0
0.10.2
0.2
0.2
0
0.2
0.20.2
0
0.20.1
0
0
0.1
0.1
0.2
0
00.10.1
0
0.2
0
0.1
0
0.100.1
0
0.1
0
0.1
0.1
0
0.2
0.2
0.1
0.1
0
0.2
0.2
0
0.1
0.2
0
0.20.1
0.1
0
0
0.2
0.1
0.2
0.2
0
0.10.2
0.1
0.1
0
0.1
0
0.10.20.10
0.1
0.1
0
0
0
0.1
0
0
0
0.1
0.1
0.1 0
0
0
0
0 0.1
0.1
000.1 00.1
0
0
0.1
0
0.10.1
0.2
0.1
0.2
0
00.1
0.2
0.10.2
0.2 0
0.20
0
0
0.1
0
0.10
000.1
0
0.1
0.1
0.1
0.10.1
0.1
0
0.1
0.2
0.1
0.20.1
0
0.1
0
0.1
0
0.20.1
0.1
0
0.2 00
0.20
0.10.1
000
0.1
0.1
00
0.1
0
0.1
0
0.1
0.1
0
0.10 0
0.1 0.1
0
00.10.1
0.100.1
00.1
0
0
0.1
0.1
00.20
00.10.1
0.100.1 0.1
00 0.10.1
0.10.100 0.1
0
000.2
0.10.1
0
0
0.2
0.10
0.1
0.1
0
0.20.1
0
00
0
0.1
0.1
00.10
0.1
0
0
0
0.1
0 000.1
0.2
0.2
0
0.10.1
0
0.1
0.1
0.1
0
0
0.1
0
0.10
0
0
0.1
0
000
0
1.71.6
1.3
1.1
1.91.7
1.2
1.2
1.2
1.2
1.3
1.7 1.3
1.9
1
1.1
1.9
1.2
1.9
1.7
1.6
1.2
1.8
1.6
1.2
1.6
1.51.3
1.11
1.6
1
11.8
1.71
1.6
1
1.5
1.5
1.7
1.7 1.8
1.31.6
1
1.7
1.5
1.4
1.5
1.5
1.5
1.4
11.611.4
1.1
1.6
1.4
1.6
1.5
1.2
11.1
1.2
1.6
1.1
1.4
1.2
1.3
1.2
1.5
1.11.4
1
1.51.41.2
1
1.4
1.1
1.2
1
1.2
1.2
1.4
1.2
11.1
1.3
1.21.2
1.1
1.3
1
1.1
1
1
1.1
1.2
1
1
1.1
1
11.1
1.1 1
11
1
1.1
11
1
11
1
1
1
1
2.2
2.1
2
2.7
2.92.8
2
2.72.7
2.5
2.3
2.5
2.62.5
2
2.3
2.1
22.1
3.6
3.9
3.9
3.8
3.93.6
3.6 3.1
3.3
3.1
3.3
3.2
3.33
14.3
14.2
14.1
13.8 12.7
10.2
9.8
9.79.5
5
7.5
5.97.1
5.3
5.8
5.45.7
5.4
4.8 4.7
4.4
4.1
4.2
4.24
80°S80°S
70°S 70°S
60°S60°S
50°S 50°S
40°S40°S
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
20°N20°N
30°N 30°N
40°N40°N
50°N 50°N
60°N60°N
70°N 70°N
80°N80°N
160°W
160°W 140°W
140°W 120°W
120°W 100°W
100°W 80°W
80°W 60°W
60°W 40°W
40°W 20°W
20°W 0°
0° 20°E
20°E 40°E
40°E 60°E
60°E 80°E
80°E 100°E
100°E 120°E
120°E 140°E
140°E 160°E
160°E
hPa
-20
-4
-3
-2
-1
0
1
2
3
4
20
Bias correction applied
Example for data monitoring – SYNOP pressure bias correction
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 31 / 95
Pressure bias correction at ECMWF
Applied to Synop, Ship, Buoy and Metar data when needed
OI and Kalman filter schemes run in parallel
– OI is used for the corrections although Kalman filtering can be switch on on request
The scheme is not applied when
– The difference between the station height and the model orography is larger then 200 hPa
– The observation is RDB flagged
– The history of the station is not long enough
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 32 / 95
Time series of Original Ps departure, Ps bias estimate and Corrected Ps departure for station 82353 (Dec-Apr ’05)
Once the sample size (30) was reached (Warm-up period) bias correction kicked in
Long-term bias of about -3hPa was recognised and corrected for Station height is thought to be correct and real reason for bias is
unknown If not bias corrected the station was just surviving the “First Guess”
check but to be rejected by the analysis check When bias corrected, the station survived all the checks and was
successfully used in the analysis
Wa
rm-u
p
≈3hPa Bias
Adaptive bias correction scheme for surface pressure data
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 33 / 95
2APR
2006
4 6 8 10 12 14 16 18 20 22 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22MAY
2006
-15-14-13-12-11-10
-9-8-7-6-5-4-3-2-10123456789
101112131415
OB
S -
FG
PRESSURESYNOP 12805 (47.7N, 16.6E)
NO. OF USED OBS: 0 ( 0 %)NO. OF OBS: 1222 BIAS: 14.5 STD: 0.8
UNCORRECTED
2APR
2006
4 6 8 10 12 14 16 18 20 22 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22MAY
2006
-15-14-13-12-11-10
-9-8-7-6-5-4-3-2-10123456789
101112131415
OB
S -
FG
PRESSURESYNOP 12805 (47.7N, 16.6E)
NO. OF USED OBS: 0 ( 0 %)NO. OF OBS: 1222 BIAS: 0.2 STD: 0.7
CORRECTED
Example for data monitoring – SYNOP pressure bias correction
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 34 / 95
Total number of obs = 2671610/AUG/2005; 00 UTC
ECMWF Data Coverage (All obs) - SYNOP/SHIP
60°S60°S
30°S 30°S
0°0°
30°N 30°N
60°N60°N
150°W
150°W 120°W
120°W 90°W
90°W 60°W
60°W 30°W
30°W 0°
0° 30°E
30°E 60°E
60°E 90°E
90°E 120°E
120°E 150°E
150°E
60°S60°S
30°S 30°S
0°0°
30°N 30°N
60°N60°N
150°W
150°W 120°W
120°W 90°W
90°W 60°W
60°W 30°W
30°W 0°
0° 30°E
30°E 60°E
60°E 90°E
90°E 120°E
120°E 150°E
150°E
Obs Type
15078 SYNOP 1957 SHIP 9681 METAR
Example for data monitoring – SYNOP pressure bias correction
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 35 / 95
Total number of obs =205010/AUG/2005; 00 UTC
ECMWF Synop/Metar/Ship Pbias corrected data
60°S60°S
30°S 30°S
0°0°
30°N 30°N
60°N60°N
150°W
150°W 120°W
120°W 90°W
90°W 60°W
60°W 30°W
30°W 0°
0° 30°E
30°E 60°E
60°E 90°E
90°E 120°E
120°E 150°E
150°E
Bias correction (hPa)-20 - -10 -10 - -5 -5 - -3 -3 - -1 -1 - 0 0 - 1 1 - 3 3 - 5 5 - 10 10 - 20
Example for data monitoring – SYNOP pressure bias correction
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 36 / 95
ECMWF’s operational analysis and forecasting system
The comprehensive earth-system model developed at ECMWF forms the basis for all the data assimilation and forecasting activities. All the main applications required are available through one integrated computer software system (a set of computer programs written in Fortran) called the
Integrated Forecast System or IFS Numerical scheme• Spectral model - TL799L91 (799 waves around a great circle on
the globe, 91 hybrid vertical levels 0-80 km (0.01 hPa))• Semi-Lagrangian time scheme • 12 minutes timestep
Prognostic variables:• wind, temperature, humidity, cloud fraction and water/ice content, pressure at surface grid-points, ozone
Grid:• Gaussian grid for physical processes, ~25 km, 76,757,590 grid points (843,490 on the surface)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 37 / 95
Spectral and grid point representations
ECMWF model uses both spectral and grid point representations
Most upper air model variables (wind, temperature) are stored as spectral fields
Horizontal derivatives of these variables are calculated in spectral space
Surface variables and upper air humidity are stored in grid point space
Dynamical tendencies and physical parameterizations are calculated in grid
point space
Resolution is the same in physical (grid point) and spectral space
Grid:
– Gaussian grid for physical processes, ~25 km, 76,757,590 grid points (843,490 / level)
– ‘Gaussian grid’ is regular in latitude, almost regular in longitude
– On regular grid (same number of points on each latitude row) points get closer together nearer the poles
– ‘Reduced Gaussian grid’ keeps distance between points nearly constant over globe
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 38 / 95
Operational model levels
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 39 / 95
Operational model grid (reduced Gaussian)
10°S 10°S
40°E
40°E
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 40 / 95
T799 orography, grid spacing ~25 km
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
0°
0° 20°E
20°E 40°E
40°E
100
200
300
500
800
1100
1400
1700
2000
2500
3000
30°S 30°S
20°S20°S
10°S 10°S
0°0°
10°N 10°N
0°
0° 20°E
20°E 40°E
40°E
100
200
300
500
800
1100
1400
1700
2000
2500
3000
Model approximations: orography and spatial resolution
High spatial resolution is needed to impose accurate boundary conditions. For
example, the representation of the orography becomes more realistic with
increased horizontal resolution.
T255 orography, grid spacing ~80 km
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 41 / 95
ECMWF model 10m wind (T799, 25 km)15.0m/s
20°S 20°S
10°S10°S
40°E
40°E
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 42 / 95
Katrina (2005 Aug): 90h forecasts - T511 versus T799
Central pressure 940hPa, 448mm/24h rain
Central pressure 909hPa, 785mm/24h rain
T511
T799
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 43 / 95
Hurricane Gordon – T799 forecast
30°N
40°N
50°N
60°N
40°W
40°W
20°W
20°W
0°
0° 20°E
20°E
ECMWF Analysis VT:Thursday 21 September 2006 06UTC Surface: mean sea level pressure
30°N
40°N
50°N
60°N
40°W
40°W
20°W
20°W
0°
0° 20°E
20°E
Wednesday 20 September 2006 00UTC ECMWF Forecast t+30 VT: Thursday 21 September 2006 06UTC Surface: mean sea level pressure
984
992
30°N
40°N
50°N
60°N
40°W
40°W
20°W
20°W
0°
0° 20°E
20°E
Monday 18 September 2006 00UTC ECMWF Forecast t+78 VT: Thursday 21 September 2006 06UTC Surface: mean sea level pressure
30°N
40°N
50°N
60°N
40°W
40°W
20°W
20°W
0°
0° 20°E
20°E
Saturday 16 September 2006 00UTC ECMWF Forecast t+126 VT: Thursday 21 September 2006 06UTC Surface: mean sea level pressure
AN 30 hrs
78 hrs 126 hrs
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 44 / 95
Limitation: Model grid box still large
Grid box 25 km x 25 km
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 45 / 95
Physical processes in the ECMWF model
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 46 / 95
Data assimilation for weather prediction
A short-range forecast provides an estimate of the atmosphere that is compared with the observations.
The two kinds of information are combined to form a corrected atmospheric state: the analysis.
Corrections are computed and applied twice per day. This process is called ‘Data Assimilation’.
The FORECAST is computed on a grid over the globe.
The meteorological OBSERVATIONS can be taken at any location in the grid.
The computer model’s prediction of the atmosphere is compared against the available observations, in near real time
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 47 / 95
Observations=
“True” state of the atmosphere
Time
Mod
el v
aria
ble
s, e
.g. t
emp
erat
ure
00 UTC 13 March
12 UTC 13 March
00 UTC 14 March
12 UTC 14 March
4D-Var Data assimilation
Analysisvalues =
Backgroundvalues =
12
-hou
r for
ecas
t
Analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 48 / 95
Observation minus model differences are computed at the observation time using the full forecast model at T799 (25 km) resolution
4D-Var finds the 12-hour forecast evolution that optimally fits the available observations. A linearized forecast model is used in the minimization process based on the adjoint method (2 minimisation loops – T95/T255)
It does so by adjusting surface pressure, the upper-air fields of temperature, wind, specific humidity and ozone
The analysis vector consists of 30,000,000 elements at T255 resolution (80 km)
A few 4D-Var Characteristics
All observations within a 12-hour period (~3,300,000) are used simultaneously in
one global (iterative) estimation problem
9z 12z 15z 18z 21z
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 49 / 95
ECMWF 4D-Var procedure
Use all data in a 12-hour window (0900-2100 UTC for 1200 UTC analysis)
1. Group observations into ½ hour time slots
2. Run the T799 (25km) high resolution forecast from the previous analysis and
compute “observation”- “model” differences
3. Adjust the model fields at the start of assimilation window (0900 UTC) so the
12-hour forecast better fits the observations. This is an iterative process
using a lower resolution linearized model T255 (80 km) and its adjoint model
4. Rerun the T799 high resolution model from the modified (improved) initial
state and calculate new observation departures
5. The 3-4 loop in repeated twice to produce a good high resolution estimate of
the atmospheric state – the result is the ECMWF analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 50 / 95
Multi-incremental quadratic 4D-Var at ECMWF
T799L91
T95L91T255L91
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 51 / 95
Analysis increments: 1st and 2nd minimization
1st minimization: T95 T increments
2nd minimization:
Additional T159 T increment
Most of the increment is formed at the lower resolution with smaller additions and
corrections obtained at the higher resolution.
Temperature level 60 (10metre). 0.2K contours (blue is negative; red is positive)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 52 / 95
Forecast versus observations
12-hour forecasttemperature change
Correction, as a resultof data assimilation
The corrections are ~10 times smallerthan the 12-hour forecast temperature change
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 53 / 95
Tropical cyclone LILI - Impact of NSCAT data in 4D-Var
No SCAT analysis
First guessMSL pressure
S.M. Leidner, L. Isaksen and R.S. Hoffman ‘Impact of NSCAT Winds on Tropical Cyclones in theECMWF 4DVAR assimilation system’ Mon. Wea. Rev. 131,1,3-26 (2003)
First guessMSL pressure
AnalysisMSL pressure
MSL pressureAnalysis
increments
NSCAT analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 54 / 95
4D-Var is using more a-synoptic data than 3D-Var
4D-Var is using more data from frequently reporting stations.The plots show the use of SYNOP surface pressure observations. Column height gives the number of observations available, while the shaded part displays those actually used in the assimilation.
4D-Var SYNOP Screening
3D-Var SYNOP Screening
3D-Var is like 4D-Var without the time dimension. The analysis is performed at synoptic times only (0000, 0600, 1200 and 1800 UTC). Mostly only data valid a synoptic time is used. The 12 hour forecast evolution is NOT an integral part of the analysis.
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 55 / 95
4D-Var versus 3D-Var and Optimum Interpolation
4D-Var is comparing observations with background model fields at the correct time
4D-Var can use observations from frequently reporting stations
The dynamics and physics of the forecast model in an integral part of 4D-Var, so observations are used in a meteorologically more consistent way
4D-Var combines observations at different times during the 4D-Var window in a way that reduces analysis error
4D-Var propagates information horizontally and vertically in a meteorologically more consistent way
More complex: needs linearized perturbation forecast model and its adjoint to solve the cost function minimization problem efficiently
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 56 / 95
4D-Var versus 3D-Var performance
6h T319 3DVAR
12h T511 3DVAR
6h T319 4DVAR
N. HEM
S. HEM
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 57 / 95
Computational cost of the model
Higher horizontal resolution
31 more vertical levels
12 min timestep instead of 15 min (T511)
Altogether 4 times more floating point operations are required to complete a 10-day forecast than with the T511 version
1.700.000.000.000.000 operations
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 58 / 95
02:00 (waiting 5h to 17h for observation to arrive)
12h 4D-Var, obs 09-21Z
18 UTC analysis (DCDA)
12h FC6h 4D-Var
21-03Z
00 UTC analysis (DA)
T799 10 day forecast
51*T255 EPS forecasts
03:40
03:55
04:00 (Waiting 1h to 7h for observations to arrive)
05:10
06:25
05:20
Time (UTC)
Dissemination
07:25
Operational schedule for 0000UTC cycle
Early delivery suite introduced June 2004
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 59 / 95
(G.-R. Hoffman)
* * *
Supercomputer performance at ECMWF 1978-2003
Mflops/s Peak performance
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 60 / 95
2004/2006 – A significant Performance increase
Peak performance7.6 Gflops per processor
IBM p690 2 x 960 processors
Peak performance5.2 Gflops per processor
8 processors per shared memory node
Switch 350 Mbytes/s
20062002
Switch 2000 Mbytes/s
(Deborah Salmond/Sami Saarinen )
IBM p575+ 2 x 2400 processors
16 processors per shared memory node
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 61 / 95
T511 1-day Forecast on IBM
CPUs
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 62 / 95
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
64x8 96x8 128x8 192x8
Sec
onds
Barrier
I /O
Serial
Comms
Parallel768
10241536
512
4D-Var T799/T95/T255 with 91 levels on present ECMWF IBM system
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 63 / 95
Time series Z500 N Hemisphere – against analysis
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 64 / 95
Time series Z500 N Hemisphere – against radiosondes
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 65 / 95
ECMWF Re-analysis project (ERA)
Main objective is to promote the use of global analysis of the state
of the atmosphere, land and surface conditions over the period
ERA-15 1979 – 1993
ERA-40 1957 - 2002
– T159L60– 3DVAR
ERA interim 1989-
– T255L91– 12h 4DVAR
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 66 / 95
Different application of the ECMWF products
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 67 / 95
Link with limited-area ensemble systems
Over Europe, there are 4 operational Limited-area EPSs (SRNWP-PEPS, COSMO-LEPS, NORLAMEPS and PEACE) that produce daily 81 forecasts with horizontal resolution ranging from 7 to 120 km, and with forecast length ranging from 30 to 120 hours. 8 further centres (Met Office, INM, DMI, HMS, MeteoSwiss, SAR, PIED-SE) are developing and testing LEPSs. Studies have shown that compared to global EPSs, limited-area EPSs are better able to predict small-scale, local phenomena.
- Boundary conditions from the global ECMWF model
This figure shows the t+96h forecast of the probability of total precipitation exceeding 20mm/d given by the EPS (left) and the COSMO-LEPS system for 29 Aug 2003 (Ticino flood).
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 68 / 95
Hydrological application - EFAS the European Flood Alert System
EFAS is a forecasting tool designed to give early-warnings for European rivers with catchments in excess of 2000 km. A pre-operational prototype is under testing at the Joint Research Center (JRC, Ispra). The system uses meteorological inputs from DWD (forecasts up to 7 days), ECMWF (high-resolution and ensemble forecasts up to 10 days) and aims to provide single and probabilistic predictions.
This figure shows the prediction of the risk of flooding from 28 Oct 2004 for the subsequent 10-days computed using the ECMWF and DWD high-resolution forecasts (left) and the EPS (right).
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 69 / 95
ECMWF operational system 2006 – Forecast Products
Data assimilation (4D-VAR)– Four-dimensional variational data assimilation based on T799 (~25 km) / T255 (~80
km) / T95 (~200 km) horizontal resolution and 91-level vertical resolution (4 times a day)
Medium-range atmospheric global model– High resolution deterministic: T799 (~25 km) 91-level high resolution model for single
deterministic forecast, twice a day up to 10 days– Ensemble: T399 (~50 km) 62-level model for 50-member ensemble forecasts, twice a
day up to 15 days Coupled ocean wave model (WAM cycle4)
– 2 versions: global and regional (European Shelf & Mediterranean)– Numerical scheme: irregular lat/lon grid, 40 km spacing spectrum with 30 frequencies
and 24 directions– Coupling: wind forcing of waves every 15 minutes, two way interaction of winds and
waves– Extreme sea state forecasts: freak waves– Wave model forecast results can be used as a tool to diagnose problems in the
atmospheric model Monthly forecast system Seasonal forecast system
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 70 / 95
Global forecasts (deterministic, fields)
Mean Sea Level Pressure + Rain (06-18UTC)
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
500 hPa height and 850 hPa temperature
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 71 / 95
Global forecasts (deterministic, fields)
T2m and 30m-winds
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
Cloud cover (high, medium, low)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 72 / 95
Global forecast to ten days
from 00 and 12 UTC at 50 km
resolution
ECMWF deterministic Ocean wave forecasts
European waters forecast to five
days from 00 and 12 UTC at 25
km resolution
AFRICA !!!!
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 73 / 95
The Ensemble Prediction System (EPS)
A Stochastic Medium-range model (EPS)
– Spans the unstable sub-space of initial conditions with a Gaussian samples of 50 T42 singular vectors + 5 per tropical target (TC)
– Runs with stochastic perturbations of physical tendencies
– TL399/L62; range = 15 days
– Schedule: twice per day:
• 00UTC (all products available before 1000UTC)
• 12UTC (all products available before 2200UTC)
Ensemble Forecasting (Thursday afternoon)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 74 / 95
EPS forecasts: time series (EPSgram)
EPSgram for Pretoria
Base Friday 27/10/06 00UTC
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 75 / 95
EPS forecasts (field probabilities)
Probability of 10m wind speed more than 10 m/s
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
Probability of precipitation more than 1mm in 24 hours
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 76 / 95
EPS forecasts (post-processed products)
Extreme forecast index for 2m temperature
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 77 / 95
Katrina forecasts (days from landfall)
4 days before landfall
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 78 / 95
Monthly forecasting
Coupled atmosphere / ocean model
Atm.: T159 (~125 km) 62 vertical levels (same model as oper)
Ocean: 29-level, 0.3° equator - 1° mid-latitudes
51 member ensemble
Runs once a week up to 32 days
Compared to 5 forecasts for same day over last 12 years
– 60 member ensemble
Results interpreted in terms of anomalies and probabilities
– For example: probability that 2m temperature averaged over day 12 to 18 is in the upper/middle/lower tercile
Products become available every Thursday at 22UTC
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 79 / 95
Monthly forecast
Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal
Base Thu 26-10-2006. Valid days 5-11 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 80 / 95
Monthly forecast
Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal
Base Thu 19-10-2006. Valid days 12-18 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 81 / 95
Monthly forecast
Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal
Base Thu 12-10-2006. Valid days 19-25 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 82 / 95
Monthly forecast
Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal
Base Thu 26-10-2006. Valid days 5-11 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 83 / 95
Monthly forecast
Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal
Base Thu 19-10-2006. Valid days 12-18 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 84 / 95
Monthly forecast
Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal
Base Thu 12-10-2006. Valid days 19-25 (30-10 to 0511)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 85 / 95
DJF03 DJF04 DJF05 DJF060.4
0.5
0.6
0.7
0.8
Monthly forecast performance over the Northern Extratropics
Monthly Forecast
Persistence of day 5-11
ROC area of probability of 2-metre temperature in upper third of climate range
Day 12-18 Day 19-32
Monthly Forecast
Persistence of day 5-18
DJF03 DJF04 DJF05 DJF060.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 86 / 95
ECMWF seasonal forecast System 2
6-month ensemble produced each month with coupled
atmosphere-ocean forecast system
ECMWF atmospheric model (same cycle as used for ERA): ~200
km (T95), 40 levels
HOPE ocean model: 29 levels; 1°x1° mid-latitudes, increased
latitudinal resolution to 0.3° at equator
40 ensemble members: 5 ocean analyses, 40 SST perturbations;
stochastic physics
Products issued as anomalies relative to model climatology (15
years of ensemble re-forecasts)
Initial date 1st of each month; forecasts issued 15th of month
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 87 / 95
ECMWF seasonal forecast System 2
Anomaly of 2m temperature
Base 01-10-2006. Valid December-January-February (DJF)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 88 / 95
ECMWF seasonal forecast System 2
Anomaly of precipitation
Base 01-10-2006. Valid December-January-February (DJF)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 89 / 95
FEB2006
MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN2007
FEB MAR
-1
0
1
2A
nom
aly
(deg C
)
-1
0
1
2
Monthly means plotted using NCEP adjusted OIv2 1971-2000 climatologyECMWF forecast from 1 Sep 2006
NINO3.4 SST anomaly plume
Forecast issue date: 15 Sep 2006
System 2
ECMWF seasonal forecast System 2
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 90 / 95
Seasonal forecasting: EUROSIP multi-model ensemble
Three models running at ECMWF:
– ECMWF – System 2– Met Office – HADCM3 model, Met Office ocean analyses– Meteo-France – Arpege/Climat, Mercator ocean analyses– Spain + Germany may join
Unified system
– All data in ECMWF operational archive– Common operational schedule (products released at 12UTC on
the 15th of each month)
Common products being developed
EUROSIP appears to be better than the individual systems
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 91 / 95
EUROSHIP seasonal forecast
EUROSHIP anomaly of 2m temperature
Base 01-10-2006. Valid December-January-February (DJF)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 92 / 95
EUROSHIP seasonal forecast
EUROSHIP anomaly of precipitation
Base 01-10-2006. Valid December-January-February (DJF)
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 93 / 95
ECMWF Products for WMO members
A new web page has been created as a single entry point for all
services to WMO members
– www.ecmwf.int/about/wmo_nmhs_access/index.html
Council enhanced in July 2006 the product set available to all
WMO members:
– For several parameters, an extension of the forecast range from day 7 to day 10
– Global products from the EPS in support of high impact weather
– Site-specific forecasts at selected locations, targeting synoptic stations in developing countries, especially the least developed
ones
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 95 / 95
ECMWF Products for ACMAD
Param level steps
u,v 925 0,24,48,72,96,120,144,168
div 925, 200 0,24,48,72,96,120,144,168
2m T 0,06,12,24,30,36,42,48,54,60,66,
72,78,84,90,96,102,108,114,120
10m u,v 0,06,12,24,30,36,42,48,54,60,66,
72,78,84,90,96,102,108,114,120
precip 0,06,12,24,30,36,42,48,54,60,66,
72,78,84,90,96,102,108,114,120
Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 96 / 95
ECMWF Web site
(www.ecmwf.int)
