Operational forecasting at ECMWF: Science, Components and Products

1
Operational forecasting at ECMWF Science,
Components and Products

• Ervin Zsoter
• ECMWF, Meteorological Operations Section
• ervin.zsoter_at_ecmwf.int

With contributions from Renate Hagedorn, David
Richardson, Antonio Garcia Mendez, Gerald van der
Grijn, Lars Isaksen and others
2
Outline

• ECMWF as an operational and research centre
• EMOS ECMWF Meteorological Observational System
• Quality control at ECMWF
• Important characteristics of the ECMWFs
  operational analysis and forecasting system
• ECMWF 4D-VAR data assimilation system
• Model computational characteristics
• Model performance
• Some applications
• Different forecast products

3
ECMWF as an organisation
ECMWF is an independent international
organization, supported by 18 member states and 8
co-operating states
Convention establishing ECMWF entered into force
on 1st Nov 1975
Co-operating states
Co-operating organisations
4
ECMWF Budget 2006
Main Revenue 2006 Member Statescontributions 27
,460,600 Co-operating Statescontributions
425,100 Other Revenue 1,454,600 Total
29,340,300
Main Expenditure 2006 Staff 12,961,900 Leaving
Allowances Pensions 1,807,500 ComputerExpendit
ure 11,785,900 Buildings 1,858,000 Supplies 927
,000 Total 29,340,300
5
Objectives of the centre

• Development of global models and data
  assimilation systems for the dynamics,
  thermodynamics and composition of the Earths
  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

6
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

7
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

8
Data sources for the ECMWF Meteorological
Operational System (EMOS)
Number of observed data assimilated in 24 hours
13th February 2006
9
Conventional observations used
BUOY MSL Pressure, Wind-10m
SYNOP/METAR/SHIP
MSL Pressure, 10m-wind, 2m-Rel.Hum.
PILOT/Profilers Wind
TEMP Land - ASAP - Dropsonde
Wind, Temperature, Spec. Humidity
Aircraft Wind, Temperature
10
(No Transcript)
11
Positive trend in the number of Radiosondes
reaching the upper Startosphere
12
Manual obs
Automatic obs
13
28 satellite data sources used in the operational
ECMWF analysis
DMSP SSM/I
NOAA AMSUA/B HIRS, AQUA AIRS
SCATTEROMETERS
GEOS
TERRA / AQUA MODIS
OZONE
14
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

15
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
16
Large increase in number of observations used

• Especially number of satellite data increases
• A scientific and technical challenge

17
Observations for one 12h 4D-Var cycle
0900-2100UTC 26 March 2006
Screened
Assimilated
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) AMVs 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) AMVs 127.000 (3.90) Radiance data 2.646.000 (81.34) Scat 122.000 (3.75) TOTAL 3.253.000 (100.00)
99 of screened data is from satellites
86 of assimilated data from satellites
18
Observations Quality control - Analysis
Data extraction

• 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

• Check out duplicate reports
• Ship tracks check
• Hydrostatic check

• Thinning
• Skipped data to avoid Over sampling
• Even so departures from FG and ANA are generated
  and usage flags also

• 4DVAR QC
• Rejections
• Used data ? increments

ANALYSIS
19
Observations Quality control - Analysis

• OI
• 3DVAR
• 4DVAR

Data input
Data assimilation

• Raw observation
• Departures (FG AN)
• Flags (data used, thinned, rejected)

• Feedback files (BUFR)
• ODB

Monthly BUFR files for different Obs types
Long term statistics
20
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

21
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

22
Blacklists
Quality problems in Asia Russia
23
Blacklists
Quality problems in Africa and southern Asia
24
Whats the benefit of using a blacklist?
All data
Ob-FG
Ob-AN
Used data Blacklist and 4DVAR QC applied
NH
Reduced random deviation
25
Whats the benefit of using a blacklist?
All data considered
26
Whats the benefit of using a blacklist?
Blacklist applied
27
Whats the benefit of using a blacklist?
Blacklist plus 4DVAR Quality Control applied
28
Sites directional setting changed by 6.5 degrees
29
Example for data monitoring SYNOP pressure bias
correction
Strong biases related to wrong station heights in
the catalogue
30
Example for data monitoring SYNOP pressure bias
correction
Bias correction applied
31
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

32
Adaptive bias correction scheme for surface
pressure data

• 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

Warm-up
3hPa Bias
33
Example for data monitoring SYNOP pressure bias
correction
34
Example for data monitoring SYNOP pressure bias
correction
35
Example for data monitoring SYNOP pressure bias
correction
36
ECMWFs 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)

37
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

38
Operational model levels
39
Operational model grid (reduced Gaussian)
40
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.
T799 orography, grid spacing 25 km
T255 orography, grid spacing 80 km
41
ECMWF model 10m wind (T799, 25 km)
42
Katrina (2005 Aug) 90h forecasts - T511 versus
T799
Central pressure 909hPa, 785mm/24h rain
T799
Central pressure 940hPa, 448mm/24h rain
T511
43
Hurricane Gordon T799 forecast
AN
30 hrs
78 hrs
126 hrs
44
Limitation Model grid box still large
45
Physical processes in the ECMWF model
46
Data assimilation for weather prediction
The FORECAST is computed on a grid over the
globe. The meteorological OBSERVATIONS can be
taken at any location in the grid. The computer
models prediction of the atmosphere is compared
against the available observations, in near real
time
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.
47
4D-Var Data assimilation
Analysis values
Background values
Observations
12-hour forecast
Model variables, e.g. temperature
True state of the atmosphere
12 UTC 13 March
00 UTC 13 March
00 UTC 14 March
12 UTC 14 March
Time
48
A few 4D-Var Characteristics
All observations within a 12-hour period
(3,300,000) are used simultaneously in one
global (iterative) estimation problem

• 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)

9z 12z 15z 18z
21z
49
ECMWF 4D-Var procedure

• Use all data in a 12-hour window (0900-2100 UTC
  for 1200 UTC analysis)
• Group observations into ½ hour time slots
• Run the T799 (25km) high resolution forecast from
  the previous analysis and compute observation-
  model differences
• 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
• Rerun the T799 high resolution model from the
  modified (improved) initial state and calculate
  new observation departures
• The 3-4 loop in repeated twice to produce a good
  high resolution estimate of the atmospheric state
  the result is the ECMWF analysis

50
Multi-incremental quadratic 4D-Var at ECMWF
T799L91
T95L91 T255L91
51
Analysis increments 1st and 2nd minimization
Temperature level 60 (10metre). 0.2K contours
(blue is negative red is positive)
2nd minimization Additional T159 T increment
1st minimization T95 T increments
Most of the increment is formed at the lower
resolution with smaller additions and corrections
obtained at the higher resolution.
52
Forecast versus observations
12-hour forecast temperature change
Correction, as a result of data assimilation
The corrections are 10 times smaller than the
12-hour forecast temperature change
53
Tropical cyclone LILI - Impact of NSCAT data in
4D-Var
First guess MSL pressure
First guess MSL pressure
Analysis MSL pressure
MSL pressure Analysis increments
NSCAT analysis
No SCAT analysis
S.M. Leidner, L. Isaksen and R.S. Hoffman Impact
of NSCAT Winds on Tropical Cyclones in the ECMWF
4DVAR assimilation system Mon. Wea. Rev.
131,1,3-26 (2003)
54
4D-Var is using more a-synoptic data than 3D-Var
4D-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.
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.
3D-Var SYNOP Screening
55
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

56
4D-Var versus 3D-Var performance
N. HEM
12h T511 3DVAR
6h T319 4DVAR
6h T319 3DVAR
S. HEM
57
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

58
Operational schedule for 0000UTC cycleEarly
delivery suite introduced June 2004
59
Supercomputer performance at ECMWF
1978-2003 Mflops/s Peak performance



(G.-R. Hoffman)
60
2004/2006 A significant Performance increase
2006
2002
IBM p690 2 x 960 processors
IBM p575 2 x 2400 processors
Peak performance 7.6 Gflops per processor
Peak performance 5.2 Gflops per processor
Switch 350 Mbytes/s
Switch 2000 Mbytes/s
8 processors per shared memory node
16 processors per shared memory node
(Deborah Salmond/Sami Saarinen )
61
T511 1-day Forecast on IBM
CPUs
62
4D-Var T799/T95/T255 with 91 levels on present
ECMWF IBM system
512
768
1024
1536
63
Time series Z500 N Hemisphere against analysis
64
Time series Z500 N Hemisphere against
radiosondes
65
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

66
Different application of the ECMWF products
67
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

68
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.

69
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

70
Global forecasts (deterministic, fields)
Mean Sea Level Pressure Rain (06-18UTC)
500 hPa height and 850 hPa temperature
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
71
Global forecasts (deterministic, fields)
T2m and 30m-winds
Cloud cover (high, medium, low)
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
72
ECMWF deterministic Ocean wave forecasts

• Global forecast to ten days from 00 and 12 UTC at
  50 km resolution

AFRICA !!!!

• European waters forecast to five days from 00 and
  12 UTC at 25 km resolution

73
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)

74
EPS forecasts time series (EPSgram)
EPSgram for Pretoria Base Friday 27/10/06 00UTC
75
EPS forecasts (field probabilities)
Probability of 10m wind speed more than 10 m/s
Probability of precipitation more than 1mm in 24
hours
Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
76
EPS forecasts (post-processed products)
Extreme forecast index for 2m temperature Base
Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC
77
Katrina forecasts (days from landfall)
4 days before landfall
78
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

79
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)
80
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)
81
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)
82
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)
83
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)
84
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)
85
Monthly forecast performance over the Northern
Extratropics
ROC area of probability of 2-metre temperature in
upper third of climate range
Monthly Forecast
Monthly Forecast
Persistence of day 5-11
Persistence of day 5-18
Day 12-18
Day 19-32
86
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 1x1 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

87
ECMWF seasonal forecast System 2
Anomaly of 2m temperature Base 01-10-2006. Valid
December-January-February (DJF)
88
ECMWF seasonal forecast System 2
Anomaly of precipitation Base 01-10-2006. Valid
December-January-February (DJF)
89
ECMWF seasonal forecast System 2
90
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

91
EUROSHIP seasonal forecast
EUROSHIP anomaly of 2m temperature Base
01-10-2006. Valid December-January-February (DJF)
92
EUROSHIP seasonal forecast
EUROSHIP anomaly of precipitation Base
01-10-2006. Valid December-January-February (DJF)
93
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

94
ECMWF Products for GTS dissemination
Param level steps
Z 500 G 0,24,48,72,96,120,144,168, 192, 216, 240
T 850 G 0,24,48,72,96,120,144,168
u,v 850, 700, 500, 200 G 0,24,48,72,96,120,144,168
Rh 850, 700 G 0,24,48,72,96,120,144,168
MSLP G 0,24,48,72,96,120,144,168
Div 700 T 0,24,48,72,96,120,144
Vort 700 T 0,24,48,72,96,120,144
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
96
ECMWF Web site (www.ecmwf.int)