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Slide 1
ECMWF Training Course - The Global Observing System - 04/2012
The Global Observing System
Stephen English and colleagues
(with special thanks to Peter Bauer )
European Centre for Medium-Range Weather Forecasts
Slide 2
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 3
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 4
ECMWF Training Course - The Global Observing System - 04/2012
Role of observations
Forecast lead time (days)
RM
S e
rror
(m
)
From C Lupu and E.Kallen
199020002010
Time (hours)
500hPa height, NH
SEVIRI 6.2 µm
Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.
Slide 5
ECMWF Training Course - The Global Observing System - 04/2012
From E. Kallen
Slide 6
ECMWF Training Course - The Global Observing System - 04/2012
Data sources: Conventional
Instrument Parameters Height
SYNOPSHIPMETAR
temperature, dew-point temperature, wind
Land: 2m, ships: 25m
BUOYS temperature, pressure, wind 2m
TEMPTEMPSHIPDROPSONDES
temperature, humidity,pressure, wind
Profiles
PROFILERS wind Profiles
Aircraft temperature, pressure wind
ProfilesFlight level data
Slide 7
ECMWF Training Course - The Global Observing System - 04/2012
Example of conventional data coverage
Aircraft – AMDAR
Synop - ship
Buoy
Temp
Slide 8
ECMWF Training Course - The Global Observing System - 04/2012
What types of satellites are used in NWP?
Advantages Disadvantages
GEO - large regional coverage - no global coverage by single satellite
- very high temporal resolution - moderate spatial resolution (VIS/IR)> short-range forecasting/nowcasting > 5-10 km for VIS/IR> feature-tracking (motion vectors) > much worse for MW> tracking of diurnal cycle (convection)
LEO - global coverage with single satellite - low temporal resolution
- high spatial resolution>best for NWP!
From P. Bauer
Slide 9
ECMWF Training Course - The Global Observing System - 04/2012
Sun-Synchronous Polar SatellitesInstrument Early morning
orbitMorning orbit Afternoon orbit
High spectral resolution IR sounder
IASI Aqua AIRSNPP CrIS
Microwave T sounder
F16, 17 SSMIS Metop AMSU-AFY3A MWTSDMSP F18 SSMISMeteor-M N1 MTVZA
NOAA-15, 18, 19 AMSU-A Aqua AMSU-AFY3B MWTS, NPP ATMS
Microwave Q sounder + imagers
F16, 17 SSMIS Metop MHSDMSP F18 SSMISFY3A MWHS
NOAA-18, 19 MHSFY3B MWHS, NPP ATMS
Broadband IR sounder
Metop HIRSFY3A IRAS
NOAA-19 HIRSFY3B IRAS
IR Imagers Metop AVHRRMeteor-M N1 MSU-MR
Aqua+Terra MODISNOAA-15, 16, 18, 19 AVHRR
Composition(ozone etc).
NOAA-17 SBUV NOAA-18, 19 SBUVENVISAT GOMOSAURA OMI, MLSENVISAT SCIAMACHYGOSAT
Slide 10
ECMWF Training Course - The Global Observing System - 04/2012
Instrument High inclination (> 60°) Low inclination (<60°)
Radio occultation
GRAS, GRACE-A, COSMIC, TerraSarXC-NOFS, (SAC-C), ROSA
MW Imagers TRMM TMIMeghatropics SAFIRE MADRAS
Radar Altimeter ENVISAT RAJASON Cryosat
Sun-Synchronous Polar Satellites (2)Instrument Early morning
orbitMorning orbit Afternoon orbit
Scatterometer Metop ASCATCoriolis Windsat
Oceansat OSCAT
Radar CloudSat
Lidar Calipso
Visible reflectance
Parasol
L-band imagery
SMOSSAC-D/Aquarius
Non Sun-Synchronous Observations
Slide 11
ECMWF Training Course - The Global Observing System - 04/2012
• Characterise the benefit of having ATOVS data from three evenly-spaced orbits
versus data from a less optimal coverage for NWP
MetOp-A
NOAA-18
NOAA-19 + NPP Aqua
NOAA-15
Ti
me
ECMWF support to EUMETSAT – LEO constellation
DMSP F16
DMSP F17
DMSP F18
FY-3B
FY-3A
ECWMF/EUMETSAT Bilateral Meeting 03/2012 SE
11
Coriolis
Slide 12
ECMWF Training Course - The Global Observing System - 04/2012
Product Status
SEVIRI Clear sky radiance Assimilated
SEVIRI All sky radiance Being tested for overcast radiances, and cloud-free radiances in the ASR dataset
SEVIRI total column ozone Monitored
SEVIRI AMVs IR, Vis, WV-cloudy AMVs assimilated
GOES AMVs
MTSAT AMVs
Data sources: Geostationary Satellites
Slide 13
ECMWF Training Course - The Global Observing System - 04/2012
LEO Sounders LEO Imagers
Scatterometers GEO imagers
Satellite Winds (AMVs)
GPS Radio Occultation
Example of 6-hourly satellite data coverage
30 March 2012 00 UTC
Slide 14
ECMWF Training Course - The Global Observing System - 04/2012
Profilers
RadiosondeSynopShip
AircraftBuoys
MoistureMass
Wind
Composition
Ozone sondesAir quality stations
Soil moistureRain gauge
Slide 15
ECMWF Training Course - The Global Observing System - 04/2012
GPSRO
Geo IR and Polar MW Imagers
AMVsScatterometersWind lidar
Geo IR Sounder
RadarGPS ZPD
PolarIR + MWsounders
MoistureMass
Wind
Composition
UV
Sub-mmVIS+NIRLidarLimb-sounders
Slide 16
ECMWF Training Course - The Global Observing System - 04/2012
Satellite data used by ECMWF
Slide 17
ECMWF Training Course - The Global Observing System - 04/2012
Slide 18
ECMWF Training Course - The Global Observing System - 04/2012
User requirements http://www.wmo-sat.info/db/
• Vision for the GOS in 2025 adopted June 2009• GOS user guide WMO-No. 488 (2007)• Manual of the GOS WMO-No. 544 (2003) (Update of satellite section being prepared for ET-SAT Geneva April 2012)
Slide 19
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 20
ECMWF Training Course - The Global Observing System - 04/2012
Combined impact of all satellite data
EUCOS Observing System Experiments (OSEs):
• 2007 ECMWF forecasting system,• winter & summer season,• different baseline systems:
• no satellite data (NOSAT),• NOSAT + AMVs,• NOSAT + 1 AMSU-A,
• general impact of satellites,• impact of individual systems,• all conventional observations.
500 hPa geopotential height anomaly correlation
3/4 day
3 days
From P. Bauer
Slide 21
ECMWF Training Course - The Global Observing System - 04/2012
Impact of microwave sounder data in NWP: Met Office OSEs
2003 OSEs:2003 OSEs:• N-15,-16 and -17 AMSUN-15,-16 and -17 AMSU• N-16 & N-17 HIRSN-16 & N-17 HIRS• AMVsAMVs• Scatterometer windsScatterometer winds• SSM/I ocean surface wind speedSSM/I ocean surface wind speed• Conventional observationsConventional observations
2007 OSEs:2007 OSEs:• N-16, N-18, MetOp-2 AMSUN-16, N-18, MetOp-2 AMSU• SSMISSSMIS• AIRS & IASIAIRS & IASI• Scatterometer windsScatterometer winds• AMVsAMVs• SSM/I ocean surface wind speedSSM/I ocean surface wind speed• Conventional observationsConventional observations
(From W. Bell)
Slide 22
ECMWF Training Course - The Global Observing System - 04/2012
State atinitial time
NWPmodel
State at time i
Observationoperator
Observationsimulations
Advanced diagnostics
Observations
AD of forecastmodel
AD of observation
operator
Sensitivity of cost to change in state at time i
Cost function J
Sensitivity of cost to change at initial time
max. 12 hours
Data assimilation:
State atinitial time
NWPmodel
State at time i
AD of forecastmodel
max. 48 hours
Sensitivity of cost to change at initial time
Analysis
Cost function J
Forecast sensitivity:
State at analysis
time
Sensitivity of cost to
observations
From P. Bauer
Slide 23
ECMWF Training Course - The Global Observing System - 04/2012
Relative FC error reduction per system
Relative FC error reduction per observation
(From C. Cardinali)
Advanced diagnostics
The forecast sensitivity (Cardinali, 2009, QJRMS, 135, 239-250) denotes the sensitivity of a forecast error metric (dry energy norm at 24 or 48-hour range) to the observations. The forecast sensitivity is determined by the sensitivity of the forecast error to the initial state, the innovation vector, and the Kalman gain.
Slide 24
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 25
ECMWF Training Course - The Global Observing System - 04/2012
Time evolution of statistics over predefined areas/surfaces/flags
Data monitoring – time series
(From M. Dahoui)
Slide 26
ECMWF Training Course - The Global Observing System - 04/2012
Selected statistics are checked against an expected range.
E.g., global mean bias correction for GOES-12 (in blue):
Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier)
Hard limits (fixed)
Email-alert
Data monitoring – automated warnings
(M. Dahoui & N. Bormann)
http://www.ecmwf.int/products/forecasts/satellite_check/
Email alert:
Slide 27
ECMWF Training Course - The Global Observing System - 04/2012
Data monitoring – automated warnings
(From M. Dahoui & N. Bormann)
Slide 28
ECMWF Training Course - The Global Observing System - 04/2012
Satellite data monitoringData monitoring – automated warnings
(From M. Dahoui & N. Bormann)
Slide 29
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 30
ECMWF Training Course - The Global Observing System - 04/2012
New data availabilities•Now
•SMOS, Suomi-NPP
•2013-2017•ADM (Doppler-lidar: Atmospheric wind vector)•SMAP (like SMOS but active + passive)•Earthcare (radar, lidar)•FY3 -> ATOVS quality
•2017-2020•Meteosat 3rd Generation •FY3 -> Metop quality
•2020+•EPS Second Generation
But don’t always focus on satellite data! RS90 radiosonde much better than older radiosondes....`advanced conventional observations’
Slide 31
ECMWF Training Course - The Global Observing System - 04/2012
REF_AT
62 N 60 N 58 N 56 N 54 N 52 N 50 N 48 N 46 N 44 N 42 N 40 N 38 N 36 N 34 N 32 N 30 NLat
100W 99 W 98 W 97 W 96 W 95 W 94W 93 W 92 W 91 W 90 W 89W 88W 87 WLon
1.1
1.9
3.0
4.3
5.9
7.6
9.3
10.9
12.5
Hei
gh
t (k
m)
-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18Observation
REF_AT
62 N 60 N 58 N 56 N 54 N 52 N 50 N 48 N 46 N 44 N 42 N 40 N 38 N 36 N 34 N 32 N 30 NLat
100W 99 W 98 W 97 W 96 W 95 W 94W 93 W 92 W 91 W 90 W 89W 88W 87 WLon
1.1
1.9
3.0
4.3
5.9
7.6
9.3
10.9
12.5
Hei
gh
t (k
m)
-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18
REF_AT
62 N 60 N 58 N 56 N 54 N 52 N 50 N 48 N 46 N 44 N 42 N 40 N 38 N 36 N 34 N 32 N 30 NLat
100W 99 W 98 W 97 W 96 W 95 W 94W 93 W 92 W 91 W 90 W 89W 88W 87 WLon
1.1
1.9
3.0
4.3
5.9
7.6
9.3
10.9
12.5
Hei
gh
t (k
m)
-24 - -21 -21 - -18 -18 - -15 -15 - -12 -12 - -9 -9 - -6 -6 - -3 -3 - 0 0 - 3 3 - 6 6 - 9 9 - 12 12 - 15 15 - 18
15 – 18
REF_AT
62N
60N
58N
56N
54N
52N
50N
48N
46N
44N
42N
40N
38N
36N
34N
32N
30N
Lat100
W99
W98
W97
W96
W95
W94
W93
W92
W91
W90
W89
W88
W87
WLon
1.1
1.9
3.0
4.3
5.9
7.6
9.3
10.9
12.5Height (km)
-24 - -21-21 - -18
-18 - -15-15 - -12
-12 - -9-9 - -6
-6 - -3-3 - 0
0 - 33 - 6
6 - 99 - 12
12 - 1515 - 18
-24 – -21
-21 – -18
-18 – -16
-16 – -12
-12 – -9
-9 – -6
-6 – -3
-3 – 0
0 – 3
3 – 6
6 – 9
9 – 12
12 – 15
Model First-Guess
Analysis
1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ)
EarthCARE
31
From S Di Michele
Slide 32
ECMWF Training Course - The Global Observing System - 04/2012
EarthCARE1D-Var Assimilation of Calipso lidar Backscatter Coefficients (km-1 sr-1)
Observation
Model First-Guess
Analysis
32
From S Di Michele
Slide 33
ECMWF Training Course - The Global Observing System - 04/2012
SMOS monitoring results
H-pol V-pol
• Monthly-average geographical mean evolution of the First-guess departures • Period Nov-2010 - August-2011
• fg departures in H-pol well correlated with snow covered areas, • Significant sources of RFI are still easy to spot with fg-departures,• In V-pol, observations are mainly overestimated.
From J. Munoz Sabater
Slide 34
ECMWF Training Course - The Global Observing System - 04/2012
ECMWF is responsible for the development of the level 2 processor and will exploit the data as soon as available.
Simulated DWL data adds value at all altitudes and well into longer-range forecasts.
S.Hem
0.0 0.5 1.0 1.51000
100
Zonal wind forecast error (m/s)
Pre
ssu
re (
hP
a)
Control+ADM
Control
Control-sondes
Active instruments: ESA’s ADMESA ADM AEOLUS Doppler Lidar for wind vector observation
From P Bauer
Slide 35
ECMWF Training Course - The Global Observing System - 04/2012
~210km~125km ~63km
~39km ~25km ~16km
Evolution of ECMWF forecast skill
From E Kallen
Slide 36
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 37
ECMWF Training Course - The Global Observing System - 04/2012
Observations used in ERA-Interim:
The ERA-40 observing system:
VTPR
TOMS/ SBUV
HIRS/ MSU/ SSU Cloud motion winds
Buoy data
SSM/I ERS-1ERS-2
AMSU
METEOSAT reprocessed
cloud motion winds
Conventional surface and upper-air observationsNCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, …
Aircraft data
1957 2002
19731979
1982 1988
1973 19791987 1991
19951998• ERA-40 observations until August 2002
• ECMWF operational data after August 2002• Reprocessed altimeter wave-height data from ERS• Humidity information from SSM/I rain-affected radiance data• Reprocessed METEOSAT AMV wind data• Reprocessed ozone profiles from GOME• Reprocessed GPSRO data from CHAMP
ERA-Interim
1989
ECMWF Reanalysis• ERA-Interim is current ECMWF reanalysis project following ERA-
15 & 40.• 2006 model cycle, 4D-Var, variational bias-correction, more data
(rain assimilation, GPSRO); 1989-1998 period available, 1998-2005 period finished, real-time in 2009.
From P. Bauer
Slide 38
ECMWF Training Course - The Global Observing System - 04/2012
From E Kallen
Slide 39
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 40
ECMWF Training Course - The Global Observing System - 04/2012
Combining NWP with CTM models and data assimilation systems
EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service
From P Bauer
Slide 41
ECMWF Training Course - The Global Observing System - 04/2012
Satellite data on CO2 and CH4 for use in MACC
Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement.
From P Bauer
Slide 42
ECMWF Training Course - The Global Observing System - 04/2012
Satellite data on reactive gases for use in MACC
Comments: Post-EPS sounder and Sentinels 4/5 should come into the picture late in period or soon after. Fire products (METEOSAT, MODIS, …) are a common requirement. From P Bauer
Slide 43
ECMWF Training Course - The Global Observing System - 04/2012
Satellite data on aerosols for use in MACC
Comment: Fire products (METEOSAT, MODIS, …) are a common requirement.
From P Bauer
Slide 44
ECMWF Training Course - The Global Observing System - 04/2012
NWP, conventional and satellite observations
General impact assessment of current observing system
Data monitoring
Future observations and observation usage
Special Applications: Climate & Chemistry
Concluding remarks
Slide 45
ECMWF Training Course - The Global Observing System - 04/2012
Concluding remarks• At ECMWF, 95% of the actively assimilated data originates from
satellites (90% is assimilated as radiances and only 5% as derived products and 5% from conventional products).
• Impact experiments demonstrate the crucial role of conventional observations!
• Ingredients for successful data implementation:- pre-launch test data, well defined formats, testing of
telecommunications, provision of detailed instrument information.- early data access after launch and active “cal/val” role for NWP
centres- near real-time data access to maximize operational use.
optimal return of investment by global user community (e.g. Metop ATOVS was used operationally only 3 months after launch despite whole new ground segment!).
• Currently most important NWP instruments at ECMWF:- high spectral resolution infrared sounders (temperature, moisture),- microwave sounders and imagers (temperature, moisture, clouds, precipitation),- GPS transmitters/receivers (temperature),- IR imagers/sounders in geostationary orbits (moisture, clouds, wind),- scatterometers (near surface wind speed, wave height)- altimeters (height anomaly),- UV/VIS/IR spectrometers (trace gases, temperature).
Slide 46
ECMWF Training Course - The Global Observing System - 04/2012
Concluding remarks
• Future upgrades to data monitoring:- Coordination with data providers, building on experience within Europe e.g. Collaboration with China over FY3.- more effective automated warning system.
• Future challenges with respect to observations:- Active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour),- Advanced imagers – synthetic aperture radiometers (soil moisture).- Geostationary high spectral resolution sounders
• Future challenges with respect to design of the Global Observing System:- In the past over-reliance on US data. European data now very important. New partnerships (e.g. China) will become increasingly important- Coordination of multi-agency programmes- Prioritisation for high benefit : low cost missions versus “new science” missions- Knowing which observations will be needed in 10-20 years time when NWP will have advanced considerably- Balancing needs of NWP, Climate and nowcasting, alongside new requirements for environmental monitoring (composition and chemistry).
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