1. The Global Observing System
Stephen English
With material kindly provided by Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen
European Centre for Medium-Range Weather Forecasts

2. Role of observations
Observations limit error growth and make forecasting possible….
Every 12 hours we assimilate ~7,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.
We monitor an additional 12,000,000.
SEVIRI 6.2 µm
RMS error (m)
Time (hours)

3. The state space MASS (temperature, pressure…)
Radiosondes, surface observations, satellite sounders, aircraft
MOISTURE (humidity, clouds, precipitation…)
Radiosondes, surface observations, satellite sounders and imagers, aircraft, radar, lidar
DYNAMICS (wind, vorticity, convergence…)
Radiosondes, surface observations, satellite imagers, satellite scatterometer/radar/lidar, aircraft
COMPOSITION (ozone, aerosol…)
Ozone sondes, surface observations, satellite sounders
SURFACE (surface type, temperature, moisture, homogeneity…)
Satellite active and passive systems, surface observations

4. Composition Mass Moisture Wind Ozone sondes Air quality stations
Soil moisture
Rain gauge
Radiosonde
Synop
Ship
Aircraft
Buoys
Profilers
Wind

5. Data sources: Conventional
Instrument
Parameters
Height
SYNOP
SHIP
METAR
temperature, dew-point temperature, wind
Land: 2m, ships: 25m
BUOYS
temperature, pressure, wind 2m
TEMP
TEMPSHIP
DROPSONDES
temperature, humidity,
pressure, wind
Profiles
PROFILERS
wind
Aircraft
temperature, pressure wind
Flight level data

6. What types of satellites are used in NWP?
Advantages Disadvantages
GEO - Regional coverage No global coverage by single satellite
- Temporal coverage
LEO - Global coverage with single satellite

7. Composition Mass Moisture Wind IR = InfraRed MW = MicroWave
Ultraviolet sensors
Sub-mm,
and near IR plus
Visible (e.g. Lidar)
Polar
IR + MW
sounders
Mass
Radar and
GPS total path delay
Moisture
Radio occultation
Geo IR Sounder
Geo IR and Polar MW Imagers
Feature tracking in imagery (e.g. cloud track winds), scatterometers and doppler winds
IR = InfraRed
MW = MicroWave
Wind

8. Metop

9. Metop

10. Example of conventional data coverage
Aircraft – AMDAR (note also have Airep and ACARs)
Buoy
Surface (synop) - ship
Balloon profiles e.g. radiosondes

11. Example of 6-hourly satellite data coverage
LEO Sounders
LEO Imagers
Scatterometers
GEO imagers
Satellite Winds (AMVs)
GPS Radio Occultation
30 March UTC

13. 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

14. User requirements and satellite data: OSCAR www.wmo-sat.info
Vision for the GOS in 2025 adopted June 2009
GOS user guide WMO-No. 488 (2007)
Manual of the GOS WMO-No. 544 (2003) (updated for ET-SAT Geneva April 2012)

15. Using DA to help design the GOS
Examples questions we use Data Assimilation techniques to study:
Would it be beneficial for the Chinese FY3 program to move to the “early morning orbit” with the Europeans occupying the “morning orbit” and the Americans the “afternoon orbit”?
Preparation for future instruments such as lidar and radar (EarthCARE).
Study using Ensemble of Data Assimilations to estimate the number of GPSRO soundings needed in future (discuss with Sean Healy if interested).

16. 2009 Experiments Enza Di Tomaso* and Niels Bormann
MetOp-A AM
Early AM PM
NOAA-17 T i m e
NOAA-16
NOAA-15
NOAA-18
NOAA-19
Aqua

17. FY3 orbit: what is the optimal orbit configuration?
“NOAA-15 experiment”
* MetOp-A * NOAA * NOAA-15
“two-satellite experiment”
* MetOp-A * NOAA-18
“NOAA-19 experiment”
* MetOp-A * NOAA * NOAA-19

18. Are 3 satellites better than 2?
3.5 months 107 cases
CY36R1 T511
“no-MW sounder experiment”
GOOD
“two-”, “three-”, “all-satellite experiment”
Are 3 satellites better than 2?
YES
Both the assimilations of NOAA-15 and NOAA-19 data have a clearly positive forecast impact in the Southern Hemisphere compared to the use of two satellites only
two-satellite RMSE – no-Mw sounder RMSE
three-satellite RMSE – no-Mw sounder RMSE
all-satellite RMSE – no-Mw sounder RMSE

19. RMS difference forecast – analysis for NOAA-15 and NOAA-19 experiments
Do orbital positions matter?
YES
NOAA-19 experiment
GOOD
NOAA-15 experiment
When averaged over the extra-Tropics the impact for the forecast of the geopotential of “NOAA-15 experiment” versus “NOAA-19 experiment” is neutral to slightly positive
RMS difference forecast – analysis for
NOAA-15 and NOAA-19 experiments

20. 2012 Experiments Tony McNally
MetOp-A
NOAA-17 AM
Early AM PM
NOAA-16
NOAA-15
NOAA-18
NOAA-19
Aqua NPP

21. 2012 experiments
Baseline 1: microwave only (NPP + METOP-A)
Baseline 2: microwave + infrared (NPP + METOP-A)

22. 3 months 90 cases
CY38R1 T511
Microwave + infrared baseline
Microwave only baseline pm
better NH NH
Early am
better SH SH

23. Preparing for future missions e.g. Aeolus and EarthCARE
1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ)
15 – 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
Observation
Analysis 23

24. New requirements in GOS for atmospheric composition
Combining NWP with CTM models and data assimilation systems
New requirements in GOS for atmospheric composition

25. Monitoring of observations
Webpages
Automatic warnings
Collaboration between users and providers
J = ½(y-H(x))TR-1(y-H(x)) + Jb
At beginning and end of minimisation, with and without QC, plus bias corrections.

26. Data monitoring – automated warnings
Selected statistics are checked against an expected range.
E.g., global mean bias correction for GOES-12 (in blue):
-alert
Soft limits (mean ± 5 stdev being checked, calculated from past statistics over a period of 20 days, ending 2 days earlier)
Hard limits (fixed)
alert:
(M. Dahoui & N. Bormann)

27. Data monitoring – automated warnings

28. Data monitoring – automated warnings
Satellite data monitoring
Data monitoring – automated warnings

29. Global Observing System is essential to weather forecasting
Technology driven….a more integrated approach now?
Mass is well observed.
Moisture – satellite observations are data rich but poorly exploited. Radar and lidar will become more important.
Dynamics – even wind observations are scarce.
Composition – NWP techniques have been successfully extended to environmental analysis and prediction but more observations are needed.
Surface – DA for surface fields is being attempted.

30. Thank you for your attention
Thanks again to Peter Bauer, Cristina Lupu, Tony McNally, Mohamed Dahoui, Erland Kallen, Enza di Tomaso, Niels Bormann, Sabatino di Michele and Richard Engelen

31. Backup slides
Detailed list of instruments for NWP and atmospheric composition
(not shown but included for information)

32. Sun-Synchronous Polar Satellites
Instrument
Early morning orbit
Morning orbit
Afternoon orbit
High spectral resolution IR sounder
IASI
Aqua AIRS
NPP CrIS
Microwave T sounder
F16, 17 SSMIS
Metop AMSU-A
FY3A MWTS
DMSP F18 SSMIS
Meteor-M N1 MTVZA
NOAA-15, 18, 19 AMSU-A
Aqua AMSU-A
FY3B MWTS, NPP ATMS
Microwave Q sounder + imagers
Metop MHS
FY3A MWHS
NOAA-18, 19 MHS
FY3B MWHS, NPP ATMS
Broadband IR sounder
Metop HIRS
FY3A IRAS
NOAA-19 HIRS
FY3B IRAS
IR Imagers
Metop AVHRR
Meteor-M N1 MSU-MR
Aqua+Terra MODIS
NOAA-15, 16, 18, 19 AVHRR
Composition
(ozone etc).
NOAA-17 SBUV
NOAA-18, 19 SBUV
ENVISAT GOMOS
AURA OMI, MLS
ENVISAT SCIAMACHY
GOSAT

33. High inclination (> 60°) Low inclination (<60°)
Sun-Synchronous Polar Satellites (2)
Instrument
Early morning orbit
Morning orbit
Afternoon orbit
Scatterometer
Metop ASCAT
Coriolis Windsat
Oceansat OSCAT
Radar
CloudSat
Lidar
Calipso
Visible reflectance
Parasol
L-band imagery
SMOS
SAC-D/Aquarius
Non Sun-Synchronous Observations
Instrument
High inclination (> 60°)
Low inclination (<60°)
Radio occultation
GRAS, GRACE-A, COSMIC, TerraSarX
C-NOFS, (SAC-C), ROSA
MW Imagers
TRMM TMI
Meghatropics SAFIRE MADRAS
Radar Altimeter
ENVISAT RA
JASON Cryosat

34. Data sources: Geostationary Satellites
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