1. The Global Observing System
Peter Bauer and colleagues
European Centre for Medium-Range Weather Forecasts

2. 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

3. 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

4. ECMWF forecasting systems
Seasonal
Forecasts
Medium-Range
Forecasts
(Deterministic and EPS)
Monthly
Forecasts
Atmospheric model
Atmospheric model
Wave model
Wave model
Ocean model
Ocean data assimilation with 10-day window every 10 days. Forcing from ocean to atmosphere through SST+ocean currents (wave model), vice versa through P-E, wind stress, heat flux.
Real Time Ocean Analysis ~8 hours
Delayed Ocean Analysis ~12 days

5. Data assimilation system (4D-Var)
The observations are used to correct errors in the short forecast from the previous analysis time.
Every 12 hours we assimilate 4 – 8,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.
This is done by a careful 4-dimensional interpolation in space and time of the available observations; this operation takes as much computer power as the 10-day forecast.

6. Satellite observing system
Data types:
Data volume:

7. Data sources: Conventional
SYNOP/SHIP/METAR:
Meteorological/aeronautical land surface weather stations (2m-temperature, dew-point temperature, 10m-wind)
Ships
 temperature, dew-point temperature, wind (land: 2m, ships: 25m)
BUOYS:
Moored buoys (TAO, PIRATA)
Drifters
 temperature, pressure, wind
TEMP/TEMPSHIP/DROPSONDES:
Radiosondes
ASAPs (commercial ships replacing stationary weather ships)
Dropsondes released from aircrafts (NOAA, Met Office, tropical cyclones, experimental field campaigns, e.g., FASTEX, NORPEX)
temperature, humidity, pressure, wind profiles
PROFILERS:
UHF/VHF Doppler radars (Europe, US, Japan)
 wind profiles
Aircraft:
AIREPS (manual reports from pilots)
AMDARs, ACARs, etc. (automated readings)
 temperature, pressure, wind profiles

8. Example of conventional data coverage

9. Data sources: Satellites
Radiances ( brightness temperature = level 1):
AMSU-A on NOAA-15/18/19, AQUA, Metop
AMSU-B/MHS on NOAA-18/19, Metop
SSM/I on F-15, AMSR-E on Aqua
HIRS on NOAA-17/19, Metop
AIRS on AQUA, IASI on Metop
MVIRI on Meteosat-7, SEVIRI on Meteosat-9, GOES-11/12, MTSAT-1R imagers
Bending angles ( bending angle = level 1):
COSMIC (6 satellites), GRAS on Metop
Ozone ( total column ozone = level 2):
Total column ozone from SBUV on NOAA-17/18, OMI on Aura, SCIAMACHY on Envisat
Atmospheric Motion Vectors ( wind speed = level 2):
Meteosat-7/9, GOES-11/12, MTSAT-1R, MODIS on Terra/Aqua
Sea surface parameters ( wind speed and wave height = level 2):
Near-surface wind speed from ERS-2 scatterometer, ASCAT on Metop
Significant wave height from RA-2/ASAR on Envisat, Jason altimeters

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

11. 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!

12. Observation numbers per cycle
EXP-HI EXP EXP-SV EXP-CLI EXP-RND
Average radiance data count per analysis from period 08/12/ /02/2009:

13. Data Assimilation – Incremental 4D-Var
T799L91
T95L91
T159L91
T255L91
T799L91
(Trémolet 2004)

14. Transfer of information between radiances and control variables
Data Assimilation – Radiances
Transfer of information between radiances and control variables
Control Variable /
state vector
Forecast
model
State at
time i
Radiative
transfer
Radiance
observations
Wind and mass,
humidity
Dynamics,
moist physics
Wind and mass,
humidity,
Clear, cloud and rain including scattering
Clear, cloud and rain
Clear sky
Clear sky
clouds and rain

15. What is the observation operator?
Example 1: Radiosonde profile of T H = spatial interpolation
Example 2: Clear-sky radiance observation H = spatial interpolation + clear-sky radiative transfer
Example 3: Cloud/rain radiance observation H = spatial interpolation + moist physical parameterizations
+ multiple scattering radiative transfer
MVIRI
Model
SSM/I
Model

16. 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

17. 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

18. Impact of microwave sounder data in NWP: Met Office OSEs
N-15,-16 and -17 AMSU
N-16 & N-17 HIRS
AMVs
Scatterometer winds
SSM/I ocean surface wind speed
Conventional observations
2007 OSEs:
N-16, N-18, MetOp-2 AMSU
SSMIS
AIRS & IASI
(W. Bell)

19. Sensitivity of analysis increments to observations
2007 GMAO/GSI system, 1.875o, 64 levels, 6-hour window;
J from analysis increments; August 2004.
temperature zonal wind
North-Pacific North Pacific
US US
satellite
conventional
total
(Zhu & Gelaro 2008)

20. Advanced diagnostics Data assimilation: Forecast sensitivity: State at
max. 12 hours
State at
initial time
NWP
model
State at
time i
Observation
operator
Observation
simulations
State at analysis time
Sensitivity of cost to change at initial time
AD of forecast
model
Sensitivity of cost to change in state at time i
AD of observation
operator
Cost function J
Observations
State at
initial time
NWP
model
time i
AD of forecast
max. 48 hours
Sensitivity of cost to change at initial time
Analysis
Cost function J
Forecast sensitivity:
Sensitivity of cost to observations

21. Advanced diagnostics
Relative FC error reduction per system
The forecast sensitivity (Cardinali, 2009, QJRMS, 135, ) 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.
Relative FC error reduction per observation
(C. Cardinali)

22. Advanced diagnostics – MW sounder denial
3 AMSU-A, 2 MHS vs 1 AMSU-A, 0 MHS
Forecast error reduction [%]
(C. Cardinali)

23. Advanced diagnostics – MW imager denial
No MW-imagers
Control
Forecast error reduction [%]
(C. Cardinali)

24. 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

25. Data monitoring – time series
Time evolution of statistics over predefined areas/surfaces/flags
(M. Dahoui)

26. Data monitoring – overview plots
Time evolution of statistics for several channels
Useful for quick and routine verifications
Can not be used for high spectral resolution sounders
RTTOV version upgrade
(M. Dahoui)

27. 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)

28. Data monitoring – automated warnings
(M. Dahoui & N. Bormann)

29. Data monitoring – automated warnings
Satellite data monitoring
Data monitoring – automated warnings
(M. Dahoui & N. Bormann)

30. 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

31. New data availabilities
2010:
Oceansat-2 (Scatterometer: surface wind vector)
DMSP F-18 SSMIS (MW T:, q-sounding, clouds and precipitation)
SMOS (MW: soil moisture)
Megha Tropiques MADRAS/SAPHIR (MW: q-sounding, clouds and precipitation)
FY-3A IRAS/MWTS/MWHS/MWRI (IR/MW: T, q-sounding, clouds and precipitation)
GOSAT FTS (Advanced IR: T, q, trace gas sounding)
2011:
NPP (Advanced IR: T, q-sounding)
ADM (Doppler-lidar: Atmospheric wind vector)
2012 and beyond:
More advanced IR sounders in polar (Metop, NPOESS) and geostationary orbits (MTG, GOES) for general sounding
More active instruments (wind, clouds, precipitation)

32. Cloudsat/CALIPSO data monitoring
(J.-J. Morcrette)

33. ECMWF usage of SMOS data
Global monitoring:
Development of model forward operator (emissivity model)
Data pre-processing (HDF2BUFR → ODB/IFS)
Implementation of passive monitoring system, diagnostics, quality control
Data assimilation study:
Impact of SMOS constrained soil moisture on medium-range forecasts
H-pol
H-pol
V-pol
H-pol
22 January UTC; 1st background departure monitoring (no q/c)

34. FG departure in m3/m3 (January 2010)
Soil moisture from ASCAT data
FG departure in m3/m3 (January 2010)
FG departure bias vs ASCAT incidence angle
Histograms of FG departures
(P. de Rosnay)

35. Active instruments: ESA’s ADM
ESA ADM AEOLUS Doppler Lidar for wind vector observation
Pressure (hPa)
Control+ADM
Control
Control-sondes
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.
Zonal wind forecast error (m/s)

36. 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

37. Areas of instability: Eady index
Eady-index as a proxy for baroclinic instability in the atmosphere
difference between seasons is rather strong;
year-to-year variability has significant seasonal dependence as well.

38. Data coverage 14/12/2008 00 UTC data density AMSU-A channel 9 EXP-HI:
EXP-SV:
EXP-CLI:
EXP-RND:
01-07/01/2009 Average SV
RND
CLI

39. Forecast impact: z500 – D08JF09
JAS08 D08JF09

40. 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

41. Observations used in ERA-Interim:
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); period available, period finished, real-time in 2009.
VTPR
TOMS/ SBUV
HIRS/ MSU/ SSU
Cloud motion winds
Buoy data
SSM/I
ERS-1
ERS-2
AMSU
METEOSAT reprocessed
cloud motion winds
Conventional surface and upper-air observations
NCAR/NCEP, ECMWF, JMA, US Navy, Twerle, GATE, FGGE, TOGA, TAO, COADS, …
Aircraft data
1957
2002
1973
1979
1982
1988
1987
1991
1995
1998
1989
The ERA-40 observing system:
Observations used in ERA-Interim:
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

42. Reanalysis as inter-calibration tool
Global mean bias corrections produced in ERA-Interim (MSU Channel 2):
Recorded warm-target temperatures, NOAA-14:
(Grody et al. 2004)
Variations in warm target
are due to orbital drift
VarBC is able to correct
the resulting calibration
errors
(D. Dee)

43. 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

44. Combining NWP with CTM models and data assimilation systems
EC FP-6/7 projects GEMS/MACC (coordinated by ECMWF) towards GMES Atmospheric Service

45. 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.

46. 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.

47. Satellite data on aerosols for use in MACC
Comment: Fire products (METEOSAT, MODIS, …) are a common requirement.

48. 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

49. 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: - early data access after launch: (1) fast monitoring of data quality – feedback to space agencies, (2) early testing of data impact in NWP data assimilation systems. - near real-time data access to maximize operational use.  optimal return of investment by global user community (example: METOP).
Currently most important NWP instruments at ECMWF: - advanced 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).

50. Concluding remarks
Future challenges with respect to observations: - active instruments – radar, lidar (wind, aerosols, clouds, precipitation, water vapour), - advanced imagers – synthetic aperture radiometers (soil moisture).
Future challenges with respect to data assimilation: - model resolution upgrades also affect data assimilation resolution, - more intelligent data thinning using ensemble methods (B) and forecast error growth metrics, - assimilation of cloud/precipitation-affected data will require revised control variable, background error statistics.
Future upgrades to data monitoring: - more sophisticated data co-location tools to compare performance between data from different sensors, - more advanced automated warning system.