1. Data assimilation of polar orbiting satellites at ECMWF
Tony McNally
ECMWF

2. Overview Data assimilation
Radiance observations from polar orbiting satellites
scientific challenges

3. Main areas of activity at ECMWF
Numerical
Weather
Prediction (NWP)
Environmental
monitoring and
modelling
Historical reanalysis for climate research

4. Numerical Weather Prediction (NWP)
Deterministic
Monthly
Seasonal

5. Estimating greenhouse forecasting trajectory forecasting trajectory
Environmental
monitoring and
modelling
Estimating greenhouse
gas concentration and
flux inversion
Monitoring and
forecasting trajectory
of dust events
Monitoring and
forecasting trajectory
of volcanic events

6. Re-analysis for climate research
Trend analysis of
climate parameters
Improved climatology
for process studies
Cleansed historical
observation data sets

7. Historical reanalysis for climate research
Numerical
Weather
Prediction (NWP)
Environmental
monitoring and
modelling
Historical reanalysis for climate research

8. Historical reanalysis for climate research
Numerical
Weather
Prediction (NWP)
DATA
ASSIMILATION
Environmental
monitoring and
modelling
Historical reanalysis for climate research

9. What is data assimilation ?
…in essence data assimilation is the combination of information from a model and observations to produce a best estimate of the state of the atmosphere (the analysis) ….

10. Key elements of the assimilation system:
Forecast model
Observations
Assimilation algorithm
Super-computer

11. The forecast model
Xt=0
Xt=t

12. The forecast model Global T1279 spectral resolution
Physical and dynamical processes
updated every 10 minutes
Global T1279 spectral resolution
(16km grid point spacing)
91 vertical levels from the surface
to 0.01hPa (approx: 80Km)

13. The forecast model 6,300,000,000,000,000 floating point operations
for a single 10 day forecast
Global T1279 spectral resolution
(16km grid point spacing)
91 vertical levels from the surface
to 0.01hPa (approx: 80Km)

14. The Observations
Yobs

15. Operational Global Observing Network

16. Operational Global Observing Network
~ 60,000,000 observations
used every 12 hours

17. The Algorithm 4D-Var (four dimensional variational analysis)

18. The 4D-Var Algorithm Jb

19. The 4D-Var Algorithm Jo

20. The Super-computer

21. Super computer configuration
June 2010

22. The assimilation of polar satellite observations

23. Some of the most important satellite instruments for NWP…
On NOAA / NASA / EUMETSAT polar orbiting spacecraft
High resolution IR Sounder (HIRS), Advanced Microwave Sounding Unit (AMSU), Atmospheric IR Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), Advanced Microwave Scanning Radiometer (AMSR), TRMM (TMI), Cross-track Infrared Sounder (CrIS)
On DMSP polar orbiting spacecraft
Special Sensor Microwave Imager (SSMI,SSMI/S)
Note: the vast majority of data comes from near-nadir passive sounders

24. Example of a modern satellite sounding instrument… IASI
8461 infra-red radiances measured
by the IASI instrument

25. What benefits do polar satellite observations bring to NWP ?

26. Evolution of ECMWF NWP forecast skill

27. Evolution of NWP forecast skill
1987*

28. Forecast skill without polar satellites ?
Anomaly correlation
geopotential height
500 hPa
S.H.: ~3 days at day 5
Anomaly correlation
geopotential height
500 hPa
N.H.: ~2/3 to 3/4 of a day at day 5

29. Forecast skill without polar satellites ?
Snowfall forecasts over North Eastern USA, 3 days in advance of the 19th December 2009 at 12z. The assimilation system with NO POLAR SATELLITES fails to predict the snow storm that caused widespread disruption to the US east coast. Contours start at 5cm and are at 5cm intervals. Red indicates more than 20cm.
NO POLAR
ECMWF OPS
VERIFICATION

30. Forecast skill without polar satellites ?
Forecasts of Mean Sea Level Pressure, 5 days in advance of the 30th October 2012 for the landfall of Hurricane Sandy. Forecasts from an assimilation system with no polar satellites fails to predict the correct landfall of the storm that caused widespread damage and loss of life to the US east coast.
ECMWF OPS
NO POLAR SAT
VERIFICATION
5 day forecast: Base time z Valid Time: z

31. What challenges do polar satellite observations present ?

32. What do these instruments measure ?
They DO NOT measure TEMPERATURE
They DO NOT measure HUMIDITY or OZONE
They DO NOT measure WIND
…these instruments measure the radiance L that reaches the top of the atmosphere at given frequency v …
…ECMWF assimilates these radiances directly
(not retrievals of temperature, humidity etc…)

33. measured by the satellite
The radiative transfer equation
measured by the satellite
Our description of the atmosphere
Surface
reflection/
scattering
Surface
emission
Cloud/rain
contribution + + +
+ ...
Planck source term* depending
on temperature of the atmosphere
Absorption in the
atmosphere
Other contributions to the
measured radiances
The RT equation is part of the 4DVar operator that maps the model state X vector into the observation space Y

34. Specific Science Challenges
Limited vertical resolution
Sensitivity to cloud and rain
Systematic error

35. 1. Limited vertical resolution

36. 1. Limited vertical resolution
Absorption
Pressure
o1 2
Frequency
Transmission Weighting function

37. 1. Limited vertical resolution
AMSUA 15 channels
IASI 8461 channels

38. 1. Limited vertical resolution
If we consider the assimilation of these radiances as correcting errors in the background state, the success will depend crucially on the size and vertical structure of the background errors (EDA / EnKF etc…)

39. 2. Sensitivity to cloud and rain

40. measured by the satellite
2. Sensitivity to cloud and rain
measured by the satellite
Our description of the atmosphere
Surface
reflection/
scattering
Surface
emission
Cloud/rain
contribution + + +
+ ...
The cloud uncertainty in radiance terms may be an order of magnitude larger than the T and Q signal (i.e. 10s of kelvin compared to 0.1s of Kelvin!

41. Weighting function non-linearity
dR/dT500 = 0
dR/dT* = 1
dR/dT500 = 1
dR/dT* = 0
full cloud at 500hPa
surface
surface

42. Sensitive areas and cloud cover
Location of sensitive
regions
Summer-2001
(no clouds)
sensitivity surviving
high cloud cover
monthly mean
high cloud cover
sensitivity surviving
low cloud cover
monthly mean
low cloud cover
From McNally (2002) QJRMS 128

43. Cloud obscured singular vector ?
Forecast impact from cloudy data!
Cloud obscured singular vector ?
500hPa analysis difference (K)
Some extra overcast observations are used – leading to some possibly important analysis differences in a sensitive area …

44. 3. Systematic error
(global influence)

45. Warm-target temperatures for MSU on NOAA-14
3. Systematic error … data
Globally averaged bias correction estimates for MSU channel 2
Warm-target temperatures for MSU on NOAA-14

46. Shifts in upper-stratospheric temperature reanalysis
3. Systematic error … model
Shifts in upper-stratospheric temperature reanalysis
Global mean temperature anomalies in the upper stratosphere
JRA-25
The transition from SSU Ch3 to AMSU-A Ch14 is clearly visible in global mean temperatures at 5hPa and above
ERA-Interim
ERA-40
The use of weak-constraint 4D-Var can (only) partially address this problem
This problem cannot be completely solved unless the forecast model is free of bias
NCEP

47. Response to Pinatubo: HIRS Ch11 bias corrections
3.Systematic error..atmosphere
Response to Pinatubo: HIRS Ch11 bias corrections
Bias corrections for HIRS Ch11 (tropical averages)
Volcanic aerosols in the lower stratosphere:
Cooling effect on radiances
Not represented in radiative transfer model
ERA-Interim: Change the bias correction
ERA-40: Change the humidity increments
Bias corrections for NOAA-12:
In ERA-Interim, correct initialisation followed by a gradual recovery
In ERA-40, bias held fixed

48. Summary
Data assimilation lies at the centre of NWP, climate re-analysis and environmental monitoring
Radiance observations from polar orbiting satellites are the single most influential component of the global observing system
Great progress has been made, but significant scientific challenges remain to advance the use of these observations

49. The 4D-Var Algorithm Sa = B - Cost function: Solution:
Correction term
Solution error covariance:
Sa = B - HB