Data assimilation of polar
orbiting satellites at
ECMWF
Tony McNally
ECMWF
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Overview
1.
Data assimilation
2.
Radiance observations from polar orbiting
satellites
3.
scientific challenges
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Main areas of activity at
ECMWF
Numerical
Weather
Prediction (NWP)
Environmental
monitoring and
modelling
Use of Satellite Data at ECMWF – Tony McNally
Historical
reanalysis for
climate research
ECMWF
Numerical
Weather
Prediction (NWP)
Deterministic
Monthly
Use of Satellite Data at ECMWF – Tony McNally
Seasonal
ECMWF
Environmental
monitoring and
modelling
Estimating greenhouse
gas concentration and
flux inversion
Monitoring and
forecasting trajectory
of dust events
Use of Satellite Data at ECMWF – Tony McNally
Monitoring and
forecasting trajectory
of volcanic events
ECMWF
Re-analysis
for climate
research
Trend analysis of
climate parameters
Improved climatology
for process studies
Use of Satellite Data at ECMWF – Tony McNally
Cleansed historical
observation data sets
ECMWF
Numerical
Weather
Prediction (NWP)
Environmental
monitoring and
modelling
Use of Satellite Data at ECMWF – Tony McNally
Historical
reanalysis for
climate research
ECMWF
Numerical
Weather
Prediction (NWP)
DATA
ASSIMILATION
Environmental
monitoring and
modelling
Use of Satellite Data at ECMWF – Tony McNally
Historical
reanalysis for
climate research
ECMWF
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) ….
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Key elements of the assimilation system:
 Forecast model
 Observations
 Assimilation algorithm
J ( x)  ( x  xb)T B 1 ( x  xb) 
( y  H[ x])T R 1 ( y  H[ x])
 Super-computer
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The forecast model
Xt=0
Use of Satellite Data at ECMWF – Tony McNally
Xt=t
ECMWF
The forecast model
Physical and dynamical processes
updated every 10 minutes
91 vertical levels from the surface
to 0.01hPa (approx: 80Km)
Global T1279 spectral resolution
(16km grid point spacing)
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The forecast model
6,300,000,000,000,000 floating
point operations
a single 10 day
forecast
Global
T1279 spectral resolution
91 vertical levels from for
the surface
to 0.01hPa (approx: 80Km)
Use of Satellite Data at ECMWF – Tony McNally
(16km grid point spacing)
ECMWF
The Observations
Yobs
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ECMWF
Operational Global Observing Network
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Operational Global Observing Network
~ 60,000,000 observations
used every 12 hours
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The Algorithm
4D-Var
(four dimensional variational analysis)
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The 4D-Var Algorithm Jb
1
J ( x)  ( x  xb) B ( x  xb) 
T
1
( y  H[ x]) R ( y  H[ x])
T
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The 4D-Var Algorithm Jo
1
J ( x)  ( x  xb) B ( x  xb) 
T
1
( y  H[ x]) R ( y  H[ x])
T
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The Super-computer
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Super computer configuration
June 2010
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The assimilation of polar
satellite observations
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
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
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Example of a modern satellite
sounding instrument… IASI
8461 infra-red radiances measured
by the IASI instrument
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
What benefits do polar
satellite observations bring
to NWP ?
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Evolution of ECMWF NWP forecast skill
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Evolution of NWP forecast skill
1987*
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
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
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Forecast skill without polar satellites ?
NO POLAR
ECMWF OPS
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.
VERIFICATION
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
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 2012-10-25-00z Valid Time: 2012-10-30-00z
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
What challenges do polar
satellite observations
present ?
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
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…)
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The radiative transfer equation
measured by the
satellite
L( )  

0
Our description of the atmosphere
 d ( ) 
B( , T ( z ))
dz

 dz 
Planck source term* depending
on temperature of the atmosphere
The RT equation is part of
the 4DVar operator that
maps the model state X
vector into the observation
space Y
Surface
+ Surface + reflection/ + Cloud/rain + ...
emission
contribution
scattering
Other contributions to the
measured radiances
Absorption in the
atmosphere
1
J ( x)  ( x  xb) B ( x  xb) 
T
1
( y  H[ x]) R ( y  H[ x])
Use of Satellite Data at ECMWF – Tony McNally
T
ECMWF
Specific Science Challenges
1. Limited vertical resolution
2. Sensitivity to cloud and rain
3. Systematic error
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
1. Limited vertical resolution
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
1. Limited vertical resolution
L( )  

Pressure
Absorption
0
 d ( ) 
B( , T ( z ))
dz

 dz 
o1 2
Frequency
Transmission Weighting function
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
1. Limited vertical resolution
AMSUA 15 channels
IASI 8461 channels
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
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…)
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
2. Sensitivity to cloud and rain
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
2. Sensitivity to cloud and rain
measured by the
satellite
L( )  

0
Our description of the atmosphere
 d ( ) 
B( , T ( z ))
dz

 dz 
Surface
+ Surface + reflection/ + Cloud/rain + ...
emission
contribution
scattering
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!
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Weighting function non-linearity
dR/dT500 = 0
dR/dT500 = 1
full cloud at 500hPa
dR/dT* = 1
surface
Use of Satellite Data at ECMWF – Tony McNally
dR/dT* = 0
surface
ECMWF
Sensitive areas and cloud
cover
Location of
sensitive
regions
Summer-2001
(no clouds)
sensitivity surviving
high cloud cover
sensitivity surviving
low cloud cover
Use of Satellite Data at ECMWF – Tony McNally
monthly mean
high cloud cover
monthly mean
low cloud cover
ECMWF
From McNally (2002) QJRMS 128
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 …
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
3. Systematic error
(global influence)
Use of Satellite Data at ECMWF – Tony McNally
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3. Systematic error … data
Globally averaged bias correction estimates for MSU channel 2
Warm-target temperatures for
MSU on NOAA-14
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
3. Systematic error … model
Shifts in upper-stratospheric temperature reanalysis
Global mean temperature anomalies in the upper stratosphere
JRA-25
ERA-Interim
ERA-40
The transition from SSU Ch3 to
AMSU-A Ch14 is clearly visible in
global mean temperatures at 5hPa
and above
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
Use of Satellite Data at ECMWF – Tony McNally
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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
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
Summary
1.
Data assimilation lies at the centre of NWP,
climate re-analysis and environmental
monitoring
2.
Radiance observations from polar orbiting
satellites are the single most influential
component of the global observing system
3.
Great progress has been made, but
significant scientific challenges remain to
advance the use of these observations
Use of Satellite Data at ECMWF – Tony McNally
ECMWF
The 4D-Var Algorithm
Cost function:
J ( x)  ( x  xb)T B1 ( x  xb)  ( y  H[x])T R 1 ( y  H[x])
Solution:
Correction term
xa  xb  [HB]T [HBHT  R]1 ( y  Hxb)
Solution error covariance:
y  Hxb)
Sa =xa Bx- b  [HB]T [HBHT  R]1 (HB
Use of Satellite Data at ECMWF – Tony McNally
ECMWF