Validation of Microwave
Moisture Retrievals Over Land
Presented by :
Matthew J. Nielsen
Cooperative Institute for Research
in the Atmosphere
Research Scope
Attempt to estimate water vapor over
land
 Created C1DOE retrieval (used AMSU
data)
 Examined analytical Jacobian
 Validated retrieval with radiosondes and
GPS

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
C1DOE retrieval
Uses Optimal Estimation to produce
layer T, skin T, emissivity, TPW, and
layer q
 Layer information calculated at 100,
200, 300, 500, 700, 850, and 1000 mb.
 Emissivities calculated at 23, 31, 50, 89,
and 150 GHz

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Cost function

The cost function used in the C1DOE is given
by:
  x  xa  S a1 x  xa    y  F xˆ  S y1  y  F xˆ 
T
T
The first term is a penalty for deviating from
the first guess (first guess and a priori are
equivalent in this retrieval). This limits the
outcome to only physical solutions.
 The second term is a penalty for deviations of
the simulated radiances from the forward
model output. This is a way to constrain the
forward model and observational errors.

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
First guess data
AGRMET: surface temperature first
guess from three hour average data
 MEM: emissivity first guess at all five
frequencies
 Radiosondes: temperature and moisture
profile first guess

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Data flow
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
AMSU
Data came from the Advanced
Microwave Sounding Unit (AMSU)
 20 channel microwave radiometer
 Ch. 1-15 used for temperature
 (AMSU-A)
 Ch. 16-20 used for water vapor
(AMSU-B)

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
AMSU-B Channelization
Ch. #
Center freq. of
channel (GHz)
No. of
pass
bands
Bandwidth
per passband
(MHz)
NEΔT
Polarization
angle
16
17
18
19
20
89.0
150.0
183.31±1.00
183.31±3.00
183.31±7.00
2
2
2
2
2
1000
1000
500
1000
2000
0.37
0.84
1.06
0.70
0.60
90-θ
90-θ
90-θ
90-θ
90-θ
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
AMSU-B Antenna Pattern
Correction
AMSU-B mainbeam only receives ~95%
of total power
 5% comes from Earth, cold space, and
satellite
 Sidelobe contamination can cause bias
up to 3 or 4 K in retrieved brightness
temperatures (corresponds to values up
to 4x the NET)

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
AMSU APC (cont.)
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Analytical Jacobian
Defined as a derivative of the forward model
with respect to the state vector parameters
 Important because it provides information on
sensitivity of forward model to changes in
state vector
 Shows performance of each channel, along
with denoting which channels have signal and
which do not
 Good for channelization and retrieval setup

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Retrieval configuration
Retrieval was run with highly accurate first
guess in order to detect bias
 Data was from September 21-September 30,
2003
 Radiosonde match-up dataset created (50 km
and two-hour window) with 555 data points
 GPS match-up dataset created (30 km and 30
min window) with 26 data points

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Validation
GPS calculations of TPW considered
highly accurate (within 1mm)
 TPW calculated from a “tropospheric
wet delay”
 Ground receivers are sent signals from
satellites to calculate delay

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Conclusions
Antenna pattern correction fixed a consistent
~3 K bias from observed Tb’s
 Jacobian illustrated where retrieval did well
and where it provided little information. Also
highlighted the channels that were best
suited to retrieve water vapor
 Retrieval bias detection showed issues near
coastlines due to poor first guess and ocean
contamination
 GPS validation yet to be satisfactory due to
dataset constraints and coastline issues

CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation
Future work

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Cloud liquid and ice module to be added
Need improved emissivity first guess Add soil
moisture module
Explore better covariance matrix options
Provide water vapor and temperature first
guess from GDAS (better spatial coverage
and able to be performed in real time)
Validate TPW with increased # of GPS
stations
Will be used in CloudSat project
CSU/CIRA Matthew Nielsen
Cooperative Research Program 2nd Annual Science Symposium/Satellite Calibration & Validation