Methods for processing cloudy AMSU observations
Stephen English1 and Fuzhong Weng2
1 Met Office, UK
2 NOAA/NESDIS, USA
Introduction
Cloud detection and analysis is important when processing microwave radiances
• the cloud liquid water path (LWP) is important in its own right
• clouds modify the measured brightness temperatures from the AMSU-A temperature and AMSU-B humidity
sounding channels
• microwave window channels are sensitive to the vertically integrated LWP whereas the sensitivity of
sounding channels to cloud liquid water depends on the altitude of the cloud
Therefore it is important to compare different methods objectively and to examine how
effectively the cloud profile, in addition to LWP, can be analysed
This poster compares five different methods for evaluating the LWP and examines the
performance of one method which analyses the vertical cloud profile
Five
techniques for liquid water cloud detection andData
analysis
using AMSU
Name Brief description
inputs

Weng1 = NESDIS day one method (Weng and Grody)
AMSU channels 1 & 2

AAPP
AMSU channels 1, 2 and 3

Weng2 = NESDIS day two method (Weng)
AMSU channels 1, 2 and SST and 10m wind fields from NWP*

1D-var1 = Separate cloud and water vapour control variables
AMSU channels 1, 2, 3, 15 and SST, 10m wind, water vapour
and temperature profiles from NWP (Met Office)

1D-var2 = Single moisture control variable
AMSU channels 1-20 and SST, 10m wind, water vapour and
temperature profiles from NWP (Met Office)
= Likelihood methods (AAPP, English)
* Note operational NESDIS product uses data from NCEP whereas this study used data from the Met Office for consistency with the 1D-vars. For
references and further information on the methods tested see the references list at the end of the poster.
Comparison
differences between the five LWPs
Data from 15-21zof
10 systematic
March 2001
Northern Hemisphere 20N-90N
The Weng2 and 1Dvar1
methods agree with the
1Dvar2 method to withn
0.05 kg m-2 at all LWP
values in the northern
hemisphere, but give
slightly lower LWP in
the southern
hemnisphere and
tropics
Southern Hemisphere 20S-90S
The AAPP and
Weng1 methods both
tend to substantially
underestimate the
LWP with respect to
1Dvar in all regions
when the LWP is
above 0.25 kg m-2
Weng2 agrees
better than
Weng1 with 1Dvar2 for LWP <
0.2kg m-2 in the
extra-tropics,
whereas Weng1
agrees most
closely in the
tropics.
Tropics 20S-20N
Comparison of cloud detection skill-scores
Explanation of skill-scores used
Definitions of clear and cloudy used to validate
The Hansen-Kuiper skill scores (HKS) is a measure of skill which is independent of the observation
frequency distribution, thus making ir a more robust measure than hit-rate or false alarm rate. The
HKS is defined as:
Clear: HIRS channel 8 Obs-FG > -4 K and AMSU Channel 4 Obs - FG < 1 K
Hansen-Kuiper score = H-F
Definition of clear and cloudy observations
where H=Hit rate and F=false alarm rate.
Clear: LWP < 70 gm-2
H = Number where LWP > 70 gm-2 and validation dataset = cloudy divided by total number where
validation dataset=cloudy
Cloudy: LWP > 70 gm-2
Cloudy: HIRS channel 8 Obs-FG < -4 K and AMSU channel 4 Obs-FG > 1 K
F = Number where LWP > 70 gm-2 and validation dataset = clear divided by total number where
validation dataset=clear
Northern Hemisphere HKS
Tropical HKS
0.9
0.80.8
0.8
0.70.7
0.7
0.60.6
0.6
Hansen-Kuipers score
Hansen-Kuipers score
Hansen-Kuipers score
0.6
0.60.7
0.0
8
9
10
0
12
Weng1
0.3
0.20.2
0.10.1
11 2 32 4 35 64 7 58 9 610 12
7
Month of the year
Month of year
0.4
0.3
0.20.2
0.10.1
0.5
0.4
0.30.3
0.20.2
AAPP
0.50.6
0.40.4
0.30.3
0
0.70.8
0.50.5
0.40.4
0.0
0.80.9
0.7
0.50.5
Southern Hemisphere HKS
1
0.8
Weng2
0.10.1
1 2 3 4 5 6 7 8 9 10 12
Month of year
Month of the year
1
The Weng2 method is clearly more skillful
than Weng1 or AAPP. The Weng2 method
does, however, show no advantage over
Weng1 in the tropics. The two 1D-vars show a
similar level of skill to the Weng2 method.
The Weng2 method also shows less monthto-month variation in skill, especially in the
northern hemisphere (note each month is
represented by data from the 15th day of each
month)
2
3
4
5
6
7
0.0
8
9
10
0
12
1
1
2 23 43 5 46 7 5 8 69 10712
Month of theMonth
year of year
0 .9
0 .8
0 .7
0 .6
0 .5
0 .4
0 .3
0 .2
0 .1
0
8
9
10
12
AAPP
Weng1
Weng2
1 Dvar1
1 Dvar2
Global
Extra tropics
Tropics
Data from 15-21z 10 March 2001
Impact of vertical cloud profile on AMSU channels 4 and 5
Five cloud scenarios
600
800
1000
1
2 Multi-level cloud 3 High cloud 4 Middle level cloud 5 Low cloud
Normalised sensivitity
400
1 Deep cloud
Normalised sensitivity with respect to cloud 5
Note: All five clouds are assumed to have the same LWP and constant LWC
with height
0.5
Cloud
Cloud
Cloud
Cloud
Cloud
0
-0.5
-1
-1.5
Ch. 4
Ch. 5
The impact of cloud on window channels, or a very low level sounding channel like AMSU channel 4 which peaks at 950 hPa, is not very
sensitive to cloud altitude or profile structure, whereas a mid-tropospheric channel like AMSU channel 5 is very sensitive to cloud altitude.
Note this example is illustrative, it does not imply all deep cloud has little impact on AMSU channel 5!
Is there real cloud profile information in the 1D-var analysis?
Cloud top using separate water
vapour and cloud control
variables in 1Dvar R=0.1
Cloud top using total
water as 1Dvar control
variable R=0.6
The figures above compare cloud top derived from the highest level in the retrieval to have liquid water present with cloud top inferred from
HIRS channel-8, which is not used in the retrieval. It can be seen that there is a significant correlation when total water is used as the control
variable. This suggests that the vertical positioning of the cloud has real skill.
1
2
3
4
5
Impact of cloud analysis on Observation - First Guess (Ob-FG) and Observation - analysis (Ob-An)
Channel 2 Ob-FG and Ob-An
The analysis of cloud significantly improves the
fit of the analysis to the observations, especially
when the total water control variable is used.
However by including or excluding temperature
in the minimisation we can see that the
temperature information still dominates, except
at very high LWP for AMSU channels 4-6. These
results show that the total water approach can
work effectively.
Channel 4: With temperature
analysis switched on, Obs-Analysis
St. Dev. = 0.25 K for all cloud
conditions
Channels 5 and 6 with temperature
analysis on
Channel 4: With temperature analysis
switched off, Obs-Analysis St. Dev. =
0.45 K for all cloud conditions
Channels 5 and 6 with temperature
analysis off
However the plots also show that significantly
more cases fail to converge in the 1D-var when
total water is analysed. It is also found that 1Dvar2 takes, on average, 20% more iterations than
1D-var1.
Problems with the total water control variable
The two 1Dvars are very similar to
each other but differences do occur
(see cloud-free area indicated by the
arrows) and the total water
sometimes gives unrealistic cloud
which is not seen in HIRS channel 8
observation minus first guess
differences
Cloud LWP in g m-2
from 1D-var2 (total
water control
variable)
Cloud LWP in g m-2
from 1D-var1
(separate cloud and
water vapour
control variables)
HIRS channel 8
Observation minus
first-guess
difference
Conclusions
The Weng2 algorithm outperforms Weng1 and AAPP, and matches the performance of
a 1D-var system for cloud LWP
Total water can work very well as a control variable but on rare occasions gives
unrealistic cloud LWP. Note problems also remain with the speed of convergence and
20% of cases fail to converge
Getting the cloud profile right is very important for successful assimilation of cloudy
AMSU data, an important issue for AMSU channels 4-6 and AMSU-B
References and further information
AAPP: www.metoffice.com/research/interproj/nwpsaf/atovs/index.html
Weng algorithm: orbit-net.nesdis.noaa.gov/arad2/MSPPS/
1Dvar: www.metoffice.com/sec5/NWP/NWPSAF/ssmi/
Acknowledgements
Godelieve Deblonde for the total water 1Dvar
code
Contact: stephen.english@metoffice.com Tel: +44 1344 854652
Met Office, London Road, Bracknell, Berkshire, RG12 2SZ