**Could forum**

**070625**

For T213 Nature run we were not satisfied the low level stratocumulus
cloud of the nature run and came up with adjustment method.

The work is posted as NCEP office note

<http://wwwt.ncep.noaa.gov/officenotes/NOAA-NPM-NCEPON-0006/01408B72.pdf>

Linked from http://www.emc.ncep.noaa.gov

It seems the cloud in the new nature run looks a lot better but we have
to use cloud information very carefully. Cloud may have to be treated
differently in simulation of DWL, radiance, and CMV.

We invited many cloud experts to the discussion forum for cloud in OSSE.

People in this E-mail list agreed or did not complain to receive bulk
mail on this forum. You can join the discussion using \"Reply to All\".
If you send message to me I will forward to the newest E-mail list.

Michiko

**\[On 2/21/07 Ad Stoffelen wrote\]**

I note your forthcoming discussion on simulating clouds for DWLs and
wonder how this relates to earlier work. For simulating ADM-Aeolus DWL
data in LIPAS we used ECMWF cloud properties and the ECMWF cloud overlap
model to simulate the probability that a laser shot hits a cloud at any
level. The simulated cloud hit probabilities are verified to be 1similar
to space lidar cloud hit rates. See the LIPAS publication in the QJRMS.
The approach may be used for other DWLs and sounders as well with some
care. We would use it again in our forthcoming DWL simulations. We would
be happy to be informed further and contribute to the discussion on this
topic.

Best regards,

Ad

**\[Dave Emmitt responded 2/21/07\]**

Steve and I have just finished looking at 7 days of the 1 x 1 degree
Nature Run. The average total cloud coverage (using ECMWF\'s overlap
functions) appears to be \~ 70%. This is less than the \~75 -80% we see
with GLAS based upon 1.25 km line integrations. We expect that the
difference is primarily in the cirrus\...particularly the Tropics. More
on that later. Last month we simulated GLAS observations using the T213
nature run (with adjusted clouds) and found results that would produce
significant understatements of coherent shots getting into the boundary
layer and overstatements of cloud free integration intervals for the
direct detection. We are getting ready (all without funding of course)
to simulate CALIPSO in the New Nature Run. Since we are using the 1 x 1
degree set, we will avoid any conclusions until we repeat the experiment
with the full resolution NR.

Lars Peter, Oreste Reales, Joe Terry, Steve Greco and I had a meeting at
GSFC on February 1 and began making plans for assessing the new Nature
Run Clouds. For the lidars we must feel comfortable with the subgrid
scale representation of penetrability. The fact that AIRS found only 4%
cloud free scenes is also noteworthy. I am focusing upon the Tropics so
if anyone wants to focus upon the extratropics and the poles, please let
me (us) know. It\'s a lot of work.

**\[Steve Greco produced the initial summary 2/22/07\]**

Partial summary of cloud statistics\* from 7 days (June 1 -- 7) of NR (1
X 1)

  ------------ ----------- ------------ ----------- ---------- -----------
  Region       Low         Middle       High        Total      Cloud Free

  Global       40          31           33          78         4

  Tropics      60          12           38          52         6

  NML          37          28           36          64         7

  SML          53          39           33          79         1
  ------------ ----------- ------------ ----------- ---------- -----------

\* Using ECMWF satellite view overlap algorithm

A diagram for distribution of cloud cover is posted at

http://www.emc.ncep.noaa.gov/research/osse/NR/Feb07/SWA_T511_cloud.ppt

**\[Michiko posted Cloud data 2/21/07\]**

Ken Campana has RTN cloud reported every hour at 1 degx1deg (64bits
real) for HCC, MCC, LCC, TCC, BCC. He has CLAVRX data reported every 6
hours at 0.5 deg x 0.5deg in grib code for TCC, HCC, MCC, LCC.

Posted at

<ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/cloud/RTN>

size 150MB per month

Jun 05 is missing.

CLAVRX data for 6 hourly 0.5 degx0.5deg grib code

TCC,HCC,MCC,LCC

Posted at

<ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/cloud/CLAVRX>

***[\[Andrew Heidinger 070411\]]{.underline}***

I am sorry but I am at an NPOESS meeting on April 12.  Thanks for
including me in this group and I am happy to participate in future
discussions.\
\
In addition to the data that Ken Campana hosts, we also have hdf data
and images available at
[[http://cimss.ssec.wisc.edu/clavr]{.underline}](http://cimss.ssec.wisc.edu/clavr)\
\
We are also happy to provide reprocessed results as well for any
specific time since 1982.  Lastly, the GEWEX cloud climatology
assessment workshop report is being finished and that report contains
analysis of several cloud climatologies that might be of interest.  I
can provide that report if there is interest.  It provides a nice sense
of the relative differences and similarities between the different data
sets.\
\
thanks,\
\
Andy Heidinger

***[\[ Ron Errico \]]{.underline}*** 6/4/07

Regarding validation of clouds in the nature run.\
\
One of the issues regarding clouds in the nature run concerns how
cloud-affected, satellite observation locations are to be specified.
Since we must settle on this before we can create such observations and
proceed with a control (current obs suite) assimilation, it is important
that we appropriately examine the model NR clouds.\
\
In the previous NCEP OSSE using an earlier T213L31 ECMWF model for the
NR, the locations of cloud cleared radiances were defined as the
locations of cloud cleared radiances obtained for a corresponding real
assimilation. So, some (likely many) cloud cleared radiances were
simulated at locations that were actually cloud covered in the NR.
Similarly, cloud track winds were simulated in regions where the NR was
cloud free. In this way, the observation locations were very easy to
specify and the numbers of cloud cleared and QC accepted observations
were identical in the OSSE and corresponding real analysis.  Also, the
clouds in this old NR were presumably much less realistic than those
produced now.\
\
The above technique may introduce unrealism if the impacts of
observations in cloud free and cloudy regions are significantly
different.  For example, clouds tend to occur in dynamically active
regions where \"things are happening\" and where magnitudes of model
errors (e.g., due to imperfect modeling of diabatic heating/cooling
associated with precipitation or turbulence) are likely large.  If in
real assimilation systems some instruments observe these active and
error-prone regions more poorly, then it may be important that the OSSE
observations simulate the same selectivity.  The questions then are:\
\
(1) For each observation type, what characteristics of the clouds are
important for defining whether a simulated observation should be
specified as being useful at each location?\
\
(2) How can the critical characteristics determined for (1) be specified
from the cloud field data provided by the NR?\
\
(3) Are these characteristics sufficiently well simulated by the NR that
we can specify the locations of useful (e.g., cloud-cleared)
observations using the NR data and, for each data type, obtain both
distributions and counts of usable observations that are realistic.\
\
For the purpose of deciding how (i.e., where) to simulate satellite
observations of wind or radiances it is neither sufficient nor necessary
to validate monthly mean cloud fields produced in the NR (although such
statistics may be relevant for other interests). Instead, the specific
application must be considered, principally by first answering (1) and
(2).  At this stage, although (1) should be answered fairly completely,
the answers to (2) likely must be simplistic.  Our goal should be to be
significantly more realistic than in the previous OSSE, although we may
need to be satisfied with much less sophisticated techniques than we
could develop over a much longer time period.  Perhaps the earlier
technique for defining locations will prove best (I hope not!), but we
should not abandon serching for a better technique too quickly.\
\
Who has the experitse and commitment to perform this investigation?\
With whom will they be consulting?

***[\[Michiko\]]{.underline}*** 6/5/07

Please find attached comments from Ron Errico regarding NR cloud. Please
note T799 NR is not a cloud resolving model, yet. Global cloud resolving
model is yet to come. We should discuss what we can do with NR we have.
Then later on we will discuss requirement for the next NR possibly a
global cloud resolving model.

***Dave Parsons 6/5/07***

Ron raises a good point about how using the model cloud fields in the
OSSEs due to the potential displacement of both sensitive areas and
cloud fields between the atmospheric measurements and the model. Some
comments on points (1) -- (3).

1.  The important characteristic of the cloud field for lidar sampling
    from space would be its optical depth.

2.  First one would need to convert the model mixing ratios, particle
    types etc to optical depth or cloud extinction. Taking the model
    hydrometer fields and determining whether you can get good lidar
    data at and below this level should be fairly straight forward in
    optically thick clouds and clear air. You will need to assume some
    threshold that would reject these thick clouds and allow high
    quality data in those thin cirrus and other optically thin cases.
    What you really need is a lidar sampling model and some assumptions
    about the pulse energy, frequency, pulse repetition rate and other
    lidar characterisitics (e.g., direct detection or heterodyne). I
    think that these issues will impact the quality and resolution of
    the data as a function of height even in clear air. Lidar instrument
    designers spend a lot of time on such models to determine the
    interplay between lidar design and the resulting data quality,
    vertical and horizontal resolution and vertical coverage. These
    lidar models are often publishable work in themselves. One example
    of the detail necessary is the Marseille and Stoffelen QJR 2003
    article. Much of what you are asking about is in the cloud
    extinction equations 5 and 6 in that article.

3.  I am not the best person to answer that question, but the Marseille
    and Stoffelen paper goes part way to addressing that issue.

On the question of who and how this would be done, I would try to push
you towards an established lidar model such as was used for ADM-AEOLUS
or the lidar work at NASA and NOAA. If you are interested in working
within NOAA, Mike Hardesty would be a good start. Lars in on the email
list so he would be a better person to give you the NASA contacts.

**\[ David Emmitt\] 6/5/07**

Appreciate the discussion prior to the meeting on Thursday. We will post

our ppt tomorrow via Michiko. Our main concern, from the lidar

simulation perspective, is to insure that the model clouds have

reasonable global statistics and are properly collocated with the

atmospheric dynamics. The clouds offer both wind observation

opportunities (lidars and CMVs) as well as obscuration of the total

column. A second concern is that the simulations of passive imagers and

sounders are affected by the same clouds affecting the active optical

sensors.

In the meantime, for the question regarding simulation of space-based

lidar observations from Nature Runs I refer you to the following web

site:
[[http://www.swa.com/laser/index.html]{.underline}](http://www.swa.com/laser/index.html).
As the main page states:

Since 1983, NASA, NOAA, U.S. DoD, CNRS, Lockheed, General Electric, and

Northrop Grumman have funded the development and application of the DLSM

(Doppler Lidar Simulation Model) for space-based and airborne Doppler

lidar wind measuring systems. The DLSM has been used extensively with

the T106, T213 and the NASA FVM Nature Runs.

A list of papers and presentations regarding the DLSM and OSSEs is

available.

Dave

G. D.

**\[ Tom Schlatter \] 6/5/07**

I think Ron\'s comments are right on the mark about how we

should treat clouds, I think his questions 1 thru 3 are the right
questions

to ask..

**\[Erik Andersson\] 6/6/07**

The discussion is about simulation of the yield and accuracy of lidar
doppler-wind-lidar data. Dave Parsons suggested the lidar model of
Marseille and Stoffelen QJR 2003 as a reference.\
\
In the paper by Tan and Andersson 2003 QJR we used the Marseille and
Stoffelen lidar model and ECMWF clouds to assess the yield and accuracy
of future ADM-Aeolus winds. We showed expected accuracy of the profile
wind data in different regions of the world and globally, as a function
of cloud conditions (assuming climatological aerosol profile).\
\
This may or may not be satisfactory for the OSSE simulations. At least
it is an example of what can be done, and what is involved.\
\
The full reference is:\
\
Tan D.G.H. and E. Andersson, 2005: Simulation of the yield and accuracy
of wind profile measurements from the Atmospheric Dynamics Mission
(ADM-Aeolus). QJRMS, 131, 1737-1757.\
\
Also available from
[[www.ecmwf.int/publications/library]{.underline}](http://www.ecmwf.int/publications/library) 
as Technical Memorandum 431.\
\
Regards, Erik

<http://www.ecmwf.int/publications/library/ecpublications/_pdf/tm/401-500/tm431.pdf>

**\[Ad Stoffelen\]**6/7/07

Gert-Jan and me will not be in the opportunity to join you this
afternoon (morning for you), but I could provide some more information
on our cloud model in LIPAS. If similar cloud modelling will be
attempted for other observation types I am happy to review the
suggestions and provide constructive comments. Some first thoughts
below.

In LIPAS we treat NR cloud cover and cloud ice water as statistical
parameters valid for \~100-km boxes in the ECMWF model. Realism of ECMWF
statistical clouds has been studied in several ways:

1\) By ECMWF in \'94

2\) By you

3\) By Beljaars et al comparing with LITE accumulated equivalent
statistical parameters.

4\) By comparing AVHRR and MeteoSat clouds with ECMWF clouds (I can dig
up a reference if you want).

Conclusion is that clouds appear in places coherent with the dynamics
and in about the right amount generally. Some problem areas are noted:
in some tropical regions, over subtropical ocean (PBL). Perhaps in
arctic regions, but here satellite data are not reliable to distinguish
low cloud from snow. We noted these problems, but did not consider them
limiting for a DWL OSSE.

Given mean cloud cover and ice content on all vertical layers in a 50-km
Aeolus observation, we use cloud cover at each layer as a probability of
cloud and draw a cloud profile for each laser shot. We take into account
the assumption of maximum cloud overlap, i.e., clouds on a next lower
level first occur below clouds on a higher level and, second, randonly
in other places. We do not assume any horizontal structure, but this
really not necessary, i.e., we can in principle rearrange the individual
shot profiles into any horizontal structure without changing the
simulated information concent in a 50-km observation. LIPAS statistics
of cloud obstruction on different levels, cloud opacity and ground
visibility compare statistically well with results from LITE and more
recently CALYPSO. Dave Emmit was the first to statistically evaluate
atmospheric lidar returns in cloudy atmospheres.

In particular cases clouds do generally compare less well, since the
correlation radius of a cloud is generally only a few 100m. In the OSSE
we are not concerned with this, but mainly with the statistical
representation and realistic co-occurence of clouds and dynamical
structures. In this light Gert-Jan and me investigated the observability
of strong wind shear with LIPAS. We found no tendency with the ocurrence
of clouds or not, in line with a series of real case studies by Lorenc,
Graham et al in \'92. Such issue could now be further studied by CALYPSO
data collocated with NWP (if you believe this is important).

If a similar method needs to be applied for (subgrid) radiance
measurements, which I recommend and appears feasible to me in principle,
I note the following

\- cloud cover depends strongly on whether you look straight up or just
over the horizon, with straight up the least cloud. In communication
this has been studied extensively. Depending on general cloud cover, the
effective increase in cloud cover for ADM-Aeolus (38 degrees incidence
angle) is up to 10-15% if I recall well (we neglected this). However,
for medium cloud cover, this goes up really fast for higher incidences
(as used by radiometers). Such issue could now be studied by CALYPSO
data (along track), and perhaps, more practically, by analysing angle
dependency of cloud masking for radiometers. I\'m not sure the latter
has been done?

\- irradiance is not isotropic in a broken cloud atmosphere, certainly
not if sun lit. I\'m not sure about bias or other error effects or how
this should be estimated from existing (ir)radiance measurements?

There are undoubtedly aspects I do not mention above that may need
further consideration. There will be a compromise between available
resources and sophistication of simulation, so I recommend a careful
planning here.

Comments or elaborations on the above are most welcome. I hope you will
have a fruitful teleconference.

Best regards,

Ad

**\[ Ronald Errico \]\
**My points are about the current suite of satellite radiance
simulations.  My concern is first that we get a good control OSSE.  I
was not thinking of future instruments at all.

**\[Erik Andersson \]**

OK, I fully agree with your comments. The spin-off discussion became
rather lidar-specific\...\
The radiance data should (also) be simulated with respect to the NR
clouds. The OSSE must not pour clear-sky radiances into the cyclone
centres. That would exaggerate their apparent impact.\
\
Erik

**\[Ron Errico\] 070608**

These multiple-people email \"discussions\" are a problem.  For example,
earlier in the week i wrote my short piece explaining the cloud
validation required for the nature run. Then all these people whom i did
not know were even part of our group added items about DWL. Next I get
an email from Erik saying something about my item, interpreting it as
regarding DWL, which it was not.  So, I sent him a note telling him my
concern about the current IR instruments, and then he understood. So, it
is easy for things to get confused.

**\[ Tom Schlatter \] 6/7/07**

In recent years sophisticated sensors indicate that there may be more
thin cirrus  clouds (some of them hardly perceptible to the eye) at high
altitudes than has  previously been supposed.  It is probable that our
best global models do not  predict these thin, high clouds.

When we simulate various observing systems, it is appropriate to ask
whether the measurements they take are sensitive to the presence of thin
cirrus.  For observing systems supplying data to current assimilation
and prediction systems,  we can compare the observed and background
values, the latter arising from the application  of a forward model.  If
these two values match well, and if the model doesn\'t predict the  high
thin clouds nor does the observing system sense them, then we can be
 confident that the forward model is adequate.   In other words, there
is no problem.  We should confirm with observing system specialists when
this is the case.\
\
On the other hand, if the obs-minus-background differences are
alarmingly large,  we suspect either that the observing system is
sensing something outside the \"purview\"  of the assimilating model or
that the forward model is faulty.  One or the other has to  be fixed
before this type of observation can be effectively assimilated.\
\
In dealing with a FUTURE observing system, lab experiments and
instrument  engineers need to tell us whether the system will be
sensitive to thin cirrus clouds.  If a Doppler Wind Lidar IS sensitive
to thin cirrus (in the sense that it will not be  able to extract
line-of-sight winds from as deep in the atmosphere as would be  possible
if there were no high clouds), then we need to alter the forward model
 accordingly.  I agree with Dave Emmitt that we should not alter the
nature run  output but rather should perform a post-processing step that
infers thin high clouds\
from the information already present in the nature run.\
\
Tom

**\[ Presnetation by Ron Errico on Radiation and Cloud subgroup meeting
on 6/22/07\]**

<http://www.emc.ncep.noaa.gov/research/osse/NR/Jun07/RADIATION/Errico_cloudtalk_fnl_070622.ppt>

Excerpt from presentation

Out line

1.  Lessons from previous OSSEs

2.  What are the important issues?

3.  Simulation of cloud cleared locations

4.  Simulation of errors remaining after QC

**Decide what characteristics of observations and their errors**

**are critical to duplicate (and by how close an approximation)**

The above decision depends on:

1.  How well the OSSE validation test criteria are to be satisfied

2.  How much development time is to be invested

**My suggestion:**

Let's not aim for perfection for the new control simulations

(i.e., the ones using current operational observation data types),

but let's strive to do significantly better than for previous OSSEs

Decide what characteristics of observations and their errors

are critical to duplicate (and by how close an approximation)

**Validation of simulated locations**

Ideally, the simulation of locations is validated, if global maps of
simulated observation locations for individual observing periods are
indistinguishable from random maps of real observation locations for
equivalent periods.

**Cloud Related Nature Run Fields**

> 2-D:
>
> Low cloud fractional coverage
>
> Medium cloud fractional coverage
>
> High cloud fractional coverage
>
> Total cloud cover
>
> Convective precipitation
>
> Stratiform precipitation
>
> 3-D
>
> Cloud liquid water content
>
> Cloud ice water content
>
> Cloud Cover

Our modest goal need not be to simulate the radiances from cloudy
regions, but more simply to get the geographical distribution and
selected innovation statistics "realistic."

This is simpler because most details regarding the clouds are
irrelevant.

Algorithm for determining cloud-cleared observation locations

For each grid box where a satellite observation is given, use the cloud
fraction to specify probability that it is a clear spot. Then use random
number to specify whether pixel is clear. Use a functional relationship
between probability and cloud fraction that we can tune to get a
reasonable distribution.

Sample functions for probability P=P(cloud fraction)

![](media/image1.png){width="4.125in" height="3.3in"}

Strawman procedure for simulating observations

1\. For each observation location ......

2\. Bilinearly interpolate cloud fractions from NR grid to location

3\. Compute P (f, tuning parameter)

4\. Select random number x from uniform distribution (0,1)

5\. If x\>P then consider cloud free for this height of cloud, otherwise
cloud covered point

6\. If cloud free, then bilinearly interpolate q,T to location and
compute radiances for these unaffected channels

7\. If cloudy region, set radiance to very small value such that QC will
detect and discard.

8\. Repeat this process with various tuning parameters for P until the
observation count and distribution look reasonable. Then use this
parameter for all further experiments.

Information required from nature run:

T, q, Ps, Ts

Surface information that affects emmisivity

**Instrument Plus Representativeness Error**

1.  Since we have no real instrument, its errors must be entirely
    simulated.

```{=html}
<!-- -->
```
2.  If different radiative transfer models (more generally called
    "forward

models" or "observation operators") are used for simulation and

assimilation, then a portion of representativness error has already been
added.

3.  The assimilation uses an interpolation algorithm (another form of
    forward

model) for interpolating from fields defined at analysis grid points to
values

specified at observation locations. Similar models are also applied to
the nature

run for simulated observation locations. Since the assimilation and N.R.
grids

differ in resolution, there is another source for differing
relationships between

grid point values. For these reasons, another portion of
representativness error

has already been added when the simulated observations were created.

**Instrument Plus Representativeness Error**

1.  Obtain an estimate of the statistics of representativeness error

due to using 2 different RT models (biases and variances)

2.  Obtain an estimate of the statistics of representativeness error

due to differing grid resolutions and bilinear interpolation

3.  Generate errors to be added to each observation by drawing

random numbers from a N(R',0) distribution. Ignore biases

since any large bias will be removed by the assimilation anyway.

Use R'\<R, R being the error value specified in the DAS.

4.  Run the assimilation for a short time and note the variance of

the innovations. Re-adjust R' so that this variance better matches

the corresponding variance computed for the real assimilation.

5.  After 1 or 2 iterations of this, use the final R' in any further
    experiments.

Note: 1 and 2 are not strictly necessary for this purpose since they
only

help in defining an initial iterate for R'. For interpreting results,
however,

they should be very helpful. Otherwise a starting value for R' can be,
e.g.,

R'=2R/3.

**\[ Comments from Ad Stoffellen \] 070622**

Dear Ron,\
\
I read your presentation with interest and the procedure you line out is
pretty much what I would do. It is not entirely clear to me whether you
intend to evaluate cloud cover versus radiance acceptance fraction for a
real data assimilation experiment. This would provide a good proxy for
cloud cover version radiance cloud contamination probability. A second
point, when you realistically simulate \"cloudy\" radiances in case of a
random number exceeding some fraction threshold (is this your
suggestion?), are you sure that the data assimilation system will always
detect this? If it does not in some cases, you will systematically
influence the simulated radiance and create a bias. (This effect is
realistic by the way.) So, I suggest this point to be avoided, clarified
or checked in the experiments.\
\
Cheers,\
\
Ad

**\[Jack Woollen \] 070625**

Ron,\
\
First, thanks for your presentation on locating NR clouds for simulating
radiance data. I hope we can get something going along those lines.\
Questions I have. Your procedure will work for AMSU, IASI, GOES
sounder/imager as well as HIRS/AIRS data?\
Did Mitch say he will develop lookup tables for cloud impact on various
representative wavebands? That would be nice to have.\
\
The paper is very good I think. I have just one small comment.\
\
1) On page three, the statement that representative error is potentially
been added \"twice\" because we add random errors as well as using high
resolution NR background,\
is somewhat misleading. It would be clearer to say that although some
representative error is introduced in the simulated observations due to
interpolation from the NR,\
additional errors must be added to the interpolated values to accurately
reflect the total error levels (instrument plus representative) found in
actual observations.\
\
Jack

**\[Michiko 070625\]**

For real data each radiance data is marked with cloud mask. So we can
produce function P(f) between probability of cloud clear (P) and total
cloud cover (f). Since there are more than one estimate of f, we will
get more than one function P. P(f) must be created for each channel.

CRTM has facility to handle six types of cloud however, when CRTM is
called from GSI, information about clouds are not used. Only
temperature, moisture, pressure, ozone seems to be used. Ron is
suggesting use CRTM without cloud liquid water content (CLWchbl) , cloud
ice water content (CIWchbl), cloud cover (CChbl). When Haibing simulated
AIRS data, he used cloud information. Probably he could generate AIRS
data without using cloud information. That will generate very optimistic
data and may be a good start.

**Cloud Mask**

NCEP receive radiance data with cloud mask. It is not clear who is
putting and algorithm for cloud mask.

**Cloud property in CRTM**

( From Mark Liu)

CRTM uses 6 type of cloud.

ice, liquid, snow, hail, rain, graupel

Cloud effective particle sizes is assigned based on cloud type if not
provided.

Typical value are, 10μm for liquid cloud, 25-30μm for ice cloud, 100μm
for rain if liquid water content is larger than certain value. NCEP
global model provide only ice and liquid cloud, cloud size has to be
assigned using only two types. However, if more types are provided cloud
size will be assigned in more detail.

Cloud size will be used to determine cloud optical property, such as
optical depth, phase coefficient, and single scattering albedo using Mie
table..

Haibing used algorithm developped by A. Slingo

**\[Walter Wolf 070625\]\
**I am not sure who puts the cloud mask with HIRS, MSU and AMSU.  The
cloud mask that goes with AIRS is from the MODIS near real-time
processing system.  The developers of this cloud mask is CIMSS.\
\
Take it easy,\
\
Walter

**\[Michiko Masutani 070626\]**

There are a few cloud schemes in radiation and CRTM and development of
cloud scheme is one of the major subject in GSI. There seems to be
various cloud clear and QC algorithm. Simulating radiance without cloud
using Ron Errico strategies will be a good start.

Steve Lord suggested that simulating and assimilating using different
cloud scheme to assess simulation cloudy radiance will be one of the
interesting subject for OSSE.