**Discussion forum for strategies of simulation data.**

Discusssion about representativeness error in November and presentation
by Ron Errico and other materials are posted at

<http://www.emc.ncep.noaa.gov/research/osse/NR/RepE/>

Summary of discussion forum

<http://www.emc.ncep.noaa.gov/research/osse/NR/RepE/RepE.Jun06-061116.doc>

Note by Ad Stoffelen and Gert-Jan

<http://www.emc.ncep.noaa.gov/research/osse/NR/RepE/Stoffelen.representation3.061116.pdf>

Presentation by Ron Errico

<http://www.emc.ncep.noaa.gov/research/osse/NR/RepE/Errico.reperror.061116.ppt>

Paper by Andrew Lorenc

<http://www.emc.ncep.noaa.gov/research/osse/NR/RepE/Lorenc.1992.TIDCCR4129.pdf>

References

<http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/references.html>

Presentation by Ron Errico about simulation of Radiance

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

Discussion forum is updated at

<http://www.emc.ncep.noaa.gov/research/JointOSSEs/forums/index.forums.html>

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

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>

**\[Some questions after meeting on Feb 22^nd^\]**

**Gert-Jan Marseille**

If T799 nature run is produced for every 1 hour no time interpolation
between archived fields is required and observations may be simulated at
1-hour resolution.\
Is this the strategy shared by the whole observation simulation group ?

**Jack Woollen**

We could adjust observational error so that we can use existing back
ground error covariance.

Michiko suggested that we should use same RTM for simulation and
assimilation to start and make sure all other problem such as cloud
problems are cleared.

In the meeting at NCEP with Jack, Yucheng, Yuanfu and Michiko discussed
that identical (fraternal) twin OSSE may be useful identify the real
problems. There are so many negative impact for very good data.

**\[Ad Stoffelen wrote on 3/6/07\]**

To those concerned,

Gert-Jan Marseille pointed me to some issues on spatial
representativeness in the document provided by Tom Schlatter.

1\) The issues of simulation of observations from the nature run and
assimilation of the simulated observations appear not clearly separated.
This is, why is x introduced in section 2.3? x_t is the nature run and
is needed to simulate the observations. x is another NWP model\'s
representation of x_t (x_t remains the reference state), but only
relevant at the stage of assimilation of simulated observations, which
stage is documented later on.

2\) Under the heading \"Application to OSSEs\" it reads \"After the
forward model is applied to the gridpoint values of the nature run, we
have an \'observation\' that contains representativeness error
(precisely as defined above) but no instrument error.\". Ealier it reads
H(x_t) = y_t + e_r which I would put rather as y_t = H(x_t)+ e_r . So,
\"after the forward model is applied to the gridpoint values of the
nature run, i.e. H(x_t), we have an \'observation\' y_t that still lacks
representativeness error e_r\". The representativeness error e_r is the
true atmospheric variance not present in x_t (since truncated) and
therefore lacking in the projection to y_t. After this variance has been
added as a random contribution to the forward model, an observation with
realistic variance appears.

In summary, both x_t and y_t are drawn from an assumed truth. Where x_t
and y_t collocate, the truncated x_t can be extended by assuming some
random local variance resolved by y_t but not by x_t. The same mechanism
may be applied when y_t is sensitive to variables not available in x_t:
the expected variance in y_t due to these variables should be added to
H(x).

3\) Grid cell volume or spectral truncation is explicitly mentioned as
the reference atmospheric cell size. In practise, as is documented in
our note on spatial representativeness, NWP models do not describe
atmospheric variance down to these scales realistically. The
determination of the truncated spatial variance spectrum in the nature
run for the relevant meteo variables should be well established for a
realistic simulation of representativeness errors.

We hope these points can be clarified.

Best regards,

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

***[Michiko Masutani 070424]{.underline}***

Initial Condition for OSSE

Jack has simulated conventional data and we are working on assimilating
data. One of the problems is to get appropriate initial condition.
Another one is constant fields.

1\) Start data assimilation from 12Z May 1st.

We agreed to start working on August 2005, however, making initial
condition for August is not a trivial matter. We could make initial
condition from NR but that will be a significant project itself.

For the nature run 12Z May 1st is analysis fields which can be compared
to any other analysis fields and real observation. Simulated observation
at 12Z May 1st can be verified against real observation to check the
simulation procedure. Simulated observation at 12Z May 1st should be
able to be assimilated with initial condition taken from operational
archive.

Jack is simulating conventional observation for May, June, July as well
as August 2005. So we can start data assimilation from 12Z May 1st to
reach August 2005. In August 2005 we can add other simulated data.

2\) SST, and ICE

Daily SST and ICE processed by NCEP is used for the Nature run. I need
confirmation from Erik. So in our data assimilation we can use NCEP SST
and ICE.

3\) Other constant fields

I wonder there are more constant fields required to simulate
observation. ECMWF agreed to provide any data required. There must be
some climatology fields used to generate nature run.

***[Yuanfu Xie 070524]{.underline}***

<http://www.emc.ncep.noaa.gov/research/osse/NR/May07/YXie.OSSErun.yy.ppt>

Yuanfu Xie summarized an experiment that assimilated perfect
\"conventional\" data\
extracted from the ECMWF T511 Nature Run into a low-resolution version
of the GFS\
using the GSI analysis package.  The simulation experiment ran for one
week beginning\
on 1 May 2005.  The analyses indicate that the simulated conventional
obs are behaving\
as expected in the assimialtion cycle. 

We notice wind fields are much closer to NR than temperature. Jack said
SATWIND is included in his data. This include SSMI wind and Quick scat
wind.

***[Jack Woollen and Daryl Kleist 070524]{.underline}***

**\[Temperature in prepbufr \]** (Jack and Daryl)

Jack and Daryl explained the historical reason for using virtual
temperature as input for the DA program. In prepbufr dry (sensible)
temperature is saved. However, GSI expects virtual temperature for RAOB
data, but the simulated data for OSSE will keep dry temperature as an
input to GSI. There is a flag TPC to indicate if the temperature is
virtual (TPC=0) or sensible (TPC=1).

The DA program was not handling dry temperature in RAOB correctly, Daryl
and Jack fixed the program and made it more flexible. The new
read_prepbufr.f90 is posted at

<http://www.emc.ncep.noaa.gov/research/JointOSSEs/NR/May07/read_prepbufr.f90>

***[Yoshiaki Sato 070525]{.underline}***

I think it needs to define what is the conventional data, especially for
AMV. AMV is very classic data and many people mention it is
\"conventional data\". Satellite retrieved wind data shares the
characteristics with the other satellite data: \# data distribution,
spatial error correlation, etc. Therefore, satellite data (i.e., SSMI
winds & Scatterometer winds) must not be included in conventional data.\
\
And this is my request: when we talk about MODIS data, please call it
\"MODIS-winds\".\
Because MODIS is sensor name and there are many products from the MODIS
sensor.

***[Michiko Masutani 070525]{.underline}***

In the meeting \"Conventional data\" was used for data simulated by Jack
which is used for the initial test. We should use another name like
\"prepbufr\" for ADPUPA ADPSFC SATWIND AIRCAR AIRCFT SFCSHP

Which data to be included in initial test is another issue. The
presentation by Yuanfu shows there is some unbalance between temperature
data and wind data. SATWIND must be reprocessed using NR cloud.

***[\[ Ron Errico \]]{.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 070606]{.underline}***

My concern is about the simulation of the current observations, not
about future observations. We first need a control OSSE and that is what
we can validate. We all need the control.

**\[ Tom Schlatter \] 070607**

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

***[Ron Errico 070607]{.underline}***

The very first thing that we must do is talk with the people who know
how each of the sat radiance instruments is used in terms of data
selection criteria.  For example, if cloud problems are detected for
some sounding, are channels weighting about the detectd clouds still
used, or are all the channels discarded.  Knowing things like this an
help us focus.\
\
I would like to get a list of these criteria for each instrument before
the meeting so that I have time to think about the implications. At the
meeting it is important that we begin by reviewing why this may be
critical (We are still not in agreement on this, and that makes
discussion difficult.), what the questions are and how they can be
answered.  Also, a collection of straw man techniques should be
described. Also, a talk about the GSI obs selection criteria should be
presented.

***[Ron Errico 070608]{.underline}***

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.

***[Ron Errico 070608]{.underline}***

I looked at our validation of your high-resol OSSE with no sat
radiances, and the rms analysis increments match very well with the real
assim, a much better match than when you included sat rads.  So, I think
that is good reason to at least try to do better this time.  I still
have hopes that an acceptable improvement will be easy to impliment.

***[Presnetation by Ron Errico on Radiation and Cloud subgroup meeting
on 6/22/07]{.underline}***

<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}
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```
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.

***[\[ Comments from Ad Stoffellen \]]{.underline}* 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]{.underline}***

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 Masutani]{.underline}* 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 for T213 NR, he used cloud information. He estimated particle
sizes based on CLWchbl CIWchbl and CChbl and compute optical depth etc.
Probably he could generate AIRS data without using cloud information.
That will generate very optimistic data and may be a good start.

**\[Walter Wolf 070625\]\
**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

**\[Ron Errico 070627\]**

I am not sure what a \"cloud mask\" precisely is. The simulation of IR
radiances from the NR by simply throwing possibly cloudy profiles, based
on cloud liquid and ice values into a version of the CRTM may or may not
be a problem. It depends on the distribution (histogram of cloud
fractions), on the realism of details of the liquid and ice profiles, on
the behavior of the cloudy CRTM, and especially on the effects of thin
clouds. This to me seems (1) unnecessary since most cloudy profiles will
be discarded and random errors added to partial account for the effects
of undetected thin clouds; (2) cloud fraction may have to be treated as
I suggest in any case, depending on the character of the histogram; (3)
if the resulting geographical distribution and/or innovations are
significantly unrealistic, you must ask where the flexibility is to
retune the simulations. My worry, just as in the case of almost everyone
misunderstanding the origins of representativeness error, some people
are thinking simplistically that just because they have a tool (CRTM)
that can produce some degree of cloudy radiances, all you need do is
throw profiles in without thinking more fully about the fundamental
issues concerning the application.

**\[Michiko Masutani 070627\]**

This is the way I understand. Please correct me if any this is wrong.

NCEP (any data assimilation) receive radiance data with cloud mask but
CRTM has a simple could scheme. Current GSI do not use the radiance data
with cloud mask and treat all radiance as clear radiance. Someone in
CIMSS in Wisconsin put all these cloud mask but we have to find out how
cloud mask is generated.

I thought what Ron suggested was to simulate and assimilate clear sky
radiance only.

Haibing came up with his own cloud scheme when he simulated AIRS data.
Based on his radiance he set cloud mask to radience data. He knew some
algoriths for AIRS cloud mask, I suppose.

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.

**\[Michiko Masutani 070627\]**

Full OSSEs

I found there are serious confusion in definition of OSSEs. OSSEs using
real data and simulating future observation from analysis is widely
conducted. The OSSE using long nature run and simulating all observation
from NR should be called differently. I wold like to suggest using
\"full OSSEs\" which is used before in this discussion forum.

\[Erik Andersson 070628\]

Michiko,\
\
A few minutes ago I sent you a grib file at T511 containing the constant
surface fields that define the vegetation. There are four of them,
labelled param=27,28,29,30\
They are\
amount of low and high vegetation\
type of low and high vegetation\
\
Our model is a bit primitive in this regard. These fields are fix, and
don\'t even change with season. (It is planned that we will use monthly
vegetation climatology within the next year or so.)\
\
The surface type is same everywhere. The only variation is land/sea. No
surface soil type variations.\
\
The model longwave emissivity is defined as follows\
Window range is 800 to 1200 cm-1\
Outside this window emis=0.99\
Inside window region it is\
REMISD  = 0.93_JPRB (dry land)\
REMISL  = 0.96_JPRB (wet land)\
REMISN  = 0.98_JPRB (snow)\
REMISS  = 0.99_JPRB (sea)\
There is a linear increase in emis between dry and wet value over land
depending on the model\'s soil moisture (top soil level)\
\
There is no notion of a cloud emissivity. The radiation from clouds
depends on the cloud optical properties via the assumptions of the RRTM
radiation scheme. This cannot be reproduced from the archived
cloud-liquid water.\
\
Erik

\[Michiko 070628\]

Erik:\
Thank you very much.\
This is very important.  Without these data NESDIS would use whatever
data they have and most likely very different from what used for NR. 
For T213 NR we could not get good results from AIRS OSSEs.  I think lack
of these information was one of the reason.\
\
I was told according to the references ECMWF use A. Slingo scheme as
well as NCEP to estimate particle size and cloud emissivity.\
\
May I post this climatology data and these information to web site,\
[ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/NR2006/clim](ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/NR2006/clim )\
This web site is indexed out and only available to people who know how
to access.  When SIVO portal become available, it will be password
protected. Still these data will be available to large number of
people.\
\
Alternatively I can keep these data to only people who are involved in
simulation and announce people that information is available.\
\
Michiko

***[Erik 070629]{.underline}***

Michiko,\
\
Please go ahead and post these constant fields on the web site. No
problem.\
\
Erik

***[Michiko 070629]{.underline}***

We have received the file contains vegetation cover etc which require in
simulation of radiance data.  Erik also provided information about
emissivity.\
\
The file is posted at\
[[ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/NR2006/clim]{.underline}](ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/NR2006/clim)\
\
I think we have to use this vegetation tom simulate radiance data.
Otherwise we\
will have noise due to inconsistency. When we assimilate data we use our
data\
used for CTRM and our model.\
\
In ctl file generated by grib2ctl.pl depicted:\
\
ANORsfc  0 162,1,0  \*\* surface Angle of sub-gridscale orography
\[rad\]\
CVHsfc  0 28,1,0  \*\* surface High vegetation cover \[(0-1)\]\
CVLsfc  0 27,1,0  \*\* surface Low vegetation cover \[(0-1)\]\
ISORsfc  0 161,1,0  \*\* surface Anisotropy of sub-gridscale orography\
LSRHsfc  0 234,1,0  \*\* surface Logarithm of surface roughness length
for heat\
SDORsfc  0 160,1,0  \*\* surface Standard deviation of orography\
SLORsfc  0 163,1,0  \*\* surface Slope of sub-gridscale orography\
SRsfc  0 173,1,0  \*\* surface Surface roughness \[m\]\
TVHsfc  0 30,1,0  \*\* surface Type of high vegetation\
TVLsfc  0 29,1,0  \*\* surface Type of low vegetation\
\
\
Note from Erik Andersson 070628:\
\
The grib file at T511 containing the constant surface fields that define
the vegetation. There are four of them, labelled param=27,28,29,30\
They are\
\
amount of low and high vegetation\
type of low and high vegetation\
\
Our model is a bit primitive in this regard. These fields are fix, and\
don\'t even change with season. (It is planned that we will use monthly\
vegetation climatology within the next year or so.)\
\
The surface type is same everywhere. The only variation is land/sea. No\
surface soil type variations.\
\
The model longwave emissivity is defined as follows\
Window range is 800 to 1200 cm-1\
Outside this window emis=0.99\
Inside window region it is\
REMISD  = 0.93_JPRB (dry land)\
REMISL  = 0.96_JPRB (wet land)\
REMISN  = 0.98_JPRB (snow)\
REMISS  = 0.99_JPRB (sea)\
There is a linear increase in emis between dry and wet value over land\
depending on the model\'s soil moisture (top soil level)\
\
There is no notion of a cloud emissivity. The radiation from clouds\
depends on the cloud optical properties via the assumptions of the RRTM\
radiation scheme. This cannot be reproduced from the archived\
cloud-liquid water.\
\
Erik

***[Ron Errico 070629]{.underline}***

This simply adds another source of rep. error, with as yet unconsidered
consequences since the NR sim. and data assim system presumably use
diff. data sets. It is not necessary to use different datasets since for
these simulations there is no consistency issue that I recognize.\
\
Ron

***[Michiko 070629]{.underline}***

Ron:\
So do you think we can use same vegetation from ECMWF for assimilation.\
This may be better.  NCEP vegetation has seasonal cycle, I believe.  The
difference between ECMWF vegetation and NCEP vegetation may be too
much.\
Michiko

***[Ron 070629]{.underline}***

Michiko:  The answer to your question goes back to the issue of
representativeness error that i brought up in March.  Simply put, any
difference between how you simulate an obs from the nature run and how
the GSi simulates an obs from the NCEP background becomes a
representiativeness error in the OSSE.  You also generally add some
portion rep. error artifically using some random number generator. A big
difference between the 2 ways these rep errors are therbey introduced is
that:\
(1)  In the former way, you don\'t know the the resulting error
characteristics (biases, variances, correlations, \"flow dependence\"
etc.) until you do some study.  It is not necessary that you know these
statistics, since your concern should be the stats of the end result
(i.e., the innovation statistics), but you should be aware that these
errors are there, having a likely complex (in this case) but unknown
characterization.\
(2) in the latter way, generally you keep rather simple; e.g., use
random numbers from a Gaussian or similar distribution with generally 0
mean and some specified variance.  In this case, you know the error
characteristics more completely and it is fairly simple to retune the
parameters to better match real innovation variances.  A drawback is
that the error stats are likely oversimplified (e.g., no correlations).\
Since you also likely add a random error even if you use approach 1, you
still have a portion of the tuning capability this provides.\
\
In the present case, you can easily experiment a bit to see what rep.
error charactersitics are introduced by using the first approach. But
whoever does this investigation must first understand what he is doing
(e.g., what rep. error is, how you measure it, and which stats are
important).\
\
Ron

***[Michiko 070629]{.underline}***

I suggest using ECMWF vegetation for simulation and NCEP vegetation for
assimilation. We could use both same vegetation.\
\
Before I forward to all, may I have comments from experts.\
Michiko

***[Ron 070629]{.underline}***

The only show-stopper that I know at this point occurs whenever the
variance of the repr. error caused by using different simulation and
assimilation models or parameters (such as vegetation) creates
innovation variances that are larger than computed for the real assim.
In this case, it seems to me that the stats cannot be corrected by
simply tuning an additive random error. In other words, if a simulation
approach implicitly creates too much error variance before any random
error is explicitly added, you must first modify the simulation approach
to reduce the implicit error variance.  My guess is that this show
stopper will be uncommon.\
\
Ron

***[Michiko 070629 12:45pm]{.underline}***

We have to clarify how vegetation index is used in model and CRTM.  It
seems we need vegetation for simulating surface albedo  but not for
assimilation.  We need to simulate and assimilate surface albedo for
MODIS.\
When we assimilate surface albedo from MODIS, do we use vegetation?\
\
Michiko

***[Tong 070629]{.underline}***

Does anyone know if the Surface Type is already in the old surface data
or the new vegetation data?\
\
Thanks\
\
Tong

***[Quanhua 070629 2:17]{.underline}***

Hi,\
\
I would use this opportunity to explain the surface model in CRTM. For
infrared sensor, we use surface emissivity. The surface emissivity is
determined\
by user inputs: surface type, sensor info (central wavelength of the
sensor channel). Vegetation type (rather than vegetation index) is
used.\
\
For microwave sensor, the surface emissivity in CRTM is computed either
using a physical model or derived from the microwave window channels.\
For a vegetable land,  the physical model is applied for the channel
frequency \< 80 GHz. Vegetation fraction and soil moisture are needed
for the microwave\
land emissivity calculation.\
\
The surface albedo is normally used for visible channel and shortwave
radiation calculation. The vegetation index may be derived from\
the visible and near-IR channels. The operational version of the CRTM
doesn\'t include\
the visible part.\
\
Thanks,

***[Yuta- Hou 070629 3:54\
]{.underline}***Quanhua

In GFS model, there are two different sets of vegetation climatology
data currently parallel running on the operational model, due to legacy
of two separate developments. This discrepancy will be addressed in the
near future with an unified land surface model.\
\
One of the data set is used in the land model that was developed and
implemented a few years ago by Ken Mitchell and others.  The data set
contains a global climatological vegetation type distribution and
vegetation cover to compute surface heat transport, etc.  The vegetation
type classification was originally developed by Dorman and Sellers
(1989) that consists of 13 distinct surface vegetation types.\
\
The other one is used by radiation related calculations to obtain
surface albedo for shortwave radiation and surface emissivity for long
wave radiation.  This part of codes was developed by me started in early
90\'s.  The surface vegetation type, a fixed climatology also with 13
different classifications (Matthews, 1983; Briegleb, 1992), was used
implicitly (e.g. used in the off-line calculations to establish albedo
tables).  For model\'s surface emissivity in the IR spectrum, the same
vegetation type data is used but is further reduced into only eight
distinct types.  By the way, in the current operational model, surface
emissivity is still using a default value of one.\
\
Yu-Tai

***[Ronald Errico wrote:\
]{.underline}***

Are the forecast model vegetation data sets or emmisivities derived from
them also used for the CRTM calculations within GSI?  In general, the
radiation paramaterization used by the forecast model is not the same as
that used by the radiance assimilation algorithm (the CRTm in this
case). Since in the OSSE context it is only the latter that concerns us,
please do not refer to the former (unless in the NCEP case they are
actually germane for some reason) since otherwise it will likely confuse
our discussion. Generally radiation parameterizations in a forecast
model are designed to model (parameterize) radiation flux divergence,
since it is the radiative heating implied by that divergence that is
critical, whereas in the direct assimilation of radiances our concern is
with parameterizing channel transmittence through the atmosphere to
mimick what a particular instrument sees. I am not an expert in this by
no means, but it is just what I was told once. Let me know if I am
wrong.\
\
\
Ron

***[Yu-Tai 070702 10:03]{.underline}***

Ron,\
\
You are right that the forecast model radiation and CRTM used in GSI are
two different types of calculations.  In a forecast model, the radiation
 computes the total radiative energy over the entire solar and thermal
spectrum.  Thus, radiances have to be integrated over all angles of a
hemisphere and then be integrated over all spectral bands to get
upward/downward fluxes.  From there surface and top fluxes and
atmospheric heating rates (divergence of fluxes) are obtained for
forecast model usage.  On the other hand, in a forward radiance model,
such as CRTM, specific and and limited number of very narrow spectral
bands are needed to be considered in the computation.  Those bands are
usually directly related to the satellite sensors specifications.\
\
Unlike in a GCM\'s radiation, where two-way whole spectral computations
are required, the forward radiance model usually needs to focus only one
direction that correlated to satellite position, and few absorption
gases.  As a result, although the two types of radiations are started
from the very same radiative transfer equations, they are diverted
considerably in their computation methods as well as the consideration
of optical properties of absorbers (gases, clouds, aerosols, etc.) and
surface radiative characteristics (albedo, emissivity).\
\
Due to the large differences between the two, I was hesitate at the
beginning to be involved with you guys work.  The email I sent out
earlier was intended for information only to respond the inquires posted
by Michiko and others.  You may ignore my part if it is not any use for
your work, and i am sorry for any confusion it may cause to anybody.\
\
Yu-Tai

***[Michiko 070702 12:27]{.underline}***

In 892 of July 2006 BAMS article by John LeMarshall.  AVHRR vegetation
cover is listed as data used by GFS.  There are vegfrac and vegtype in
fixed file, which Yu-tai told me he does not use.   I suspect if ECMWF
does not use vegetation cover Erik sent us in operational forecasting,
but for they may used for nature run.  Then how can we make simulation
consistent?\
\
I need help in putting together intelligent questions to ask to ECMWF.\
\
\
Michiko

***[Ron Errico 070702 2:28pm]{.underline}***

Michiko: I first ask \"consistent with what?\"  The nature run does not
include sat obs as part of its run.  The sat obs are the only radiance
issue that concerns us for the OSSE at this time (maybe later we can
concern ouselves with the nature of model error (difference) in the NCEP
vs ECMWF NWP models).  The issue is exactly what I stated back in March
about representativeness error and what i have tried to explain several
times since then. Is it better to have a source of rep. error that is
implicitly drawn from a non-random distribution with rather complex
relationships that may be qualitatively but not quantitatively
charactertistic of the real observing system, or better to draw from an
over simplified random distribution tuned to the correct variance?\
I don\'t know the answer. It should be relatively easy to perform some
simple exps. to compare the two approaches before actually performing
the OSSE.

***[Michiko 070702 3:21]{.underline}***

Ron:\
What kind of two approach are you thinking about?\
I will put together all your E-mails and read though all.\
Michiko

***[Ron Errico 070707]{.underline}***

About the 2 methods of creating rep error:\
One is to add values drawn from a random number generator.\
The other is to use different forward models(interpolation and radiative
 transfer).  The first you can totally control but is more difficult to
design to be totally realistic.  The second is hard to adjust (since it
yields the error determined by an algorithm less adjustable) but may be
more realistic (e.g., it will create biases and correlations, although
it is unclear hw they may be related to those of the real errors).  Even
if you use the second method, you will still have to use the first to at
least simulate the instrument error.  If that addition does not
necessary to yield the correct 0-B stats, then the rep. error you
already have is not realistic.\
\
There is a way to do this testing off line. 

***[Michiko 070711]{.underline}***

That what I understand for vegetation. Please let me know how if I
understand correctly.

= = = = = = = =

At NCEP monthly climatology vegetation(cover and type) and surface type
are used for GSI, fcst model and Land model. Land model read from fixed
file and does horizontal and time interpolation to model grid save to
sfc file. GSI and forecast model uses what ever in sfc file.

There is a confusion in language. Radiation scheme in fcst model does
not use vegetation but uses surface emissivity based on vegetation and
surface type. Forecast model uses table based on Matthews scheme to get
emissivity and Land model uses other table based on MODIS data. CRTM has
model to evaluate surface emissivity. CRTM handle much narrower wave
band but it gives more detail and uses more than vegetation and surface
type.

Erik said sfc long wave emissivity is defined, in ECMWF model

Window range is 800 to 1200 cm-1

Outside this window emis=0.99

Inside window region it is

REMISD = 0.93_JPRB (dry land)

REMISL = 0.96_JPRB (wet land)

REMISN = 0.98_JPRB (snow)

REMISS = 0.99_JPRB (sea)

There is a linear increase in emis between dry and wet value over land

depending on the model\'s soil moisture (top soil level)

When we simulate radiation data we have to discuss if we use ECMWF
scheme or CRTM.

ECMWF uses annual mean vegetation and surface type. I think we should
use ECMWF surface to simulate data and use NCEP monthly climatology to
assimilate. Yutai told me model may not be so sensitive to monthly
varying vegetation and surface type.

**Summary if the meeting on august 20 and 23rd.**

**\[Summary of meetings and discussion\]**

***[- Talk by Chris Barnet and other information]{.underline}***

He talked about how AIRS data are simulated and how to estimate
observational errors.

There are products for estimated clear sky radiance data using
extrapolation. There is a effort to assimilate retrieval data in Univ
MD. There are nine versions of SARTA based on various stages of AIRS.
There were two major warm up episodes.

Comments from Jim Jung:

JCSDA has made up its mind not to use retrieval and clear sky radiance
from NESDIS. However, it may be worthwhile to look into these products
to evaluate the performance of GSI and CRTM.

Comments from Paul:

When we do simulations the data response function used for simulation
and coefficient used for assimilation must match. For example, the AIRS
response function has not been changed from version 5 and the matching
coefficient for CRTM is version 4. This mismatch will not be corrected
by bias correction.

***[-Calibration for OSSE with T213 NR]{.underline}***

Michiko went over various problems encountered in the OSSE with T213 NR.

1\. General agreement in data impact test and detailed differences.
Trouble in the tropics.

2\. Problems caused by constant SST.

3\. How to handle surface topography and surface data.

4\. Systematic error using obs-anl.

***[- Procedure of simulation of Radiance for existing
instruments-]{.underline}***

This proposed procedure is based on the summary from the meeting on 8/20
and follow up discussions. Initially, we would produce files for HIRS,
AMSU, AIRS, and GOES channels.

We will work on two streams, A and B, in parallel:

A. Development of codes and scripts from NR grib code to BUFR formatted
radiance data.

1\) Retrieve the information about radiance data from the archive.
(RADINFO)\
2) Generate soundings as a necessary first step, separate from any cloud
masking or error assignments. We would use the orbital data patterns
from BUFR dump files. (DB91L). DB91L can be saved either 6 hourly or
every 6 min depending on the amount of data.

3\) DB91L using RTN simulated radiance data to produce BUFR files
containing the simulate the clear sky radiances. (SIMRAD) First we will
use ad hoc sampling strategies. If TCC is less than a certain amount
(for example, 10%) simulate clear sky radiance.

B. Development of sampling strategies and study of error characteristics
presented by Ron Errico.

1\) How big are differences between radiances produced by different RT
algorithms for identical input profiles.  I have asked her to use 2
different RTMs that I think are options in the CRTM. This is to check
whether we can in fact use a different RTM to simulate radiances fro the
NR than used in the GSI-DAS.\
2) What is the distribution of relevant cloud parameters in the NR that
we hope to use to determine whether clouds are affecting radiances at
observation locations. She is starting by looking at the distribution of
cloud fractions for H,M and L clouds at a single time. What this
distribution looks like will inform us of our options for proceeding.\
\
\
**\[Proposed task for next few months\]**

Simulation of radiance for existing instruments:

Jack Woollen

He offered to work on A1-A2. DB91L for task A is in BUFR format and
contains only basic data.

Haibing and Tong Zhu will work on task A3.

Read DB91L and generate SIMRAD using adhoc sampling and error
assignment.

Haibing will work on AIRS AMSU and HIRS data and Tong Zhu will work on
GOES data.

Tong Zhu may continue to GOESR project by generating DB91L for GOESR
himself

Yuanfu and Michiko will provide help for Haibing.

Runhua , Ron Errico, and Emily will work on task B.

For task (B), Jack will generate DB91L for October 1, 2005 for T511NR
containing all model and surface variables in binary format, not BUFR.
GMAO work on one day data and select the list of variables. For the
selected variables DB91L will be generated for the first two or three
weeks of October 2005 from T511NR.