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<tbody>
<tr class="odd">
<td colspan="3"><img src="media/image1.jpeg"
style="width:0.675in;height:0.58264in" /></td>
<td colspan="11"><p><strong><u>PROPOSAL COVER PAGE</u></strong></p>
<p>(Date Nov 16, 2004)</p></td>
<td colspan="5"><p>NRA: NNH04ZYS008N</p>
<p>Proposal Number:</p>
<p><u>MAP/04-0000-0110</u></p>
<p>(Above assigned by SYS-EYFUS)</p></td>
</tr>
<tr class="even">
<td colspan="8"><strong>Name of Submitting Institution:</strong></td>
<td colspan="4">Bay Area Environmental Research Institute</td>
<td colspan="6"><strong>Congressional District:</strong></td>
<td>12</td>
</tr>
<tr class="odd">
<td><strong>Proposal Title:</strong></td>
<td colspan="18"><p>An Innovative Data Assimilation Technique for
Assimilating</p>
<p>Satellite Radiances Into a Global Aerosol Model</p></td>
</tr>
<tr class="even">
<td colspan="19"><p><strong><u>Certification of Compliance with
Applicable Executive Orders and US Code</u></strong></p>
<p>By submitting the proposal identified in this <em>Cover
Sheet/Proposal Summary</em> in response to this Research Announcement,
the Authorizing Official of the proposing institution (or the individual
proposer if there is no proposing institution) as identified below:</p>
<ul>
<li><p>certifies that the statements made in this proposal are true and
complete to the best of his/her knowledge;</p></li>
<li><p>agrees to accept the obligations to comply with NASA award terms
and conditions if an award is made as a result of this proposal;
and</p></li>
<li><p>confirms compliance with all provisions, rules, and stipulations
set forth in the two Certifications contained in this NRA [namely,
(i<em>) Assurance of Compliance with the NASA Regulations Pursuant to
Nondiscrimination in Federally Assisted Programs</em>, <em>and (ii)
Certifications, Disclosures, And Assurances Regarding Lobbying and
Debarment &amp; Suspension</em>]<em>.</em></p></li>
</ul>
<p>Willful provision of false information in this proposal and/or its
supporting documents, or in reports required under an ensuing award, is
a criminal offense (U.S. Code, Title 18, Section 1001).</p>
<p><strong><u>NASA PROCEDURE FOR HANDLING PROPOSALS</u></strong></p>
<p>This proposal shall be used and disclosed for evaluation purposes
only, and a copy of this Government notice shall be applied to any
reproduction or abstract thereof. Any authorized restrictive notices
that the submitter places on this proposal shall also be strictly
complied with. Disclosure of this proposal for any reason outside the
Government evaluation purposes shall be made only to the extent
authorized by the Government.</p></td>
</tr>
<tr class="odd">
<td colspan="4"></td>
<td colspan="5"><strong>Principal Investigator Name:</strong></td>
<td colspan="6">Robert Bergstrom</td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="4"></td>
<td colspan="5"><strong>Organization:</strong></td>
<td colspan="6">Bay Area Environmental Research Institute</td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="4"></td>
<td colspan="5"><strong>Mailing Address:</strong></td>
<td colspan="6">560 Third St. West</td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="4"></td>
<td colspan="5"><strong>City, State Zip:</strong></td>
<td colspan="6">Sonoma CA 95476</td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="4"></td>
<td colspan="5"><strong>Telephone Number:</strong></td>
<td colspan="6">(707) 938-9387</td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="4"></td>
<td colspan="5"><strong>Fax Number:</strong></td>
<td colspan="6">(707) 938-3984</td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="4"></td>
<td colspan="5"><strong>Email Address:</strong></td>
<td colspan="6"><a
href="mailto:Philip.B.Russell@nasa.gov">bergstrom@baeri.org</a></td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="4"></td>
<td colspan="5"><strong>Principal Investigator Signature:</strong></td>
<td colspan="6"></td>
<td colspan="4"></td>
</tr>
<tr class="odd">
<td colspan="4"></td>
<td colspan="5"></td>
<td colspan="6"><strong>Date:</strong></td>
<td colspan="4"></td>
</tr>
<tr class="even">
<td colspan="5"><p><strong>Authorized Institutional</strong></p>
<p><strong>Official Name:</strong></p></td>
<td colspan="8"><p>Marion Williams</p>
<p>Executive Director</p></td>
<td colspan="6"><p>R. Stephen Hipskind,</p>
<p>Acting Division Chief</p></td>
</tr>
<tr class="odd">
<td colspan="5"><strong>Organization:</strong></td>
<td colspan="8">Bay Area Environmental Research Institute</td>
<td colspan="6">NASA Ames Research Center</td>
</tr>
<tr class="even">
<td colspan="5"><strong>Department</strong></td>
<td colspan="8"></td>
<td colspan="6">Earth Science Division</td>
</tr>
<tr class="odd">
<td colspan="5"><strong>Mailing Address</strong></td>
<td colspan="8">560 Third St. West</td>
<td colspan="6">MS 245-5</td>
</tr>
<tr class="even">
<td colspan="5"><strong>City, State Zip:</strong></td>
<td colspan="8">Sonoma CA 95476</td>
<td colspan="6">Moffett Field, CA, 94035-1000</td>
</tr>
<tr class="odd">
<td colspan="5"><strong>Telephone Number:</strong></td>
<td colspan="8">(707) 938-9387</td>
<td colspan="6">(650) 604-5076</td>
</tr>
<tr class="even">
<td colspan="5"><strong>Fax Number:</strong></td>
<td colspan="8">(707) 938-3984</td>
<td colspan="6">(650) 604-3625</td>
</tr>
<tr class="odd">
<td colspan="5"><strong>Email Address:</strong></td>
<td colspan="8"><a
href="mailto:Steve.Hipskind@nasa.gov">williams@baeri.org</a></td>
<td colspan="6"><a
href="mailto:Steve.Hipskind@nasa.gov">Steve.Hipskind@nasa.gov</a></td>
</tr>
<tr class="even">
<td colspan="5"></td>
<td colspan="8"></td>
<td colspan="6"></td>
</tr>
<tr class="odd">
<td colspan="5"><strong>Authorized Institutional Official
Signature:</strong></td>
<td colspan="8"><strong>Date:</strong></td>
<td colspan="6"><strong>Date:</strong></td>
</tr>
<tr class="even">
<td colspan="19"><strong>Co-Investigators/Collaborators:</strong></td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Name</strong></td>
<td colspan="4"><strong>Telephone</strong></td>
<td colspan="5"><strong>Email</strong></td>
<td colspan="5"><strong>Institution</strong></td>
<td colspan="3"><strong>Address</strong></td>
</tr>
<tr class="even">
<td colspan="2">Howard Houben Co-I</td>
<td colspan="4">(650) 604-3381</td>
<td colspan="5"><u>houben@humbabe@arc.nasa.gov</u></td>
<td colspan="5">BAERI</td>
<td colspan="3">Moffett Field, CA 94035</td>
</tr>
<tr class="odd">
<td colspan="2">Bob Chatfield Co-I</td>
<td colspan="4">(650) 604-3386</td>
<td colspan="5"><u>jlivingston@mail.arc.nasa.gov</u></td>
<td colspan="5">NASA Ames</td>
<td colspan="3">Moffett Field, CA 94035</td>
</tr>
<tr class="even">
<td colspan="2">Phil Russell Co-I</td>
<td colspan="4">(805) 658-2637</td>
<td colspan="5"><a
href="mailto:jredemann@mail.arc.nasa.gov">jredemann@mail.arc.nasa.gov</a></td>
<td colspan="5">NASA Ames</td>
<td colspan="3">Moffett Field, CA 94035</td>
</tr>
<tr class="odd">
<td colspan="2">Hong Guan Co-I</td>
<td colspan="4">(650) 604-3903</td>
<td colspan="5"><a
href="mailto:guan@clio.arc.nasa.gov">guan@clio.arc.nasa.gov</a></td>
<td colspan="5">BAERI</td>
<td colspan="3">Moffett Field, CA 94035</td>
</tr>
<tr class="even">
<td colspan="2">Phil Rasch Col</td>
<td colspan="4">(303) 497-1368</td>
<td colspan="5"><a href="mailto:pjr@ucar.edu">pjr@ucar.edu</a></td>
<td colspan="5">Nat’l. Center for Atmos. Res.</td>
<td colspan="3">Boulder, CO 80305</td>
</tr>
<tr class="odd">
<td colspan="2">Sundar Christopher Col</td>
<td colspan="4">(256) 961-7872</td>
<td colspan="5"><a
href="mailto:sundar@nsstc.uah.edu">sundar@nsstc.uah.edu</a></td>
<td colspan="5">Univ. of AL, Huntsville</td>
<td colspan="3">Huntsville, AL 35806</td>
</tr>
<tr class="even">
<td colspan="2">Tami Bond Col</td>
<td colspan="4">(217) 244-5277</td>
<td colspan="5"><a href="mailto:yark@uiuc.edu">yark@uiuc.edu</a></td>
<td colspan="5">Univ. of IL, Urb.-Champ</td>
<td colspan="3">Urbana, IL 61801</td>
</tr>
<tr class="odd">
<td colspan="7"><strong>Budget:</strong></td>
<td colspan="3"><strong>BAER</strong></td>
<td colspan="7"><strong>NASA Ames</strong></td>
<td colspan="2"><strong>Total</strong></td>
</tr>
<tr class="even">
<td colspan="7">Year 1 (April 2005-March 2006):</td>
<td colspan="3"></td>
<td colspan="7"></td>
<td colspan="2"></td>
</tr>
<tr class="odd">
<td colspan="7">Year 2 (April 2006-March 2007):</td>
<td colspan="3"></td>
<td colspan="7"></td>
<td colspan="2"></td>
</tr>
<tr class="even">
<td colspan="7">Year 3 (April 2007-March 2008):</td>
<td colspan="3"></td>
<td colspan="7"></td>
<td colspan="2"></td>
</tr>
<tr class="odd">
<td colspan="7"><strong>Total:</strong></td>
<td colspan="3"></td>
<td colspan="7"></td>
<td colspan="2"></td>
</tr>
</tbody>
</table>

**Table of Contents**

COVER PAGES i-iii

Abstract/Executive Summary 1

1.  Background and Rationale 2

    1.  The NCAR MATCH/CAM aerosol model 3

    2.  Radiative Transfer Model 4

    3.  Obervations of Satellite Aerosols 4

    4.  Application of Data Assimilation to Aerosol Effects in Climate
        Modeling 7

2.  Detailed Description of Proposed Work 10

    1.  Run the ACE Asia case (April 2001) with the MATCH model 10

    2.  Adapt the radiative transfer code to match the GMS-5 sensor 13

    3.  Develop the data assimilation algorithms 15

    4.  Apply the assimilation scheme to the numerical simulation and\
        compare the results to other data 16

    5.  Assimilate data from other satellites 16

    6.  Other test cases -- SAFARI 2000, PRIDE, INDOEX, INTEX 17

3.  Summary of Planned Activities by Year 17

4.  References 18

5.  Management Plan 21

6.  Current Support 22

7.  Budget 23

8.  Vitae 24

9.  Letters from Collaborators 31

**[Abstract/Executive Summary]{.underline}**

We propose to apply an innovative data assimilation approach to
satellite radiance data for input into a global aerosol model. The goal
is to improve the assessment of aerosol effects on atmospheric radiation
and climate through more accurate evaluations of aerosol sources and
better constrained modeling of aerosol transport and evolution. Data
assimilation has been used by several previous investigators to input
satellite measurements of aerosol optical depth into aerosol numerical
models. We will instead directly assimilate radiance data, enabling us
to work simultaneously with many different satellites and other data
sets in a more consistent fashion. Major advances in numerical weather
prediction have followed the use of satellite radiance data (rather than
retrievals) to constrain dynamical fields. We can expect similar
advances in the understanding of aerosols and aerosol processes if this
approach is applied to aerosol modeling. The required data assimilation
calculations will also be more efficient when the primary assimilation
variable is the same as the primary observational variable. This allows
the application of four-dimensional variational techniques that combine
observations from different times to fully constrain the aerosol model
parameters, eliminating many a priori assumptions about the physical
properties of those aerosols. A similar approach has been used on a
routine basis to assimilate radiances from the Martian Global Surveyor
into a Martian General Circulation Model.

We have assembled an experienced research team that has expertise in all
aspects of the proposed research: numerical aerosol modeling,
atmospheric chemistry, radiative transfer and data assimilation. We
propose to use the NCAR Model for Atmospheric Transport and CHemistry
(MATCH) aerosol model as our aerosol numerical model. This model
contains complete aerosol simulation capabilities and routines for data
assimilation (of AOD) that we can adapt for our purposes. We will use a
previously developed radiative transfer model that has been used to
analyze moderate resolution (10 nm) radiometer data as our forward model
to predict the radiances from the aerosol concentrations given by the
aerosol model. Our initial test area and time frame will be the ACE Asia
region of April 2001. We propose to start by using the geostationary
GMS-5 satellite data as it has better time resolution than the polar
orbiting satellites. The East Asian aerosol is of great current interest
and has been the subject of a number of studies. The aerosol in this
region is extremely complex as it results from windblown dust, urban
pollution, biomass burning and sea salt. After establishing our
methodology we will use other polar-orbiting satellite data (MODIS,
SeaWiFS, TOMS) that contain more spectral information to improve our
simulations. We will attempt to focus on source areas to attempt to get
more information about the sources of the Asian aerosol. We will also
investigate other test experiments (SAFARI, PRIDE, INDOEX,INTEX) that
include examples of biomass burning aerosols, desert dust or urban
pollution.

We are proposing a three-year study. The tasks for the first year will
be to develop the specific numerical tools needed, acquire the satellite
data and sources and meteorological data for the aerosol model. The
second year will be to run the ACE Asia case with data assimilation and
compare to observational data. The third year will be to expand our
methods to other satellites and other test areas.

If this proposal is selected for funding, we will participate in the GMI
Science Team meetings and make our assimilation techniques available to
the community. This technique has application to other remote sensing
problems such as CO and other atmospheric gases.

1.  **[Background and Rationale]{.underline}**

Climate modeling plays a crucial role in the assessment of the potential
climate changes due to anthropogenic emissions. One of the most
fundamental and pressing issues in climate change research is the effect
of atmospheric aerosols. The Intergovernmental Panel on Climate Change
(*IPCC,* 1994 and 2001) has identified aerosol radiative forcing as a
major uncertainty in the global radiative energy budget.

The difficulty of predicting aerosol effects by climate models stems
from the variability (in both amount and properties) of the atmospheric
aerosol. Previous studies of the climatic effect of atmospheric aerosol
have restricted the aerosol properties to a fixed set of \"typical\"
values or aerosol models. This has limited the applicability of the
results.

The advent of satellite and surface data has created the opportunity of
using this data to improve estimates of the aerosol effects. A major
thrust of NASA\'s Earth Science Strategic Plan is the inclusion of
satellite data into climate models. Merging data with predictions from a
numerical model is characterized by the term \"data assimilation.\" In
recent years there is a large body of research that has been created by
developing and using techniques of data assimilation. For example, major
improvements in weather forecasting have resulted from the use of
satellite data into weather prediction models (Kalnay, 2003).

Several investigators have used data assimilation into their numerical
aerosol model (Collins et al 2002,2003; Filmore et al 2004; Yue et al
2003; Wang et al 2004). These investigators used derived aerosol optical
depths (AOD) from the satellite measurements. We propose to extend their
work by using satellite radiance values to assimilate into a global
aerosol model. We will use the NCAR MATCH model as the global aerosol
model, the visible channel of the geostationary satellite GMS-5 as the
initial satellite measurements and the ACE Asia region as the initial
test area. Once the numerical assimilation technique is established we
will expand the research to include other satellite data and other
areas.

We expect to gain in three ways from our direct assimilation of radiance
data. First, data from all satellites are treated in the same way and
are easily combined to increase the data's influence on the modeling
results. Second, the (transformed) Kalman filer that mediates between
model predictions and observations is readily computed when the
observational quantity is the same as the primary assimilation variable,
allowing for optimal model increments at little computational cost.
Third, this computational saving makes four-dimensional assimilation a
real possibility, allowing the data to constrain the model source terms
and aerosol evolutions.

This proposed work responds to the new Climate Global Modeling
Initiative part of the Modeling NRA.[^1] If successful this assimilation
technique will be made available to the scientific community,
particularly the GMI project scientists. We will participate in GMI
meetings to facilitate this technology transfer.

1.  **[The NCAR MATCH/CAM aerosol model]{.underline}**

The Model for Atmospheric Transport and CHemistry (MATCH) (Rasch et al.,
1997) is a chemical transport model forced with meteorological fields
from an external source. The aerosol species included in the model are
sulfate, soil dust, black carbon and organic carbon, and sea salt. The
model has been used in simulations of the aerosol during the INDOEX
program (Collins et al 2002) and the ACE Asia program. The NCAR
investigators have also used the model to input the aerosol optical
depth results from MODIS and AVHRR (Collins et al 2003, Filmore et al
2003).[^2]

As discussed by Collins et al (2002), the aerosol species in the current
MATCH aerosol model of sulfate, soil dust, black and organic carbon are
computed from prognostic equations including surface sources, wet and
dry deposition, and transport by resolved and subgrid-scale motion. The
vertical profile of sea salt is computed from the 10 m wind speed. The
dust is resolved in 4 size bins and each bin is represented by a
lognormal distribution. The other aerosols are represented by a single
mass-mixing ratio for each species. Currently the aerosols are assumed
to be externally mixed.

The formulation of the sulfur cycle is described in the works of Barth
et al. (2000) and Rasch et al. (2000). The emissions inventory for SO2
is from Smith et al. (2001). The sources for mineral dust are based upon
the approach of Tegen and Fung (1994) with modifications to the soil
mobilization to improve agreement with the TOMS absorptive-aerosol index
from Herman et al. (1997). The emissions of carbonaceous aerosols
include contributions from biomass burning (Liousse et al., 1997),
fossil fuel burning (Penner et al., 1993), and a source of natural
organic aerosols resulting from terpene emissions (Collins et al 2002).
The carbon emissions will be updated with the recent work of Bond et al
(2004). As discussed below it is likely that the satellite data will
yield information about the aerosol sources.

The model is integrated using meteorological fields from either the
NCAR/NCEP reanalysis or aviation data (Collins et al 2002). The data is
stored on a Gaussian T62 grid with 28 sigma levels at 6-hourly
intervals. At the equator, the horizontal resolution is 209 km. The
fields are temporally interpolated to each model time step.[^3]

2.  **[Radiative Transfer Model]{.underline}**

A radiative transfer calculation is needed to predict the radiance that
is seen by the satellite from the predicted aerosol concentration
fields.[^4] In previous studies (Bergstrom et al 2003, 2004a) Dr.
Bergstrom developed a numerical radiative transfer model specifically
for analysis of medium resolution (10 nm) solar radiometer data. We
propose to adapt this radiative transfer code to predict top of the
atmosphere radiances that would be seen by a number of satellite
instruments.

A description of the model is contained in Bergstrom et al (2003,
2004a). The major features of the model include the K-distribution
method for O~2~,O~3~,CO~2~, and H~2~O absorption coefficients, the
multiple scattering code DISORT, and a high resolution representation of
the solar spectrum. The inputs are the vertical profiles of the gases,
aerosol and clouds; as well as the spectral scattering and absorption
properties of the aerosols and clouds, the spectral surface reflectance
and the solar and azimuthal angles.

The K-distribution technique has been discussed a number of times and is
an effective method for treating the line absorption of atmospheric
gases. The significance of the K- distribution technique is that it also
allows for the accurate inclusion of scattering and absorption of gases
and aerosols at a saving of several orders of magnitude computational
effort compared to a line-by-line calculation.

The line-by-line code LBLRTM is be used to compute the gas absorption
coefficients at a high resolution (typically 0.001 cm-1). For each band
and temperature and pressure combination the coefficients are then
sorted in cumulative probability space. Sixteen Gaussian quadrature
points are typically used to compute a number of absorption
coefficients. The resulting values are interpolated (linear in
temperature and logarithmically in pressure). The H2O/CO2 and H2O/O2
overlap was included. This requires defining the ratio of the
overlapping gas amounts as an additional variable and then an
interpolation in that ratio. Since both the solar source function and
the gaseous absorption coefficients have a fine structure at a
resolution of 0.001 cm^-1^ to integrate over a spectral interval, the
pairing of absorption coefficient and solar source function must be
maintained. The same requirement holds for the instrument filter
function.

A major concern in building a radiative transfer code for a numerical
model is speed. Generally, radiative transfer codes take a significant
amount of time compared to the dynamics. This has led to a large number
of approximate techniques and codes that endeavor to compute the radiant
fluxes or radiances with a minimum amount of time. For example, the
Joint Center for Satellite Data Assimilation is funding an effort to
develop a Community Radiative Transfer Model (CRTM). This model will be
used to assimilate cloud radiances into numerical weather prediction
models. We will follow the development of this code to see if there are
techniques that will be applicable to our research.

3.  **[Satellite Observations of Aerosols]{.underline}**

Satellite sensors of aerosols \[Kaufman et al., 2002; Ramanathan et al.,
2002\] are essential to developing an accurate description of their
global distribution and variability. Originally, aerosol effects were
detected by satellite instruments (e.g., AVHRR, UARS and TOMS), which
were not explicitly designed for aerosol work.[^5] Spaceborne aerosol
sensors launched in the past few years---such as MODIS, MISR, POLDER and
OMI --- have made the study of tropospheric aerosols from space more
quantitative than was possible with their predecessors.

The methodology for determining the aerosol effects from a satellite
remote measurement is the solution of the radiative transfer equation.
The radiance observed by a satellite at wavelength λ and angles μ,φ can
be simply written as

I(μ~s~, μ~o~,φ~o~) = μ ~o~ I~s~\*R \[μ~s~, μ ~o~, φ~o~,ω, τ,
P(Θ),brdf(μ~1~, μ~2~)\] (1)

where I~s~ is the solar intensity, R is the reflectance function, brdf
is the bidirectional reflection function of the surface, ω is the albedo
for single scattering, τ is the optical depth and P(Θ) is the scattering
distribution function. The reflectance function is determined by solving
the radiative transfer equation.

The reflectance function depends upon the scattering and absorption
properties of the aerosols in the particular area viewed by the
satellite instrument. Given the variability of the atmospheric aerosol,
these absorption and scattering properties can not be known a priori. By
measuring only the satellite radiance the three unknowns (ω, τ, P(Θ))
can not be uniquely determined[^6]. Therefore investigators have been
forced to develop \"typical\" aerosol models to use for the values of
aerosol absorption (originally assumed to be zero) and scattering
distribution function (usually assumed to be that of spherical particles
with some size distribution). The reflectance function is then computed
and tabulated. For a given satellite radiance and set of angles the
closest value in the table is used estimate the aerosol optical depth.

With the advent of multi-spectral instruments (SeaWiFS, MODIS, MISR)
more information about the aerosols can be inferred. The TOMS group has
shown that their data can be used to detect the existence of absorbing
particles (Torres et al 1998). Both Higurashi and Nakajima (2003) and
Hsu et al (2004) have used SeaWiFS data to differentiate between four
different types of aerosols. The MODIS team routinely publishes
information about particle sizes. New advances such as the work of Hsu
et al (2004) that differentiates between absorbing dust particles from
different regions will undoubtedly occur as the scientific community
gets more experience with the satellite data.

We propose to work with the following satellite data sets:

  -------------------------- ------------------------- ----------------------------
  [Instrument]{.underline}   [Satellite]{.underline}   [solar
                                                       wavelengths]{.underline}
                                                       [^7]

  S-VISSR                    GMS-5                     500 - 800 nm; one band

  MODIS                      Terra, Aqua               420 - 940 nm; 15 bands

  SeaWiFS                    OrbView-2                 412 - 1050 nm; 9 bands

  TOMS(OMI)                  Nimbus-7                  312 - 360 nm; 6 bands

  AVHRR                      NOAA-7, -9, -11, -14      580 - 1100 nm; two bands

  MISR                       Terra                     446 - 830 nm; 4 bands
  -------------------------- ------------------------- ----------------------------

Since our initial test area is the ACE Asia region, we propose to use
the visible channel aboard the geo-stationary GMS-5 satellite following
Wang et al (2002). As discussed below, this data set, while having only
one visible wavelength channel with somewhat broad spectral sensitivity,
has the advantage of better spatial and temporal coverage than the polar
orbiting satellites.

We will use the radiances measured by the satellite sensor to assimilate
into the numerical model for two reasons. First, we want to avoid the
inconsistency of having two aerosol models - one for the satellite
retrieval and one for the aerosol numerical model (as discussed by
Collins 2002). Secondly, as set out below, there are computational
advantages to having the assimilated variable be the actual measurement.

We have already downloaded the GMS-5 data for April 2001. We will need
to determine the occurrence of clouds (Wang et al 2002) and can use the
infrared channels of GMS-5 to accomplish this.

Once we have the assimilation tools developed we will extend the
research to other satellites. The Ames Sunphotometer group has
considerable experience and expertise in working with satellite data.
Co-I Dr. Russell, who leads the group, brings to the proposed study this
familiarity with the satellite data and its relation to a variety of
measurements, in situ and remote, made within the atmosphere. Table 1
shows the large number of validation experiments and resulting
publications carried out by Dr. Russell\'s group.

**Table 1.** Validation of satellite derived spectral AOD published by
the Ames Sunphotomer Group

+------+----------+-----------+----------+-----------+---------------+
| Se   | Campaign | Region    | Surface  | Period    | Publication   |
| nsor |          |           |          |           |               |
+------+----------+-----------+----------+-----------+---------------+
| A    | TARFOX   | US East   | Ocean    | July 1996 | Veefkind et   |
| VHRR |          | Coast     |          |           | al., 1999     |
+------+----------+-----------+----------+-----------+---------------+
| A    | ACE 2    | Canary    | Ocean    | June/July | Durkee et     |
| VHRR |          | Islands   |          | 1997      | al., 2000     |
|      |          |           |          |           |               |
|      |          |           |          |           | Livingston et |
|      |          |           |          |           | al., 2000     |
|      |          |           |          |           |               |
|      |          |           |          |           | Schmid et     |
|      |          |           |          |           | al., 2000     |
+------+----------+-----------+----------+-----------+---------------+
| AT   | TARFOX   | US East   | Ocean    | July 1996 | Veefkind et   |
| SR-2 |          | Coast     |          |           | al., 1999     |
+------+----------+-----------+----------+-----------+---------------+
| AT   | SAFARI   | Namibian  | Ocean    | September | Schmid et     |
| SR-2 | 2000     | Coast     |          | 2000      | al., 2003b    |
+------+----------+-----------+----------+-----------+---------------+
| G    | ACE-Asia | Eastern   | Ocean    | April     | Wang et al,   |
| MS-5 |          | Asia      |          | 2001      | 2003b         |
+------+----------+-----------+----------+-----------+---------------+
| GO   | PRIDE    | Puerto    | Ocean    | June/July | Livingston et |
| ES-8 |          | Rico      |          | 2000      | al., 2003     |
|      |          |           |          |           |               |
|      |          |           |          |           | Wang et al.,  |
|      |          |           |          |           | 2003a         |
+------+----------+-----------+----------+-----------+---------------+
| MAS  | TARFOX   | US East   | Ocean    | July 1996 | Tanré et al.  |
|      |          | Coast     |          |           | 1999          |
+------+----------+-----------+----------+-----------+---------------+
| MISR | SAF      | Southern  | Ocean &  | September | Schmid et     |
|      | ARI-2000 | Africa    | Land     | 2000      | al., 2003b    |
+------+----------+-----------+----------+-----------+---------------+
| MISR | ACE-Asia | Eastern   | Ocean    | April     | Kahn et al.,  |
|      |          | Asia      |          | 2001      | 2004          |
+------+----------+-----------+----------+-----------+---------------+
| MISR | CLAMS    | US East   | Ocean    | July/Aug. | Redemann et   |
|      |          | Coast     |          | 2001      | al., 2004     |
+------+----------+-----------+----------+-----------+---------------+
| M    | PRIDE    | Puerto    | Ocean    | June/July | Livingston et |
| ODIS |          | Rico      |          | 2000      | al., 2003     |
|      |          |           |          |           |               |
|      |          |           |          |           | Levy et al.,  |
|      |          |           |          |           | 2003          |
+------+----------+-----------+----------+-----------+---------------+
| M    | SAF      | Southern  | Ocean &  | September | Schmid et     |
| ODIS | ARI-2000 | Africa    | Land     | 2000      | al., 2003b    |
+------+----------+-----------+----------+-----------+---------------+
| M    | CLAMS    | US East   | Ocean    | Ju        | Levy et al.,  |
| ODIS |          | Coast     |          | ly/August | 2004          |
|      |          |           |          | 2001      |               |
|      |          |           |          |           | Redemann et   |
|      |          |           |          |           | al., 2004     |
+------+----------+-----------+----------+-----------+---------------+
| M    | ACE-Asia | Eastern   | Ocean    | April     | Chu et al.,   |
| ODIS |          | Asia      |          | 2001      | 2004          |
+------+----------+-----------+----------+-----------+---------------+
| M    | EVE      | US CA     | Ocean    | April     | Redemann et   |
| ODIS |          | Coast     |          | 2004      | al., in prep. |
+------+----------+-----------+----------+-----------+---------------+
| POAM | SOLVE-2  | Arctic    | Ocean &  | Jan/Feb   | Russell et    |
|      |          |           | Land     | 2003      | al., in prep. |
+------+----------+-----------+----------+-----------+---------------+
| SAGE | SOLVE-2  | Arctic    | Ocean &  | Jan/Feb   | Russell et    |
| 3    |          |           | Land     | 2003      | al., in prep. |
+------+----------+-----------+----------+-----------+---------------+
| Sea  | ACE-Asia | Eastern   | Ocean    | April     | Hsu et al.,   |
| WiFS |          | Asia      |          | 2001      | 2002          |
+------+----------+-----------+----------+-----------+---------------+
| TOMS | PRIDE    | Puerto    | Ocean    | June/July | Livingston et |
|      |          | Rico      |          | 2000      | al., 2003     |
+------+----------+-----------+----------+-----------+---------------+
| TOMS | SAF      | Southern  | Land     | September | Schmid et     |
|      | ARI-2000 | Africa    |          | 2000      | al., 2003b    |
+------+----------+-----------+----------+-----------+---------------+

4.  **[Application of Data Assimilation to Aerosol Effects in Climate
    Modeling]{.underline}**

Data assimilation combines a forecast model and observations to produce
an analysis of the state of the atmosphere that is consistent with both
the governing equations of the model and the data (Ghil and
Malanotte-Rizzoli, 1991). This analysis can be thought of as a weighted
average of the model predicted state and the measured state (Talagrand,
1997). Determining the optimal average requires that both the model
forecast errors and the observational errors be known. The latter can
clearly be inferred from the statistical properties of the data
themselves. Model errors are another matter. While there are a number of
approaches to determining forecast errors for well-tested terrestrial
numerical weather prediction models (e.g., by making ensemble forecasts
\-- on the assumption that the range of variability in those forecasts
brackets the true variability in the atmosphere \-- or from
fore-knowledge of the intrinsic co-variability in the atmosphere,
usually associated with physical space assimilation (Daley and Barker,
2001; Kalnay, 2003), such methods are not appropriate for aerosol
retrievals about which we know much less. An alternative approach is to
use the formalism of the model forecast error covariance matrix to
impose additional constraints on an otherwise underdetermined problem
(e.g., to enforce a balance condition that controls gravity waves in a
dynamical assimilation or to predetermine the particle size distribution
in an aerosol retrieval (Cosme et al., 2004).

However, at least for research purposes, it is preferable to minimize
such a priori assumptions. We therefore adopt a data assimilation method
that has been developed for planetary science applications \-- where
there is minimal advance knowledge about the properties of the system
being measured. By choosing the primary assimilation variable to be the
observed quantity (i.e., satellite radiances), it is possible to
estimate both the instrumental and forecast errors from the data
themselves. This approach also leads to a major reduction in the
computational requirements of the data assimilation.

Data assimilation is usually divided into two possible approaches:
sequential or variational. Both solve the same optimization problem
(which is in fact the same problem solved by all data retrievals). The
differences arise from the relative importance of prior knowledge (the
background state) and the new data in determining the analysis solution.

Operationally, given:

-   a predictive model M (in this case the MATCH model which includes
    the physics of atmospheric aerosol transport and chemistry) which
    has computed a future state b with forecast error covariance matrix
    B;

-   an observation operator H (in our case, the k-distribution radiance
    forward model) that transforms from the model variables to
    observable quantities;

-   a set of observations y (here the GMS-5 satellite data), with
    observational error covariance matrix R;

we seek a new model state x (i.e., a three-dimensional distribution of
aerosol properties) that minimizes the cost function

J = 1/2 (x - b)^T^ B^-1^ (x - b) + 1/2 (HMx - y)^T^ R^-1^ (HMx - y). (2)

After some manipulation, it can be shown that the solution is

x = b + K (y - HMb), (3)

where the Kalman filter K is defined by

K = BM^T^H^T^ \[R + HMBM^T^H^T^\] ^-1^. (4)

Thus, the theory of data assimilation is straightforward, but as
discussed above it requires knowledge of the forecast error covariance
matrix B. Furthermore, this large matrix must be propagated in time by

B \<\-- MBM^T^,

to retain the optimality of the solution. This last calculation is
beyond the capabilities of numerical weather prediction centers to this
day. Therefore, they must estimate B by other means. While those methods
have shown some success in dynamic meteorology, we do not yet have
enough experience to be sure the same approaches will work for aerosol
assimilations.

For example, the current MATCH model performs a three-dimensional
assimilation of AOD (Collins et al 2002, 2003, Filmore et al 2004). The
observations are incorporated only at the time of observation, so M in
the above relations is the identity operator. H is a spatial
interpolator from the model grid to the point of observation. It is
assumed that separate observations by nadir-viewing satellites are
independent, so B is diagonal (and is computed based on assumed
correlation lengths).

In order to avoid these restrictive assumptions, we propose to use
observation space assimilation (Houben, 2003), whereby the principal
variable to be fit is not x, but HMx (i.e., the satellite radiances, in
the case of our global aerosol observations). The sequential
assimilation scheme then becomes

> HMx = (I - HMK) HMb + HMKy, (5)

where

> HMK = HMBM^T^H^T^ \[R + HMBM^T^H^T^\] ^-1^

and

I - HMK = R \[R + HMBM^T^H^T^\] ^-1^.

So the fitted variable (HMx) is clearly a weighted average of the
prediction (HMb) and the observation y. And the weighting factors are
estimated from the data themselves as (Courtier, 1997; Menard et al.,
2000)

R = \<(\<y\>-y)(\<y\>-y)^T^\> (6)

and

\[R + HMBM^T^H^T^\] = \<(y-HMb)(y-HMb)^T^\>. (7)

It is important to note that the resulting efficient estimate of
HMBM^T^H^T^or HBH^T^ that comes from this formulation is only available
when the primary assimilation variable is the same as the observed
variable.

If, in addition, adjoints of the radiative transfer model H and the
aerosol transport model M are available, we can use variational
techniques to invert HMx and derive x. This solution gives all the
advantages of four-dimensional variational assimilation in that
information measured at a given time can propagate forward and backward
in time to improve the knowledge of the state. Furthermore, data can be
accumulated over a long enough assimilation window to assure that the
solution x is overdetermined.

The adjoint of the radiative transfer model is straightforward. In fact,
adjoint methods can easily be used to solve atmospheric radiation
problems (Box et al., 1988). The adjointing of the aerosol transport and
chemistry model will be more complicated and constructing it will be an
important part of the effort under this proposal. However, even in the
absence of this code, the solution determines an analysis increment that
can be used to sequentially update the model state in much the same way
as is currently done.

Co-I Dr. Houben has used the proposed technique to assimilate Thermal
Emission Spectrometer data (infrared radiances) from the Mars Global
Surveyor spacecraft into a Martian general circulation model. More than
10,000 spectra per day are assimilated and used to determine
approximately 10,000 initial atmospheric state variables. The radiation
code is a two-stream model with multiple scattering for the emission
from the 15 micron band of CO~2~. When the model state is propagated
forward in time from these initial conditions using the general
circulation model, the observed data are reproduced with a high degree
of precision (See Figure 1). The assimilation is validated by comparing
model results with independent observations of temperature and surface
pressure (such as those derived by radio occultation measurements).

![](media/image2.png){width="6.417361111111111in"
height="3.498611111111111in"}

**Figure 1** (a) Root-mean-square residuals as a function of Thermal
Emission Spectrometer channel for the direct assimilation of some
1,000,000 infrared spectra over 80 Mars days, in units of
erg/cm^2^/s/sr/wavenumber. Average observed radiances are on the order
of 50 in these units. Results are improved somewhat (dashed curve) when
the global average surface pressure of the assimilated state is
increased by 16% over the precalculated model value (black curve). (b)
Biases (in the same units) are small (black curve) and can be further
diminished by rescaling the K-distribution coefficients used in the
radiative forward model with adjustments on the order of 1% (dashed
curve). This is equivalent to making a small adjustment in the channel
center wavenumbers, a reasonable recalibration of the instrument. (c)
Model surface pressure compared to independent radio occultation values
for two occultation seasons (approximately 1000 ingress and egress
occultations). Consistent with the results in (a), observed northern
hemisphere surface pressures (black line and points) exceed the ab
initio model values by 12%.

1.  **[Detailed description of Proposed Work]{.underline}**

We are proposing to develop the numerical tools and test our methodology
on the problem of understanding and predicting the aerosols in the East
Asian region. There are several reasons for selecting this region.
First, the aerosol has been the subject of a number of field programs
and our group has a good set of observational data (Bergstrom et al
2004a). Second, the area is of increasing interest since the aerosol is
thought to be impacting US air quality (Van Curren, 2003). Third the
aerosol is particularly difficult since it consists of intermixed layers
of dust, biomass burning and urban air pollution (Huebert et al 2003).

The Aerosol Characterization Experiment Asia (ACE Asia) intensive field
program was carried out during April-May 2001 (Huebert et al, 2003). Its
purpose was to investigate the outflow of aerosols from East Asia during
the spring months with particular emphasis on mineral dust outbreaks and
on air pollution transport. The ACE Asia intensive program consisted of
measurements made aboard two aircraft (the Twin Otter and C130), a NOAA
ship the Ron Brown, ground stations at Gosan and various locations in
Japan and in conjunction with several satellite sensors (SeaWiFS, MODIS,
MISER, AVHRR). The study was also coordinated with the APEX program and
the TRACE-P study.

The initial results of the study have been described in a number of
papers (JGR special section 2003) that discuss various aspects of the
aerosols measured during ACE Asia. (Huebert et al 2003.) The study
observed aerosols from pollution sources, biomass burning and mineral
dust from the inland Chinese deserts. Of particular interest were
several outbreaks of mineral dust (22 March, 6-11 April and 23 April).

One of the most important findings of ACE Asia is that the aerosols of
the coast of East Asia are often mixtures of both mineral dust and
pollution particles. This combination of particle types is highly
variable and makes attribution of the aerosol absorption, for example,
very difficult. Bergstrom et al (2004a) investigated the spectral
absorption properties of the ACE Asian aerosol and found that there was
a range of possible absorption due to dust and black carbon
combinations.

1.  **[Run the ACE Asia case (April 2001) with the MATCH
    model]{.underline}**

We have obtained the MATCH code and with the help of Phil Rasch and Dani
Bundy of NCAR and have it running on the NASA Ames Research Center
Advanced Supercomputing (NAS) computer. Our initial testing shows that
running it with NCEP aviation data at T62 takes about 30 min wall time
for every day of simulation. At present we are running an ACE Asia base
simulation with the NCEP Aviation meteorological inputs for the time
period of March 12 to May 9, 2001.

One of the unique computational capabilities of the Ames Research Center
is the new Columbia supercomputer. This machine is specifically designed
for input of satellite data into numerical models. While the current
computers that we are using are sufficient for the present, we will
request Columbia computer time at a later date.

The aerosol optical depth results from the MATCH simulation for April
12, 2001 with no assimilation are shown in Figures 2 and 3. April 12,
2001 is chosen since it has been extensively studied (Bergstrom 2004a)
and the aerosol plume contains both black carbon and mineral dust. The
optical depth results show the aerosol plume containing dust and urban
pollution extending fro the mainland northward over the Pacific Ocean.

![](media/image3.wmf){width="6.4222222222222225in"
height="6.376388888888889in"}

Figure 2. The aerosol optical depth at 550 nm for (a) total aerosol -
upper left; (b) dust - upper right; ( c) sulfate - lower left and (d)
sea salt - lower right.

![](media/image4.wmf){width="6.498611111111111in"
height="6.251388888888889in"}

Figure 3. The aerosol optical depth at 500 nm for (a) black carbon
hydrophylic - upper left; (b) organic carbon hydrophlic - upper right; (
c) black carbon hydrophobic - lower left and (d) organic carbon
hydrphobic - lower right.

These simulation results can be compared to the total aerosol optical
depth results for a previous MATCH run, Figure 4, on the NCAR computer
with AVHRR data assimilation (Bergstrom et al 2004b - these results were
supplied by Bill Collins and David Filmore of NCAR).

![](media/image5.png){width="6.491666666666666in"
height="3.5722222222222224in"}

Figure 4 Total aerosol optical depth at 550 nm for April 12, 2001 with
AVHRR aerosol optical depth product assimilated.

As seen the aerosol optical depth pattern is quite similar since the
only difference (aside from the color scheme) is the use of the AVHRR
assimilation in the earlier run. However the optical depth of the
unassimilated run is larger than the assimilated run. The assimilated
run appears to be in better agreement with the in situ data (Bergstrom,
2004a).

2.  **[Adapt the radiative transfer code to match the GMS-5
    sensor]{.underline}**

The spectral response of the GMS-5 visible sensor is shown in Figure 5.
We will tailor the bands of the radiative transfer model to match the
GMS-5 instrument response. We will break up the spectral region into
several bands to correctly account for H2O and CO2 in the region.

We will also modify the code to calculate radiances instead of fluxes.
This entails an expansion in a Fourier series for the azimuthal angle
(Chandrasekar, 1960).

To obtain aerosol information over land the surface reflectance must
also be known. Originally, the aerosol retrievals were only over water
since the reflectance

![](media/image6.png){width="2.5833333333333335in"
height="3.0861111111111112in"}

> Figure 5 Spectral response of the GMS-5 channel 1 instrument.

of water is a relatively well-known small value. More recent work by the
MODIS group has shown that the surface reflectance can be known well
enough to permit some retrievals over land.

Wang et al. (2002) were been able to compute AOD\'s at 670 nm for the
ACE Asia case. (See Fig 7 below). We will use their suggested
calibration values to convert the GMS-5 data to radiance units of
w/m2-str.[^8] We can use the GMS-5 IR channels to help discriminate
clouds and the ratio to look for dust (Durkee et al 2000).

There is a great deal known about the aerosol properties that were
observed during the ACE Asia (and APEX and TRACE-P) program. We can use
this information to describe the initial aerosol properties that are
used by the model.

The ACE Asia dust particles are non- spherical. The effects of
non-sphericity on satellite remote sensing are have been studied, but no
consensus has emerged as to how to treat such particles. We will attempt
to use non-spherical phase functions in the model.

As mentioned above, we will try to make the radiation code as
computationally efficient as possible. This means using as few K
coefficients and number of streams as possible.

We have plotted the data from the GMS-5 visible channel for April 12,
2001 in Figure 6. The importance of clouds is quite apparent in the
Figure 6. Both Professor Christopher and Dr. Higurashi have extensive
experience with cloud screening techniques. This will be an important
part of this research[^9].

![](media/image7.wmf){width="3.0972222222222223in"
height="3.0972222222222223in"}

Figure 6. The GMS-5 channel 1 reflectance data for April 12, 2001..

The results of an optical depth retrieval from this GMS-5 data from
Christopher (2004) is shown below In Figure 7. The figure is from the
research described in Wang et al 2002.

![](media/image8.jpeg){width="6.497916666666667in"
height="3.1013888888888888in"}

> Figure 7 - The total aerosol optical depth at 670 nm derived from the
> GMS-5 channel 1 data from Christopher (2004).

3.  **[Develop the data assimilation algorithms]{.underline}**

The techniques that we are developing are general and can be applied to
any modeling effort.  Specifically, the code for assimilating satellite
radiance data is a special treatment of radiative transfer that only
needs to reproduce the observations at specific times and places.  The
observables are usually outgoing radiances in a few spectral bands. 
This code is much easier to generate (and less demanding of resources)
and easier to use than a full radiative transfer model that is required
to compute radiative forcing for all times and places in a general
circulation model.  Ultimately, it would be desirable for instrument
teams to provide forward models for their observations, which could then
be adjointed by standard techniques (see below).

The production and use of adjoints for data assimilation is now well
understood (Talagrand 1997, Courtier, P., and O. Talagrand, 1990).  In
general, we are solving the problem Hx=y, where y is the data and x is
the model state and H is the model.  Even if the model is linear and H
is a matrix, it may not be invertible (because it\'s not square, for
example).  On the other hand, the problem H^T^Hx = H^T^y is always
invertible.  However, it is too large and complicated to write out
explicitly.  Instead, we can use a technique like conjugate gradients
(Press et al 1992) to solve the problem as long as we know H and HH^T^
implicitly.  (Knowing H and HH^T^ implicitly means given a vector b, we
can calculate Hb and/or HH^T^b as the case may be).  To produce H^T^
from a linearized version of H, we can also go line by line in the code
and convert the algebraic operations (additions and multiplications) to
their adjoints.  This is extremely laborious, but straightforward. In
fact, this is how we computed the adjoint of the radiative transfer
model for the Mars TES data discussed above.

  While constructing the adjoint of the radiative transfer code is
relatively easy[^10] (Box et al. 1988), the advection code is not
simple.  There are some reported techniques to adjointing
semi-Lagrangian transport codes (Tanguay, M., and S. Polavarapu, 1999,
Polacarapu et al 1996), it is not guaranteed that the techniques will
work with the MATCH mode, which can use methods other than
semi-Langrangian transport.  Even though the advection equation is also
linear, the techniques used to assure positive definitiveness and mass
conversation may not be so simple. 

We will compute the adjoint of the radiative transfer model during the
first year and the transport model over the first two years.  Even with
just the radiative transfer adjoint, we can do 3-d assimilation (like
the MATCH model now does for optical depth), only with a better handle
on the aerosol properties and combining different observations.  When we
have got the transport code done, we will add the 4th dimension \--
time.

4.  **[Apply the assimilation scheme to the numerical simulation and
    compare the results to other data.]{.underline}**

Once we have the adjoints, applying them to the simulation will be
relatively straightforward. As set out above, we will use the data to
estimate the best fit of the variables. We will update the model with
the most recent carbon inventory (Bond et al 2004).

We can compare the results of the simulations with a variety of in situ
data. For example. The AERONET network has continuous record that has
been used to compare against numerical simulations and satellite
retrievals. Also, the ground based sites of the APEX and ACE Asia
campaigns have data available that we can compare against.

There are also many experiments we can perform with regard to the ACE
Asia aerosol. We can focus on particular source regions where the
aerosol should be dominated by a particular type of aerosol such as
biomass burning, urban air pollution or mineral dust. In an interesting
paper (van der Werf et al, 2004), investigators evaluated the
contribution of fires from different continents to variability in these
greenhouse gases using satellite-based estimates of fire activity,
biogeochemical modeling, and an inverse analysis of atmospheric CO
anomalies. We will attempt a similar type of analysis to obtain
information about the sources of the Asian aerosol. Co-I Dr. Chatfield
has a great deal of experience in this field.

We can investigate the Asian dust transport. Chin et al (2003) found
that it was necessary to explicitly lift the dust out of the boundary
layer to get the proper amount of dust transport across the Pacific. In
general, proper description of dust sources have been a difficult task
for numerical models (Grini et al, 2004).

5.  **[Assimilate data from other satellites]{.underline}**

As set out in Table 1 above, we will work with other satellite data. We
will have to go through the same procedure as discussed above to adapt
the radiative transfer model to the satellite sensor. The multi-channel
satellite data of SeaWiFS and MODIS will be particularly interesting.
One of the advantages of our assimilation techniques is that we can work
with multiple satellite data set in a consistent manner.

Dr. Akiko Higurashi derived results for the optical depth during ACE
Asia from the SeaWiFS data (Bergstrom et al 2004a). Dr. Higurashi has
agreed to work with us as a collaborator on using her four-channel
technique. She is now working with both SeaWiFS and MODIS data.

6.  **[Other test cases - SAFARI 2000, PRIDE, INDOEX,
    INTEX]{.underline}**

There are a number of interesting field experiments that should we will
investigate.\
SAFARI 2000 observed a significant amount of biomass burning mixed in
with other aerosols (Schmid et al 2002), well-defined industrial
emissions from the High Veld and intermittent moderate dust events.
Dr.'s Chatfield, Russell and Bergstrom have experience with the SAFARI
2000 data as has Professor Christopher. Southern Africa provides simpler
case with far more data than the mixed aerosol flowing into the
Equatorial Atlantic from West Africa during its burning season. We will
use the Global Observations of Forest and Land Cover Dynamics
(GOFC/GOLD) effort as guidance for regionalization of emissions (Prins
et al 2004), making a critical usage of MODIS fire products as the GOFC
assessment continues (Justice et al., 2002). We expect that higher
resolution regional emissions for approximately 5-month burning season
will be used. Our colleagues at the University of Witwatersrand (Profs.
Luanne Otter and Stuart Piketh) have supplied Southern African related
datasets developed since the main SAFARI-2000 publications, and will
advise us on rational approaches to industrial and biomass burning
emissions. A SAFARI-2000 biomass burning emissions estimate led by
Robert Scholes may still be expected.

Dr.\'s Chatfield and Guan have a small project to make forward-model
studies of the transport of biomass burning emission around Southern
Africa and mechanisms of escape to the free troposphere \[Chatfield et
al., 2002\]. As the adjoint model is developed and applied to the
ACE-Asia region, they will also work to understand and perhaps improve
the meteorology of emissions lofting and the formation of various
"rivers of smoke" (Schmid et al, 2002, and references). A wide variety
of new information regarding properties and timing of smoke emissions
(Reid et al., 2004, Korontzi et al., 2004) will be recognized for this
regional application. The large database of observations for SAFARI-2000
will ensure that our ultimate assimilation efforts have a proper
independent test. (SAFARI-2000 data disks and U. of Washinton aircraft
dataset http://cargsun2.atmos.washington.edu/sys/research/safari/)

The PRIDE experiment has provided much information about the impact of
the African Dust plume. Professor Christopher and Jun Wang have recently
completed a study where they assimilated satellite data into an
interactive meso-scale model (Wang et al 2004). We will explore our
assimilation technique for the PRIDE experiment.

There are a number of other important field experiments where we can
test our technique (e.g. INDOEX, INTEX). The only limitation is time and
money.

2.  **Summary of Planned Activities by year**

    1.  [Year One:]{.underline}

a\. Adapt the radiative transfer model to the GMS-5 sensor

> b\. Compute the adjoint of the radiative transfer model
>
> c\. Set up the ACE Asia runs
>
> d\. Do the data assimilation on the runs with only the radiative
> transfer model
>
> e\. Work on the adjoint of the transport and chemistry model
>
> f\. Direct simulation of during emissions for SAFARI 2000

3.2 [Year Two:]{.underline}

a\. Compare results with ground and in situ ACE Asia data

b\. Convert radiation code to work with MODIS and SeaWiFS data

c\. continue developing the adjoint of the transport and chemistry model

c\. Run simulations with three satellites

d\. compare the results with the data

> e\. Comparison and reporting of direct simulations for SAFARI 2000
>
> 3.3 [Year Three]{.underline}

a\. Finish developing the adjoint for the transport and chemistry module

a\. Start data assimilation simulations for SAFARI

b\. Start data assimilation simulations for PRIDE

**4. References**

Barth, M. C., P. J. Rasch, J. T. Kiehl, C. M. Benkovitz, and S. E.
Schwartz, Sulfur chemistry in the National Center for Atmospheric
Research Community Climate Model: Description, evaluation, features and
sensitivity to aqueous chemistry, *J. Geophys. Res.,* 105, 1387-- 1415,
2000.

Bergstrom, R. W., P. Pilewskie, B. Schmid, and P. B. Russell, Estimates
of the spectral aerosol single scattering albedo and aerosol radiative
effects during SAFARI 2000, *J. Geophys. Res.*, 108(D13), 8474,
doi:10.1029/2002JD002435, 2003.

Bergstrom, R.W., P. Pilewskie, J. Pommier, M. Rabbette, P. B. Russell,
B. Schmid, J. Redemann, A. Higurashi, T. Nakajima, and P. K. Quinn,
Spectral absorption of solar radiation by aerosols during ACE-Asia, *J.
Geophys. Res.,* Vol. 109, D19S15, doi:10.1029/2003JD004467, 2004a.

Bergstrom, R.W, Pilewskie, P. , Russell, P. B., Ramirez, S. and T. C.
Bond, Effect of the Variability of the Absorption Properties of Light
Absorbing Carbon (LAC) Particles on Direct Radiative Forcing, IGAC
Confernce, New Zealand September, 2004b.

Blanchard, D. C., and A. H. Woodcock, Production, concentration, and
vertical distribution of the sea salt aerosol, *Ann. N. Y. Acad. Sci.,*
338, 330-- 347, 1980.

Bond, T.C. D. G. Streets, K. F. Yarber, S. M. Nelson, J.-H. Woo, and Z.
Klimont, A technology-based global inventory of black and organic carbon
emissions from combustion *J. Geophys. Res.,* Vol. 109, D14203,
doi:10.1029/2003JD003697, 2004.

Box, M. A., S. A. W. Gerstl, and C. Simmer, Application of the adjoint
formulation to the calculation of atmospheric radiative effects,
*Beitr.* *Phys. Atmosph.,* 61,303-311 1988.

Chin, M., P. Ginoux, R. Lucchesi, B. Huebert, R. Weber, T. Anderson, S.
Masonis, B. Blomquist, A. Bandy, and D. Thornton, A global aerosol model
forecast for the ACE-Asia field experiment, *J. Geophys. Res.,*
108(D23), 8654, doi:10.1029/2003JD003642, 2003.

Chu, D. A., L. A. Remer, Y. J. Kaufman, B. Schmid, J. Redemann, K.
Knobelspiesse, J.-D. Chern, R. Li, F. Chang, J. Livingston, P. Russell,
Characterization of aerosol properties by MODIS during ACE-Asia
experiment, *J. Geophys. Res.*, submitted, 2004.

Chylek et al, Aerosol radiative forcing and the accuracy of satellite
aerosol optical depth retrieval, *J. Geophys. Res*. 108,
doi:10.1029/2003JD004044 ,2003.

Christopher, S., Satellite Remote Sensing of East (ACE)-Asia
Aerosols from Geostationary GMS-5 Imager, figure displayed on website,
http://www.atmos.uah.edu/%7Esundar/Research/ace-asia.htm,fromWang et al
2002 below, 2004

Collins, W. D., P. J. Rasch, B. E. Eaton, D. W. Fillmore, J. T. Kiehl,C.
T Beck, C. S. Zender, Simulation of aerosol distributions and radiative
forcing for INDOEX: Regional climate impacts, *J. Geophys. Res.,* Vol.
107, No. D19, 8028, doi:10.1029/2000JD000032, 2002.

Collins et al, Global Aerosol Modeling for Climate Studies Using
Assimilation of EOS Satellite Observations, 2002 and 2003 Progress
reports, Grant NASA (W-19942), 2003

Cosme, E., R. Menard, and M. O\'Neill, First experiments in assimilation
of ODIS reflectances in an aerosol model, Eos Trans. AGU, 85(17), *Jt.
Assem. Suppl.,* Abstract A53C-06, 2004.

Courtier, P. Dual formulation of four-dimensional variational
assimilation, *Quart. J. Roy. Meteor. Soc.,* 123, 2449-2461, 1997.

Courtier, P., and O. Talagrand,  Variational assimilation of
meteorological observations with the direct and adjoint shallow-water
equations.  *Tellus* 42A, 531-549, 1990.

Daley, R., and E. Barker, *NAVDAS Source Book*, NRL Publication
RL/PU/7530-01-441, 161pp., 2001.

Durkee, P. A., K. E. Nielsen, P. J. Smith, P. B. Russell, B. Schmid, J.
M. Livingston, B. N. Holben, D. R. Collins, R. C. Flagan, J. H.
Seinfeld, K. J. Noone, E. Öström, S. Gassó, D. A. Hegg, L. M. Russell,
T. S. Bates, and P. K. Quinn. Regional aerosol properties from satellite
observations: ACE-1, TARFOX and ACE-2 results. *Tellus,* B 52, 484-497,
2000.

Fillmore, D.W., W.D. Collins and A.J. Conley, Aerosol direct radiative
forcing - estimates from a global aerosol analysis with MODIS
assimilation. American Geophysical Union Fall Meeting, Dec. 8-12, 2003,
San Francisco, California. *EOS Trans. AGU* , vol. 84 (no.46), p. F67,
2003:

Ghil, M., and P. Malanotte-Rizzoli, Data assimilation in meteorology and
oceanography, *Adv. Geophys.,* 33, 141-246, 1991.

Grini, A., G. Myhre, C. S. Zender, I. S.A. Isaksen, Model simulations of
dust sources and transport in the global atmosphere. Effects of soil
erodibility and wind speed variability, *J. Geophys. Res.,* in press
2004.

Herman, J. R., P. K. Bhartia, O. Torres, C. Hsu, C. Seftor, and E.
Celarier, Global distributions of UV-absorbing aerosols from Nimbus
7/TOMS data, *J. Geophys. Res.*, 102, 16,911 -- 16,922, 1997.

Higurashi, A., and T. Nakajima, Detection of aerosol types over the East
China Sea near Japan from four-channel satellite data, *Geophys. Res.
Lett.,* 29(17), 1836, doi:10.1029/2002GL015357, 2002.

Houben, H., Planetary meteorology by onboard data assimilation, Roger
Daley Memorial Symposium, Montreal, Canada, 2003.

Houben, H., and C. L. Weeks, Mars Global Surveyor meteorology by
sequential data assimilation, Bull. A. A. S., 35, 936, 2003.

Hsu, N. C., Tsay, S, Herman, J Holben, B, Comparisons of satellite
retrieval of aerosol properties from SeaWiFS and TOMS to the AERONET
measurements, *EOS Trans. AGU*, 82 (47), Spring Meet. Suppl., Abstract
A51B-03, 2002.

Hsu, N.C., S.C. Tsay, M.D. King, and Jay R. Herman, IEEE Trans.
Geoscience Remote Sensing, Vol. 42, no. 3, 557 Aerosol Properties Over
Bright-Reflecting Source Regions, 2004,

Huebert, B. J., T. Bates, P. B. Russell, G. Shi, Y. J. Kim, K. Kawamura,
G. Carmichael, and T. Nakajima, An overview of ACE-Asia: Strategies for
quantifying the relationships between Asian aerosols and their climatic
impacts, *J. Geophys. Res., 108*(D23), 8633, doi:10.1029/2003JD003550,
2003.

Intergovernmental Panel on Climate Change (IPCC), *Climate Change, 1994:
Radiative Forcing of Climate Change and an Evaluation of IPCC IS92
Emission Scenarios*, 339 pp., Cambridge Univ. Press, New York, 1995.

Intergovernmental Panel on Climate Change (IPCC*), Climate Change 2001:
The Scientific Basis*, 881 pp., Cambridge Univ. Press, New York, 2001.

Kahn R.A., et al., Environmental Snapshots for Satellite Multi-Angle
Aerosol Retrieval Validation During the ACE-Asia Field Campaign *J.
Geophys. Res.,* accepted 2004.

Kalnay, E., *Atmospheric Modeling, Data Assimilation and
Predictability*, Cambridge U. Press, 341pp., 2003.

Kaufman, Y., D. Tanré, and O. Boucher, A satellite view of aerosols in
the climate system, *Nature*, 419, 215-223, 2002.

King, M. D., Y. J. Kaufman, D. Tanré, and T. Nakajima, "Remote sensing
of tropospheric aerosols from space: Past, present, and future," *Bull.
Amer. Meteorol. Soc.*, vol. 80, pp. 2229--2259, 1999.

Lamarque, J.-F., B. Khattatov, J. Gille, and G. Brasseur, Assimilation
of Measurement of Air Pollution from Space (MAPS) CO in a global three
dimensional model, *J. Geophys. Res.,* 104, 26,209-- 26,218, 1999.

Le Marshall, J., F. Weng, S. Lord, L.-P. Riishojgaard and P. Phoebus,
Joint Center for Satellite Data Assimilation, NOAA Science Center, Camp
Springs, MD Presented at AMS meeting, 2004.

Levelt, P. F., B. V. Khattatov, J. C. Gille, G. P. Brasseur, X. X. Tie,
and J. Waters, Assimilation of the MLS ozone measurements in the global
three-dimensional chemistry-transport model ROSE, *Geophys. Res. Lett.*,
25, 4493-- 4496, 1998.

Levy, R.C., L.A. Remer, J.V. Martins, Y.J. Kaufman, A. Plana-Fattori, J.
Redemann, B. Wenny, Evaluation of the MODIS aerosol retrievals over
ocean and land during CLAMS, *J. Atmos. Sci.*, special issue on CLAMS,
accepted, 2004.

Liousse, C., F. Dulac, H. Cachier, and D. Tanre, Remote sensing of
carbonaceous aerosol production by African savanna biomass burning, *J.
Geophys. Res.*, 102, 5895 -5911, 1997.

Livingston, J. M., V. Kapustin, B. Schmid, P. B. Russell, P. K. Quinn,
T. S. Bates, P. A. Durkee, P. J. Smith, V. Freudenthaler, D. S. Covert,
S. Gassó, D. A. Hegg, D. R. Collins, R. C. Flagan, J. H. Seinfeld, V.
Vitale, and C. Tomasi, Shipboard sunphotometer measurements of aerosol
optical depth spectra and columnar water vapor during ACE 2 and
comparison to selected land, ship, aircraft, and satellite measurements.
*Tellus*, B 52, 594-619, 2000.

Livingston, J. M**.,** et al., Airborne sunphotometer measurements of
aerosol optical depth and columnar water vapor during the Puerto Rico
Dust Experiment, and comparison with land, aircraft, and satellite
measurements, *J. Geophys. Res.*, 108 (D19), 8588,
doi:10.1029/2002JD002520, 2003.

Lorenc, A. C., Analysis methods for numerical weather prediction, *Q. J.
R. Meteorol. Soc.,* 112, 1177--1194, 1986.

Menard, R., S. E. Cohn, L.-P. Chang, and P. M. Lyster, Stratospheric
assimilation of chemical tracer observations using a Kalman filter, part
I, Formulation, *Mon. Wea. Rev.*, 128, 2654-2671, 2000.

Michalsky, J. et al, Rotating shadowband radiometer development and
analysis of spectral shortwave data, ARM Science team meeting, 1999.

Penner, J. E., H. Eddleman, and T. Novakov, Towards the development of a
global inventory for black carbon emissions, *Atmos. Environ.,* 27,
1277-- 1295, 1993.

Polavarapu, S., M. Tanguay, R. Menard, and A. Staniforth,  The tangent
linear model for semi-Lagrangian schemes.  *Tellus* 48A, 74-95, 1996.

Press, W.H., S.A. Teukolsky, W.T. Vettering and B.Flannery, *Numerical
Recipes, 2^nd^ Ed.,* Cambridge Press, (1992)

Prins et al., \* Report on the Joint GOFC/GOLD Fire and CEOS LPV Working
Group Workshop on Global Geostationary Fire Monitoring Applications.
EUMETSAT, Darmstadt, Germany, 23 -- 25 March 2004

Ramanathan, V., T. S. Bates, J. E. Hansen, D. J. Jacob, Y. J. Kaufman,
J. E. Penner, M. J. Prather, S. E. Schwartz, and J. H. Seinfeld,
*National Aerosol-Climate Interactions Program (NACIP), A National
Research Imperative*, White Paper, <http://www-NACIP/ucsd.edu>

Rasch, P. J., M. C. Barth, J. T. Kiehl, S. E. Schwartz, and C. M.
Benkovitz, A description of the global sulfur cycle and its controlling
processes in the National Center for Atmospheric Research Community
Climate Model, Version 3, *J. Geophys. Res.,* 105, 1367--1385, 2000.

Rasch, P. J., N. M. Mahowald and B. E. Eaton, Representations of
transport, convection and the hydrologic cycle in chemical transport
models: Implications for the modeling of short lived and soluble
species, *J. Geophys. Res*, 102, 28127-28138, 1997:

Redemann, J., S. Masonis, B. Schmid, T. Anderson, P. Russell, J.
Livingston, O. Dubovik, A. Clarke, Clear-column closure studies of
aerosols and water vapor aboard the NCAR C-130 in ACE-Asia, 2001, *J.
Geophys. Res.* *108*(D23), 8655, doi:10.1029/2003JD003442, 2003.

Redemann, J., B. Schmid, J. A. Eilers, R.A. Kahn, R. C. Levy, P. B.
Russell, J. M. Livingston, P. V. Hobbs, W. L. Smith Jr., B. N. Holben,
Suborbital measurements of spectral aerosol optical depth and its
variability at sub-satellite grid scales in support of CLAMS, 2001, *J.
Atmos. Sci.*, special issue on CLAMS, accepted 3/29, 2004.

Schmid, B., J. M. Livingston, P. B. Russell, P. A. Durkee, H. H.
Jonsson, D. R. Collins, R. C. Flagan, J. H. Seinfeld, S. Gassó, D. A.
Hegg, E. Öström, K. J. Noone, E. J. Welton, K. J. Voss, H. R. Gordon, P.
Formenti, and M. O. Andreae, Clear sky closure studies of lower
tropospheric aerosol and water vapor during ACE 2 using airborne
sunphotometer, airborne in-situ, space-borne, and ground-based
measurements, *Tellus*, B 52, 568-593, 2000.

Schmid B., J. Redemann, P. B. Russell, P. V. Hobbs, D. L. Hlavka, M. J.
McGill, B. N. Holben, E. J. Welton, J. Campbell, O. Torres, R. A. Kahn,
D. J. Diner, M. C. Helmlinger, D. A. Chu, C. Robles Gonzalez, and G. de
Leeuw, Coordinated airborne, spaceborne, and ground-based measurements
of massive, thick aerosol layers during the dry season in Southern
Africa, *J. Geophys. Res*, 108, doi:10.1029/2002JD002297, 2003b.

Smith, S. J., H. Pitcher, and T. M. L. Wigley, Global and regional
anthropogenic sulfur dioxide emissions, Global Biogeochem. Cycles,
29,99-- 119, 2001.

Talagrand, O., Assimilation of observations, an introduction, *J.
Meteor. Soc. Japan,* 75, 191-209, 1997.

Tanguay, M., and S. Polavarapu,  The adjoint of the semi-Lagrangian
treatment of the passive tracer equation.  *Mon. Wea. Rev.* 127,
551-564, 1999.

Tegen, I., and I. Fung, Modeling of mineral dust in the atmosphere:
Sources, transport, and optical thickness, J. Geophys. Res., 99,
22,897-- 22,914, 1994.

Tanré, D., Remer, L. A., Kaufman, Y. J.; Mattoo, S.; Hobbs, P. V.;
Livingston, J. M.; Russell, P. B.; Smirnov, A. Retrieval of aerosol
optical thickness and size distribution over ocean from the MODIS
airborne simulator during TARFOX. *J. Geophys. Res*. 104 (D2) 2261-2278,
1999.

Torres, O., P.K. Bhartia, J.R. Herman, Z. Ahmad and J. Gleason,
Derivation of aerosol properties from satellite measurements of
backscattered ultraviolet radiation, Theoretical Basis, *J. Geophys.
Res*.,103, 17099-17110, 1998.

VanCuren, R.A., Asian aerosols in North America: Extracting the chemical
composition and mass concentration of the Asian continental aerosol
plume from long-term aerosol records in the western United States, *J.
Geophys. Res,*108 doi:10.1029/2003JD003459, 2003

Veefkind J. P., G. de Leeuw, P. A. Durkee, P. B. Russell, P. V. Hobbs,
and J. M. Livingston, Aerosol optical depth retrieval using ATSR-2 and
AVHRR data during TARFOX, *J. Geophys. Res.*, *104*, 2253-2260, 1999.

Wang J., S. A. Christopher, J. S. Reid, H. Maring, D. Savoie, B. N.
Holben, J. M. Livingston, P. B. Russell, and Shi-Keng Yang. GOES-8
Retrieval of Dust Aerosol Optical Thickness over the Atlantic Ocean
during PRIDE. *J. Geophys. Res.*, 108, doi:10.1029/2002JD002494, 2003a

Wang, J., S.A. Christopher, F. Brechtel, J. Kim, B. Schmid, J. Redemann,
P.B. Russell, P. Quinn, and B.N. Holben, Geostationary Satellite
Retrievals of Aerosol Optical Thickness during ACE-Asia, *J. Geophys.
Res.*, *108*, doi:10.1029/2003JD003580, 2003b.

Wang, J., U.S. Nair, and S.A. Christopher. GOES 8 aerosol optical
thickness assimilation in a mesoscale 2 model: Online integration of
aerosol radiative effects, , *J. Geophys. Res.*, *108*,
doi:10.1029/2004JD004827, 2004

Yu, H, R. E. Dickinson, M. Chin ,Y. J. K.aufman ,B. N. Holben,V.
Geogdzhayev and M. I. Mishchenko, Annual cycle of global distributions
of aerosol optical depth from integration of MODIS retrievals and GOCART
model simulations, *J. Geophys. Res.*, *108*, doi:10.1029/2002JD002717,
2003.

**5. Management Plan**

## 5.1 Roles of Co-PIs and Co-I's

Drs. Robert Bergstrom and Howard Houben will be the Co-Principal
Investigators. They will be responsible for the overall scientific
direction, project management, administration, and communications with
NASA HQ. They will further be responsible for completing the work on
time and within budget. Dr. Bergstrom will be also responsible for the
radiative transfer modeling since he has developed the radiative
transfer code. Dr. Houben will be responsible for the data assimilation
numerical tools since he has performed similar

Dr. Robert Chatfield, Philip Russell and Hong Guan (Co-Investigators)
will assist the PIs in the proposed activities. Dr. Chatfield who is a
member of the GMI science team will coordinate the efforts with the GMI
project. He is a respected expert in atmospheric chemistry modeling and
has extensive experience in biomass aerosol and gases transport. Dr.
Chatfield's expertise and contacts will be particularly useful in
evaluating the SAFARI simulations. Dr. Russell brings an extensive
familiarity with the satellite data and its relation to a variety of
measurements, in situ and remote, made within the atmosphere. Dr. Guan
has been running global circulation simulations at the Canadian Weather
Service and at NASA Ames for many years. She is also an expert in cloud
modeling and her experience will be useful in cloud screening and
attempts to determine the aerosol indirect effect. She will be in charge
of running the numerical simulations.

> **5.2 Role of Collaborators**

We are extremely fortunate that we have obtained offers of assistance
from Dr.\'s Phil Rasch, Akiko Higurashi and Professors Tami Bond and
Sundar Christopher. Dr. Rasch is one of the authors of the MATCH code
and is currently in charge of the Community Atmospheric Model (CAM). He
has already given us valuable assistance in acquiring and setting up the
MATCH model. Professor Bond is one of the foremost experts in
carbonaceous aerosols and has recently published a detailed inventory.
She has graciously agreed to provide this inventory to us. Professor
Christopher leads one of the most active groups in remote sensing and
has worked with the GMS-5 data to determine aerosol optical depth over
the ACE Asia region. He has offered to give us the benefit of his
expertise in the processing of the GMS-5 data. Dr. Higurashi has
pioneered the development of the 4-channel retrieval from satellite data
that distinguishes between four different types of aerosols. She will
help us with the adaptation of her technique into our assimilation
method.

The emails from Dr.\'s Rasch and Higurashi and Professors Bond and
Christopher expressing their interest in this project are attached.

**6. Current Support**

# CURRENT SUPPORT OF Co-PI (Robert Bergstrom)

+--------------------------+-----------------------+------------------+
| **Short Title**          | **Agency/Task No.**   | **Duration / WY  |
|                          |                       | for PI**         |
|                          | **Total award**       |                  |
+--------------------------+-----------------------+------------------+
| Quantification of        | NOAA, OGP,            | 3/2003-2/2006    |
| aerosol radiative        | Aerosol-Climate       |                  |
| effects by integrated    | Interactions Program. | 0.2 WY           |
| analysis of airborne     |                       |                  |
| measurements, satellite  | \$000k/yr (Co-I; P.B. |                  |
| retrievals, and          | Russell PI)           |                  |
| radiative transfer       |                       |                  |
| models                   |                       |                  |
+--------------------------+-----------------------+------------------+
| Satellite-Sunphotometer  | NASA EOS-IDS          | 4/2003-3/2006    |
| Studies of               |                       |                  |
| A                        | \$000k/yr (Co-I; P.B. | 0.35 WY          |
| erosol-Chemistry-Climate | Russell PI)           |                  |
| Coupling                 |                       |                  |
+--------------------------+-----------------------+------------------+

# CURRENT SUPPORT OF Co-PI (Howard Houben)

+--------------------------+-----------------------+------------------+
| **Short Title**          | **Agency/Task No.**   | **Duration / WY  |
|                          |                       | for PI**         |
|                          | **Total award**       |                  |
+--------------------------+-----------------------+------------------+
| Tidal Dissipation in     | NASA Planetary        | 1/2002-12/2004   |
| Giant Planets Point of   | Atmospheres           |                  |
| Contact: Dr. Denis Bogan |                       | 0.2 WY           |
|                          | \$00.0k/yr (PI)       |                  |
+--------------------------+-----------------------+------------------+
| Validation of Mars       | NASA Mars Data        | 6/2002-5/2005    |
| Global Surveyor          | Analysis Program      |                  |
| Meteorology Point of     |                       | 0.2 WY           |
| Contact: Dr. Herbert     | \$00.0k/yr (PI)       |                  |
| Frey                     |                       |                  |
+--------------------------+-----------------------+------------------+

**7. [Budget]{.underline}**

BUDGET INFORMATION DELETED

**[Travel]{.underline}**

TRAVEL BUDGET INFORMATION DELETED

**8. Vitae**

**Dr. Robert W. Bergstrom**

B.S., Mechanical Engineering, Oregon State University (1968). M.S. and
Ph.D., Mechanical Engineering, Purdue University (1969 and 1972). J.D.,
Law, Stanford University (1983). Member of the California Bar
(1983-present).

Post-Doctoral fellow, Mainz University, Germany (1972-1973). National
Research Associate, NASA Ames Research Center (1974-1977) . Senior
Scientist, Systems Application Inc. in San Rafael CA (1997 -- 1980).
Associate, Law Firm of McCutchen, Doyle, Brown and Enersen, San
Francisco (1983-1984). Assistant Regional Counsel, U.S. EPA, Region IX,
San Francisco, (1984-1992).

Founder of the Bay Area Environmental Research Institute, San Francisco,
CA (1992). Currently, Director of Research overseeing 30 employees.
Principal Investigator on a several Cooperative Agreements with NASA
Ames Research Center.

[Selected Relevant Publications]{.underline}

Bergstrom, R.W., P. Pilewskie, J. Pommier, M. Rabbette, P. B. Russell,
B. Schmid, J. Redemann, A. Higurashi, T. Nakajima, and P. K. Quinn,
Spectral absorption of solar radiation by aerosols during ACE-Asia, J.
Geophys. Res., Vol. 109, D19S15, doi:10.1029/2003JD004467, (2004)

Bergstrom, R.W., P. Pilewskie, B. Schmid, P.B. Russell, \"Comparision of
Measured and Predicted Aerosol Radiative Effects during SAFARI 2000,\"
,J. Geophys. Res.,doi:1029/2002JD002435, (2003).

Bergstrom, R.W., P.B. Russell, P. Hignett, \"The Wavelength dependence
of black carbon particles: Predictions and Results fro the TARFOX
experiment and Implications for the Aerosol Single Scattering Albedo,\"
J. Atmos. Sci., 59, 567-577, (2002).

Pilewskie, P. et al. (including R.W. Bergstrom), \"The Discrepancy
between measured and modeled downwelling solar irradiance at the ground:
Dependence on water vapor.\" Geophys. Res. Lett[.]{.underline},
[25]{.underline}, 137-14, (2000).

Bergstrom, R.W. and P.B. Russell, \"Estimation of aerosol radiative
effects over the mid-latitude North Atlantic region from satellite and
in situ measurements,\" Geophys. Res. Lett., [26]{.underline},
1731-1734, (1999).

Sokolik, I., O.B. Toon and R.W. Bergstrom, "Modeling the Radiative
Characteristics of Airborne Mineral Aerosols at Infrared Wavelengths,"
J. Geophys. Res., [103]{.underline}, 8813-8826, (1998).

Pilewskie, P. et al. (including R.W. Bergstrom), \" Observations of the
spectral distribution of solar irradiance at the ground During SUCCESS,"
Geophys. Res. Lett[.]{.underline}, [25]{.underline}, 1141-1144, 1998.

Russell, P.B. S. Kinne and R.W. Bergstrom: "Aerosol climate effects:
Local radiative forcing and column closure experiments," J. Geophys.
Res. [102]{.underline}, 9397, (1997)

Kinne, S. et al (including R.W. Bergstrom), \"Cirrus Cloud Radiative and
Microphysical Properties from Ground Observations during FIRE '91,\" J.
Atmos. Sci., [54]{.underline}, 2320 (1997).

**Dr. Howard Houben**

Cornell University, Ithaca, New York, 1972-1978. Ph.D. in Astronomy,
August 1978.

City College of New York, New York, New York, 1967-1971. B.S. in
Phyysics, June 1971.

Bay Area Environmental Research Institute

Principal Investigator, beginning May 2002

Space Physics Research Institute

Research Scientist, May 1990 to April 2002

Mycol, Incorporated, Sunnyvale, California

Research Scientist, September 1980 to April 1990

National Research Council (Tenure at NASA-Ames Research Center)

NRC Resident Research Associate, September 1978 to September 1980

[Selected Relevant Publications:]{.underline}

Houben, H., and C.L. Weeks, 2003. Mars Global Surveyor meteorology by
sequential data assimilation, *Bull. A. A. S.,* 35, 936.

Houben, H., 2003. Planetary meteorology by onboard data assimilation,
Roger Daley Memorial Symposium, Montreal, Canada.

Houben, H., and R.W. Bergstrom, 2001. Meteorology for the Mars Global
Surveyor mapping year based on Thermal Emission Spectrometer data
assimilation, *EOS Trans. AGU,* 47, Fall Meeting Supplement, Abstract
P32E-08.

Houben, H., 1999. Assimilation of Mars Global Surveyor meteorological
data, *Adv. Spa. Res.,* 23, 1899-1902.

Houben, H., 1999. The Martian diurnal water cycle, *Adv. Spa. Res.,* 23,
1587-1590.

Houben, H., R.M. Haberle, R.E. Young, and A.P. Zent, 1997. Evolution of
the Martian water cycle, *Adv. Spa. Res.,* 19, 1233-1236.

Houben, H., R.M. Haberle, R.E. Young, and A.P. Zent, 1997. Modeling the
Martian seasonal water cycle. *J. Geophys. Res.* 102, 9069-9083.

Haberle, R.M., H. Houben, J.R. Barnes, and R.E. Young, 1997. A
simplified three-dimensional model for Martian climate studies. *J.
Geophys. Res.* 102, 9051-9067.

Hamill, P., H. Houben, R. Young, R. Turco, and J. Zhao, 1996.;
Microphysical processes affecting the Pinatubo volcanic plume, in *The
Mount Pinatubo Eruption Effects on the Atmosphere and Climate*, G.
Fiocco, D. Fua, and G. Visconti, eds., NATO ASI Series, Vol. I42,
Springer-Verlag, Berlin, pp. 49-59.

Young, R.E., H. Houben, and O.B. Toon, 1994. Radiatively forced
dispersion of the Mt. Pinatubo volcanic cloud and induced temperature
perturbations in the stratosphere during the first few months following
the eruption. *Geophys. Res. Lett.* 21, 369-372.

**Dr. Robert B. Chatfield**

MS 245-5 NASA Ames Research Center

Moffett Field, CA 94035

650-604-5490; FAX: 650-604-3625; chatfield@clio.arc.nasa.gov
ftp://science.arc.nasa.gov/pub/chatfield/pub

  ----------------------------- -------- --------- -----------------------
  Colorado State University     Ph.D.    1982      Atmospheric Science

  University of Washington      M.S.     1976      Atmospheric Sciences

  Rice University               B.A.     1969      Math (Chemical Physics)
  ----------------------------- -------- --------- -----------------------

Dissertation: *Remote tropospheric SO2: Cloud transport of reactive
sulfur emissions,* with      Paul J. Crutzen, Nobel Prize winner, at the
Max-Planck -Institut für Chemie    

[Professional Experience]{.underline}

[NASA Ames Research Center]{.underline} (1990-2004) Researcher,
Atmospheric Chemist

Leader of a small research group. Focus: the study of source, chemical,
and transport processes where they clearly require improvement in global
simulations. Basic technique: reconcile detailed, situation-specific
studies of emis­sions and observed chemical composition (from aircraft
and satellites), and deposition, and so to check the closure of chemical
and particulate budgets. We use advanced statistical techniques and the
NCAR Finite Volume version of Community Atmospheric Model, and our own
flexible, 0-,1-,2-, or 3-dimensional models for synoptic-to-global
transport and transformation for idealized *or* highly
experiment-specific analyses. We are collaborating with other centers
including Goddard Space Flight Center, University of São Paulo,
University of Virginia, University of North Carolina, Harvard, and
others. Strong connections with those studying land use, biomass
burning, surface deposition of trace species, and gaseous emissions from
soils, plants, and combustion, as they compose the great global
biogeochemical cycles.

[National Center for Atmospheric Research]{.underline}, Atmospheric
Chemistry Division

PostDoctoral fellow and Research Scientist, 1984--1990; work on
transport parameterizations, oxidant chemistry, and field observations.

[Contributions]{.underline}

Publications break significant new and tend establish new paths of
research. Here are some contributions that have had a significant impact
on this field:

-remote tropospheric O3 sondes values interpreted \[1977\];
Demonstrating transport of Indian Ocean Brown Cloud ozone pollution to
the remote Atlantic \[2003\].

-conceived, analyzed and wrote a universally cited paper on a major
biogenic emission, isoprene, Zimmerman et al. \[1978\].

-showed that cumulonimbus clouds had extraordinary effects on the global
upper troposphere. The role of (CH3)2S a source of new aerosol there is
now a commonplace.

-demonstrated the importance of clouds in moving radical reservoirs like
the peroxides which determine tropospheric cleaning power (OH radicals
and O3 buildup) \[1984\].

-demonstrated and estimated a simple "two-stream" model of planetary
boundary layer transport that has become a standard for models that can
easily treat atmospheric chemistry and physics \[1987\]

-demonstrated a fundamental limitation in the simulation of the
atmospheric chemistry of tropospheric ozone while also providing an
origin for high smog ozone over the Equatorial Atlantic \[1990\].
Presented wavelet analysis pointing to a separate role for lightning
\[2002\].

-demonstrated a fundamental anomaly in the reactive nitrogen chemistry
of the background troposphere, possibly implying "re-NO*x*-ification"
\[1995\]

-gave a quantitative mechanistic explanation for the "Great African
Plume" and the "Subtropical Global Plume" describing the pollution of
the Atlantic and global tropics from biomass burning \[1988, 2000\].

-provided provocative evidence that aircraft NO*x* plays an
environmentally significant role in the troposphere above 6 km, and
other sources have limited effects \[1999\].

[Honors, Science Teams, Scientific Societies]{.underline}

-- *Science Teams*: NASA Global Modeling Initiative, Global Tropospheric
Experiment,

EOS Interdisciplinary Science

-- *NASA Professional Development Program at NASA Headquarters,
2000--2001.*

-- *American Geophysical Union*

-- *Annual Invited Lecturer* at U.C. Berkeley.

[Selected publications. See web page for recent papers in PDF and recent
presentations.]{.underline}

Chatfield, R., H. Guan, A.M. Thompson, and H. Smit, Mechanisms for the
Intraseasonal Variability of the Tropospheric Ozone during the Indian
Winter Monsoon, submitted to *Atmospheric Environ*., 2004.

Chatfield, R.B., H. Guan, A.M. Thompson, J.Witte, Convective Lofting
Links Indian Ocean Air Pollution to Paradoxical South Atlantic Ozone
Maxima, *Geophys. Res. Lett., 31,* L06103, doi:10.1029/2003GL018866*,*
2004.

Chatfield R. B., Z. Guo, G. W. Sachse, D. R. Blake, and N. J. Blake, The
subtropical global plume in the Pacific Exploratory Mission-Tropics A
(PEM-Tropics A), PEM-Tropics B, and the Global Atmospheric Sampling
Program (GASP): How tropical emissions affect the remote Pacific, J.
Geophys. Res., 107 (D16), doi:10.1029/2001JD000497, 2002.

Folkins, I., and R.B. Chatfield, Impact of acetone on ozone production
and OH in the upper troposphere at high NO*x*, *J. Geophys. Res.,
105*,11,585--11,599, 2000

Chatfield, R.B., "Atmospheric Composition and Structure"and "Atmospheric
Motions and the Greenhouse Effect," *Earth System Science: Processes and
Issues*, ed., G. Ernst, Cambridge University Press, 2000.

Chatfield, R.B., J.A. Vastano, L. Li, G.W. Sachse, and V.S. Connors, The
Great African Plume from biomass burning: A three-dimensional study of
Trace-A carbon monoxide, *J. Geophysical Res., 103,* 28,059-28,077,
1998.

Chatfield, R.B., J.A. Vastano, H.B. Singh, and G.W. Sachse, A
generalized model of how fire emissions and chemistry produce African /
oceanic plumes (O3, CO, PAN, smoke) seen in Trace-A*, J. Geophysical
Res, 101,* 24,279--24,306, 1996.

Chatfield, R.B., Anomalous HNO3/NO*x* ratio of remote tropospheric air:
Conversion of nitric acid to formic acid and NOx?, *Geophys. Res. Lett.,
21,* 2705--2708, 1994.

Chatfield, R.B., Ephemeral Biogenic emissions and the earth\'s radiative
and oxidative environment, in *Scientists on Gaia*, MIT Press, ed.
Stephen Schneider, 1991

Chatfield, R.B., and A. C. Delany, Convection links biomass burning to
increased tropical ozone: However, models will tend to overpredict O3*,
J. Geophysical Res, 95,* 18473--18488, 1990.

Chatfield, R.B., and P. J. Crutzen, Are there interactions of iodine and
sulfur species in marine air photochemistry?, *J. Geophysical Res, 95,*
22319--22341, 1990.

Ferek, R.J., R.B. Chatfield, and M.O. Andreae, Vertical distribution of
dimethylsulfide in the ma­rine atmosphere: implications for the
atmospheric sulfur cycle*, Nature, 320,* 514--516, 1986.

Chatfield, R.B. and P.J. Crutzen, Sulfur dioxide in remote oceanic air:
Cloud transport of reactive precursors, *J. Geophys. Res., 89*,
7111--7132, 1984.

Zimmerman, P.R., R.B. Chatfield, J. Fishman, P.J. Crutzen, and P.L.
Hanst, Estimates of the production of CO and H2 from the oxidation of
hydrocarbon emissions from vegetation, *Geophys. Res. Lett., 5,*
679--682, 1978.

**Dr. Philip B. Russell**

Ph.D. and M.S., Physics, Stanford University (1971 and 1967, Atomic
Energy Commission Fellow). M.S., Management, Stanford University (1990,
NASA Sloan Fellow). B.A., Physics, Wesleyan University (1965, Magna cum
Laude; Highest Honors).

Chief, Atmospheric Experiments Branch (1982-89), Acting Chief, Earth
System Science

Division (1988-89), Chief, Atmospheric Chemistry and Dynamics Branch
(1989-95), Research Scientist (1995-present), NASA Ames Research Center.

Physicist to Senior Physicist, Atmospheric Science Center, SRI
International (1972-82).

Postdoctoral Appointee, National Center for Atmospheric Research
(1971-72, at University of Chicago and NCAR).

NASA Ames Honor Award (2002, for excellence in scientific research).
NASA Ames

Associate Fellow (1995, for excellence in atmospheric science). NASA
Space Act Award (1989, for invention of Airborne Autotracking
Sunphotometer). NASA Exceptional Service Medal (1988, for managing
Stratosphere-Troposphere Exchange Project). Member, Phi Beta Kappa and
Sigma Xi.

Currently, Member, Science Teams for NASA's Earth Observing System
Inter-Disciplinary Science (EOS-IDS) and Solar Occultation Satellites
(SOSST).

Previously, Mission Scientist for Jetstream 31 aircraft in the 2004
INTEX and ITCT

projects. Mission Scientist for ACE-Asia C-130 flights addressing
aerosol-radiation interactions. Co-coordinator for the CLEARCOLUMN
component of IGAC's Second Aerosol Characterization Experiment (ACE-2).
Coordinator for IGAC's Tropospheric Aerosol Radiative Forcing
Observational Experiment (TARFOX). Member, Science Teams for SAGE II and
SAGE III (satellite sensors of stratospheric aerosols, ozone, nitrogen
dioxide, and water vapor). Member, Science Team for the Asian Pacific
Regional Aerosol Characterization Experiment (ACE-Asia) of the
International Global Atmospheric Chemistry (IGAC) Project.

Previously, Editor-in-Chief (1994-95) and Editor (1993, 1996),
*Geophysical Research Letters*. Chair, American Meteorological Society
Committee on Laser Atmospheric Studies (1979-82). Member, AMS Committee
on Radiation Energy (1979-81). Member, National Research Council
Committee on Army Basic Research (1979-81).

Previously, Project Scientist, Small High-Altitude Science Aircraft
(SHASA) Project to

develop the Perseus A Remotely Piloted Aircraft (RPA, 1992-94). Member,
NASA Red Team on Remote Sensing and Environmental Monitoring of Planet
Earth (1992-3). Leader, NASA Ames Earth Science Advanced Aircraft (ESAA)
Team (1990-94).

Patent, \"Airborne Tracking Sunphotometer Apparatus and System\" (U.S.
Pat. No. 4,710,618, awarded 1987)

Over 114 peer-reviewed publications. Selected publications relevant to
this NRA are included in section 5 of this proposal.

**Dr. Hong Guan**

1995 Ph.D. In Department of Atmospheric and Oceanic Sciences, McGill
University, Montreal, Quebec, Canada

1989 M.Sc. In Institute of Atmospheric Physics, Academia Sincia,
Beijing, China

1984 B.S. In Nanjing Institute of Meteorology, Nanjing, China

[Professional Experiences]{.underline}

NASA Ames Research Center (BAERI),USA

May,2002-Present, Senior Scientist

In charge of running large numerical models and publishing the results,
supercomputer has been

intensively used in the recent years. In charge of analysis of SSFR data
for cirrus clouds in CRYSTAL-

FACE

Cloud Physics Research Division, Meteorology Service of Canada, Canada

a\) 2001-2002, Research Scientist

b\) 1996-2001, Contract Scientist

Studied the warm frontal precipitation structures with different scales
(synoptic scale, mesoscale, and

cloud scale) using Canadian MC2 model. Test and compare various
convective (Kuo, Frisch-Chappell,

Kain-Fritsch) and condensation (Sundqvist, Mixed-phase, Hsie, and
Kong-Yau) parameterization

schemes. Simulated the stratus clouds and their radiation property
observed in the RACE with high

resolution, test the sensitivity of the cloud on longwave and shortwave
radiation, and explore cloud-

radiation interaction. Improve radiative transfer model in CMC.Assess
the accuracy of GEM forecast

meteorological and cloud fields through extensive test cases. Test and
compare various cloud and aircraft

icing forecast algorithms in CMC using in-situ aircraft observations.

Department of Atmospheric and Oceanic Sciences, McGill University,
Montreal, Quebec, Canada

1989-1995, Ph.D Candidate (Oct. 1992-Oct. 1993 maternity leave)

Develop a three-dimensional longwave radiative transfer model and
calculate

longwave radiative cooling rates in cumulus and stratiform clouds.
Validate radiative

transfer model with LOTRAN 6. Explore the effects of cloud top
inhomogeneity on

radiative cooling rates.

Study the interaction between longwave radiation and a cumulus cloud
using an

interactive radiation-cloud dynamics model.

Institute of Atmospheric Physics, Academia Sinica

1987-1989, M.Sc. research

Develop a scheme to retrieve the vertical distribution of stratospheric
aerosols using

the radiance observations during twilight periods and Monte Carlo
simulations.

Beijing Meteorological Bureau, Beijing, China

1984-1986, Assistant engineer in Radar Meteorology Div.

Operational radar observation, analysis and forecast.

Honors and Awards

B.Sc. (2nd place among 33 students) in the Department of Atmospheric
Physics, Nanjing

Institute of Meteorology (1984).

M.Sc. Received three annual graduate awards at Academia Sinica
(1986-1989).

Ph.D. Research Assistantship and Fellowship in Department of Atmospheric
and Oceanic

Sciences, McGill University, Montreal, Canada (1989-1995).

Canadian NSERC Post Doc. Scholarship. (1997)

Group Achievement Award Crystal-Face Science Team (2002)

[Journal Publications]{.underline}

R. B. Chatfield, H. Guan, A. M. Thompson, H. Smit, 2004: Mechanisms for
the intraseasonal variability of the tropospheric ozone during the
Indian Winter Monsoon. Submitted to J.G.R.

R. B. Chatfield, H. Guan, A. M. Thompson, J.Witte, 2004: Convective
Lofting Links Indian Ocean Air Pollution to Paradoxical South Atlantic
Ozone Maxima, Geophys. Res. Lett., 31.

Manfred Wendisch, Peter Pilewskie, Evelyn Jakel, Sebastian Schmidt, John
Pommier, Steve Howard, Haflidi H. Jonsson, Hong Guan, Marc Schroder and
Bernhard Mayer, 2004: Airborne measurements of areal apectral surface
albedo over different sea and land surfaces. J.G.R., 109,D08203,
doi:10.1029/2003JD004392.

Guan, H., S. G. Cober, G. A. Isaac, and A. Méthot, 2003: Validation of
four cloud forecast schemes with in-situ aircraft measurements. Wea.
Forecasting, 17, 1226-1235.

Guan, H., S. G. Cober, and G. A. Isaac, 2001: Verification of
supercooled water forecasts with in-situ aircraft measurements. Wea.
Forecasting. 16,145-155.

Guan, H., A. Tremblay, G. A. Isaac, K. B. Strawbridge and Catharine M.
Banic, 2000:

Numerical simulations of stratus clouds and their sensitivity to
radiation ¾ A RACE

case study, J. Appl. Meteor., 39, 1881-1893 .

Szeto, K. K. and H. Guan, 2000: Coarse-grid and cloud-resolving
simulations of a midlatitude cyclonic cloud system: Implications for the
parameterization of layer clouds in GCMs. J. Climate., 13, 1972-1978.

Szeto, K. K., A. Tremblay, H. Guan, D. R. Hudark, R. E. Stewart, and Z.
Cao, 1999: On the dynamics of freezing rain storms over eastern Canada.
J. Atmos. Sci., 56, 1261-1281.

Guan, H., M. K. Yau, and R. Davies, 1997: The effects of longwave
radiation in a small

cumulus cloud, J. Atmos. Sci., 55, 2201-2214.

Guan, H., R. Davies, and M. K. Yau, 1995: Longwave radiative cooling
rates in axially

symmetric clouds, J. Geophys. Res., 100, 3213-3220.

Guan, H., D. Lu, and B. Wu, 1992: Retrieval of the profile of
stratospheric aerosols from the intensity of skylight during twilight
periods, Scientia Atmospheric Sinica, 16, 216-227.

Ye, Z., and H. Guan, 1986: A numerical study of evolution of the
nocturnal urban boundary layer. Scientia Atmospheric Sinica, 10, 78-88.

**9. Letters from Collaborators**

**From:** yark@uiuc.edu

**Subject:** **NASA proposal**

**Date:** November 7, 2004 12:22:09 PM PST

**To:** bergstrom@baeri.org

Dear Dr. Bergstrom:

I acknowledge that I am identified by name as a collaborator on your
proposal entitled \"Quantification of aerosol radiative effects by data
assimilation of satellite radiance measurements into a global aerosol
model.\" This proposal will be submitted by the Bay Area Environmental
Research Institute to the NASA Research Announcement NNH04ZYS008N
entitled \"Modeling, analysis, and prediction including contributions
from climate variability and change focus area.\" As stated in the
proposal, I intend to provide you with data sets of our global black and
organic carbon inventories, including current-year updates as they
become available.

Best regards,

Tami Bond

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Assistant Professor <yark@uiuc.edu>

Dept. of Civil & Environmental Engineering ofc 217-244-5277

University of Illinois at Urbana-Champaign fax 217-333-6968

Newmark Civil Engineering Laboratory, MC-250

205 N. Mathews Ave.

Urbana, IL 61801

**From:** pjr@ucar.edu

**Subject:** **Letter of commitment**

**Date:** November 12, 2004 8:32:39 AM PST

**To:** bergstrom@baeri.org

**Reply-To:** pjr@ucar.edu

Hi Bob,

This email indicates my interest in participating in your project

entitled \'Quantification of aerosol radiative effects by data

assimilation of satellite measurements into a global aerosol model\',

as a collaborator and advisor. We have already provided the MATCH

assimilation program to you and I am happy to continue on the project,

by providing further advice on the model, and sharing our experience
with

modeling and assimilating aerosols for this project.

Regards

Phil

\--

Phil Rasch, Climate Modeling Section, National Center for Atmospheric
Research

Mail \--\> P.O. Box 3000, Boulder CO 80307

Shipping \--\> 1850 Table Mesa Dr, Boulder, CO 80305

email: <pjr@ucar.edu>, Web: <http://www.cgd.ucar.edu/cms/pjr> Phone:
303-497-1368, FAX: 303-497-1324

![](media/image9.jpeg){width="6.49375in" height="8.403472222222222in"}

**From:** hakiko@nies.go.jp

**Subject:** **Re: NASA Proposal**

**Date:** November 14, 2004 6:27:42 PM PST

**To:** bergstrom@baeri.org

Dear Dr. Bergstrom,

Thank you for your email.

Yes, I will accept your offer of participation. I\'m very glad you\'re
involving my work.

I\'m trying to apply the 4-channel algorithm on global scale. Therefore
global segmented data with latitudinal and longitudinal resolution of
0.25 degrees are processing for MODIS and SeaWiFS data now.

Thank you.

Sincerely,

Akiko

\--

Akiko Higurashi

Atmospheric Environment Division

National Institute for Environmental Studies

16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

Phone: +81-29-850-2423, Fax: +81-29-850-2960

E-mail: <hakiko@nies.go.jp>

[^1]: The NRA states as the goals of the new Climate Global Modeling
    Initiative:

    > \(a\) Incorporation and evaluation of state-of-the-art algorithms
    > and inclusion of new surface, *in situ* and satellite remote
    > sensing data into the GMI model with the goal of understanding
    > their relative importance in determining simulated atmospheric
    > composition. High priority will be given to studies incorporating
    > new satellite-based meteorological fields, wet scavenging
    > processes, emission of source gases, boundary layer processes,
    > microphysical processes, chemical mechanisms and numerical
    > approaches. Research efforts towards off-line incorporation of GCM
    > processes such as convective parameterizations, hydrological cycle
    > and aerosol-cloud interactions are also encouraged.
    >
    > \(b\) Evaluation of GMI simulation results with available surface,
    > *in situ* and satellite remote sensing data. Priority will be
    > given to studies utilizing available and forthcoming measurements
    > from satellite instruments and aircraft platforms.
    >
    > \(c\) Studies of the impact of anthropogenic activity and climate
    > change on atmospheric composition at both regional and global
    > scales, incorporating the coupling between the stratosphere and
    > the troposphere incorporating satellite observations.
    >
    > \(d\) Studies of the radiative impact of atmospheric composition
    > as simulated by the GMI model. Priority will be given to efforts
    > that include coordination with climate models and interactive
    > chemistry-climate research.

[^2]: The NCAR scientists are currently merging the MATCH model
    capability with the newly released CCSM and CAM models. At present,
    the CAM model does not have the capability to carry the aerosol
    model. In the near future all simulations will be run with the CAM
    model. For the work discussed here, the MATCH model is sufficient.
    Using data assimilation to derive the aerosol indirect effect will
    require an interactive model such as the new CAM model.

[^3]: The MATCH model is one of the models participating in the AEROCOM
    comparison. There are fifteen such models of which five are US
    models - information on AEROCOM is available at
    http://nansen.ipsl.jussieu.fr/AEROCOM/aerocomhome.html.

[^4]: In data assimilation terminology, the radiative transfer model is
    termed a forward model

[^5]: For description of satellite remote sensing of aerosols see King
    et al 1999 and Kaufman et al 2002.

[^6]: While the single scattering albedo and optical depth are each one
    number the function P(Θ) requires more one number to characterize
    it. Usually, the scattering distribution function is assumed to be
    the represented by a lognormal distribution of spherical particles
    of mode radius, r~o~.

[^7]: Not all bands can be used for aerosol detection; the H2O bands
    prevent retrieval in certain solar spectral regions (King, et al
    1999; Bergstrom et al 2002). There are also slight differences in
    wavelengths for TOMS and AVHRR for different spacecraft.

[^8]:

[^9]: Ultimately, we will be able to compute cloud radiances with these
    techniques. However, that effort is beyond the scope of work
    presented here.

[^10]: While straightforward, use of the adjoint in atmospheric
    radiative transfer is actually not particularly common. Only in a
    few cases (Michalsky et al 1999) has the adjoint method been used.