**Proposed MODIS-Atmosphere Collection 006 Changes**

*Version 04* (06/06/08)

***[Source Key]{.underline}***

*Blue = from Bryan Baum's PPT at Atmosphere Breakout 5/16/08 (Input from
multiple team members.)*

*Green = from Steve Platnick, Michael King, & Gala Wind (06OD Cloud
Optical Properties)*

*Red = from Paul Hubanks (Level 3)*

*Violet = from Bo-Cai Gao (05_L2 Water Vapor and 06CD Cirrus Detection
)*

*Orange = from Rich Frey (06CT Cloud Top Properties and 07_L2 Atm
Profile)*

**[Aerosol]{.underline} (04_L2)** Christina Hsu, Lorraine Remer, Shana
Mattoo, Allen Chu

-   Extend the Deep Blue aerosol retrieval through the entire Terra
    archive

-   Create a "MODIS aerosol product" that combines the traditional and
    Deep Blue products

-   Include POLDER nonsphericity information for a better MODIS aerosol
    retrieval for nonspherical aerosol

-   Implement a better urban product

-   Further considerations

    -   Examine aerosols in the proximity of clouds

-   Urge the Deep Blue Aerosol development team to fix their L2 "Deep
    Blue Aerosol Type" flags ... reverse smoke and sulfate flags
    (2=Sulfate, 3=Smoke) so they match the QA flags for the standard
    aerosol retrieval. (See QA Plan.)

> ![](media/image1.wmf){width="6.495833333333334in"
> height="0.7763888888888889in"}

**[Water Vapor]{.underline} (05_L2)** Bo-Cai Gao

-   Improve QA for the near-IR water vapor products, and thoroughly
    screen out pixels saturated over bright clouds.

**[Cloud]{.underline} (06_L2)**

***Cloud Optical Properties (06OD)*** Steve Platnick, Michael King, Gala
Wind

-   Integrate low cloud temperature retrievals into the MOD06OD
    algorithm to include non-unity emissivity (from optical thickness
    retrieval)

-   Update current MOD06OD multilayer flag to include other techniques
    and approaches

-   Improve cirrus cloud retrievals of τc, re

    -   Improve detection using a combination of IR and 1.38 µm bands

    -   Perform additional retrieval of τc using IR and/or 1.38 µm bands
        as an augmentation of the current solar reflectance approach

-   Modify table look-up libraries and solution algorithm

    -   Add more small τc in libraries to reduce interpolation errors
        for thin clouds

    -   Remove asymptotic algorithm for thick clouds, replacing it with
        more τc libraries; no impact on solutions but simplifying
        algorithm maintenance

    -   Include ocean BRDF to accommodate, especially, thin cloud
        retrievals over ocean

-   Partly cloudy pixels

    -   Better use of 250 m cloud mask (at least over ocean) for QA of
        MOD06OD and CT retrievals

    -   Coakley-type spatial variance vs. temperature approach (Note:
        Rich Frey doesn't think the Coakley algorithm should be included
        in this list. However, he could see calculating a variance
        statistic of some kind using band 2 and including a flag in the
        L2 output that indicates a non-uniform scene. I think the cloud
        team should discuss this more before we commit to anything.)

-   Integrate low cloud window IR temperature retrievals into the
    MOD06OD algorithm to include non-unity emissivity (from optical
    thickness retrieval). (11/06)

-   Update current MOD06OD multilayer flag to include other
    techniques/approaches (e.g., Pavolonis and Heidinger). (11/06)

-   Partly cloudy pixels: Better use of 250m cloud mask (at least over
    ocean) for QA of MOD06OD and CT retrievals. (11/06)

-   Replace LUT/asymptotic approach with LUT only. Need non-Lambertian
    surface BRDF for ocean library generation. (04/07)

-   Phase of thin cirrus. Cloud mask correctly detects the clouds with
    IR window difference and 3.9-11 tests, but we are getting a liquid
    water phase while IR is undetermined. Clearly, if the mask can
    detect the cirrus then we ought to use those mask tests to help in
    the phase logic. (04/07)

> \* \[removed from STM notes\] CSR. According to Gala Wind, the default
> for 250 m tests is to set all of the 250 m pixels to clear, and then
> change to cloud upon reading such as result from cloud mask. But the
> logic should be reversed, i.e., all 250 m pixels are set to cloudy and
> then changed to clear upon reading such a result from the cloud mask.
> Either approach is irrelevant unless there are missing 250 m L1B
> pixels. This is the likely reason why CSR was showing thick ice cloud
> striping due to the faulty QA in the Jan. 2007 L1B LUT delivery. The
> old/original LUT was then used to reprocess data so isn't a cause of
> immediate widespread concern but this is an error in the logic.

-   Either provide scaled optical thickness in the data set AND/OR
    include a vector of ice cloud g and ssa in the data set, so: (1)
    users could scale optical thickness from our retrievals to their own
    library of g values in non-absorbing bands, and g & ssa for
    absorbing bands (e.g., a broadband code in a climate model). (2)
    users could scale our effective radius to their own library of re by
    scaling ssa. (03/08)

-   Despite tradition, we don't believe that delta transmittance should
    be included in ice cloud radiative transfer calculations. For MODIS
    C5, eliminating delta transmittance reduces g for re\>10 µm and
    reduces the slope of g vs. re. This mitigates some of the need for
    roughened particles. (05/08)

-   Additional thin cirrus algorithms: incorporation of 1.38 µm thin
    cirrus optical thickness algorithm; Pavolonis/Heidinger beta 1DVAR.
    (05/08)

-   Pursue Aqua cold focal plane adjustment in L1B production. Jack
    Xiong says Vermote has done something along these lines. (05/08)

***Cloud Top Properties (06CT)*** Rich Frey, Kathy Strabela, Paul Menzel

-   Implement "top-down" method of final cloud top pressure choice for
    Aqua

-   Avoid CO2 slicing solutions in water clouds and IRW solutions in ice
    or mixed phase clouds

-   Output cloud (geopotential) heights along with cloud top pressures

-   Run algorithm at 1 km resolution

-   Include cloud overlap / phase at 1 km

-   Include multiple cloud top pressure solution flag for window channel
    retrievals

-   Investigate inversion detection for low level water cloud to be
    located below inversion

    -   Survey globe to see frequency of occurrence of low level
        inversions in GDAS dataset used as ancillary information

        -   Are these correctly found over ocean, land, and coastlines?

        -   Are they found at the correct height, when present?

> This initial work has already been done. The use of GDAS to identify
> inversions is not perfect, but leads to much improvement in L2 output
> in marine stratus cloud regimes. I would simply include one bullet
> that states: GDAS temperature profile data will be used to identify
> regions where temperature inversions exist and an alternative IRW
> method will be implemented.

***Cirrus Detection (06CD)*** Bo-Cai Gao

-   Improve cirrus reflectance retrievals over dry high elevation areas,
    such Tibet Plateau, Andes Mountains, Antarctic and Greenland. The
    collection 05 cirrus reflectance products over these areas are
    slightly contaminated by surface reflection effects.

**[Atmospheric Profile]{.underline} (07_L2)** Eva Borbas, Suzanne
Seemann, Rich Frey

-   Update surface emissivity data base to current version

-   Investigate the dry bias in Aqua TPW and make adjustments to
    improve.

-   Perform a more thorough evaluation of the ozone product through
    intercomparisons with TOMS and AIRS and make  adjustments to
    algorithm

-   Evaluate the current radiance bias adjustments in Aqua and Terra
    algorithms and make updates.

-   Look into whether we can include all profiles at 101 levels in
    direct broadcast or at the DAAC, and an ozone profile instead of
    just TOZ.

-   Assess the TPW Low and TPW High products and possibly change the
    levels of integration to make them more useful.

-   Improve QA/QC flags and screening for bad input MOD02L1B data.

-   Examine the MOD07 Level 3 products for consistency with other long
    term datasets (NVAP).

-   Perform an experimental combined retrieval with AIRS, for at least a
    few cases.

-   Making Aqua and Terra DAAC code uniform

-   Have the Atmosphere Profile development team fix (remove the noise
    in fill regions) all 07_L2 Atmosphere Profile Usefulness and
    Confidence QA Flags. This problem might stem from the QA flags not
    being initialized as 0's; but this is unclear.

> ![](media/image2.wmf){width="3.115972222222222in"
> height="2.459722222222222in"}

**[Cloud Mask]{.underline} (35_L2)** Rich Frey, Steven Ackerman

-   Implement angle-dependent 0.86 μm thresholds for day ocean

-   Implement day/night, land/water dust detection algorithm

-   Lower 1.38 μm thresholds to "thin cirrus" values, but keep thin
    cirrus flag for users (all scene types except snow/ice)

-   Investigate cloud test using variability of 0.86 μm reluctances in a
    3x3 region for day ocean

-   Investigate angle and location-dependent 0.66 μm thresholds for day
    land

-   Investigate cloud test using variability of 3.75 μm BTs for night
    ocean

-   Investigate use of 7.2-11 μm BTDs in polar day scenes

-   Use 11 μm BTs and surface data to help screen out false snow from
    both maps (night) and NDSI (day)

-   Tune-ups:

    -   3.9-11 μm cloud test threshold/algorithm for night coastlines
        and shallow water (eliminate uncertain results as much as
        possible)

    -   3.9-11 μm cloud test thresholds for night land in moist
        environments (e.g., Amazon)

    -   Adjust Antarctic night cloud test thresholds

**[Level-3 Gridded Product]{.underline} (08_D3, 08_E3, 08_M3)** Paul
Hubanks

-   Add joint histogram of cloud optical thickness and cloud top
    pressure for combined phase to more easily compare with ISCCP joint
    histogram

-   Add smoke only, dust only, and sulfate only aggregations of Deep
    Blue aerosol. Aggregate aerosol single scattering albedo for dust
    from Deep Blue algorithm. Make sure to sync any L2 Deep Blue Aerosol
    Type flag change to L3.

-   Modify Cloud Effective Radius (Re) liquid water cloud histograms and
    joint histograms to start at 4.0. In addition, use the newly defined
    Research L3 boundaries for Cloud Effective Radius (Re) joint
    histograms

> New Bin Bounds for C006 = 4, 6, 8, 10, 12, 14, 16, 18, 20, 25, 30
>
> Old Bin Bounds for C005 = [2]{.mark}, 4, 6, 8, 10, [12.5, 15,
> 17.5]{.mark}, 20, 25, 30

-   Increase number of bins in the marginal histograms (especially for
    ice cloud effective radius). Check all the Research L3 histogram bin
    bounds against the Operational L3 (change the Operational L3 where
    appropriate). Increase \# of bins and reduce histogram bin sizes for
    Re Ice esp in the 20 to 32 range but perhaps along entire range.

-   Add Cloud Top Height parameter to L3. Add new marginal histograms
    for this SDS (develop a most useful definition of histogram bin
    boundaries). Q: Are new joint histograms based on this parameter
    envisioned?

-   Modify L3 code to compute median. Add median statistics to some
    parameters of L3. (esp Cloud Optical Property parameters)

-   Fix File Spec "long name" for Cloud Fraction Histogram Counts
    (delete at 10 intervals)

-   Use uncertainty to develop new QA-weighted means of Cloud Optical
    Property parameters. (check with S.Platnick)

-   Check with all L2 development teams to ensure any changes to L2 HDF
    files (new parameters, new or changed QA, etc.) are accounted for in
    the L3 (sync the L3 to L2)