The MODIS cloud optical and microphysical products: Collection
6 updates and examples from Terra and Aqua
Steven Platnick,
Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA
Kerry G. Meyer,
Goddard Earth Science Technology and Research, Universities Space Research Association,
Columbia, MD 21046 USA
Michael D. King [Fellow, IEEE],
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303 USA
and the Texas A&M University Institute of Advanced Study
Galina Wind,
Science Systems and Applications, Inc., Lanham, MD 20706 USA
Nandana Amarasinghe,
Science Systems and Applications, Inc., Lanham, MD 20706 USA
Benjamin Marchant,
Goddard Earth Science Technology and Research, Universities Space Research Association,
Columbia, MD 21046 USA
G. Thomas Arnold,
Science Systems and Applications, Inc., Lanham, MD 20706 USA
Zhibo Zhang,
Department of Physics, University of Maryland – Baltimore County, Baltimore, MD 21250 USA
Paul A. Hubanks,
ADNET Systems, Inc., Bethesda, MD 20817 USA
Robert E. Holz,
Space Science and Engineering Center, University of Wisconsin – Madison, Madison, WI 53706
USA
Ping Yang,
Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77845 USA
William L. Ridgway, and
Science Systems and Applications, Inc., Lanham, MD 20706 USA
Jérôme Riedi
Laboratoire d’Optique Atmosphérique, Université de Lille – Sciences et Technologies, Villeneuve
d’Ascq, France
Abstract
NASA Public Access
Author manuscript
IEEE Trans Geosci Remote Sens
. Author manuscript; available in PMC 2018 April 12.
Published in final edited form as:
IEEE Trans Geosci Remote Sens
. 2017 January ; 55(1): 502–525. doi:10.1109/TGRS.2016.2610522.
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The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra
and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and
night pressure, temperature, and height) and cloud optical properties (optical thickness, effective
particle radius, and water path for both liquid water and ice cloud thermodynamic phases–daytime
only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015
for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical
property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical
and microphysical algorithm changes include: (i) new ice cloud optical property models and a
more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the
skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius
retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals
for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide
diagnostic information for pixels having observations that fall outside the LUT solution space, and
(vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively
can result in significant changes relative to C5, though the magnitude depends on the dataset and
the pixel’s retrieval location in the cloud parameter space. Example Level-2 granule and Level-3
gridded dataset differences between the two collections are shown. While the emphasis is on the
suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.
Index Terms
Aqua; cloud remote sensing; clouds; Moderate Resolution Imaging Spectroradiometer (MODIS);
MOD06; MYD06; Terra; satellite applications; terrestrial atmosphere
I. Introduction
Since the launch of NASA’s Terra satellite on 18 December 1999, followed by Aqua on 4
May 2002, the Moderate-resolution Imaging Spectroradiometer (MODIS) has become one
of the most widely used satellite remote sensing platforms for Earth science investigations.
Designed to provide global observations of the Earth’s atmosphere, land, and oceans [1]–[4],
MODIS measures reflected solar and emitted thermal radiation in 36 spectral channels
ranging from the visible (VIS) to the infrared (IR) at a native spatial resolution of 250 m
(0.66 and 0.87 μm channels), 500 m (five channels including 3 shortwave-infrared [SWIR]),
and 1 km (all others). MODIS provides unique spectral and spatial capabilities for retrieving
cloud properties. The NASA operational cloud product (Earth Science Data Set names
MOD06 and MYD06 for Terra and Aqua MODIS, respectively, though for simplicity the
modifier MOD will subsequently be used for both Terra and Aqua since the respective
algorithms are nearly identical) [5] contains pixel-level retrievals of cloud top properties
(pressure, temperature, and height during both day and night) and cloud optical and
microphysical properties (cloud optical thickness [COT], effective particle radius [CER],
and derived water path [CWP] for both liquid water and ice cloud thermodynamic phases
during daytime only) (see [6]).
The cloud top properties algorithm, which relies on CO
2
-slicing channels (13–14 μm
spectral region) and two IR window channels [7]–[8], has heritage with the High resolution
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Infrared Radiation Sounder (HIRS) (see [9]); spatial resolution is at both 5 km and 1 km for
C6. The 1 km cloud optical and microphysical product algorithm makes primary use of six
VIS, near-infrared (NIR), SWIR, and midwave-infrared (MWIR) MODIS channels, as well
as several thermal IR channels. Relative to previous generation global imagers such as the
Advanced Very High Resolution Radiometer (AVHRR), MODIS has a number of additional
spectral channels, including window channels centered near 1.6 and 2.1 μm that, in addition
to an AVHRR heritage channel near 3.7 μm, provide cloud microphysical information. The
basic physical principle behind the simultaneous retrieval of COT and CER is the bi-spectral
solar reflectance method first described in [10] and applied to airborne data. MOD06-
specific heritage work is also described in [11] and [12] (microphysical retrievals using the
AVHHR 3.7 μm channel), [13] (1.6–2.1 μm retrievals over snow/ice surfaces), and
thermodynamic phase retrievals [14].
MODIS (re)processing streams are referred to as data collections. A major increment in the
collection number denotes comprehensive changes to the instrument calibration and science
algorithms. Collection 5 (C5) was completed in calendar year 2006, while a reprocessing to
C5.1 was completed in calendar year 2010. MODIS Atmosphere Team Collection 6 (C6)
Aqua Level-2 (L2), or pixel-level, reprocessing began in December 2013 and was completed
in early May 2014 (data acquisition dates 4 July 2002 through 31 December 2013); Aqua
forward processing began on 1 January 2014. Atmosphere Team C6 Terra L2 reprocessing
began in November 2014 and was completed in March 2015 (data acquisition dates 24
January 2000 through 31 December 2014); Terra forward processing began on 1 January
2015. Atmosphere Team Level-3 (L3) (re)processing for Terra and Aqua began in October
2014 and was completed in March 2015.
Basic MOD06 optical property algorithm details are described in the C5 Algorithm
Theoretical Basis Document (ATBD) addendum [15] and original ATBD [16]. An overview
of the MODIS cloud product algorithms (at the time of Collection 4) along with example
results is provided in [16] and [17]. Collection 5 algorithm-related work is described in
various publications, e.g., ice radiative models [18]–[19], multilayer cloud detection [20]–
[21], Clear Sky Restoral filtering [22]–[23], pixel-level retrieval uncertainties [24], and
global aggregated statistics [25]. Evaluation-specific investigations include cloud phase
[26]–[27], view angle biases [28]–[30], and the impacts of non-plane-parallel clouds [22],
[31]–[32].
Due to the significant number of algorithm and dataset changes implemented in the latest
collection, an overview paper of the C6 MOD06 cloud optical and microphysical property
product is warranted. Here we focus on key changes with respect to C5 and the resulting
impact to granule-level and global cloud property statistics. The MOD06 cloud optical and
microphysical retrieval algorithm is numerically intensive, depending on explicit forward
radiative calculations for cloud, gas, and surface interactions. Updates for C6 are
representative of evolving passive imager cloud retrieval science as spectral information
from MODIS and other capable sensors continues to be explored (e.g., synergistic A-Train
[33] studies have provided important constraints on ice particle radiative models [34]).
Meanwhile, the climate modeling community continues to improve its ability to exploit the
product (see [23]) and cloud assessment reports [35]–[36] acknowledge the challenges in
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establishing cloud climate data records. Note that the MOD06 product should not be
confused with a separate MODIS cloud product developed specifically for Clouds and the
Earth’s Radiant Energy System (CERES) processing [37, 38]; comparisons between many
CERES Edition-2 and MODIS C5 products are given in [38].
II. Summary of Collection 6 Updates
The C6 MOD06 cloud property product is the culmination of extensive multi-year
development and testing. While the theoretical basis of the retrievals remains unchanged
from C5, numerous algorithm updates and enhancements have been implemented that
increase algorithm sophistication and performance. Note that only updates to the cloud
optical and microphysical property retrievals are discussed here; updates to the cloud top
property and IR-derived thermodynamic phase algorithms, including the new native 1 km
resolution retrievals, are detailed in
Baum et al.
[8]. Notable updates to the optical and
microphysical property retrievals include:
A.
Radiative transfer and look-up table (LUT) improvements that reduce algorithm
complexity and maintenance by eliminating the use of asymptotic theory, reduce
linear interpolation errors by optimizing LUT grid point locations and separating
the single and multiple scattering components, and include a new single-habit ice
cloud radiative model based on the severely roughened aggregate of solid
columns [39] that has been shown to provide better retrieval consistency with IR
and lidar-based COT retrievals [34].
B.
A redesigned cloud thermodynamic phase algorithm, based on a variety of
independent tests with assigned weights, that provides improved skill in
comparison with collocated lidar and polarimeter-based phase products.
C.
Separate spectral retrievals of COT, CER, and derived CWP for channel
combinations that include the 1.6, 2.1, and 3.7 μm channels that allow for
independent Level-3 aggregation and ease retrieval inter-comparisons.
D.
Separate Science Data Sets (SDSs) for lower quality scenes identified by C5-like
Clear Sky Restoral algorithms (see [22]) that flag pixels not expected to be
overcast (referred to as “partly cloudy” retrievals), a 1 km sub-pixel 250 m
reflectance heterogeneity index, and an updated multilayer cloud detection
scheme [40], [20]–[21]; this information can be used for improved retrieval
quality assessment.
E.
Retrieval failure metrics that provide diagnostic information for pixels where the
reflectance observations fall outside the LUT solution space.
F.
Improved pixel-level retrieval uncertainty estimates that include scene-dependent
L1B uncertainties [41], cloud model and surface albedo error sources (cloud
effective variance, ocean surface wind speed and direction), and 3.7 μm emission
error sources; note these uncertainties do not include estimates of 3D radiative
transfer biases or ice habit model error sources.
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G.
Updated handling of surface reflectance, including a new dynamic 8-day
sampling surface spectral albedo dataset derived from gap-filled C5 Aqua+Terra
MODIS data (MCD43B3,
Schaaf et al.
[42]), adoption of land spectral
emissivities consistent with the cloud-top property algorithm [43], and wind
speed interpolated bidirectional reflectance properties of water surfaces based on
the parameterization of
Cox and Munk
[44].
H.
A new L1B re-aggregation scheme for Aqua MODIS that accounts for focal
plane misalignment between the 250 m resolution channels (0.66 and 0.87 μm)
and the 500 m resolution channels (0.47, 0.55, 1.24, 1.6, and 2.1 μm); note that
all maintained atmospheric products for Aqua MODIS use the new re-aggregated
L1B, including the Dark Target [45] and enhanced Deep Blue [46] aerosol
products.
A more detailed discussion of the C6 MOD06 cloud optical and microphysical property
retrieval algorithm is provided in Section III below (note that the above lettering scheme is
consistent with Section III organization), followed by a discussion of the impacts of the C6
updates on the Level-3 global gridded cloud property statistics and best-practice guidance
for MOD06 product users.
III. C6 Algorithm Details
A. Cloud Radiative Models
The simultaneous retrieval of cloud optical thickness (COT) and effective particle radius
(CER) can be achieved by simultaneously measuring the cloud reflectance in two spectral
channels having a different amount of cloud particle absorption (e.g., VIS/NIR and SWIR,
respectively) and comparing the measurements with theoretical forward model calculations,
as demonstrated with airborne data [10] (see also [47]–[53], [11]–[12]). For previous
MOD06 collections (C5 and earlier), the theoretical forward model calculations used
asymptotic theory ([10], [51] and references therein) for optically thick atmospheres,
coupled with a forward calculated LUT containing spectral cloud reflectance and fluxes at
four discrete optically thin COT values. For C6, asymptotic theory has been replaced with
cloud reflectance and emissivity LUTs containing the complete range of COT values. This
change simplifies code maintenance such that multiple algorithm paths for optically thin and
optically thick atmospheres, followed by interpolation between them, are no longer required;
in addition, more optically thin COTs are included in the new LUTs. Note that for optically
thick atmospheres, the resulting reflectance computations are the same as those obtained
from asymptotic theory.
For the C6 LUTs, cloud top reflectance is calculated for six spectral channels, namely the
non-absorbing 0.66, 0.86, and 1.24 μm channels that are primarily sensitive to COT, and the
absorbing 1.6, 2.1, and 3.7 μm channels sensitive to CER. Effective cloud and surface
emissivities [12] are also calculated for the 3.7 μm channel, whose TOA radiance has both
solar and thermal components due to its location in the MWIR. The plane-parallel discrete-
ordinates radiative transfer (DISORT) algorithm [54] is used for the forward RT
calculations, ignoring above-cloud atmospheric gaseous absorption in all channels and
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