AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-2633-2018The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction retrieval algorithm: theoretical basisOMPS LP V1 aerosol extinction retrieval algorithm: theoretical basisLoughmanRobertrobert.loughman@hamptonu.eduBhartiaPawan K.ChenZhongXuPhilippeNyakuErnesthttps://orcid.org/0000-0002-1863-4074TahaGhassanhttps://orcid.org/0000-0001-8362-6516Department of Atmospheric and Planetary Sciences, Hampton University, Hampton, Virginia, USAAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USAScience Systems and Applications, Inc. (SSAI), 10210 Greenbelt Road, Suite 600, Lanham, Maryland 20706, USAScience Applications International Corporation (SAIC), Lanham, Maryland, USAGESTAR, Columbia, Maryland, USARobert Loughman (robert.loughman@hamptonu.edu)4May20181152633265115August20174October20172March201816March2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/2633/2018/amt-11-2633-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/2633/2018/amt-11-2633-2018.pdf
The theoretical basis of the Ozone Mapping and Profiler Suite (OMPS) Limb
Profiler (LP) Version 1 aerosol extinction retrieval algorithm is presented.
The algorithm uses an assumed bimodal lognormal aerosol size distribution
to retrieve aerosol extinction profiles at 675 nm from OMPS LP radiance
measurements. A first-guess aerosol extinction profile is updated by
iteration using the Chahine nonlinear relaxation method, based on
comparisons between the measured radiance profile at 675 nm and the radiance
profile calculated by the Gauss–Seidel limb-scattering (GSLS) radiative
transfer model for a spherical-shell atmosphere. This algorithm is discussed
in the context of previous limb-scattering aerosol extinction retrieval
algorithms, and the most significant error sources are enumerated. The
retrieval algorithm is limited primarily by uncertainty about the aerosol
phase function. Horizontal variations in aerosol extinction, which violate
the spherical-shell atmosphere assumed in the version 1 algorithm, may also
limit the quality of the retrieved aerosol extinction profiles significantly.
Introduction
Most of the aerosols found in the Earth's atmosphere occur in the planetary
boundary layer, due to the wide variety of aerosol sources that exist at the
surface (dust, smoke, sea salt, etc.). But a secondary peak in aerosol
abundance typically occurs in the stratosphere , extending
from the tropopause to an altitude of approximately 30 km
(; ). The stratospheric aerosol layer
consists primarily of hydrated sulfuric acid (H2SO4) droplets
, generated by the oxidation of tropospheric sulfur
dioxide (SO2) and carbonyl sulfide (OCS) that has entered the stratosphere
through troposphere–stratosphere exchange processes . The
stratospheric aerosol layer is enhanced by volcanic eruptions that inject
SO2 into the stratosphere, creating a layer of H2SO4 droplets that
spreads quickly in the horizontal directions (and much more slowly in the
vertical direction), slowly dissipating over a period from months to several
years. Volcanic eruptions also may inject ash particles directly into the
stratosphere, and mineral dust from the ablation of meteors also can augment
the stratospheric aerosol layer . Several competing
influences therefore affect the stratospheric aerosol layer, including
volcanic activity, stratosphere–troposphere exchange, stratospheric transport
processes, gas-to-droplet conversion rates, and particle sedimentation. As a
result, the stratospheric aerosol concentration varies widely in space and in
time.
Aerosols in the stratosphere play key roles in the chemistry of that region,
particularly including heterogeneous ozone destruction
(; ;
; ). Monitoring stratospheric
aerosols as a tracer for stratospheric air mass motion has also provided
useful insight (; ). The most
significant climate impact of changes in the distribution of stratospheric
aerosols occurs due to backscattering of solar radiation, which increases
the planetary albedo and cools the troposphere (;
; ). The magnitude of this effect varies
significantly with latitude, solar zenith angle, etc. . A
recent review of the observations and processes of stratospheric aerosol and
how they impact the Earth's climate is presented in .
Occultation measurements
The primary global record of stratospheric aerosol abundance has been derived
from solar occultation (SO) measurements. (This kind of data will be
indicated as “solar occultation transmission (SOT)”, to avoid confusion
with the notation for sulfur oxide gases.) The Stratospheric Aerosol
Measurement (SAM)/Stratospheric Aerosol and Gas Experiment (SAGE) series of
missions pioneered this technique, with the long-lived SAGE II instrument
(1984–2005) providing a particularly valuable continuous data record
(; ;
). An overview of the large variation of stratospheric
aerosol optical depth during the SAM/SAGE time period can be found in Fig. 1
of . These SOT measurements provide unmatched altitude
resolution, precision, and accuracy for stratospheric aerosol monitoring:
transmission profiles are produced on a 0.5 km grid with estimated vertical
resolution of 0.7 km . The SAGE aerosol extinction coefficient
βa retrieval has targeted accuracy and precision = 5 %,
and analysis of the version 4 product indicates accuracy and precision
performance on the order of 10 % for the 15–25 km altitude range
. The Polar Ozone and
Aerosol Measurement (POAM) satellite series has further
provided SOT measurements in the polar regions. Comparison between POAM III
and SAGE II data indicates relative differences of ±30 % in
βa, with some hemispheric differences evident
. The MAESTRO instrument also launched aboard the SCISAT
satellite in 2003 . This mission has provided aerosol
extinction profiles based on SOT measurements, as described by
and .
Daily coverage provided by the OMPS LP
instrument mounted on the SNPP satellite. The tangent point for the LOS
corresponding to each observation is indicated, with red, white, and yellow
circles depicting the left, center, and right slit observations, respectively.
The primary drawbacks of SOT observations made from a low Earth orbit are the
limited number of profiles measurable (30 occultations per day) and the lack
of flexibility concerning the locations monitored (which are determined
entirely by the orbit of the satellite). In addition to SOT measurements,
occultation measurements involving other sources of light are also possible.
The SAGE III instrument also performs lunar occultations, but it does not
produce βa profiles based on lunar occultation measurements
. The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument has provided
stellar occultation monitoring of the stratospheric aerosol layer
. Since numerous bright stars can be used as the source
of photons, this method offers the potential for increased geographic
coverage as compared to SOT (but with a much dimmer source of light). Comparisons of
GOMOS stellar occultation βa retrievals to SAGE II, SAGE III,
and POAM III βa data indicate agreement at the 10–25 %
level in the lower stratosphere .
The lack of global stratospheric βa profile measurements from
SOT since the SAGE II, POAM III, and Meteor-3M SAGE III missions ended (in
2005, 2005, and 2006, respectively) has left a vacancy. Limb-scattering (LS)
data have been combined with occultation data to produce a
merged time series, which will aid in tracking the evolution of aerosol
plumes from volcanic eruptions that contribute aerosol to the upper
troposphere and lower stratosphere (UTLS) . After an
absence of over a decade, the recent installation of a SAGE III instrument on
the International Space Station in February 2017 promises
to resume the valuable SOT data set for stratospheric βa
monitoring.
Limb-scattering (LS) measurements
Several recent missions have provided LS measurements, including the Optical
Spectograph and InfraRed Imaging System (OSIRIS) , the
Scanning Imaging Absorption spectroMeter for Atmospheric
CartograpHY (SCIAMACHY) , Meteor-3M SAGE III
(which made LS measurements in addition to occultation
measurements), and the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler
(LP) . These instruments measure profiles of the LS sunlight
across the ultraviolet (UV), visible, and near-infrared (NIR) spectral
regions.
As illustrated in Fig. 1 of , LS measurements are
possible throughout the entire sunlit hemisphere, permitting much better
spatial coverage and sampling than SOT measurements. But LS retrievals of
stratospheric βa are significantly more challenging,
requiring radiative transfer (RT) models to simulate the diffuse radiation
field, which must include all orders of atmospheric scattering as well as
surface reflection. Careful tangent height registration of the measured
radiance profiles and cloud screening are also
required. The LS radiance is also susceptible to stray-light (SL) contamination
(see Fig. 2 of ). Finally, the LS radiance depends upon both
the scattering properties (especially the phase function) and the extinction
coefficient for the aerosols, while occultation measurements are only
sensitive to the latter property.
The single-scattering angle (SSA, or Θ) as a
function of latitude for the SNPP OMPS LP instrument. June and December
solstice conditions are illustrated by the red and blue lines, respectively.
Note that near-polar latitudes may be observed twice (during the ascending
and descending nodes of the orbit), which provides useful diagnostic
information.
Each LS mission team has developed its own methodology to retrieve
stratospheric βa profiles from limb radiance measurements,
but all of the retrieval algorithms involve the comparison of measured LS
radiance profiles with simulated radiance profiles that are generated by a RT
model. In the case of OSIRIS, the “color index” of measured LS radiances at
470 and 750 nm is compared to radiances calculated by the SASKTRAN
(; ) model. The evolution of
βa during the OSIRIS mission has been investigated in a
series of papers (; ;
). Comparison between version 5 OSIRIS retrievals and the
version 4 SAGE III record indicates agreement to within 10 % for
βa in the 15–25 km altitude range . The
retrieval of aerosol size information from OSIRIS data has also been
investigated (; ) to produce the
version 6 OSIRIS aerosol product. The version 6 algorithm combines the
infrared imager 1.53 µm channel with OSIRIS data to allow retrieval
of both βa and aerosol mode radius, based on an assumed
aerosol mode width value.
For the SCIAMACHY mission, the initial βa retrievals were
performed by , using a modified version of the algorithm under
development for the eventual OMPS LP mission .
present an approach to retrieve stratospheric aerosol
number density from SCIAMACHY LS data in the O2 A-band. More recent work
(; ; ) describes an
approach that uses the color-index approach introduced by .
The global average difference between SAGE II (version 7) and SCIAMACHY
(version 1.1) βa data is 10 %, with larger relative
differences (up to 40 %) at specific latitudes and altitudes
. The SCIATRAN RT model provides the
radiance simulations in this case.
The SAGE III instrument that flew on the Meteor-3M satellite made LS
measurements as a research product, from which retrievals of ozone
and aerosol were derived. These
retrieval algorithms were the predecessors for the initial OMPS LP algorithm
, which used the Gauss–Seidel limb-scattering (GSLS) RT model described in
to provide the simulated radiances. Comparison to
coincident SAGE II SOT data indicated bias < 5 % and
precision = 25–50 % for βa retrievals from SAGE III LS
data .
The βa retrieval algorithm described by
was applied to early OMPS LP observations. It was
modified slightly to assess the aftermath of the Chelyabinsk bolide
explosion, as documented by . This paper describes the new
OMPS LP Version 1 (V1) βa retrieval algorithm. Section 2
briefly describes the OMPS instruments (particularly the LP instrument) and
the Suomi National Polar-Orbiting Operational Environmental Satellite System
Preparatory Project (SNPP) satellite on which OMPS was initially installed. Section
3 focuses on the necessary radiance calculations, while Sect. 4 describes the
retrieval algorithm in detail. Section 5 contains error analysis of the
retrieved aerosol extinction profiles. Finally, a preliminary evaluation of
the retrieval results is presented in Sect. 6. We conclude with a summary and
description of proposed future work in Sect. 7.
The OMPS LP Instrument
The LP instrument is part of the Ozone Mapping and Profiler Suite (OMPS),
whose primary purpose is to monitor the ozone layer. The LP instrument design
was guided by the preceding Shuttle Ozone Limb Sounding
Experiment (SOLSE) and Limb Ozone Retrieval Experiment (LORE) sensors and was
built by Ball Aerospace Technology Corporation under contract from the
Integrated Program Office. The instrument makes a series of simultaneous
observations of the Earth's entire sunlit limb through three vertical slits,
producing a set of three radiance profiles: the line of sight (LOS) for one
set of observations (called the “center slit”) is oriented along the
orbital track, while the other two sets (called the “left” and “right”
slits) are offset by 4.25∘ from the orbital track. The ground track
of the resulting sequence of observations is illustrated in
Fig. .
OMPS LP is installed in a fixed orientation relative to the SNPP spacecraft,
which is in a sun-synchronous orbit with a 13:30 ascending node and mean
altitude of 833 km above the Earth's surface. As a result of this
orientation, the single-scattering angle (Θ) observed by the LP
instrument varies with latitude as shown in Fig. . Most notably,
Northern Hemisphere observations (with latitude > 0∘) generally
correspond to forward-scattered beams (Θ<90∘), while
Southern Hemisphere observations (latitude < 0∘) correspond to
backscattered beams (Θ> 90∘). As a result, the relative
strength of the aerosol scattering signal is much larger in Northern
Hemisphere OMPS LP measurements, as shown in Fig. : the aerosol
phase function (Pa) increases by a factor of approximately 50
over the course of a typical orbit, as the SNPP satellite travels from its
southernmost observation to its northernmost observation. (All observations
for which the solar zenith angle at the tangent point θT<85∘ are processed by the OMPS LP V1 software.) The variation of the
Pa at several latitudes over the course of a year due to the OMPS
LP orbit is shown in Fig. .
The aerosol phase function (for the Θ values
shown in Fig. ) as a function of latitude for the SNPP OMPS LP
instrument. June and December solstice conditions are illustrated by the red
and blue lines, respectively. Due to the variation of the aerosol phase
function with latitude and season, the SNPP OMPS LP observations are most
sensitive to aerosols in the NH winter and least sensitive to those in the SH. The
aerosol size distribution described in Table 1 for the V1 aerosol extinction
retrieval algorithm is assumed.
The seasonal variation of
the aerosol phase function at several latitudes for the SNPP OMPS LP orbit.
The aerosol size distribution described in Table 1 for the V1 aerosol
extinction retrieval algorithm is assumed.
The OMPS LP instrument permits radiance observations for the 290–1000 nm
wavelength range. Dispersion is provided by a prism, which provides images
whose spectral resolution varies greatly with wavelength (from ≈1 nm in the UV to ≈ 30 nm in the NIR). At the wavelength of
interest for the V1 βa retrieval algorithm (675 nm), the
spectral resolution is 15 nm. For further information about the OMPS LP
instrument characteristics, please consult ,
, and .
Radiance calculationThe GSLS radiative transfer model
The GSLS RT model is built from the previous models described by
, as summarized in . The model
atmosphere is specified by input pressure, temperature, absorbing gas number
density, and βa profiles. Radiances are calculated using
Rayleigh and Mie scattering cross sections at 675 nm, using the
user-provided aerosol microphysical and optical properties. Ozone
cross sections are averaged over the spectral width of the OMPS LP bandpass
(15 nm). This approach is significantly faster than performing a full
radiance convolution, and it produces radiance errors < 1 %. The viewing
geometry is specified by the solar zenith angle and relative azimuth angle at
the tangent point (TP) for the LOS, denoted by θT and ϕT,
respectively, and illustrated in Fig. .
The GSLS model calculates radiances at several wavelengths λ and
tangent heights h. For single-scattering (SS) calculations, the solar beam
attenuation is calculated to each point along the LOS, including the
curvature of the spherical atmosphere as well as the variation of solar
zenith angle and solar beam attenuation along the LOS. The attenuation of the
scattered beam along the LOS is also calculated accounting for the curvature
of the atmosphere. Recent updates to the GSLS model described in
reduce SS radiance errors that were as great as 4 % in
the comparisons to the 0.3 % level.
Illustration of the limb-scattering viewing
geometry, including definitions of the tangent altitude h and tangent
point. The solar zenith angle and solar azimuth angle at the tangent point
are indicated by θT and ϕT, respectively. Adapted from Fig. 1
of . Note that a frequently committed error in the
definition of ϕT (; ;
) has been corrected: a beam with Θ=0∘
(scattered exactly forward) has ϕT=0∘.
The multiple-scattered (MS) radiances observed by a LS instrument originate
from illumination of the limb LOS by photons that have been scattered within
the atmosphere or reflected by the underlying surface. These photons are
scattered for the final time at some point along the limb LOS and then
transmitted from that point to the observer. The diffuse upwelling radiance
(DUR) from below the LOS provides the primary source of illumination that
produces MS photons, containing the combined effects of molecular scattering,
aerosol scattering, cloud scattering, and surface reflection. For the V1
βa retrieval, the DUR is estimated as described in Sect.
.
The MS source function is calculated at one or more points along the LOS
using the pseudo-spherical version of the RT model described by
. In the GSLS model, the MS source
functions were calculated only at the TP (solar zenith
angle =θT). This was updated in to calculate
the MS source functions at multiple solar zenith angles along the LOS,
increasing the accuracy of the MS radiances. Total radiance errors that had
reached 10% in the comparisons decrease to 1–3 % in
the updated comparisons presented by .
The GSLS model described by was used for retrieval
applications on missions including SOLSE/LORE ,
SAGE III (; ;
), GOMOS , SCIAMACHY ,
and OMPS LP . These retrieval algorithms generally
performed well despite the shortcomings of the version of
the GSLS model, but development of a more accurate version of the GSLS model
was considered desirable to improve the algorithms further, as well as for
the purpose of interpreting residuals (differences between measured radiances
and radiances calculated for the desired model atmosphere). The
version of GSLS has therefore been implemented for the V1
algorithm described in this paper.
The diffuse upwelling radiance (DUR)
The horizontal extent of the limb LOS covers thousands of kilometers, and the
underlying scene generally includes variable surface types, broken clouds at
various locations and levels, etc. The current GSLS model lacks the
capability to model the full complexity of such a scene, even if its
properties were known. To estimate the DUR, the V1 βa
retrieval algorithm uses a simple Lambertian model of the reflecting surface,
characterized by its reflectivity R. Radiances simulated by the GSLS RT
model using a Lambertian surface (placed at sea level) are used to estimate
an effective scene reflectivity from a measurement, by tuning the value of
R used in the GSLS model until the calculated radiance matches the measured
value for a given set of viewing and illumination conditions.
The R value at which the calculations match the measurement is sometimes
called the “Lambert-equivalent reflectivity”, or LER. It does not equal the
true reflectivity of the surface, since the scene generally contains clouds,
aerosols, etc. below the LOS that are not properly captured in the GSLS model
atmosphere, and variations in terrain height are also ignored. This approach
has been extensively used for nadir-viewing applications such as ozone
profile retrievals from the SBUV satellite series and ozone total column
retrievals from the TOMS satellite series , and it was suggested
by . Approximate treatment of DUR in the V1 OMPS LP
βa retrieval algorithm is justified by the relative
insensitivity of the normalized radiances used by the βa
retrieval to DUR, as demonstrated in Sect. .
Finally, note that the model atmosphere for the GSLS model used in the V1
βa retrieval algorithm is constrained to be one-dimensional
(i.e., the atmospheric properties vary only with altitude). A two-dimensional
SS version of GSLS (allowing atmospheric properties to vary along the LOS as
well as with altitude) has recently been developed , and a
full MS version of this model is currently under development.
Aerosol properties
The LS radiance is affected by several aerosol properties. The V1 algorithm
described in this paper employs assumptions for several of these properties
in order to deduce the βa based on observations of the LS
radiance I(λ, h).
Aerosol shape and optical properties
First, the stratospheric aerosols are assumed to be spherical droplets of
sulfuric acid (H2SO4). Mie theory is used to calculate the aerosol
scattering and extinction properties, based on the aerosol refractive index
values given in Table . These assumptions exclude numerous
processes that may contribute significantly to the stratospheric aerosols
found at particular places and times (e.g., volcanic ash, meteoric dust,
various tropospheric aerosols that enter the stratosphere). However, the
assumption that “aged” aerosol in the Junge layer is dominated by such
H2SO4 droplets agrees with observations dating back to the earliest
studies of stratospheric aerosol and is assumed in all
previous LS βa retrieval algorithms. The assumption is less
supportable under “perturbed” stratospheric conditions (such as the
immediate aftermaths of volcanic eruptions), as noted by , or
at the upper and lower boundaries of the Junge layer, which may have more
meteoric content above and more tropospheric aerosol near the tropopause.
Aerosol optical properties and aerosol size
distribution (ASD) assumed in the V1 OMPS LP aerosol extinction retrieval.
Aerosol size distributions assumed in several recent
LS aerosol extinction retrieval algorithms.
MissionSourcer0 (µm)σα(525/1020)OMPS (V0.5)0.061.732.34OSIRIS (V5)0.081.62.44SCIAMACHY (V1.1)0.111.372.82Nyaku, fine mode0.051051.438332.07, coarse mode0.20251.15Aerosol size distribution (ASD)
In the V1 algorithm, the ASD is modeled as a bimodal lognormal (LN)
distribution, as specified in Table . This ASD is defined by
Eq. ():
dN(r)dr=∑i=12Nir2πlnσiexp-12ln(r/ri)lnσi2.
Five independent parameters are required to specify the shape of the bimodal
LN ASD: two median radii (r1 and r2), two mode widths (σ1 and
σ2), and one more parameter indicating the relative sizes of the aerosol
concentration associated with each mode (N1, N2). In this work, the
mode with the smaller median radius value (r1) is called the “fine mode”,
while the other mode is the “coarse mode”. Therefore the relative sizes of
the aerosol modes are described by the “coarse-mode fraction” fc
= N2/(N1+N2). (Changes in the absolute values of N1 and N2 alter
the magnitude of the βa for a given distribution but do not
change the shape of the ASD for a given fc value.)
The ASDs used in several other LS βa retrieval algorithms are
given in Table . These properties have typically been taken from
the long record of balloon-borne optical particle counter (OPC) data provided
by T. Deshler's group at the University of Wyoming. But this data set indicates that the
ASD varies considerably with time, location, and altitude. For example, the
V1.1 SCIAMACHY ASD is taken from Fig. 3c in
(excluding the coarse mode). and
cite as the source of the V5 OSIRIS ASD,
which resembles Fig. 5b of that reference (again excluding coarse-mode
particles). uses the 2012–2013 data provided by the University of
Wyoming website for Laramie as the basis of the bimodal LN ASD for
sensitivity studies, as cited earlier in . Unfortunately,
the OPC data corrections described by occurred
after the OSIRIS, SCIAMACHY, and Nyaku ASDs described in this paragraph were
defined, so none of those ASDs reflect the corrected version of the OPC data.
Illustration of the aerosol size distributions used
in several recent LS aerosol extinction retrieval algorithms, including
OSIRIS (V5), SCIAMACHY (V1.1), and OMPS (V0.5 and V1). The aerosol size
distribution studied by is also represented.
Ångström exponent α(525/1020) derived from SAGE II SOT
measurements during its measurement history. This picture corresponds to
measurements at altitude 20 km for the 0–10∘ N latitude bin. Cases
for which the measured aerosol extinction at 1020 nm <4×10-6 km were excluded from this analysis (L. Thomason, private
communication).
Variation of
Ångström exponent α(525/1020) with aerosol properties for the
V1 OMPS LP aerosol extinction retrieval algorithm characteristics. Each curve
shows the variation of (α(525/1020) with fc for a given
set of median radii and mode widths. In addition to the “base” curve (which
uses the V1 characteristics listed in Table ), several curves show
how the value of α(525/1020) changes as the values of r1,σ1,r2, and σ2 (in Table ) are perturbed by ±10 %.
The apparent lack of consistency in the stratospheric aerosol ASD poses a
significant problem for efforts to retrieve βa from LS
measurements, as discussed further in Sect. . A single-mode LN ASD
is assumed in stratospheric βa retrievals by the V5 OSIRIS
, V1.1 SCIAMACHY , and intermediate
V0.5 OMPS LP retrievals, as shown in Table . The assumed median
radius (r0), mode width (σ), and resulting Ångström coefficient
α(525/1020) (defined below in Eq. ) are shown in
Table , and several single-mode and bimodal LN ASDs are shown in
Fig. . Table also includes the properties of the
bimodal LN ASD analyzed by .
α(525/1020)=-lnβa(525nm)/βa(1020nm)ln525/1020
For the V1 OMPS LP βa retrieval algorithm, we introduce the
added complexity of the bimodal LN ASD because it generally describes the
properties of stratospheric aerosol observations better
. The fine- and coarse-mode properties of the V1 OMPS
ASD (given in Table ) were selected based on the data found in
Table 1a of . These observations were taken on
23 August 1991, in the aftermath of the eruption of Mt. Pinatubo, and are
based on in situ measurements by impactor samplers flown on an ER-2 aircraft
in the lower stratosphere. The intention of this choice was to keep the
observed fine mode for stratospheric aerosols (with properties broadly
similar to the single-mode LN ASDs shown in Table ), while
introducing the possibility of a coarse mode of larger aerosols. The
recent eruption of Mt. Pinatubo causes fc=0.36 in the selected
data, which is much larger than one would expect in the
background stratosphere. Therefore the relative prominence of the coarse mode
was reduced for the V1 OMPS LP βa algorithm by tuning the
fc value, based on the following considerations drawn from the
available stratospheric aerosol data record:
The SAGE satellite series (particularly SAGE II) provides a long-term
record of βa profiles for stratospheric aerosols at several
wavelengths. The βa wavelength variation can be expressed by
the Ångström coefficient α, which is defined by Eq. ()
based on observations of βa at 525 and 1020 nm. The SAGE II
zonal mean α value for the tropics at 20 km is shown in
Fig. . Except for volcanically perturbed periods, the
observed α value at this altitude is relatively constant at α≈2. This value tends to grow with altitude above the peak of the
stratospheric aerosol layer, approaching α≈2.5 at 30 km.
Figure shows how α varies with coarse-mode fraction
fc, for fine- and coarse-mode fraction values in the vicinity of
the V1 OMPS LP ASD values (r1, σ1, r2, and σ2 in
Table ). For these assumed fine- and coarse-mode properties, the
value of α is extremely sensitive to fc. If one assumes
that the fine and coarse modes are correctly specified, this implies that
fc can be determined with great precision based on the observed
value of α. The V1 OMPS LP βa retrieval algorithm uses
fc=0.003 in conjunction with the values of
r1,σ1,r2, and σ2 to produce α=2.
The differences among the V1 algorithm assumed Pa and the phase
functions associated with other LS βa retrievals are shown in
Figs. – for 675, 756, 869, and 1020 nm. To
assess the sensitivity of the V1 ASD, we also present Fig. , in
which r1,σ1,r2, and σ2 in Table are perturbed
by 10% (while fc is also adjusted to maintain α≈2). This analysis shows the greatest percentage change in Pa (up
to 30 %) for Θ>90∘ when σ1 is perturbed.
Figure also includes the Pa when r1, σ1,
r2, and σ2 remain at their default values (shown in
Table ), but fc is varied:
in one case fc=0.0012 (producing α=2.5), while in the
other case fc=0.006 (producing α=1.5).
As expected, the Pa becomes more “Rayleigh-like” as
α increases, but the change in Pa is relatively modest
(generally < 10 %) except for small scattering angles (Θ<30∘). The impact of the Pa is discussed further in
Sect. .
The aerosol phase function at 675 nm as a function
of SSA (or Θ) for the aerosol size distributions listed in
Tables –.
The aerosol phase function at 756 nm as a function
of SSA (or Θ) for the aerosol size distributions listed in
Tables –.
The aerosol phase function at 869 nm as a function
of SSA (or Θ) for the aerosol size distributions listed in
Tables –.
The aerosol phase function at 1020 nm as a
function of SSA (or Θ) for the aerosol size distributions listed in
Tables –.
Properties of altitude-normalized radiances (ANRs)
As explained in Sect. , the V1 algorithm uses ANRs rather than radiances to define the measurement vector
y. The ANR is defined as ρ=I(λ,h)/I(λ,hn), with the radiance at the tangent height h of interest
divided by the radiance at a selected normalization tangent height
hn>h. For the V1 algorithm, hn=40.5 km. In
Fig. , ρ at 675 nm is calculated for a range of scattering
angles using the V1 OMPS LP ASD. The βa, ozone, pressure, and
temperature profiles are fixed for the radiance calculations shown in
Fig. , in order to isolate the dependency of ρ on Θ
and R. In Fig. , h and hn are 25.5 and 40.5 km,
respectively.
Aerosol phase function sensitivity to perturbations
of the aerosol size distribution, using the V1 aerosol size distribution as a
baseline. Note that the ±10 % perturbations of r1,σ1,r2,
and σ2 also involved adjustments of fc to keep α≈2. Two other perturbations are shown, in which the values of r1,σ1,r2, and σ2 remain at the V1 aerosol size distribution
values, but fc is varied: CMF0.0012AE2.5 (red line) indicates
that fc=0.0012 (producing α=2.5), and
CMF0.0060AE1.5 (black line) indicates that fc=0.06 (producing
α=1.5)
Altitude-normalized radiances (ANR, or ρ) at
675 nm as a function of SSA (or Θ) under aerosol-free and aerosol
conditions, with both non-reflecting (R=0) and perfectly reflecting (R=1) surface conditions. The values of h and hn are 25.5 and
40.5 km, respectively.
When aerosols are excluded from the model atmosphere, Fig. shows
that the ρ is insensitive to both Θ and R. But when aerosols
are included, several effects emerge:
ρ is sensitive to Θ due to the strong variation of the
Pa with Θ, as shown in Fig. . For cases in
which R is low, the variation of ρ with Θ can be estimated by
the variation of the phase function ratio Pa/PR, in
which the Pa is divided by the Rayleigh phase function
PR. The phase function ratio varies with Θ as shown in
Fig. .
ρ also shows some dependence on R when aerosols are included.
However, this effect is relatively small compared to the effect of R on the
radiance, which can reach 100 % at large values of R.
As noted above, ρ decreases with increasing Θ, showing similar
behavior to the Pa/PR ratio when the underlying scene
is dark. But this decrease becomes more gradual for brighter scenes, in which
the ρ dependence on Θ is flattened out. As the underlying scene
becomes brighter, the limb radiance is influenced more by DUR. This upwelling
radiation illuminates the LOS from a variety of directions, reducing the
influence of the solar scattering angle Θ on ρ. As a result,
ρ becomes less sensitive to the details of Pa(Θ) as R
increases.
The ratio of APF (or Pa) to RPF (or
PR) as a function of SSA (or Θ) for the V1 OMPS LP aerosol
size distribution. This ratio declines by a factor of ≈50 between
forward (Θ=0∘) and backward (Θ=180∘) scattering
conditions.
Daily zonal mean aerosol scattering index (ASI, or
y) measured by the SNPP OMPS LP instrument. This picture corresponds to
center slit observations on 23 September 2015. The x axis is labeled with
both the event number (solid) and tangent point latitude (italics). The color
scale is nonlinear, designed to highlight relatively small y values
in the SH.
Retrieval algorithmAerosol scattering index (ASI)
The V1 algorithm uses the ASI as its measurement
vector y. The ASI is defined as y(λ,h)=(ρm-ρR)/ρR, where ρm is the measured ANR and ρR is the ANR
calculated assuming an aerosol-free (and therefore purely
Rayleigh scattering) atmosphere bounded by a Lambertian reflecting surface of
reflectivity R. The value of R is derived from 675 nm sun-normalized
radiances measured at hn=40.5 km, as discussed in
Sect. . The radiance calculation that determines R assumes that no
aerosols are present along the LOS at hn=40.5 km, which forces
y=0 at hn. We initially assume a climatological ozone profile
to account for the weak ozone absorption at 675 nm. The ozone estimate is
then updated at the final step of the retrieval, as described in
Sect. .
For an optically thin LOS, the ANR is approximately the sum of
ρa (the ANR due to aerosol scattering) + ρR (the ANR due
to Rayleigh scattering). In that case, the measured ANR =ρm≈ρa+ρR, and therefore the ASI =y≈ρa/ρR. It is also true under these conditions
that ρa≈βa×Pa. However,
under more general conditions the scattering contributions cannot be treated
independently: attenuation of Rayleigh-scattered photons by aerosols (and
vice versa) can cause y to become negative at some altitudes. This
indicates that the aerosol attenuation effect has exceeded the aerosol
scattering effect. This behavior can be seen in Fig. ,
particularly at the southern end of the orbit (where the OMPS LP aerosol
signal is weakest). Figure shows a strong hemispheric contrast
in y, which simply reflects the variation of Pa with
Θ.
Finally, note that use of y (and its dependence on ρ) is
best suited for a circumstance in which an “aerosol-free” layer lies above
the aerosols of interest. That implicit assumption is consistent with the
fact that H2SO4 droplets evaporate completely in the 30–35 km
altitude range, due to the warmer stratospheric temperatures at that level
. But use of y makes us unable to detect aerosol
scattering that has a constant mixing ratio with height (relative to
molecular scattering), so the contributions of other aerosol sources such as
meteoric smoke require further investigation.
Inverse model
The V1 algorithm uses OMPS LP radiance measurements at a single wavelength
(675 nm) to estimate the βa profile. This wavelength was
selected primarily to provide aerosol information to the V2.5 ozone code that
uses a wavelength triplet (consisting of 510, 600, and 675 nm) to retrieve
the ozone profile . Since both βa and
Pa have strong wavelength dependence in the stratosphere, aerosol
profiles derived from a wavelength near the Chappuis ozone band are expected
to minimize aerosol-related errors in the ozone retrieval.
Several additional advantages make selecting a wavelength near 700 nm
optimal for OMPS LP aerosol retrievals. Wavelengths < 500 nm feature weak
ozone absorption, but large Rayleigh scattering obscures the aerosol signal.
OMPS LP also measures wavelengths longer than 675 nm, but these tend to be
more affected by internal instrument SL. The OMPS LP instrument
was designed and characterized primarily with the goal of ozone retrieval,
and therefore successful characterization of SL at the longer wavelengths is
an ongoing project. Longer wavelengths are also more sensitive to the
highly uncertain ASD than 675 nm (see
Figs. –), making 675 nm attractive for
βa retrievals.
The V1 algorithm uses the Chahine nonlinear relaxation
method to obtain the βa from the OMPS LP
measurements. Since ASI is roughly proportional to βa, we use
ASI as the measurement vector y, which is updated iteratively as
shown in Eq. (), based on the notation of ,
Sect. 6.8:
xin+1=xinyimyin.
The symbol xin represents the state vector
(βa) at altitude zi after n iterations of the
retrieval algorithm. The measurement vector yim represents the
measured y at tangent height hi=zi. The GSLS RT model
calculates the ASI vector yin at each iteration, using the
βa profile given by xin. The iterative
process is initialized with a nominal first-guess aerosol profile
xi0 derived from 2000–2004 SAGE data (shown as
Fig. ), which does not vary with latitude or season.
Figure shows the daily zonal mean βa retrieved
from the y values shown in Fig. . Note that the hemispheric
asymmetry shown in the y figure is not repeated in the
βa figure.
The first-guess aerosol extinction (or AE) profile
used in the V1 OMPS LP aerosol extinction retrieval algorithm.
Daily zonal mean aerosol extinction for center slit
observations on 23 September 2015 (derived from the y measurements
shown in Fig. ).
Daily zonal mean aerosol scattering index (ASI, or
y) residuals for center slit observations on 23 September 2015
(derived from the y measurements shown in Fig. ).
The retrieval is constrained to limit changes within a single iteration:
xi can increase by no more than a factor of 2, while decreases are limited
to be a factor of 5 or less in each iteration. The algorithm executes just
three iterations, which constrains the final solution at each altitude xi3
within the range of values xi0/125≤xi3≤8xi0. The retrieval
algorithm sets xi to zero for observations with weak aerosol signals
(where yim<0.01). Data at altitudes for which a cloud has been
detected by the algorithm described by are flagged. An example
of the residual (difference between measured and calculated) y is presented
in Fig. , which demonstrates general convergence to the
±2 % level except at altitudes < 15 km and for regions near the
South Pole (where the SNPP OMPS LP aerosol signal is weakest).
Ozone correction
The V1 βa algorithm operates independently from the ozone
retrieval algorithm . As noted in Sect. , a
climatological ozone profile is assumed during the iterations of the
βa retrieval. After those three iterations are complete, an
approximate ozone correction is applied as follows. For λ1,λ2, and λ3=510, 600, and 675 nm,
respectively, we define Y(h,λi)=Yi in terms of the measured
radiance (Im) and the calculated radiance (Ic) at
each wavelength:
Yi=lnIm(h,λi)Ic(h,λi).
Based on these three Y values, we define a three-parameter fit:
Yi=a+bλi+cσi,
where σi = the ozone absorption cross section averaged over the OMPS
LP bandpass centered at λi. The c parameter represents the
sensitivity of the ozone slant column density with respect to the first
guess and can be determined from Eq. ():
c=(Y2-Y1)(λ3-λ2)-(Y3-Y2)(λ2-λ1)(σ2-σ1)(λ3-λ2)-(σ3-σ2)(λ2-λ1).
The ozone-corrected value of Y at 675 nm is therefore denoted by
Yc(λ3):
Yc(λ3)=Y(λ3)expcσ(λ3).
A similar correction is also applied to the value of Y at the normalization
tangent height to obtain Yc(hn,λ3).
Error analysis
This section describes the most significant categories of uncertainty that we
anticipate will limit the accuracy and precision of the V1 retrievals.
Quantitative estimates of the anticipated error are provided when possible,
but a full algorithm error budget is beyond the scope of this study.
Unfortunately, many uncertainties are difficult to quantify for the full
range of possible conditions.
To provide an overall context for assessing the significance of various error
sources, we begin by detailing the process used to estimate the atmospheric
number density profile used in the V1 βa retrieval
algorithm. This profile is derived from the operational geopotential height
product provided by the NASA Global Modeling and Assimilation Office (GMAO),
which has reduced quality at altitudes above 35 km. The resulting
uncertainty has been estimated by comparisons with the Modern-Era
Retrospective analysis for Research and Analysis, Version 2 (MERRA-2) fields,
which incorporate Microwave Limb Sounder
(MLS) temperature profiles above 35 km .
This comparison indicates both noise and bias at the 1–2 % level for
calculation of radiances at h=40 km.
We therefore neglect error sources that exist below this 1–2 % “floor”
level and concentrate on error sources that exceed that threshold. This
criterion eliminates both stray light and random error associated with the
OMPS LP measurements, which typically are < 1 %.
Uncertainty due to measurement errors
As defined in Sect. , our measurement vector y is influenced
by four radiances (all at λ=675 nm): the measured radiance at the
tangent height of interest hi and the normalization tangent
height hn, and the calculated radiance (excluding aerosol from
the model atmosphere) at the same tangent heights. The primary source of
error in y appears to be the SL error at
hn. OMPS LP stray light acts roughly as an additive effect
and therefore affects the measured radiance at
hn much more strongly than the other radiances that form
y, due to the roughly exponential decrease of I with tangent
height. Internal analysis suggests that this error is 1 %, and it therefore
produces fractional error in x=0.01/y. Stray-light error therefore
becomes most significant at altitudes and latitudes where y is small
(<0.1). As shown in Fig. , this condition is most likely to
occur near the top of the Junge layer (h≈35--40 km) and/or near
the South Pole (where SNPP OMPS LP provides unfavorable viewing conditions
for βa retrieval, with large Θ producing small
Pa values).
Contour plot showing the ratio of the Ångström
coefficient α for a given aerosol size distribution to the V1 aerosol
size distribution α≈2. Cases for which this ratio is within
±5 % of 1 are highlighted in white. The coarse-mode properties are
fixed in this example at the V1 aerosol size distribution values (r2=0.32µm; σ2=1.6), while the fine-mode properties vary in
the vicinity of the V1 aerosol size distribution values (r1=0.09µm; σ1=1.4). Red circles indicate the individual
cases calculated to create this figure.
The background contour plot is the
same as in Fig. . This time, red circles appear only for
cases in which the Ångström coefficient ratio is within ±5 % of 1
and the aerosol phase function is within ±10 % of the V1
aerosol size distribution value at Θ=60∘. Nearly every
aerosol size distribution that satisfies the Ångström coefficient ratio
criterion also satisfies the aerosol phase function criterion for this case.
Identical to
Fig. except that the aerosol phase function
comparison is done for Θ=120∘. For this viewing geometry,
the aerosol phase function criterion is much more useful in determining the
aerosol size distribution properties: Note the smaller number of red circles
(relative to Fig. ), centered around the true
values of r1 (0.09 µm) and σ1 (1.4).
Uncertainty due to radiative transfer limitations
The GSLS radiative transfer model used in the V1 OMPS LP βa
retrieval algorithm contains several limitations that affect the retrieved
βa profiles. The most significant issues are listed below, in
order of priority.
Uncertainty in the aerosol scattering phase function Pa
As described in Sect. , we have selected a bimodal LN ASD to
calculate the assumed Pa used in the V1 βa
retrieval algorithm. However, we cannot expect that any single ASD will be
correct for the full range of OMPS LP observations. And even if a single ASD
were suitable, many plausible combinations of r1,σ1,r2,σ2,
and fc exist that would fit the criterion stated in
Sect. (α≈2) equally well, as shown in
Fig. . Whether these “plausible” ASDs produce significantly
different Pa values depends strongly on Θ. As shown in
Fig. , the Pa for backscattered directions varies
much more strongly with Θ than the Θ=30–90∘
directions. The sensitivity of Pa to ASD for the cases shown in
Fig. is illustrated in
Figs. –.
Since ρa is approximately proportional to Pa for
optically thin LOS, differences between the assumed and true Pa
values map directly into βa errors in the V1 algorithm.
Figure therefore predicts that the OMPS LP
βa retrievals for Θ=120∘ will be greatly
affected by the assumed ASD in the retrieval, while
Fig. shows that the OMPS LP βa
retrievals for Θ=60∘ will be nearly insensitive to the
assumed ASD. The preceding analysis roughly estimates the possible error that
may result in the V1 OMPS LP βa retrievals, but it provides
no clear method to estimate the error in a
single retrieval at a particular place, time, and altitude. This topic will
be explored more thoroughly in a future publication.
Uncertainty due to LOS variation in atmospheric properties
As noted in Sect. , the RT model in the V1 OMPS LP βa
retrieval assumes that the atmospheric properties vary only with altitude.
This assumption is used to retrieve βa for each measured
image, independent of the neighboring images. But the maps of retrieved
βa values regularly feature large horizontal variations,
particularly latitudinal variations (see Fig. ). Many such
features persist at particular latitude ranges for which stratospheric
dynamics are known to cause steep horizontal gradients in βa
at a given altitude.
The viewing geometry of OMPS LP (looking backwards along the sun-synchronous
orbital track) exacerbates this problem, due to the zonal gradients in
βa seen in Fig. , but LOS variations of
atmospheric properties affect all limb-viewing retrieval methods. Past limb
missions have developed a two-dimensional retrieval strategy that allows
variation of the retrieved quantity both along the LOS and with altitude. The
MLS (limb emission) mission and OSIRIS (LS) mission
have made notable progress in this area. The V1 OMPS LP
algorithm remains a 1-D solution (with βa varying only with
altitude). This assumption is likely to affect the retrieval most strongly at
the edge of the tropics (where βa tends to have a large
horizontal gradient), in the Northern Hemisphere (where y varies
rapidly with Θ), and at the edges of a fresh volcanic cloud.
Uncertainty due to approximate treatment of DUR
The limb LOS is illuminated from above (overwhelmingly by direct solar
radiation) and from below (by photons scattered within the underlying
atmosphere and/or reflected by the underlying surface). The latter source of
radiation is modeled as described in Sect. : A Lambertian surface is
assumed to lie beneath the model atmosphere (which is not updated outside the
range at which the βa is retrieved during the iteration
process). This assumption allows one to determine R, the effective
Lambertian surface reflectivity that is consistent with the measured radiance
at hn=40.5 km.
This assumption provides a first-order estimate of the DUR, but this estimate
will generally be imperfect for the following reasons:
The simple assumptions described above generally fail to represent the
true conditions below a given LOS in multiple ways: the atmosphere will
generally include clouds and aerosols below the LOS that are not included in
the model atmosphere. The true bi-directional reflectance distribution model (BRDF)
of the scene will also generally be
non-Lambertian. In such cases, the upwelling radiation in the model
calculation will have a different angular distribution than the upwelling
radiation in the true atmosphere.
For an inhomogeneous underlying scene, the effective LER may also vary
with h, due to the varying solid angle that contributes to I(h). The
difference between LER (h=40 km) and LER (h=50 km) is typically
slight (see Fig. ), implying that this is a minor effect, but
more research is needed to assess whether any systematic relationships exist.
LER retrieved from radiances at h= 40 km (blue line) and
50 km (green line). Center slit observations from orbit 20234 are used in
this example. Again, as noted in Fig. , the OMPS LP aerosol
extinction retrieval is insensitive to LER.
Inverse model errors
This section includes several effects unrelated to the radiative transfer
model that affect the V1 OMPS LP βa retrieval, again listed
in order of priority.
Large aerosol extinction
As noted in Sect. , the algorithm limits possible variation of
the retrieved βa value. As a result, the retrieval often
“saturates” at the maximum allowed value when the βa is
large relative to the first-guess profile. At higher extinction values,
the retrieval will also be more influenced by inhomogeneity along the LOS,
since the LS radiance will be more influenced by the LOS segment nearest the
sensor (see item 3 below).
Cloud detection algorithm
The current cloud detection algorithm detects clouds well, but
it sometimes also flags fresh volcanic aerosols as clouds. Since retrieval of
such aerosols is quite complicated for several reasons discussed earlier (LOS
inhomogeneity, uncertainty about the appropriate Pa due to a
mixture of aerosol types and shapes, etc.), we have not attempted to fix this
error.
Poor convergence
The algorithm often does not converge well for scenes in which the y
has large horizontal gradient. We believe that this occurs because of 2-D
effects discussed earlier in Sect. , which produce an asymmetry in
the LS radiance contribution function. Under optically thick conditions, the
LS radiance will be influenced by the atmosphere near the satellite much
more than the atmosphere far from the satellite at a given altitude.
This effect is illustrated in Fig. 6c of
. Fixing this problem will require the development of a 2-D
aerosol algorithm.
Ozone correction errors
The 675 nm radiances used in the V1 OMPS LP βa retrieval
algorithm lie within the Chappuis ozone absorption band, and therefore the
βa estimate is influenced by possible differences between the
true ozone profile and the ozone profile that is assumed in the calculation
of yin in Eq. (). We therefore apply the ozone correction
described in Sect. to reduce this source of error. This
correction produces the largest percentage change in the retrieved
βa value when the following conditions are met:
the a priori ozone concentration differs significantly from the true
ozone concentration,
y is relatively small for a given βa value,
the βa value itself is small.
The first condition is most likely to occur for regions with highly variable
ozone profiles. The second condition will prevail for regions that are viewed
by OMPS LP at large Θ values, where the corresponding Pa
value is small. The third condition occurs primarily in regions with low
βa values, typically where sinking air prevails in the
mid-stratospheric region.
The largest ozone corrections therefore typically appear near the South Pole,
where minima for both the y and βa at a given
altitude tend to occur, as shown in Figs. and ,
respectively. The ozone profile also exhibits large variation in this region,
partly due to the formation of the Antarctic spring ozone hole. Under these
extreme conditions, the ozone correction produces changes in the retrieved
βa value as large as 20 %. For a more typical case in the
tropics, the βa changes by < 3 % when the ozone
correction is applied.
OMPS LP V1 (blue line) and OSIRIS V5.07 (red line)
retrieved aerosol extinction daily zonal means at selected altitudes from
2012 to 2016, at latitudes between 10 and 0∘ S. The OSIRIS data
set reports aerosol extinction at 750 nm, so the OMPS aerosol extinction was
converted from 674 to 750 nm by using the Ångström coefficient consistent
with the aerosol size distribution assumed in the OMPS LP V1 algorithm.
Preliminary evaluation of retrieval results
In this section, we will only present an early qualitative evaluation of OMPS
LP V1 βa data in comparison with profiles derived from OSIRIS
LS radiances and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observation (CALIPSO; ) backscattered lidar measurements. A
detailed validation paper for the OMPS LP βa retrievals is in
preparation.
Figure shows OMPS LP V1 and OSIRIS V5.07 retrieved
βa in the tropics. In general, the two data sets agree to
within 25 %. OSIRIS daily means are noisier because of its relatively
limited coverage, which provides fewer profiles for a given day than
OMPS. Both OMPS and OSIRIS show enhanced aerosol values at 18.5 and 20.5 km
following the tropical volcanic eruptions of Nabro (June 2011) and Kelut
(February 2014). Transport of the plume associated with Calbuco (which
erupted in the Southern Hemisphere in May 2015) is also evident. At 20.5 km,
OMPS measurements are lower than OSIRIS during the peak of the Kelut plume, most
likely caused by the retrieval's restriction on the number of iterations (see
Sects. and ), although differences between the
OMPS LP and OSIRIS coverage patterns can contribute to such differences. At
30.5 km, both instruments clearly show the quasi-biennial oscillation (QBO)
signature of enhanced βa values during easterly shear
conditions of the QBO during early 2012, 2013–2014,
and 2016, caused by enhanced aerosol lofting. The lower values of
βa in 2012 and 2015 are associated with westerly shear
conditions of the QBO, causing downward aerosol transport.
Monthly zonal mean aerosol extinction profiles at
750 nm derived from CALIPSO, OSIRIS, and OMPS LP measurements during the
aftermath of the Kelut eruption in 2014.
Figure shows monthly zonal mean βa profiles at
750 nm derived from CALIPSO, OSIRIS, and OMPS LP measurements during 2014.
This time series is averaged from 5 to 0∘ S, and altitudes
15–35 km are illustrated. CALIPSO data were provided by and
. The three instruments track Kelut injection of volcanic
aerosol at 20 km and the upward lofting of the aerosol to higher altitudes
(≈ 25 km) within a few months. The CALIPSO data are based on a
series of narrow lidar swaths, so its coverage differs from OSIRIS and OMPS
LP coverage. Vertical resolution differences might also explain some the
differences seen among the three instruments.
Conclusions
The OMPS LP V1 aerosol extinction (βa) retrieval algorithm is
summarized in this document. The V1 algorithm differs from the most
recently published OMPS LP algorithm (given in ) in
several ways:
the βa profile is retrieved at a single wavelength,
675 nm;
the retrieval uses the solution method;
the assumed ASD is bimodal lognormal, guided by the aerosol
properties measured by with the coarse-mode fraction tuned
to produce Ångström coefficient α(525/1020)≈2.
The main motivation for these changes was to produce a simpler algorithm that
works with the best-characterized OMPS LP radiances. The resulting
βa profiles are more stable and permit more straightforward
analysis of the radiance residuals. Initial comparisons with coincident
OSIRIS and CALIPSO βa data show similar spatial and temporal
variation over the lifetime of the OMPS LP instruments.
The accuracy of the absolute value of the OMPS LP βa remains
variable, primarily due to uncertainty about the appropriate ASD to be used.
The V1 ASD selection was guided by the Ångström coefficient measured by SAGE
II during volcanically quiescent periods. But the lack of contemporaneous
global observations of the ASD presents a significant challenge for all LS
βa retrievals, particularly for observations at Θ>90∘ (Southern Hemisphere conditions for OMPS LP). The
recently launched ISS SAGE III instrument is capable of both SOT and LS
observations, which should provide valuable information to reduce uncertainty
in the Pa for stratospheric aerosols.
Future work to improve the OMPS LP βa algorithm will begin by
adding consideration of additional wavelengths. Longer wavelengths are
sensitive to lower tangent heights that typically saturate at 675 nm due to
interference by Rayleigh scattering and are also more sensitive to small
aerosol signals (such as OMPS LP encounters in the Southern Hemisphere).
Additional wavelengths also will allow us to asses the self-consistency of
the measured βa wavelength variation with the Mie theory
prediction for the assumed ASD. A 2-D algorithm will also improve performance
in the vicinity of large horizontal variations. The ability to allow the ASD
to vary with height will also be valuable, given better ASD information.
The retrieved profiles produced by the V1 OMPS LP aerosol
extinction retrieval algorithm are publicly available at the following site:
https://ozoneaq.gsfc.nasa.gov/data/ozone/ (NASA, 2018).
The Suomi NPP daily files for the aerosol extinction product are labeled as
“OMPS Limb Profiler –Suomi NPP – LP-L2–AER675-DAILY”.
The authors declare that they have no conflict of
interest.
Acknowledgements
This paper includes material that first appeared at the 8th and 9th
Atmospheric Limb Workshops, which were hosted by Chalmers University
(Gothenburg, Sweden, September 2015) and the University of Saskatchewan
(Saskatchewan, Canada, June 2017), respectively. This research was supported
by NASA Goddard Space Flight Center through SSAI subcontracts 21205-12-043
and 21702-17-010. The authors recognize the contributions of the SAGE,
OSIRIS, SCIAMACHY, CALIPSO, and University of Wyoming teams to maintaining
high-quality stratospheric aerosol data, and particularly thank Larry
Thomason, Terry Deshler, Adam Bourassa, Landon Rieger, Christian von Savigny,
Alexei Rozanov, and Jean-Paul Vernier for helpful insights into the
stratospheric aerosol problem. Surendra Bhatta contributed significantly to
preparing the figures and references. The NASA, SSAI, and NOAA OMPS teams
supported this research and contributed many useful discussions, including
Larry Flynn, Matt DeLand, Jack Larsen, and Tong Zhu. Finally, several summer
research students contributed to studies that have improved this work,
including Nelson Ojeda, Ryan McCabe, and Ashley Orehek.Edited by: Christian von Savigny Reviewed by:
two anonymous referees
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