Satellite measurements enable quantification of atmospheric
temperature, humidity, wind fields, and trace gas vertical profiles. The
majority of current instruments operate on polar orbiting satellites and
either in the thermal and mid-wave or in the shortwave infrared spectral
regions. We present a new multispectral instrument concept for improved
measurements from geostationary orbit (GEO) with sensitivity to the boundary
layer. The JPL GEO-IR Sounder, which is an imaging Fourier transform
spectrometer, uses a wide spectral range (1–15.4 µm) encompassing
both reflected solar and thermal emission bands to improve sensitivity to
the lower troposphere and boundary layer. We perform retrieval simulations
for both clean and polluted scenarios that also encompass different
temperature and humidity profiles. The results illustrate the benefits of
combining shortwave and thermal infrared measurements. In particular, the
former adds information in the boundary layer, while the latter helps to
separate near-surface and mid-tropospheric variability. The performance of
the JPL GEO-IR Sounder is similar to or better than currently operational
instruments. The proposed concept is expected to improve weather
forecasting as well as severe storm tracking and forecasting and also benefit local
and global air quality and climate research.
Introduction
The Program of Record (PoR) of current and planned satellite observations,
as described in the 2017 US Earth Science Decadal Survey (National Academies of Sciences, Engineering, and Medicine, 2018), includes
a range of spectrally resolved radiance measurements in the thermal and
shortwave infrared (TIR and SWIR) wavelength regions that provide key
information on atmospheric temperature (TATM), water vapor (H2O), and a
range of trace gases (see Table 1 for a definition of spectral range
designations). The TIR region can be further subdivided into mid-wave,
longwave, and very longwave infrared (MIR, LWIR, and VLWIR) regions. Profiling
of key gases including CO, CH4, and CO2 with sensitivity to
planetary boundary layer (PBL) abundances was identified as a gap in current
capability in the 2017 Decadal Survey, as was the promise of multispectral
approaches for addressing this gap. In fact, combining radiances from the
(thermal-emission-dominated) TIR and (solar-reflection-dominated) SWIR
spectral regions has been shown to increase the vertical information content
for these gases, providing improved information on near-surface variations
relative to retrievals from the thermal alone (e.g., Christi and Stephens, 2004; Worden et al., 2010, 2015;
Kuai et al., 2013; Fu et al., 2016; Zhang et al., 2018; Schneider et al., 2021). Such retrievals have the potential
to extend the utility of satellite products for air quality forecasting,
greenhouse gas monitoring, and carbon cycle research. In addition, combining
TIR and SWIR infrared radiances also offers opportunities for increasing the
vertical information of H2O retrievals in the PBL, another topic
highlighted by the Decadal Survey and by the NASA Decadal Survey PBL
Incubation Study Team (Teixeira et al., 2021). Under clear-sky conditions, the SWIR provides
sensitivity to H2O (e.g., Noël et al., 2005; Trent et al., 2018; Nelson et al., 2016), CO (e.g., Buchwitz et al., 2004; Deeter et al., 2009;
Landgraf et al., 2016; Borsdorff et al., 2017, 2018), CH4 (e.g., Buchwitz et al., 2005; Frankenberg et al., 2006; Yokota et al., 2009; Hu et al., 2018; Parker et al., 2020)
and CO2 (e.g., Buchwitz et al., 2005; Yokota et al., 2009; O'Dell et al., 2018) throughout the full
atmospheric column, providing complementary information to the TIR radiances
that are strongly sensitive to the details of the profile of TATM, H2O,
and trace gases but have variable sensitivity to the PBL, depending on
surface and atmospheric conditions.
Spectral ranges and their designations used in this study.
Table 2 shows a list of current and planned missions making
spectrally resolved, spaceborne TIR and SWIR measurements. In low Earth
orbit (LEO), the MOPITT instrument on the Terra platform has been providing
a record of TIR + SWIR CO for over 2 decades (Buchholz et al., 2021). GOSAT provides spectrally resolved TIR and SWIR radiances on the same platform,
with coverage of SWIR CO2 and CH4 bands, as well as H2O
absorption (Trent et al., 2018), but not SWIR CO. The TROPOMI instrument on the
Sentinel-5P satellite flies in formation with the CrIS instrument on the
Suomi-NPP satellite, providing nearly coincident observations of TIR and SWIR as well as
presenting opportunities for multispectral retrievals of CO and CH4.
Measurements from geostationary (GEO) orbit can provide contiguous
horizontal (∼4 km) and temporal (full sounding disk coverage
in 1–2 h) resolution not possible from LEO (e.g., Schmit et al., 2009). The IRS
instrument onboard the Meteosat Third Generation Sounder platform will track
the four-dimensional structure of TATM and H2O (Holmlund et al., 2021). The GIIRS
instrument on the Fengyun-4 meteorological satellite has similar
capabilities (Yang et al., 2017). Adkins et al. (2021) describe in comprehensive detail the value of
a hyperspectral IR sounder in GEO orbit. Based on this report, an advanced
high-resolution IR sounder has been recommended for the Geostationary
Extended Observations (GeoXO) mission (https://www.nesdis.noaa.gov/next-generation-satellites/geostationary-extended-observations-geoxo, last access: 25 February 2022).
However, none of the current or planned instruments and missions listed in Table 2
provide TIR + SWIR measurements from GEO on the same platform.
Current and planned missions making spaceborne, spectrally resolved measurements of TIR and SWIR radiances. Note that MOPITT was designed to also offer measurements of CH4, although that did not materialize (hence the gray shading).
Here, we describe an instrument concept, called the JPL GEO-IR Sounder, that
would provide profiling of TATM, H2O, CO, CH4, and CO2, as
well as numerous other species important for air quality and the
hydrological cycle, from a geostationary platform. The JPL GEO-IR Sounder is
an imaging Fourier transform spectrometer that utilizes high-speed digital
focal plane arrays to record simultaneous TIR and SWIR spectra from each
pixel of the array (640×480 or 1024×1024 format). The
primary advantages of this sounder include the following:
coincident spatial and temporal retrievals of trace gases and TATM using
both SWIR and TIR bands multiple times per day;
combined TIR and SWIR retrievals provide for enhanced vertical resolution
with PBL visibility for TATM, humidity, and multiple trace gases;
capability for retrievals of 4D winds from combinations of TATM and
H2O temporal imagery as recently described using GIIRS data (Ma et al., 2021; Yin et al., 2021); and
provision of data products that are not readily obtained by combining
retrievals from PoR LEO and GEO sounders.
This paper is organized as follows: in Sect. 2, we describe the scenarios
used in the simulations. Section 3 provides brief descriptions of the
radiative transfer (RT), instrument, and inverse models. We discuss the
considerations imposed on simulated JPL GEO-IR Sounder retrievals in Sect. 4. In Sect. 5, we present results for TATM, H2O, and trace gas
retrievals from simulated GEO-IR Sounder measurements for both individual
spectral regions and combinations. The relevance of these simulated
retrievals for observing system simulation experiments is discussed in
Sect. 6. We arrive at some preliminary conclusions in Sect. 7. In
particular, we show that the JPL GEO-IR Sounder would, for the first time,
enable high-spatial- and temporal-resolution simultaneous retrievals in the
TIR and SWIR, which together provide more vertical profile information and
improved sensitivity to the PBL than either spectral region alone.
Scenarios
Representative atmospheric conditions, including TATM, H2O and
pollutant distributions, surface temperature, and other interferents, are
needed to understand satellite instrument performance. Using the Weather
Research and Forecasting model coupled to Chemistry (WRF-Chem) simulations
at 4 km spatial resolution over the continental United States (Mary Barth,
personal communication, 2012), we examined about 200 atmospheric profiles at six
local times for 2 d in July 2006 over 17 locations that represent a
range of diurnal meteorological conditions and a variety of air quality
scenarios. For the purposes of these simulations, we assume clear-sky
conditions. Simulation of conditions with significant aerosol loading and
cloud interference adds significant complexity and is beyond the scope of
this study. We calculate molecular absorption coefficients using the
Line-By-Line Radiative Transfer Model (LBLRTM; Clough et al., 2005).
The main goal of these simulations is to evaluate the retrieval
characteristics of TATM, H2O, and trace gases for different instrument
configurations. From our database of over 200 summertime atmospheric
profiles over the continental US, we selected two representative daytime
atmospheres: one near Houston to support the weather-focused observing
system simulation experiment (OSSE) analyses and the background trace gas
case and another in West Virginia that has more enhanced trace gas
pollutants near the surface. Note that we kept the solar and viewing
geometry as well as the surface albedo constant in order to isolate the
effects of different boundary layer trace gas concentrations. Figure 1 shows
the profile plots for TATM, H2O, and trace gases that we examine in
this paper (O3, CO, CH4, and CO2) at the two locations.
Scenarios considered in the simulations.
The emissivity is obtained from a database structured by month and
latitude–longitude coordinates. To populate the database, we used a global
land use and land cover classification system developed by the US
Geological Survey (Anderson et al., 1976) and mapped them into spectra from the ECOSTRESS
spectral library (Baldridge et al., 2009; Meerdink et al., 2019;
http://speclib.jpl.nasa.gov/, last access: 25 February 2022), as described in the TES Algorithm
Theoretical Basis Document (Beer et al., 2002). The albedo is calculated from the
emissivity using Kirchoff's law.
The location and times of the WRF-Chem profiles were used to calculate the
solar viewing geometry, assuming a geostationary satellite at 95 W. The NOAA
solar position calculator was used to verify the solar zenith and solar
azimuth calculations
(http://www.srrb.noaa.gov/highlights/sunrise/azel.html, last access: 25 February 2022).
ModelsRadiative transfer model
We use the accurate and numerically efficient
two-stream-exact-single-scattering (2S-ESS) RT model (Spurr and Natraj, 2011; Xi et al., 2015).
This forward model is different from a typical two-stream model in that the
two-stream approximation is used only to calculate the contribution of
multiple scattering to the radiation field. Single scattering is treated in
a numerically exact manner using all moments of the scattering phase
function. High computational efficiency is achieved by employing the
two-stream approximation for multiple-scattering calculations. The exact
single-scattering calculation largely eliminates biases due to the severe
truncation of the phase function inherent in a traditional two-stream
approximation. Therefore, the 2S-ESS model is much more accurate than a
typical two-stream model and produces radiances and Jacobians that are
typically within a few percent of numerically exact calculations and in most
cases with biases much less than a percent. This model has been widely used
for the remote sensing of greenhouse gases and aerosols (Xi et al., 2015; Zhang et al., 2015,
2016; Zeng et al., 2017, 2018). Aerosols are not included in the analysis since the
main objective was to investigate the impact of combining multiple spectral
bands and of varying instrument parameters. However, the RT model has the
capability of handling generic aerosol types.
The 2S-ESS RT model is used to generate monochromatic radiances at the top
of the atmosphere for the atmospheric profiles and surface conditions near
Houston over the entire spectral range considered for the JPL GEO-IR
Sounder. Figure 2 shows the spectral radiance computed on a 0.002 cm-1
wavelength grid. We also calculate the individual contributions of each
absorbing gas to the radiance. The gaseous absorption features have
different spectral distributions and line strengths, which can be used to
identify spectral windows for profile retrievals and recognize interfering
gases that also absorb strongly in the same channels.
Simulated top-of-the-atmosphere monochromatic radiances (black) in
the 650–7000 cm-1 wavelength range for an atmospheric profile near
Houston. Also shown are radiances corresponding to (red) O3, (green)
CO2, (blue) H2O, (orange) CH4, and (purple) CO absorption.
Instrument model
This section starts with a brief description of the spectrometer, primarily
to define the terms used in the instrument model. We then detail the focal
plane arrays and the optical filter that determine the bandpasses of the
instrument. The processing steps of the instrument model are then explained.
Finally, we show some of the resulting spectra produced by the model.
Optics overview
The JPL GEO IR Sounder uses a Michelson interferometer, which modulates the
light that passes through it. The interferometer is characterized by two
main parameters: the spectral resolution, which is directly proportional to
the maximum optical path difference (MOPD) between the two arms of the
interferometer, and the optical throughput or étendue, which is given by
the product of the area of the aperture stop and the angular field of view
(AΩ). From geostationary orbit, a ground pixel of 2.1 km subtends an
angle of 58.7 µrad, and for a focal plane array (FPA) of
1024×1024 pixels, the overall FOV is 60 mrad; this fits well
within the Fourier transform spectrometer (FTS) design parameters. In
parallel with the light from the target scene, a beam from an internal
metrology laser travels through the interferometer. This laser is used to
precisely measure the optical path difference to within a small fraction of
the laser wavelength. An imaging FTS (IFTS) shares many of the principles of
the traditional FTS; the main difference is that the detector is replaced
with an FPA. The main challenge in the IFTS design is in the FPA, which must
operate at a high frame rate (0.5–1 kHz) and high dynamic range (14–16 bits) to properly digitize the interferograms.
Focal plane arrays
The JPL GEO-IR Sounder FPA optics use a dichroic to split the
interferometer output along the wavelength dimension: radiation from 1 to 5.3 µm is sent towards FPA 1, and radiation from 5.3
to 15.4 µm is directed to FPA 2. Whereas FPA 2 is a
single-color detector handling its full domain at all times, FPA 1 is a
dual-color detector. The two colors of FPA 1 are operated sequentially,
recording either the 1 to 3 µm domain (SWIR; FPA 1a) or the
3 to 5.3 µm domain (MWIR; FPA 1b). This dual-color
operation is implemented inside the FPA by having two distinct detectors in
an optical “sandwich”. It is designed to minimize the effect of photon noise
in the low-light MWIR and SWIR domains. Furthermore, the SWIR FPA 1a
bandpass is narrowed by a triple-band optical filter tailored to the
regions that contain absorption bands of interest (Fig. 3). As listed in
Table 3, the SWIR domains of interest are (1) 4210–4350 cm-1, (2) 4810–4900 cm-1, (3) 6000–6150 cm-1, and (4) 6170–6290 cm-1.
Based on previous optical filter studies, we allow 200 cm-1 for the
filter slope on either side. Since the gap between the first two domains
would therefore be small and the signal there is low, these have been
merged (4210–4900 cm-1). Domains (3) and (4) have also been combined
(6000–6290 cm-1). In addition, the 1.27 µm oxygen band
(7780–8010 cm-1) will be used to measure the light path. We believe
that it is best to specify the 50 % transmission points for the filter
bands, as that is where the slope is maximum and hence most easily verified.
With a 200 cm-1 transition region, the 50 % point will be 100 cm-1 outside the bandpasses. Hence, the final triple-band filter
configuration is 4110–5000 cm-1 (2.000–2.433 µm), 5900–6390 cm-1 (1.565–1.695 µm), and 7680–8110 cm-1 (1.233–1.302 µm). The triple-band filter physically covers the two-color FPA 1. It is intended to limit the photon flux only in the SWIR mode of
operation, with the detector that is sensitive over the 1–3 µm
domain (FPA 1a). The filter must also be transparent over the 3–5.3 µm domain of the other shared detector (FPA 1b). It may be
possible to combine the first band of the triple-band filter (2–2.433 µm) with this MWIR transparency need (3–5.3 µm), but this has
not been simulated in this study.
Spectral ranges used in this study for simulated retrievals of CO, CH4, and CO2.
MoleculeSpectral rangesRelevant for(cm-1)Carbon monoxide (CO)2000–2250Air quality and carbon cycle4210–4350(combustion and fire emissions)1210–1380Greenhouse gas monitoring and carbon cycle (wetlands, oil and gas, agriculture)Methane (CH4)4210–43506000–6150Carbon dioxide (CO2)650–11002250–2450Greenhouse gas monitoring and carbon cycle4810–4900(human emissions, status of land and ocean carbon sinks)6170–6290Instrument model description
The instrument model for the JPL GEO IR Sounder allows us to explore the
instrument trade space and its effect on retrieved atmospheric composition.
It includes the ability to convolve synthetic spectra and Jacobians with the
instrument line shape (ILS). The model performs the following steps.
It reads synthetic data from the radiative transfer model. The radiance
spectrum is extended using blackbody curves simulating the Earth and the
Sun, and it is converted to a photon flux spectrum. After this step, the spectrum
is in units of photons m-2 sr-1(cm-1)-1 s-1.
It convolves the spectrum with the theoretical FTS ILS, given as
2Lsinc(2σL), where L is the MOPD and sinc(x)=sin(πx)/πx.
This expression of the ILS has unit area, and hence the convolution does not
change the overall magnitude or the units of the spectrum. It does, however,
reduce the spectral resolution, broadening all sharp features. In the same
step, we resample the spectrum on a coarser grid; i.e., we “decimate” the
spectrum. For example, in the current simulations, we reduce the wavenumber
interval by a factor of 50 from 0.002 to 0.1 cm-1.
It scales the spectrum by the étendue of the instrument. After this
step, the units of the spectrum are photons (cm-1)-1 s-1.
It applies further scaling to account for the single output design (whereby
half the light is sent through the instrument and the other half sent back
to the source), losses in the metallic coatings and at the uncoated optical
interfaces (i.e., compensator and back side of beamsplitter), the efficiency
of the beamsplitter coating, the quantum efficiency of the detector, and the
integration time of the analog-to-digital converter. After this step, the
units of the spectrum are photoelectrons per wavenumber.
It applies bandpass limits caused either by an optical filter or the working
domain of the detector.
It applies the Fourier transform to convert the spectrum into an
interferogram.
It computes the number of photoelectrons counted in each interferogram data
sample. From this, we can compute the photon noise. Subsequently, white
noise is added to the interferogram with a root mean square amplitude
matching the computed photon noise.
It simulates the interferogram digitization, which is performed for each pixel within
the read-out integrated circuit of the two FPAs.
It produces the final spectrum by Fourier transform. The signal-to-noise
ratio (SNR) is then evaluated by computing the noise level in blacked-out
regions on either side of the instrument bandpass and by locating the
maximum signal within the bandpass.
Spectral results
Figure 3 shows a JPL GEO-IR Sounder model spectrum for FPA 1a, covering
the SWIR domain. Figure 4 shows a similar spectrum for the VLWIR, LWIR, and
MWIR FPA bands: FPA 2 covers the VLWIR and LWIR domains, and FPA 1b
covers the MWIR domain. The spectral ranges include the range utilized by
existing TIR sounders (AIRS, CrIS, IASI) and selected bands in the SWIR. In
particular, the FPA 2 spectral range contains critical information for
radiance assimilation by weather forecasting algorithms (see, e.g., Eresmaa et al., 2017).
The spectral resolution (MOPD) of the JPL GEO-IR Sounder is configurable.
For these simulations, we choose to look at three possible MOPD options: a
CrIS-like spectral resolution (0.8 cm MOPD, 0.625 cm-1 resolution,
described as nominal spectral resolution or NSR in Table 4), an intermediate
option (2 cm MOPD, 0.25 cm-1 resolution), and a high-spectral-resolution option (5 cm MOPD, 0.1 cm-1 resolution, described as full
spectral resolution or FSR in Table 4). In order to make an “apples to
apples” comparison, we consider the same integration time (1 ms per
interferogram point) for these three options. The integration time is driven
by the high-spectral-resolution option. The native and binned
(footprint-averaged) ground sampling distance (GSD) is also indicated in
Table 4.
Simulated JPL GEO-IR Sounder spectrum in the SWIR domain. The SWIR
domain is subdivided into discrete bands using a triple-band interference
filter to maximize the SNR in spectral regions of interest (CO2,
CH4, CO, H2O, and O2).
Simulated JPL GEO-IR Sounder spectrum in the VLWIR, LWIR, and MWIR
domains. Note the logarithmic scale.
Comparison of the JPL GEO-IR Sounder with other state-of-the-art
instruments.
InstrumentGIIRSIRSCrISJPL GEO-IRSounderStatusIn space2023 launchIn spaceThis studyNationalityChinaEUUSUSOrbitGEOGEOPolarGEOLongitude (∘)104.7 E0–45 En.a.75–137 WSpacecraftDedicatedDedicatedDedicatedHosted payloadGSD, nadir (km)16 (prototype)4144.2 (binned)12 (follow-ons)2.1 (native)Spectral range (cm-1700–1130680–1210650–1095unless otherwise indicated)1650–22501600–22501210–1750650–10 000a0.55–0.75 µm2155–2550Resolution (cm-1)0.6250.6250.625NSRb=0.625,FSR =0.1Full disk revisit time (h)2–31121–2
a FTS instrument capability. b NSR: nominal spectral resolution. FSR: full spectral resolution.
FSR mode decreases retrieval biases caused by interfering absorbers.
Inverse model
We use an optimal estimation approach (Rodgers, 2000) and perform linear retrievals
from simulated radiances described in the previous section. The spectral
differences of the modeled and satellite-measured radiances and the
differences of the species profile and the a priori profile are mathematically
minimized: weighted by the measurement error and the a priori constraint. The
species profile can then be optimally derived.
The a priori constraint vectors for TATM and H2O are obtained from forecast
fields from the NASA Global Modeling and Assimilation Office, supplied for
use within the TES retrieval algorithm (Bowman et al., 2006). A priori constraint matrices are
constructed using the method described in Kulawik et al. (2006) from an
altitude-dependent combination of zeroth-, first-, and second-order derivatives
of the profiles. For TATM and H2O, the square roots of the diagonals of
the respective constraint matrices are on the order of 1.8–2.2 K and
15 %–18 %, respectively. A priori vectors for O3, CO, and CH4 are taken
from calculations using the Model for OZone And Related chemical Tracers
(MOZART3) (Brasseur et al., 1998; Park et al., 2004) that were performed for the purpose of
construction of trace gas climatologies for the Aura mission. For O3,
the square root of the diagonal of the constraint matrix is on the order of
25 % in the troposphere, 40 % in the stratosphere, and 15 % above. For
CO, this is set to 30 % over the entire atmosphere, while for CH4,
the values range from 2 %–10 %. The constraint matrices for CO are the same
as those used by the MOPITT algorithm (Deeter et al., 2010). For CO2, the a priori vector
and constraint used are described in Kulawik et al. (2010). The square root of the
diagonal of the constraint matrix ranges from 1.2 %–2 %. We note that these
profile constraints were developed for TIR instruments and therefore may
not capture strong near-surface variability. There could be scope for
increasing the near-surface information content via development of updated
constraints, although that work is outside the scope of this study.
The end-to-end retrieval analysis provides averaging kernels, which describe
the sensitivity of the retrieved atmospheric state to the true state;
degrees of freedom for signal (DOFS), which denote the pieces of vertical
information contained in the retrieved profile; and retrieval errors. These
metrics are used for evaluating the retrieval results for a variety of
spectral bands as well as spectral and spatial resolutions.
Considerations for simulated retrievals
For the retrieval simulations described here, we consider a somewhat
idealized scenario. Simulations have been performed for clear-sky,
no-aerosol conditions. In retrievals from actual measured radiances, even
for a clear-sky, non-scattering atmosphere, there is always some forward
model error due to, e.g., uncertainties in spectroscopy, interfering species,
and the treatment of the surface. With real data, these kinds of
uncertainties can lead to significant systematic errors in the retrievals,
particularly for well-mixed greenhouse gases such as CH4 and CO2.
For the simulations presented here, we have considered only the error term
associated with measurement noise.
The measurement noise associated with the simulated radiance is obtained
using the instrument model described in Sect. 3.2. The JPL GEO-IR Sounder
concept is configurable in terms of spectral range and spectral resolution,
with a native spatial resolution that corresponds to a 2.1 km footprint on
the ground. Different configurations of the instrument concept will affect
the number of photons available in each channel and therefore impact the
SNR. For a given integration time, lower spectral resolution
leads to correspondingly higher SNR. The SNR of the observed radiance
spectra can be increased by increasing the integration time. For
geostationary observations, this leads to a trade-off between measurement
noise and temporal resolution. An increase in the throughput (étendue) leads
to lower noise (Schwantes et al., 2002).
In retrievals from real data, higher spectral resolution can offer
advantages in terms of ability to distinguish between the target molecule
and interfering spectral signatures from other molecules with features in
the spectral range of interest, despite the increase in measurement noise.
In the results presented in this study, that advantage in reduction of
systematic error is not accounted for. The SNR can also be increased by
aggregating spatially. For example, aggregating four 2.1 km footprints would
increase the SNR by a factor of 2. Depending on the application of the
measurements, there may be some advantage to trading spatial resolution for
a gain in SNR.
ResultsTATM and H<inline-formula><mml:math id="M116" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O retrievals
High spectral resolution is necessary to provide the vertically resolved
TATM and H2O information critical for numerical weather prediction and
for many other applications including local extreme weather conditions and
global climate change. Current satellite-based TATM and H2O retrievals
mainly utilize TIR spectral measurements. Here we also examine information
gained from adding SWIR measurements. Tables 5 and 6 list the possible
choices of frequency range for TATM and H2O retrievals. Some of these
spectral ranges are used in current operational missions, while some are
candidates for future missions. We compare results for three values of
spectral resolution and for two values of spatial resolution.
DOFS for TATM retrievals for three spectral (MOPD) and two spatial
(GSD) resolution scenarios. The values shown here are for the Houston
profile.
* Instrument noise is reduced by a factor of 5 through footprint averaging
for the SWIR only, providing an effective GSD of 21 km.
Examining the above DOFS tables, we see competing effects of spectral
resolution (MOPD) and measurement noise. As described in Sect. 3.2, the
measurement noise (noise-equivalent spectral radiance, NESR) is estimated
for a fixed integration time for both the 2.1 and 4.2 km ground sampling
distance (GSD) configurations. The NESR for the MOPD =0.8 cm instrument
is therefore smaller than that for the MOPD =2 or 5 cm instruments.
Typically, however, the higher-spectral-resolution instruments provide
larger DOFS than the NSR instrument. For H2O retrievals, the optimal
DOFS are provided by the intermediate-resolution instrument.
The differences in DOFS for the two GSD values are obvious. This shows the
trade-off between spatial resolution and retrieval vertical resolution as well as
precision (not listed). Both GSDs provide high-precision, high-vertical-resolution TATM and H2O retrievals. We estimate the tropospheric
vertical resolution for TATM to be 1.5–2 km with <0.5 K precision
and for H2O to be 1–2 km with ∼5 % precision. In
comparison, representative tropospheric values for AIRS are 1 km for TATM
and 2 km for H2O (Irion et al., 2018).
The selection of spectral regions also affects the TATM and H2O
products. For example, using the VLWIR + LWIR + MWIR domain provides much
more sensitivity compared to using MWIR alone. Figure 5 shows averaging
kernel plots for TATM and H2O for the 4.2 km GSD option for four
spectral band combinations: VLWIR + LWIR, MWIR, SWIR, and
VLWIR + LWIR + MWIR + SWIR. The characteristics of the TIR TATM and H2O
retrievals are very similar to those obtained by currently operating
instruments. We note that the sensitivity of SWIR retrievals is mostly near
the surface. Further, the measurement noise in the SWIR was reduced by a
factor of 5 in these figures by averaging 25 pixels, thereby reducing the
effective GSD to 21 km. Note that this is worse than the 15 km AIRS/CrIS
native resolution but better than the 45 km that the TATM and H2O
products are typically reported on. Further, while 5×5 pixels may
be required for trace gas retrievals in the SWIR (see Sect. 5.2), which is
therefore a little worse than AIRS/CrIS, we measure TIR and SWIR at the same
time, eliminating bias from observing with separate instruments.
Plots of averaging kernel rows for (top) TATM and (bottom)
H2O. The spectral ranges are (from left to right) VLWIR + LWIR, MWIR,
SWIR, and VLWIR + LWIR + MWIR+ SWIR. These results are for the Houston case.
Trace gas retrievals
Among many possible detectable trace gases from the extended spectral
radiance measurements, we selected to examine profile retrieval
characteristics for O3, CO, CH4, and CO2 for the given
instrument configurations (see Table 3 for retrieval spectral ranges). Table 7 lists DOFS for the chosen trace gases for the West Virginia scenario.
Results for the FSR option are largely similar to those for the intermediate-spectral-resolution instrument and are hence not shown. The DOFS in Table 7
are broadly consistent with previously published work on species profile
retrievals from satellite observations (Beer, 2006; Connor et al., 2008; Deeter et al., 2009, 2015; George et al.,
2009; Kulawik et al., 2010; Worden et al., 2010, 2013; Clerbaux et al., 2015; Fu et al., 2016; Smith and Barnet, 2020). For a given spectral
resolution instrument, the higher DOFS in retrievals for the larger GSD case
for all species are due to the reduced measurement noise. For a given GSD,
the DOFS are slightly higher for the NSR case compared to the MOPD =2 cm
case, but the differences are small. It is worth reiterating that these
simulated retrievals represent an idealized scenario in which we assume
perfect knowledge of interfering species in the spectral range for any given
target species. In this scenario, with a constant integration time, the NSR
option provides results similar to the MOPD =2 cm option due to the
trade-off between spectral resolution and instrument noise.
Trace gas retrieval configurations and DOFS for the West Virginia
profile. TATM and H2O are simultaneously retrieved when listed.
* Instrument noise is reduced by a factor of 5 through footprint averaging
for the SWIR only, providing an effective GSD of 21 km.
Figure 6 shows averaging kernel plots for CO for MWIR- and SWIR-only
scenarios and for combined MWIR + SWIR retrievals. The combination of
wavelength regions provides improved sensitivity to the lower troposphere
compared to either spectral region alone. CO2 retrievals (Fig. 7)
benefit the most from the combination of VLWIR + MWIR + SWIR retrievals. The
SWIR domain adds sensitivity in the lower troposphere and near the surface.
The characteristics of the CO2 retrievals are in good agreement with
OCO-2/3 observations. For CH4 (Fig. 8), the addition of SWIR bands
also provides noticeable enhancement in lower tropospheric and near-surface
sensitivity. For CO retrievals, the contribution of the SWIR to the
near-surface sensitivity is less pronounced. The stronger contribution of
SWIR measurements to the total DOFS for CH4 and CO2 compared to CO
is a result of three factors: (1) lower top-of-the-atmosphere solar
irradiance in the CO spectral region relative to the CH4 and CO2
regions, (2) lower surface albedo, and (3) larger absorption, primarily by
H2O and CH4. Our results for O3 are broadly consistent with
published results for LWIR satellite observations (e.g., Nassar et al., 2008; Smith and Barnet, 2020).
Figures 6–8 use the same effective GSD of 21 km in the SWIR as described in
Sect. 5.1.
Plots of averaging kernel rows for CO retrievals. The spectral
ranges are (from a to c) MWIR, SWIR, and MWIR + SWIR. These results
are for the West Virginia case.
Plots of averaging kernel rows for CO2 retrievals. The
spectral ranges are (from a to c) VLWIR + MWIR, SWIR, and VLWIR + MWIR + SWIR. These results are for the West Virginia case.
Plots of averaging kernel rows for CH4 retrievals. The
spectral ranges are (from a to c) LWIR, SWIR, and LWIR + SWIR. These
results are for the West Virginia case.
Discussion: use of GEO-IR information in data assimilation and observing
system simulation experiments
We have focused in this paper on the characteristics of the measurements and
retrievals that we expect to obtain from the GEO-IR observing platform.
While this paper does not deal directly with the use of this information in
a data assimilation system, the results we have presented lay the necessary
groundwork for future work in this area. In particular, the detailed
characterization of uncertainties in the TATM and H2O retrievals
provided by this study can be directly incorporated into a set of weather
forecast OSSEs. We have begun this research and will report on the results
in a subsequent paper. Note that for a weather forecast OSSE to be
credible, it is crucial to represent the synthetic measurements as
accurately as possible. TATM and H2O precision and total error are
reported in Table 8; it can be seen that the errors for the MWIR-only
configuration are on the order of the errors in CrIS and AIRS retrievals,
while the full-spectrum JPL GEO-IR Sounder configuration yields total errors
that are smaller than those from either CrIS or AIRS. As such, assimilation
of information from JPL GEO-IR Sounder measurements is expected a priori to have as
much or greater impact on weather forecasts compared with existing
hyperspectral sounders. Note that the total error in the full-spectral-range
TATM and H2O retrievals is equivalent to, or less than, the uncertainty
reported for radiosonde measurements of these quantities (Rienecker et al., 2008, Table 3.5.2).
Estimates of total and precision errors for JPL GEO-IR Sounder,
CrIS, and AIRS TATM and H2O retrievals in the troposphere. Note that
data used for CrIS and AIRS retrievals were obtained near Houston, Texas, in
August 2020. Averaged retrieved cloud optical depths are limited to less
than 0.1, consistent with mostly clear-sky conditions.
We also note that there will be particular advantages and challenges in
assimilating the high-temporal-resolution data that will be available from
the JPL GEO-IR Sounder. The clear advantage is the ability to observe
rapidly evolving processes (e.g., the environment around thunderstorms and
hurricanes; see, e.g., Li et al., 2018). This information is not available from the
current LEO constellation. However, many modern data assimilation systems
are configured for assimilation of intermittent data (at best hourly in
operational data assimilation systems). While four-dimensional variational
data assimilation (4D-Var) is capable of ingesting data at non-synoptic
times, assimilation of sub-hourly data remains challenging. It is likely
that all but the most rapid-update data assimilation systems will require
modification to make best use of the high-time-frequency geostationary
soundings provided by the JPL GEO-IR Sounder.
Conclusions
In this paper, we present an end-to-end retrieval study for a proposed FTS
instrument covering the entire infrared spectral range from 1–15 µm
from a geostationary satellite orbit. An instrument model is used to derive
realistic measurement radiance and noise for several diurnal observations
over small ground footprints (e.g., 2.1 km). We perform TATM and trace gas
profile retrievals for the JPL GEO-IR Sounder that covers the entire VLWIR,
LWIR, MWIR, and SWIR spectral domains. Retrieval characteristics, such as
DOFS and measurement error, are examined in order to evaluate the
performance of several instrument configurations. These configurations
include VLWIR-, LWIR-, MWIR-, and SWIR-only, their combinations, and
different spectral and spatial resolutions for a realistic geostationary
observing system making field-of-view observations at fixed time intervals.
Two summertime atmospheres are used: a scenario near Houston as a clean-air
case and one in West Virginia representing a polluted scenario. We analyze
TATM, H2O, O3, CO, CH4, and CO2 profile retrievals.
High spectral resolution can provide improved ability to distinguish
absorption lines of the target species from interferents. In the case of
species (such as O3) for which much of the total column lies in the
stratosphere, higher spectral resolution also provides enhanced ability to
separate the tropospheric signal from the stratospheric signal. When the
total integration time is fixed, there is a trade-off between spectral
resolution and noise. In the idealized retrievals presented here, we assume
perfect knowledge of interfering species. In this case, three different
MOPDs provide comparable results in terms of DOFS. However, in the real
world, we would expect higher spectral resolution to offer advantages in
terms of reduction in systematic errors.
Compared to single-spectral-region instruments, e.g., only LWIR or MWIR,
combinations of VLWIR, LWIR, MWIR, and SWIR enhance the sensitivity of the
retrievals to the lower troposphere. In our analyses, we find that the
contributions from the SWIR in the combined measurements are noticeable for
both trace gas and TATM retrievals, especially when the ground pixels are
averaged to reduce measurement noise in the SWIR. In particular, the SWIR
measurements add information in the lower troposphere and for near-surface
species retrievals.
We limit the spatial resolution choices to GSD =2.1 and 4.2 km in our
simulations. Especially for multi-band retrievals, the results are
realistically adequate for many research applications for both ground
sampling footprints. We compare performance metrics (e.g., NESR and SNR) for
the proposed instrument with values for several current and past satellite
instruments in multiple spectral bands. The performance of the JPL GEO-IR
Sounder is similar to or better than currently operational instruments. At
the same time, the JPL GEO-IR Sounder provides much higher spatial and
temporal resolution as well as a wider range of trace gases than current
instruments that combine TIR and SWIR. The derived retrieval characteristics
(e.g., DOFS and retrieval errors) also compare favorably with currently
available products.
Code and data availability
The code and data are available from the authors upon request. The LBLRTM code is archived on GitHub: https://github.com/AER-RC/LBLRTM (AER-RC, 2020).
Author contributions
SPS, YHW, and LID conceived the work. VN provided the radiative transfer
model, led the simulated retrieval work, and prepared the paper. ML,
JFB, and ZCZ assisted with the retrievals. ML provided the trace gas
absorption and inverse models. JLN provided the profiles for the
simulations. JFB provided the instrument model. VHP and SPS
helped analyze the simulation results. LW, JAR, and DJP provided the
connection with OSSEs. All listed authors contributed to the review and
editing of this paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
A portion of this research was carried out at the Jet Propulsion Laboratory,
California Institute of Technology, under a contract with the National
Aeronautics and Space Administration (80NM0018D0004). The authors
acknowledge Susan Kulawik for helpful discussions on retrieval constraints.
Financial support
This research has been supported by the National Oceanic and Atmospheric Administration (grant no. BAA‐NOAA‐GEO‐2019) and the Jet Propulsion Laboratory Advanced Concepts Program.
Review statement
This paper was edited by Lars Hoffmann and reviewed by three anonymous referees.
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