Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100–1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient “super-emitter” point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.
Methane is a greenhouse gas emitted by anthropogenic sources including
livestock, oil–gas systems, landfills, coal mines, wastewater management, and
rice cultivation. Wetlands are the dominant natural source. The atmospheric
concentration of methane has risen from 720 to 1800 ppb since preindustrial
times (Hartmann et al., 2013). The resulting radiative forcing on an emission
basis is 0.97 W m
The United Nations Framework Convention on Climate Change (UNFCCC) requires
individual countries to report their annual anthropogenic greenhouse gas
emissions following bottom-up inventory guidelines from the International
Panel on Climate Change (IPCC, 2006). As an example, Fig. 1 shows the US
anthropogenic methane emission inventory for 2012 compiled by the
Environmental Protection Agency (US EPA, 2016) and reported to the UNFCCC.
The inventory uses advanced IPCC Tier
US national anthropogenic emission inventory for methane in 2012
compiled by the US EPA (2016). Units are Tg a
Targeted atmospheric measurements of methane can quantify emissions on small scales (point source, urban area, oil–gas basin) by measuring the ratio of methane to a co-emitted species whose emission is known (Wennberg et al., 2012) or by using a simple mass balance approach (Karion et al., 2013; Peischl et al., 2016; Conley et al., 2016). Quantifying emissions on larger scales, with many contributing sources, requires a more general approach where an ensemble of atmospheric observations is fit to a 2-D field of emissions by inversion of a 3-D chemical transport model (CTM) that relates emissions to atmospheric concentrations. This inversion is usually done by Bayesian optimization accounting for errors in the CTM, in the observations, and in the prior knowledge expressed by the bottom-up inventory. We obtain from the inversion a statistically optimized emission field, and differences with the bottom-up inventory point to areas where better understanding of processes is needed. A large number of inverse studies have used surface and aircraft observations to quantify methane emissions on regional to global scales (Bergamaschi et al., 2005; Bousquet et al., 2011; Miller et al., 2013; Bruhwiler et al., 2014).
Satellite instruments for measuring tropospheric
methane
Satellites provide global and dense data that are particularly well suited
for inverse analyses. Measurement of methane from space began with the IMG
thermal infrared instrument in 1996–1997 (Clerbaux et al., 2003).
Measurement of total methane columns by solar backscatter began with
SCIAMACHY in 2003–2012 (Frankenberg et al., 2006) and continues to the
present with Greenhouse Gases Observing Satellite (GOSAT) launched in 2009
(Kuze et al., 2016). Satellite measurements of atmospheric methane have been
used to detect emission hotspots (Worden et al., 2012; Kort et al., 2014;
Marais et al., 2014; Buchwitz et al., 2016) and to estimate emission trends
(Schneising et al., 2014; Turner et al., 2016). They have been used in global
inverse analyses to estimate emissions on regional scales (Bergamaschi et
al., 2007, 2009, 2013; Monteil et al., 2013; Cressot et al., 2014; Wecht et
al., 2014a; Alexe et al., 2015; Turner et al., 2015). The TROPOMI instrument
scheduled for launch in 2017 will vastly expand the capability to observe
methane from space by providing complete daily global coverage with
7
Table 1 lists the principal instruments (past, current, planned, proposed)
measuring methane from space. Atmospheric methane is detectable by its
absorption of radiation in the shortwave infrared (SWIR) at 1.65 and
2.3
Configurations for observing methane from space in the shortwave
infrared (SWIR) and in the thermal infrared (TIR). Here
All instruments launched to date have been in polar sun-synchronous low Earth orbit (LEO), circling the globe at fixed local times of day. They detect methane in the nadir along the orbit track, and most also observe off-nadir (at a cross-track angle) for additional coverage. Unlike other instruments, GHGSat focuses not on global coverage but on specific targets with very fine pixel resolution and limited viewing domains. Geostationary instruments still at the proposal stage would allow a combination of high spatial and temporal resolution over continental-scale domains, and could observe either in the SWIR or in the TIR following the configurations of Fig. 2.
Figure 3 shows typical vertical sensitivities for solar backscatter
instruments in the SWIR and for thermal emission instruments in the TIR.
Instrument sensitivity extending down to the surface is desirable for
inferring methane emissions. This is achieved in the SWIR, where the
atmosphere is nearly transparent unless clouds are present (Frankenberg et
al., 2005). SWIR instruments measure the total atmospheric column of methane
with near-uniform sensitivity in the troposphere. This column measurement can
be related to emissions in a manner that is not directly sensitive to local
vertical mixing. Measurements in the TIR require a thermal difference between
the atmosphere and the surface (
Typical sensitivities as a function of atmospheric pressure for
satellite observation of atmospheric methane in the SWIR (solar
backscatter) and in the TIR. The sensitivities are the elements of the
averaging kernel vector
Figure 4 shows the atmospheric optical depths of different gases in the SWIR,
highlighting the methane absorption bands at 1.65 and 2.3
Atmospheric optical depths of major trace gases in the spectral
region 1.5–2.5
Methane retrievals at either 1.65 or 2.3
The viewing geometry of the satellite measurement is defined by the solar
zenith angle
The methane vertical column density
Solar backscatter measurements in the SWIR require a reflective surface. This
largely limits the measurements to land, although some ocean data can be
obtained from specular reflection at the ocean surface (sun glint). Clouds
affect the retrieval by reflecting solar radiation back to space and
preventing detection of the air below the cloud. Even partly cloudy scenes
are problematic because the highly reflective cloudy fraction contributes
disproportionately to the total backscattered radiation from the pixel. A
major advantage of finer pixel resolution is thus to increase the probability
of clear-sky scenes (Remer et al., 2012). The GOSAT retrievals exclude cloudy
scenes by using a simultaneous retrieval of the oxygen column in the
0.76
Global and US distributions of methane dry-air column mole
fractions (X
Two different methods have been used for methane retrievals at
1.65
Figure 5 shows the global and US distributions of methane (X
The SCIAMACHY and GOSAT global distributions show commonality in patterns. Values are highest in East Asia, consistent with the Emissions Database for Global Atmospheric Research (EDGAR) inventory (European Commission, 2011), where the dominant contributions are from rice cultivation, livestock, and coal mining. Values are also high over central Africa and northern South America because of wetlands and livestock. Over the US, both SCIAMACHY and GOSAT feature high values in the South Central US (oil–gas, livestock) and hotspots in the Central Valley of California and in eastern North Carolina (livestock). There are also high values in the Midwest that are less consistent between the two sensors and could be due to a combination of oil–gas, livestock, and coal mining sources.
TROPOMI will observe methane in the 2.3
Observations of methane in the TIR are available from the IMG, AIRS, TES,
IASI, and CrIS instruments (Table 1). These instruments observe the
temperature-dependent blackbody radiation emitted by the Earth and its
atmosphere. Atmospheric methane absorbs upwelling radiation in a number of
bands around 8
Multispectral retrievals in the SWIR and TIR combine the advantages of both approaches and provide some vertical profile information, as demonstrated by Herbin et al. (2013) using the combination of SWIR and TIR data from GOSAT, and by Worden et al. (2015) using the combination of SWIR from GOSAT and TIR from TES. This could enable separation between the local/regional methane enhancement near the surface and the higher-altitude methane background (Bousserez et al., 2015). Such multi-spectral retrievals are not yet produced operationally because of computational requirements and because of limitations in the quality and calibration of spectra across different detectors (Hervé Herbin, personal communication, 2016).
The MERLIN lidar instrument scheduled for launch in 2020 (Kiemle et al.,
2011) will measure methane in the pencil of 1.65
Other instruments in Table 1 are presently at the proposal stage. All use
solar backscatter. CarbonSat (Buchwitz et al., 2013) is designed to measure
methane globally with an unprecedented combination of fine pixel resolution
(2
Satellite observations require careful calibration and error characterization for use in inverse analyses. Errors may arise from light collection by the instrument, dark current, spectroscopic data, the radiative transfer model, cloud contamination, and other factors. Kuze et al. (2016) give a detailed description of GOSAT instrument errors as informed by 5 years of operation. Errors may be random, such as from photon count statistics, or systematic, such as from inaccurate spectroscopic data. They may increase with time due to instrument degradation.
Random error (precision) and systematic error (accuracy) have very different
impacts (Kulawik et al., 2016). Random error can be reduced by repeated
observations and averaging. As we will illustrate in Sect. 4, instrument
precision can define the extent of spatial/temporal averaging required for
satellite observations to usefully quantify emissions. Systematic error, on
the other hand, is irreducible and propagates in the inversion to cause a
corresponding bias in the emission estimates. A uniform global bias is not
problematic for methane since the global mean concentration is well known
from surface observations, but a spatially variable bias affects source
attribution by aliasing the methane enhancements relative to background.
Buchwitz et al. (2015) refer to this spatial variability in the bias as
“relative bias”. It can arise, for example, from different surface
reflectivity, aerosol interference, sloping terrain, or unresolved
variability in CO
Validation of satellite data requires accurate suborbital observations of methane from surface sites, aircraft, or balloons. Direct validation involves comparison of single-scene satellite retrievals to suborbital observations of that same scene. The suborbital observations must be collocated in space and time with the satellite overpass, and they must provide a full characterization of the column as observed by the satellite. Although direct validation is the preferred means of validation, the requirements greatly limit the conditions under which it can be done. Indirect validation is a complementary method that involves diagnosing the consistency between satellite and suborbital data when compared to a global 3-D CTM as a common intercomparison platform (Zhang et al., 2010). It considerably increases the range of suborbital measurements that can be used because collocation in space and time is not required. Indirect validation can also be conducted formally by chemical data assimilation of the different observational data streams into the CTM.
The standard benchmark for direct validation of solar backscatter satellite
observations is the worldwide Total Carbon Column Observing Network (TCCON)
(Wunch et al., 2011). TCCON consists of ground-based Fourier transform
spectrometer (FTS) instruments staring at the Sun and detecting methane
absorption in the direct solar radiation spectrum. This measures the same
dry-air column mole fraction X
Dils et al. (2014) and Buchwitz et al. (2015) present direct validation of
the different operational SCIAMACHY and GOSAT retrievals using TCCON data.
Relative bias is determined using pairs of TCCON sites. They find a
single-observation precision of 30 ppb and a relative bias of 4–13 ppb for
SCIAMACHY in 2003–2005, which are good enough for inverse applications, but
they worsen after 2005 to 50–82 ppb (precision) and 15 ppb (relative bias). For GOSAT,
they report single-observation precisions of 12–13 ppb for the CO
TIR measurements are most sensitive to the middle–upper troposphere (Fig. 3) and aircraft vertical profiles provide the best resource for direct validation. Wecht et al. (2012) and Alvarado et al. (2015) evaluated successive versions of TES methane retrievals with data from the HIPPO pole-to-pole aircraft campaigns over the Pacific (Wofsy, 2011). Alvarado et al. (2015) report that the latest Version 6 of the TES product has a bias of 4.8 ppb. Crevoisier et al. (2013) find that IASI observations are consistent with aircraft observations to within 5 ppb.
Use of satellite observations in inverse modeling studies cannot simply rely on past validation to quantify the instrument error. This is because the instrument calibration may drift with time, optics and detectors may degrade, and errors may vary depending on surface and atmospheric conditions. It is essential that error characterization be done for the specific temporal and spatial window of the inversion. Opportunities for direct validation may be sparse but indirect validation with the CTM to be used for the inversion is particularly effective. Such indirect validation can exploit all relevant suborbital data collected in the window to assess their consistency with the satellite data. This has been standard practice in inversions of SCIAMACHY and GOSAT data and has resulted in correction factors applied to the data as a function of latitude (Bergamaschi et al., 2009, 2013; Fraser et al., 2013; Alexe et al., 2015; Turner et al., 2015), water vapor (Houweling et al., 2014; Wecht et al., 2014a), or air mass factor (Cressot et al., 2014).
The general approach for inferring methane emissions from observed atmospheric concentrations is to use a 3-D CTM describing the sensitivity of concentrations to emissions. The CTM simulates atmospheric transport on the basis of assimilated meteorological data for the observation period and a 2-D field of gridded emissions. It computes concentrations as a function of emissions by solving the mass continuity equation that describes the change in the 3-D concentration field resulting from emissions, winds, turbulence, and chemical loss. In Eulerian CTMs, the solution to the continuity equation is done on a fixed atmospheric grid. In Lagrangian CTMs, often called Lagrangian particle dispersion models (LPDMs), the solution is obtained by tracking a collection of air particles moving with the flow. Eulerian models have the advantage of providing a complete, continuous, and mass-conserving representation of the atmosphere. LPDMs have the advantage of being directly integrable backward in time, so that the source footprint contributing to the concentrations at a particular receptor point is economically computed. Eulerian models can also be integrated backward in time to derive source footprints by using the model adjoint (Henze et al., 2007). LPDMs have been used extensively for inverse analyses of ground and aircraft methane observations, where the limited number of receptor points makes the Lagrangian approach very efficient (Miller et al., 2013; Ganesan et al., 2015; Henne et al., 2016). Satellite observations involve a considerably larger number of receptor points, including different altitudes contributing to the column measurement. For this reason, all published inversions of satellite methane data so far have used Eulerian CTMs. A preliminary study by Benmergui et al. (2015) applies an LPDM to inversion of GOSAT data.
The CTM provides the sensitivity of concentrations to emissions at previous times. By combining this information with observed concentrations we can solve for the emissions needed to explain the observations. Because of errors in measurements and in model transport, the best that can be achieved is an error-weighted statistical fit of emissions to the observations. This must account for prior knowledge of the distribution of emissions, generally from a bottom-up inventory, in order to target the fit to the most relevant emission variables and in order to achieve an optimal estimate of emissions consistent with all information at hand.
The standard method for achieving such a fit is Bayesian optimization. The
emissions are assembled into a state vector
In the absence of better information, error PDFs are often assumed to be
Gaussian (Rodgers, 2000). We then have
The optimization problem
Equation (7) can be solved analytically if the relationship between emissions
and atmospheric concentrations is linear, such that
Analytical solution to the inverse problem provides full error
characterization of the solution through
Equations (8)–(10) further require the multiplication and inversion of large
matrices of dimensions
There is danger in over-interpreting the posterior error covariance matrix
The limitation on the size of the emission state vector can be lifted by
solving Eq. (7) numerically instead of analytically. This is done by applying
iteratively the adjoint of the CTM, which is the model operator
The iterative procedure in the adjoint method is as follows. Starting from
the prior estimate
Markov Chain Monte Carlo (MCMC) methods are yet another approach to solving the
Bayesian inverse problem. Here the posterior PDF
There are other ways of expressing the prior information than as
(
Inverse methods for constraining emissions can be applied not only to current
observing systems but also to formally evaluate the capability of a proposed
future instrument to improve current knowledge. Given an observational plan
and error specifications for the proposed instrument, we can compute the
expected observational error covariance matrix
The simple error analysis described above to assess the value of a future instrument is sometimes loosely called an observing system simulation experiment (OSSE). However, the OSSE terminology is generally reserved for a more rigorous test (and an actual “experiment”) of the benefit of adding the proposed instrument to the current observing system, including realistic accounting of CTM errors. A standard OSSE setup is illustrated in Fig. 6. The OSSE uses two CTMs driven by different assimilated meteorological datasets for the same period. The first model (CTM1) produces a synthetic 3-D field of atmospheric concentrations from an emission inventory taken as the “true” emissions (A in Fig. 6). For the purpose of the exercise, CTM1 is taken to have no error and so describes the true 3-D field of atmospheric concentrations. This true atmosphere is then sampled synthetically with the current observing system, adding instrument noise as stochastic random error, so that the resulting synthetic data mimic the current observing system. Inversion of these data returns emissions optimized by the current observing system (B in Fig. 6) We then add the proposed instrument to the observing system, again adding instrument noise as random error on the basis of the instrument specifications, and invert the data using the previously optimized emissions (B) as prior estimate. The resulting optimized emissions (C in Fig. 6) can be compared to the “true” emissions (A) and to the prior emissions (B) to quantify the value of the proposed instrument and its advantage relative to the current observing system. The use of two independent assimilated meteorological data sets is important for this exercise as it allows for realistic accounting of the CTM error component. Such an OSSE setup is frequently used to evaluate proposed meteorological instruments, and it has previously been applied to the evaluation of a geostationary instrument for tropospheric ozone (Zoogman et al., 2014) but not so far for methane.
Generic design of an observing system simulation experiment (OSSE) to evaluate the potential of a proposed new atmospheric instrument to improve knowledge of emissions relative to the current observing system.
There are a number of issues requiring care in the application of inverse methods to infer methane emissions from observations of atmospheric methane, some of which are specific to satellite observations.
A first issue relates to the resolution of the emission field (state vector)
to be optimized by the inversion. Methane originates from a large number of
scattered sources, with emission factors that are poorly known and highly
variable for a given source sector. It is therefore of interest to optimize
emissions with fine spatial resolution, and for some sources also with fine
temporal resolution. The resolution of the emission state vector can in
principle be as fine as the grid resolution and time step of the CTM used as
a forward model. However, the amount of information contained in the
observations places limits on the extent to which emissions can actually be
resolved. Satellite data sets may be large but the data are noisy. If the
dimension of the emission state vector is too large relative to the
information content of the observations, then the Bayesian optimization
problem is underconstrained and the solution may be heavily weighted by the
prior estimate. This is known as the smoothing error and the associated error
covariance matrix is (
Figure 7 illustrates the smoothing problem in an inversion of methane emissions over North America using SCIAMACHY. The cure is to reduce the dimension of the emission state vector by aggregating state vector elements and optimizing only the aggregate (Fig. 7). However, this introduces another type of error, known as aggregation error, because the relationship between aggregated state vector elements is now imposed by the prior estimate (Kaminski et al., 2001). As shown by Turner and Jacob (2015) and illustrated in Fig. 7, it is possible to define an optimal dimension of the emission state vector by balancing the smoothing and aggregation errors. For a multi-annual GOSAT data set this implies a spatial resolution of the order of 100–1000 km in methane source regions (Turner et al., 2015). The state vector of emissions can be reduced optimally by hierarchical clustering (Wecht et al., 2014a) or by using radial basis functions with Gaussian PDFs (Turner and Jacob, 2015).
Effect of smoothing and aggregation errors in a high-resolution
inversion of methane emissions using SCIAMACHY observations of methane
columns for summer 2004. The top left panel shows the correction factors to
prior emissions when attempting to optimize emissions at the native
Inverse analyses require high-quality gridded bottom-up inventories as prior
estimates to regularize the solution and interpret results. All inversions of
methane satellite data so far have relied on the EDGAR bottom-up inventory
for anthropogenic emissions with 0.1
The need for improved, finely gridded bottom-up inventories for inverse
analyses is well recognized. Wang and Bentley (2002) disaggregated the
Australian national inventory to guide inversion of surface observations at
Cape Grim, Tasmania. Zhao et al. (2009) disaggregated the California Air
Resources Board (CARB) statewide inventory to a
0.1
The standard assumption of Gaussian error PDFs for the prior estimate allows for the possibility of negative methane emissions. Although soils can be a weak sink for methane (Kirschke et al., 2013), negative emissions are generally unphysical. Small negative values may be acceptable as noise, and can be removed by averaging them with neighboring positive values. The analytical solution to the Bayesian inverse problem requires Gaussian error PDFs (Sect. 3.1), but numerical solutions do not. Adjoint-based inversions may use lognormal (Wecht et al., 2014a) or semi-exponential (Bergamaschi et al., 2013) error distributions to prevent negative solutions. Lognormal errors can be used in the analytical solution by adopting as state vector the logarithm of emissions. Miller et al. (2014) present additional approaches for imposing positivity of the solution, including (1) application of Karush–Kuhn–Tucker (KKT) conditions, and (2) MCMC methods with sampling domain restriction. These approaches will tend to bias the solution by enforcing zero values for a subset of the state vector (KKT conditions) or by artificially inflating the PDF of the prior estimate in the vicinity of zero (MCMC methods).
Observations from the HIPPO pole-to-pole aircraft campaigns over the Pacific in 2010–2011 indicate background concentrations of tropospheric methane varying with latitude from 1750 to 1800 ppb in the Southern Hemisphere to 1850–1900 ppb at high northern latitudes (Wofsy, 2011). The mid-latitude background varies on synoptic scales under the alternating influence of high-latitude and low-latitude air masses. This variability in background is comparable to the magnitude of concentration enhancements in methane source regions, so that accurate accounting of the global methane background and its variability is essential for regional inversions. Local source inversions may be able to use regional background information upwind of the source instead (Krings et al., 2013).
Observations at remote sites from the NOAA Earth System Research Laboratory (ESRL) network (Dlugokencky et al., 2013; Andrews et al., 2014) accurately characterize the seasonal latitude-dependent background, and one can then rely on the CTM used as forward model in the inversion to resolve the synoptic variations in that background. Global inversions of satellite data have exploited the NOAA ESRL network data in different ways. Bergamaschi et al. (2009, 2013), Fraser et al. (2013), and Alexe et al. (2015) included the data in their inversions together with the satellite data. Cressot et al. (2014) conducted separate inversions with NOAA ESRL and satellite data, and demonstrated consistency between the two. In limited-domain inversions such as on the continental scale of North America, the background must be specified as a time- and latitude-dependent boundary condition. This has been done by Miller et al. (2013) using the NOAA ESRL data as boundary conditions, in Wecht et al. (2014a) by optimizing the boundary conditions as part of the inversion, and by Turner et al. (2015) by using results from a global inversion as boundary conditions for the continental-scale inversion.
The main sink for methane is oxidation by the OH radical in the troposphere, with a lifetime of 9 years constrained by global observations of methyl chloroform (MCF) (Prather et al., 2012). OH is produced photochemically and its concentration is controlled by complex chemistry that is not well represented in models (Voulgarakis et al., 2013). However, the loss of methane is sufficiently slow so that variability in OH concentrations affects methane concentrations only on seasonal, interannual, and interhemispheric scales (Bousquet et al., 2006). It does not affect the regional-scale gradients relevant to inverse analyses of satellite data. Global inverse analyses generally compute the methane sink by using specified global 3-D monthly fields of OH concentrations from an independent simulation of tropospheric oxidant chemistry that are compatible with the MCF constraint (Bergamaschi et al., 2013; Houweling et al., 2014). Cressot et al. (2014) optimized methane and MCF emissions together in their inversion, thus allowing for adjustment of OH concentrations within the uncertainty range allowed by MCF. Specifying OH concentrations is not an issue for limited-domain inversions with spatial boundary conditions because the modeling domain is then ventilated on a timescale considerably shorter than the 9-year methane lifetime. In that case, information on the methane sink is effectively incorporated in the boundary conditions.
Inversions of satellite methane data require a proper accounting of the
stratosphere. The stratosphere contributes about 5 % of the total methane
column in the tropics and 25 % at high latitudes (Ostler et al., 2015).
Methane enters the stratosphere in the tropics and is transported to high
latitudes on a timescale of about 5 years. Over that time it is
photochemically oxidized by OH, O(
A number of observational data sets are available to evaluate the
stratospheric methane simulation in CTMs. These include balloons (Bergamaschi
et al., 2013), TCCON stratospheric retrievals (Saad et al., 2014), and
satellite observations by solar occultation and in the limb (de Mazière
et al., 2008; von Clarmann et al., 2009; Minschwaner and Manney, 2014).
Bergamaschi et al. (2013) presented a detailed evaluation of their CTM with
balloon observations as a prelude to inversion of SCIAMACHY data, and this
led them to limit their inversion to the 50
Estimation of prior and observational error covariances is crucial for
inverse modeling. Observational error is the sum of instrument and CTM
errors. We discussed in Sect. 2.2 the characterization of instrument error by
validation with suborbital data. CTM error variance can be estimated by
intercomparison of different CTMs (Patra et al., 2011) and added to the
instrument error variance in quadrature. An alternative is to estimate the
total observational error variance by the residual error method (Heald et
al., 2004), which uses statistics of differences between the observations and
the CTM simulation with prior emissions. In that method, systematic
difference (bias) is assumed to be caused by error in emissions (to be
corrected in the inversion), The remaining residual difference (averaging to
zero) defines the total observational error, including contributions from
instrument and CTM errors. This method has the merit of being consistent with
the premise that the observational error is random. The CTM error variance
can then be deduced by subtracting the instrument error variance.
Application to SCIAMACHY and GOSAT shows that the instrument error tends to
be dominant (Wecht et al., 2014a; Turner et al., 2015). Error correlation
populating the off-diagonal terms of the observational error covariance
matrix is typically specified as an
Error in the prior bottom-up emission inventory can be estimated by propagation of errors in the variables used to construct the inventory (US EPA, 2016), or by comparison of independently generated inventories such as the WETCHIMP intercomparison for wetlands (Melton et al., 2013) or regional anthropogenic inventories in the US (Maasakkers et al., 2016). Error PDFs are usually assumed to be normal or lognormal, but more skewed PDFs may better capture the occurrence of “super-emitters” (Zavala-Araiza et al., 2015). Errors may be scale-dependent, such that spatial aggregation of emission grid squares in the inversion decreases the error variance (Maasakkers et al., 2016). The prior error covariance matrix is usually taken to be diagonal, but some error correlation would in fact be expected for a given source sector. This is accounted for in the geostatistical inversion approach (Eq. 11) by assuming coherence in source patterns.
Sources completely missing from the prior bottom-up inventory pose a particular difficulty for inverse modeling, because inverse methods applied to an underconstrained problem will tend to correct emissions where the prior estimate indicates them to be. Simply increasing the error on the prior estimate is not a satisfactory approach because the inverse solution may then misplace emissions. Before conducting the inversion it is important to compare the CTM simulation using prior emissions to the observations, and diagnose whether any elevated values in the observations that are absent in the simulation could represent missing sources.
Most inversions of SCIAMACHY and GOSAT satellite data for atmospheric methane
have been done on the global scale, estimating emissions at the resolution of
the CTM used as a forward model (typically a few hundred km) by applying an
adjoint method (Bergamaschi et al., 2009, 2013; Spahni et al., 2011; Monteil
et al., 2013; Cressot et al., 2014; Houweling et al., 2014; Alexe et al.,
2015). Fraser et al. (2013) estimated monthly methane fluxes over
continental-scale source regions by using an analytical method with a Kalman
filter. Wecht et al. (2014a) and Turner et al. (2015) used continental-scale
inversions for North America to estimate emissions at up to 50 km resolution
in source regions through optimal selection of the state vector, with Turner
et al. (2015) applying an analytical inversion to characterize errors. Fraser
et al. (2014) and Pandey et al. (2015) optimized both methane and CO
Inversions of methane fluxes using GOSAT data show consistency with observations from NOAA ESRL surface sites, both in joint inversions (Bergamaschi et al., 2009, 2013; Fraser et al., 2013; Alexe et al., 2015) and in independent evaluations (Turner et al., 2015). GOSAT observations are sparse, with observation points separated by about 260 km, but still provide considerably more information on methane emissions at the continental scale than the surface network observations (Fraser et al., 2013; Alexe et al., 2015). This is particularly true in the tropics, where methane emissions are large but surface observations are few (Bergamaschi et al., 2013; Cressot et al., 2014; Houweling et al., 2014).
Inversions of SCIAMACHY and GOSAT data have revealed important biases in bottom-up inventories of methane emissions. Monteil et al. (2013) and Spahni et al. (2011) find large errors in wetland emission models. Bergamaschi et al. (2013) find that 2003–2010 growth in Chinese emissions is less than estimated by EDGAR. Inversion results in the US show that EDGAR emissions in the South Central US are too low while emissions along the East Coast are too high (Wecht et al., 2014a; Alexe et al., 2015; Turner et al., 2015).
Ultimately, the application of satellite data to improve understanding of methane emissions requires that the optimized estimates from the inversions be related to specific source sectors and processes in the bottom-up inventories. SCIAMACHY observations over wetlands have been used in this manner to improve bottom-up models of wetland emissions (Bloom et al., 2010, 2012; Spahni et al., 2011). Application of satellite observations to improve anthropogenic emission inventories has so far been stymied by poor representation of emission patterns in the inventories. For example, the EDGAR underestimation in the South Central US cannot be confidently attributed to livestock or oil–gas sectors because EDGAR emission patterns for these sectors are incorrect (Maasakkers et al., 2016).
Satellite data sets for correlative variables could help relate methane observations to source sectors but this has received little attention so far. Bloom et al. (2012) combined methane data from SCIAMACHY with water height data from the GRACE satellite instrument to improve their bottom-up inventory of wetland methane emissions. Worden et al. (2012) combined measurements of methane and CO from TES to quantify methane emissions from Indonesian fires. TIR measurements of ammonia are available from the TES, IASI, and CrIS satellite instruments (Shephard et al., 2011; Van Damme et al., 2014; Shephard and Cady-Pereira, 2015) and provide a fingerprint of agricultural emissions (Zhu et al., 2013) but have yet to be exploited in combination with methane satellite data. Interpretation of the ammonia data is complicated by gas–aerosol partitioning with ammonium. In addition, ammonia is mainly emitted by manure and fertilizer, whereas methane is mostly emitted by enteric fermentation and the sources may not be collocated. Ethane provides a marker for oil–gas emissions but is observed from space only by solar occultation with sensitivity limited to the upper troposphere (González Abad et al., 2011). In addition, the ethane / methane emission ratio is highly variable. TROPOMI will provide data for both methane and CO from common SWIR retrievals. Beyond constraining the combustion source of methane, the CO observations could be valuable for decreasing model transport errors in joint methane–CO inversions (Wang et al., 2009).
Future satellite instruments listed in Table 1 will have higher pixel
resolution, spatial density, and temporal frequency than SCIAMACHY or GOSAT.
Several studies have examined how these attributes will improve the
capability of methane flux inversions. Wecht et al. (2014b) conducted an
inversion of methane emissions in California at
Bousserez et al. (2016) explored the potential of geostationary observations
to constrain methane emissions on the continental scale of North America over
weekly and monthly timescales. Again they used a CTM with
Bovensmann et al. (2010) examined the potential of CarbonSat to detect methane point sources by inversion of the Gaussian dispersion plume, and Rayner et al. (2014) did the same for geoCARB. We review their results in the next section.
Here we present a simple analysis of the potential of future satellite instruments for observing regional and point sources from space. Observation requirements are somewhat different for climate policy and for point source monitoring purposes. From a climate policy standpoint, the goal is to quantify annual mean emissions with emphasis on the regional scale and source attribution. This plays to the strength of satellites, as repeated observations of the same scene measure the temporal average with improved precision and also smooth out the temporal variability that can bias estimates from short-term field campaign data. From a point source monitoring standpoint, on the other hand, we may be most interested in detecting large leaks or venting from facilities emitting far more than would be expected on the basis of normal operations (the so-called “super-emitters”). Here the advantage of satellite data is spatial coverage, but a requirement is to have a localized and detectable signal on short timescales, with detection and localization often being more important than precise quantification.
For conceptual purposes we define detection and quantification as the ability
to observe the methane enhancement
Nominal capability for observing regional and point sources of methane from space.
We first examine the capability of satellite instruments to quantify
emissions from a large source region by taking as an example the Barnett Shale
in Northeast Texas, a 300
Cumulative frequency distribution of spatially resolved annual
mean methane emissions in the contiguous US. The left panel shows the
distribution of emissions at 0.1
Table 2 summarizes the capabilities of the SWIR instruments in Table 1 to
quantify such a source. GOSAT views 2–3 pixels for a
300
Consider now the problem of detecting individual point sources through
observations of the corresponding source pixels. We estimate for the
different solar backscatter instruments of Table 1 the detection threshold
at the scale of a satellite pixel for a single observation pass, by
assuming low emissions in neighboring pixels (to characterize a local
background) and clear skies (for favorable retrieval conditions). The
enhancement
Pixel resolution of the satellite instrument can be a limiting factor for
detecting individual point sources because these are often clustered on a
1–10 km scale (Lyon et al., 2015). For a satellite instrument with pixel
resolution
Comparison of the detection thresholds in Table 2 with the emission
distributions in Fig. 8 offers insight into the capabilities of the different
instruments for resolving point sources. With a detection limit of
4 t h
GHGSat and CarbonSat are designed for observation of point sources. If it
meets its specifications of Table 1, GHGSat will have a single-pass detection
threshold of 0.24 t h
Bovensmann et al. (2010) give a CarbonSat detection threshold of
0.24 t h
Several approaches have been used to exploit downwind plume information for inferring point source emissions, including (1) inverse modeling with source strength and dispersion parameters as state variables (Krings et al., 2011, 2013), (2) integrating the flux over the plume cross-section normal to wind direction (Conley et al., 2016), and (3) summing the above-background mass in all plume pixels and relating this integrated mass enhancement to emission by using a relationship from known sources or a plume dispersion model (Frankenberg et al., 2016). Choice of the best approach may depend on the level of meteorological information available and the ability of the instrument to map the observed plume structure, which in turn depends on the pixel size, the measurement noise, the ability to define the local background, and the complexity of the flow including the effect of wind shear (Rayner et al., 2014).
Geostationary observations can in principle achieve high precision together with fine pixel resolution because the viewing geometry allows much longer observation times. But there is competing demand for spatial coverage. Currently proposed geostationary missions (Table 1) expect to achieve 0.2–1 % precision for pixels 2–5 km in size, limited in part by their stated mission objectives of observing continental-scale domains several times a day. With this implementation and the assumptions above, a regional source such as the Barnett Shale is strongly constrained on a single-pass basis but the capability to detect transient point sources is limited (Table 2). Point sources could be detected more effectively from geostationary orbit by adopting longer viewing times per pixel and/or using finer pixels. This could be achieved by limiting the domain of observation or by using “special observations” where the instrument is maneuvered to stare at specific points of interest. For example, detection of an anomaly in emissions, either from the satellite or from suborbital observations, could motivate targeted observation by the satellite to localize and quantify the anomaly. A schedule of alternate days for continental-scale mapping and for special observations could be effective in quantifying emissions at the national and regional scales while also providing fast detection and quantification of point sources.
Airborne remote sensing offers another way to observe methane emissions from
point sources, using the same techniques as satellite remote sensing but with
much higher spatial resolution. The methane airborne mapper (MAMAP) (Krings et al., 2011) retrieves methane
in the SWIR at 1.6
We have reviewed the capabilities for observing atmospheric methane from space and their utility for improving knowledge of methane emissions through inverse analyses. Observations in the shortwave infrared (SWIR) are of most interest for quantifying emissions because they are sensitive to the full atmospheric column down to the surface. Retrievals combining the SWIR and the thermal infrared (TIR) would isolate the lower tropospheric contribution to methane and thus reduce uncertainties in accounting for the free tropospheric background and the stratosphere.
Current SWIR observations from the GOSAT satellite are of high quality but
sparse. Through inverse analyses and annual averaging they can quantify
emissions in source regions on a 100–1000 km scale. The TROPOMI instrument,
to be launched in 2017, will be able to map emissions daily on that scale and
will also have the capability to detect and quantify large point sources. As
such it will significantly enhance the value of satellite measurements to
serve the needs of climate policy. The GHGSat instrument launched in 2016
with 50
The ultimate goal of top-down inverse analyses of atmospheric observations is to guide the improvement of bottom-up emission inventories relating emissions to the underlying processes. There is the opportunity to gain considerable synergy between top-down and bottom-up approaches by using high-quality bottom-up inventories as prior estimates in inversions, and then using inversion results to improve the inventories. Exploiting this synergy requires the construction of finely gridded, sector-resolved bottom-up inventories including error estimates.
Geostationary observations (still at the proposal stage) hold considerable potential for monitoring methane emissions from space. The geostationary orbit allows sustained staring at individual pixels, providing a unique opportunity to infer emissions with both high spatial and temporal resolution on national scales. This also enables the characterization of diurnally varying sources such as from wetlands (Mikkela et al., 1995) and manure (Wood et al., 2013), where LEO sun-synchronous observations at a single time of day might provide a biased estimate. Current geostationary mission proposals emphasize hourly mapping of emissions at the continental scale. This limits their pixel resolution and their precision. It is not clear that high-frequency continental-scale mapping from geostationary orbit is of much value if sufficient information is already available from a LEO instrument such as TROPOMI. It may be more effective for a geostationary mission to focus on selective observation of point sources and source regions, enabling finer pixel resolution and longer viewing times to resolve emissions at local scale including transient sources.
More work needs to be done in exploiting correlative observations to increase the value of methane satellite data, but the task is difficult because of the uniqueness of methane sources. Observations of ammonia from space are becoming mature and provide a marker of agricultural operations, though the sources of ammonia (fertilizer, manure) only partly overlap with the sources of methane (enteric fermentation, manure). Joint observations of methane and CO, as from TROPOMI, may help to reduce model transport error in inversions through methane–CO error correlations. Satellite mapping of surface properties can provide important correlative information, as already demonstrated for wetlands. Satellite data for soil moisture, gas flaring, and imagery of point sources could be integrated with available methane data to more effectively constrain methane emissions.
Suborbital observations of methane from aircraft and from the ground are essential partners of satellite observation. Suborbital observations have a unique capability for correlative measurements such as methane isotopes and ethane that can provide additional constraints in inversions. They can confirm methane anomalies detected from space, and pinpoint sources with far greater accuracy (down to the device scale) than is achievable from space. Suborbital platforms are also essential for continual validation of the satellite data. The prospect of improving satellite observations in the near future calls for the construction of a comprehensive atmospheric methane observing system to monitor emissions from global to local scales through coordination with improved suborbital observations, bottom-up inventories, and atmospheric transport models.
The gridded US methane inventory of Maasakkers et al. (2016) used to produce
Fig. 8 is available at
This work was funded by the NASA Carbon Monitoring System, by the NASA GEO-CAPE Atmospheric Sciences Working Group, by the ExxonMobil Upstream Research Company, and by the US DOE Advanced Research Projects Agency – Energy. Kelly Chance acknowledges funding from the Smithsonian Astrophysical Observatory. We thank Helen Worden for pointing out an error in the original submitted paper. Edited by: B. N. Duncan Reviewed by: two anonymous referees