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.

The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O₂) A-band at 0.765 microns and the carbon dioxide (CO₂) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO₂ dry-air mole fraction, XCO₂. Variations of XCO₂ in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO₂. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO₂ field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO₂ distribution could be misinterpreted as evidence for CO₂ fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s⁻¹ across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO₂. Each of these soundings has an unprecedented combination of spatial resolution (< 3 km²/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 10⁴), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

We present a new solar irradiance reference spectrum representative of solar minimum conditions between solar cycles 24 and 25. The Total and Spectral Solar Irradiance Sensor‐1 (TSIS‐1) Hybrid Solar Reference Spectrum (HSRS) is developed by applying a modified spectral ratio method to normalize very high spectral resolution solar line data to the absolute irradiance scale of the TSIS‐1 Spectral Irradiance Monitor (SIM) and the CubeSat Compact SIM (CSIM). The high spectral resolution solar line data are the Air Force Geophysical Laboratory ultraviolet solar irradiance balloon observations, the ground‐based Quality Assurance of Spectral Ultraviolet Measurements In Europe Fourier transform spectrometer solar irradiance observations, the Kitt Peak National Observatory solar transmittance atlas, and the semi‐empirical Solar Pseudo‐Transmittance Spectrum atlas. The TSIS‐1 HSRS spans 202–2730 nm at 0.01 to ∼0.001 nm spectral resolution with uncertainties of 0.3% between 460 and 2365 nm and 1.3% at wavelengths outside that range.

Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.

A unified framework that connects emissions with satellite‐observed column amounts is derived from first principles. The emission information originates from the inner product of the horizontal wind and the gradient of column amount, which is more accurate than the horizontal flux divergence as used in previous studies. Additionally, the topographical and chemical effects are accounted for through fitted scale height and chemical lifetime. This framework is applied to derive NO _x and CO emissions over the CONUS from TROPOspheric Monitoring Instrument NO ₂ and CO observations. High‐resolution (0.04°) emission mapping over the CONUS reveals unprecedented details, including CO emissions in major cities and NO _x emissions from large cities, power plants, and major roadways. Monthly resolved NO _x emissions show decrease and rebound after the COVID‐19 pandemic. This framework is integrated with the physical oversampling algorithm and can be readily applied to other products from the new‐generation satellite instruments.

Air pollution disproportionately burdens communities of color and lower‐income communities in US cities. We have generally lacked city‐wide concentration measurements that resolve the steep spatiotemporal gradients of primary pollutants required to describe intra‐urban air pollution inequality. Here, we use observations from the recently launched TROPospheric Ozone Monitoring Instrument (TROPOMI) satellite sensor and physics‐based oversampling to describe nitrogen dioxide (NO₂) disparities with race, ethnicity, and income in 52 US cities (June 2018–February 2020). We report average US‐urban census tract‐level NO₂ inequalities of 28 ± 2% (race‐ethnicity and income combined), with many populous cities experiencing even greater inequalities. Using observations and inventories, we find diesel traffic is the dominant source of NO₂ disparities, and that a 62% reduction in diesel emissions would decrease race‐ethnicity and income inequalities by 37%. We add evidence that TROPOMI resolves tract‐scale NO₂ differences using relationships with urban segregation patterns and spatial variability in column‐to‐surface correlations.