ALBERT

Description: Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source category distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at an unprecedented resolution of 1–5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can estimate flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using large eddy simulations under a range of background wind speeds of 1–10 m s−1 and source emission rates ranging from 10 to 1000 kg h−1. The surrogate measurements mimic plume mapping performed by the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5 m spatial resolution. We make use of the integrated total methane enhancement in each plume, denoted as integrated methane enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our analysis shows that the IME corresponds to the flux rate nonlinearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the associated background wind speed. This allows us to invert source flux rate based solely on the IME and the plume shape itself. On average, the error estimate based on randomly generated plumes is approximately 30 % for an individual estimate and less than 10 % for an aggregation of 30 plumes. A validation against a natural gas controlled-release experiment agrees to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geographical areas in airborne field campaigns and future space-based observations.

Description: The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

Description: Fugitive emissions from waste disposal sites are important anthropogenic sources of the greenhouse gas methane (CH4). As a result of the growing world population and the recognition of the need to control greenhouse gas emissions, this anthropogenic source of CH4 has received much recent attention. However, the accurate assessment of the CH4 emissions from landfills by modeling and existing measurement techniques is challenging. This is because of inaccurate knowledge of the model parameters and the extent of and limited accessibility to landfill sites. This results in a large uncertainty in our knowledge of the emissions of CH4 from landfills and waste management. In this study, we present results derived from data collected during the research campaign COMEX (CO2 and MEthane eXperiment) in late summer 2014 in the Los Angeles (LA) Basin. One objective of COMEX, which comprised aircraft observations of methane by the remote sensing Methane Airborne MAPper (MAMAP) instrument and a Picarro greenhouse gas in situ analyzer, was the quantitative investigation of CH4 emissions. Enhanced CH4 concentrations or CH4 plumes were detected downwind of landfills by remote sensing aircraft surveys. Subsequent to each remote sensing survey, the detected plume was sampled within the atmospheric boundary layer by in situ measurements of atmospheric parameters such as wind information and dry gas mixing ratios of CH4 and carbon dioxide (CO2) from the same aircraft. This was undertaken to facilitate the independent estimation of the surface fluxes for the validation of the remote sensing estimates. During the COMEX campaign, four landfills in the LA Basin were surveyed. One landfill repeatedly showed a clear emission plume. This landfill, the Olinda Alpha Landfill, was investigated on 4 days during the last week of August and first days of September 2014. Emissions were estimated for all days using a mass balance approach. The derived emissions vary between 11.6 and 17.8 kt CH4 yr−1 with related uncertainties in the range of 14 to 45 %. The comparison of the remote sensing and in situ based CH4 emission rate estimates reveals good agreement within the error bars with an average of the absolute differences of around 2.4 kt CH4 yr−1 (±2. 8 kt CH4 yr−1). The US Environmental Protection Agency (EPA) reported inventory value is 11.5 kt CH4 yr−1 for 2014, on average 2.8 kt CH4 yr−1 (±1. 6 kt CH4 yr−1) lower than our estimates acquired in the afternoon in late summer 2014. This difference may in part be explained by a possible leak located on the southwestern slope of the landfill, which we identified in the observations of the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) instrument, flown contemporaneously aboard a second aircraft on 1 day.

Description: Visible–shortwave infrared imaging spectroscopy provides valuable remote measurements of Earth's surface and atmospheric properties. These measurements generally rely on inversions of computationally intensive radiative transfer models (RTMs). RTMs' computational expense makes them difficult to use with high-volume imaging spectrometers, and forces approximations such as lookup table interpolation and surface–atmosphere decoupling. These compromises limit the accuracy and flexibility of the remote retrieval; dramatic speed improvements in radiative transfer models could significantly improve the utility and interpretability of remote spectroscopy for Earth science. This study demonstrates that nonparametric function approximation with neural networks can replicate radiative transfer calculations and generate accurate radiance spectra at multiple wavelengths over a diverse range of surface and atmosphere state parameters. We also demonstrate such models can act as surrogate forward models for atmospheric correction procedures. Incorporating physical knowledge into the network structure provides improved interpretability and model efficiency. We evaluate the approach in atmospheric correction of data from the PRISM airborne imaging spectrometer, and demonstrate accurate emulation of radiative transfer calculations, which run several orders of magnitude faster than first-principles models. These results are particularly amenable to iterative spectrum fitting approaches, providing analytical benefits including statistically rigorous treatment of uncertainty and the potential to recover information on spectrally broad signals.

Description: At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral interference in the shortwave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the combined sources of cooling towers and cooling ponds. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher-resolution Google Earth imagery. Real-time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow-up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.

Description: At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral mixing in the short wave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the cooling towers of one power plant. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher resolution Google Earth imagery. Real time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.

Description: Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source categories distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at unprecedented resolution of 1–5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can estimate flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using Large Eddy Simulation under a range of background wind speeds of 1–10 m/s and source emission rates ranging from 10 to 1000 kg/hr. The surrogate measurements mimic plume mapping performed by the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5m spatial resolution. We make use of the integrated total methane enhancement in each plume, denoted as Integrated Methane Enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our analysis shows that the IME corresponds to the flux rate non-linearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the associated background wind speed. This allows us to invert source flux rate based solely on the IME and the plume-shape itself. On average, the error estimate based on randomly generated plumes is approximately 30 % for an individual estimates and less than 10 % for an aggregation of 30 plumes. A validation against a natural gas controlled release experiment agree to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geographical area in airborne field campaigns and future space-based observations.

Description: Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud influencing factors that contribute to winter-summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, not high, clouds; the largest differences occur between the surface and 950 hPa. Stratifying cloud amount by cloud influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Inter-group differences in low cloud amount are found to be a function of the dependence of low cloud amount on the lower tropospheric thermodynamic characteristics. We find that models with a larger low cloud amount in winter produce more cloud ice, whereas models with a larger low cloud amount in summer produce more cloud liquid. Thus, the parameterization of ice microphysics, specifically the ice formation mechanism (deposition vs. immersion freezing) and cloud liquid and ice partitioning, contributes to the inter-model differences in the Arctic cloud annual cycle and provides further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.

Description: The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes having outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4–1.8 microns can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005–2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal trends corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extra tropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

Description: We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (EnMAP, PRISMA, EMIT, SBG) scheduled for launch in 2019–2025. These instruments are designed to map the Earth's surface with a sampling distance as fine as 30 × 30 m2 but they have spectral resolution of 7–10 nm in the 2200–2400 nm band that should also allow useful detection of atmospheric methane. We simulate scenes viewed by EnMAP (10 nm spectral resolution, 180 signal-to-noise ratio) using the EnMAP End-to-End Simulation Tool with superimposed methane plumes generated by large-eddy simulations. We retrieve atmospheric methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estimation algorithm. We find an EnMAP precision of 4–13 % for atmospheric methane depending on surface type, allowing effective single-pass detection of 100+ kg h−1 methane point sources depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resolution. We simulated the EnMAP capability with actual plume observations over oil/gas fields in California from the airborne AVIRIS-NG sensor (3 × 3 m2 pixel resolution, 5 nm spectral resolution, SNR 200–400). We spectrally and spatially downsampled AVIRIS-NG images to match EnMAP instrument specifications and found that we could successfully detect point sources of ~ 100 kg h−1 over bright surfaces. Estimated emission rates inferred with a generic Integrated Mass Enhancement (IME) method agreed within a factor of 2 between EnMAP and AVIRIS-NG. Better agreement may be achieved with a more customized IME method. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.