How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling the complex and often aerosol laden, humid, urban microclimates, atmospheric transport and profile monitoring, spatial resolution, temporal cycles (diurnal and seasonal which involve interactions with the surrounding environment diurnal and seasonal cycles) and representative measurement approaches given traffic realities. Promising approaches incorporate contemporaneous airborne remote sensing and in situ measurements, nocturnal surface surveys, with ground station measuremen

Towards space based verification of CO₂ emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

Carbon dioxide (CO₂) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO₂, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO₂ emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO₂ emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m. LT) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO₂ emissions from large power plants (≥5 Mt CO₂ yr⁻¹) with a systematic error of less than ~4.9% and a random error of less than ~6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than ~12.4% and the random error was less than ~13%. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Therefore, we recommend the CarbonSat constellation configuration that achieves daily global coverage

The semianalytical cloud retrieval algorithm for SCIAMACHY II. The application to MERIS and SCIAMACHY data

International audienceThe SemiAnalytical CloUd Retrieval Algorithm (SACURA) is applied to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data. In particular, we derive simultaneously cloud optical thickness (COT) and cloud top height (CTH), using SCIAMACHY measurements in the visible (442 nm, COT) and in the oxygen A-band (755?775 nm, CTH). Some of the results obtained are compared with those derived from the Medium Resolution Imaging Spectrometer (MERIS), which has better spatial resolution and observes almost the same scene as SCIAMACHY. The same cloud algorithm is applied to both MERIS and SCIAMACHY data. In addition, we perform the vicarious calibration of SCIAMACHY at the wavelength 442 nm, using MERIS measurements at the same wavelength. Differences in the retrieved COT for the same cloud field obtained using MERIS and SCIAMACHY measurements are discussed

MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: instrument description and performance analysis

Abstract. Carbon dioxide (CO2) and Methane (CH4) are the two most important anthropogenic greenhouse gases. CH4 is furthermore one of the most potent present and future contributors to global warming because of its large global warming potential (GWP). Our knowledge of CH4 and CO2 source strengths is based primarily on bottom-up scaling of sparse in-situ local point measurements of emissions and up-scaling of emission factor estimates or top-down modeling incorporating data from surface networks and more recently also by incorporating data from low spatial resolution satellite observations for CH4. There is a need to measure and retrieve the dry columns of CO2 and CH4 having high spatial resolution and spatial coverage. In order to fill this gap a new passive airborne 2-channel grating spectrometer instrument for remote sensing of small scale and mesoscale column-averaged CH4 and CO2 observations has been developed. This Methane Airborne MAPper (MAMAP) instrument measures reflected and scattered solar radiation in the short wave infrared (SWIR) and near-infrared (NIR) parts of the electro-magnetic spectrum at moderate spectral resolution. The SWIR channel yields measurements of atmospheric absorption bands of CH4 and CO2 in the spectral range between 1.59 and 1.69 μm at a spectral resolution of 0.82 nm. The NIR channel around 0.76 μm measures the atmospheric O2-A-band absorption with a resolution of 0.46 nm. MAMAP has been designed for flexible operation aboard a variety of airborne platforms. The instrument design and the performance of the SWIR channel, together with some results from on-ground and in-flight engineering tests are presented. The SWIR channel performance has been analyzed using a retrieval algorithm applied to the nadir measured spectra. Dry air column-averaged mole fractions are obtained from SWIR data only by dividing the retrieved CH4 columns by the simultaneously retrieved CO2 columns for dry air column CH4 (XCH4) and vice versa for dry air column CO2 (XCO2). The signal-to-noise ratio (SNR) of the SWIR channel is approximately 1000 for integration times (tint) in the range of 0.6–0.8 s for scenes with surface spectral reflectances (SSR)/albedo of around 0.18. At these integration times the ground scene size is about 23 × 33 m2 for an aircraft altitude of 1 km and a ground speed of 200 km/h. For these scenes the actual XCH4 or XCO2 dry air column retrieval precisions are typically about 1% (1 σ). Elevated levels of CH4 have been retrieved above a CH4 emitting landfill. Similarly the plume of CO2 from coal-fired power plants can be well detected and tracked. The measurements by the MAMAP sensor could enable estimates of anthropogenic, biogenic and geological emissions of localized intense CH4 and CO2 sources such as anthropogenic fugitive CH4 emissions from oil and gas industry, coal mining, disposal of organic waste, CO2 emissions from coal-fired power plants, steel production or geologic CH4 and CO2 emissions from seepage and volcanoes. Appropriate analysis of the measurements of MAMAP potentially also yields natural CH4 emissions from less intense but extensive sources such as wetlands

Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign

Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources. For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed. The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 h−1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1σ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended

Validation of SCIAMACHY top-of-atmosphere reflectance for aerosol remote sensing using MERIS L1 data

Aerosol remote sensing is very much dependent on the accurate knowledge of the top-of-atmosphere (TOA) reflectance measured by a particular instrument. The status of the calibration of such an instrument is reflected in the quality of the aerosol retrieval. Current data of the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument (operated with the data processor version 5 and earlier) give too small values of the TOA reflectance, compared e.g. to data from MERIS (Medium Resolution Imaging Spectrometer), both operating on ENVISAT (ENVIronmental SATellite). This effect causes retrievals of wrong aerosol optical thickness and disables the processing of aerosol parameters. <br><br> From an inter-comparison of MERIS and SCIAMACHY TOA reflectance, for collocated scenes correction factors are derived to improve the insufficient SCIAMACHY L1 data calibration for data obtained with the processor 5 for the purpose of aerosol remote sensing. The corrected reflectance has been used for tests of remote sensing of the aerosol optical thickness by the BAER (Bremen AErosol Retrieval) approach using SCIAMACHY data

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

Methane (CH_4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH_4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼2 kg/h to 5 kg/h through ∼5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign

The UV-A and visible solar irradiance spectrum: inter-comparison of absolutely calibrated, spectrally medium resolution solar irradiance spectra from balloon- and satellite-borne measurements

International audienceWithin the framework of the ENVISAT/-SCIAMACHY satellite validation, solar irradiance spectra are absolutely measured at moderate resolution in the UV/visible spectral range (in the UV from 316.7–418 nm and the visible from 400–652 nm at a full width half maximum resolution of 0.55 nm and 1.48 nm, respectively) from aboard the azimuth-controlled LPMA/DOAS balloon gondola at around 32 km balloon float altitude. After accounting for the atmospheric extinction due to Rayleigh scattering and gaseous absorption (O3, and NO2), the measured solar spectra are compared with previous observations. Our solar irradiance is +1.6% larger than the re-calibrated Kurucz et al. (1984) solar spectrum (Fontenla et al., 1999, called MODTRAN 3.5) in the visible spectral range (435–650 nm), +1.5% larger in the (370–415 nm) wavelength interval, but -4% smaller in the UV spectral range (316.7–370 nm), when the Kurucz spectrum is convolved to the spectral resolution of our instrument. The same comparison with the SOLSPEC solar spectrum (Thuillier et al., 1997, 1998a, b) confirms the somewhat larger solar irradiance (+1.7%) measured by the balloon instrument from 435–500 nm, but not from 500–650 nm, where the SOLSPEC is -1.3% lower than MODTRAN 3.5. Comparison of the SCIAMACHY solar spectrum from channels 1 to 4 (– re-calibrated by the University of Bremen –) with MODTRAN 3.5 indicates an agreement of +0.2% in the visible spectral range (435–585 nm). With this calibration, the SCIAMACHY solar spectrum is congruent with the balloon observations (-1%) in the 316.7–370 nm wavelength range, but both are up to -5%/-3% smaller than MODTRAN 3.5 and SOLSPEC, respectively. In agreement with findings of Skupin et al. (2002) our study emphasizes that the present ESA SCIAMACHY level 1 calibration is systematically +15% larger in the considered wavelength intervals when compared to all available other solar irradiance measurements

Scientific and Technical Assistance for the Deployment of a Flexible Airborne Spectrometer System During C-MAPExp and COMEX

The COMEX (CO2 and MEthane eXperiment) campaign supports the mission definition of CarbonSat and HyspIRI (Hyperspectral Infrared Imager) by providing representative airborne remote sensing data MAMAP (Methane Airborne MAPper) for CarbonSat; the Airborne Visual InfraRed Imaging Spectrometer (Classic & Next Generation) AVIRISC/AVIRISNG for HyspIRI as well as ground-based and airborne insitu data. The objectives of the COMEX campaign activities are (see Campaign Implementation Plan (RD4)): 1. Investigate spatial/spectral resolution tradeoffs for CH4 anomaly detection and flux inversion by comparison of MAMAPderived emission estimates with AVIRIS/AVIRISNG derived data. 2. Evaluate sunglint observation geometry on CH4 retrievals for marine sources. 3. Characterize the effect of Surface Spectral Reflectance (SSR) heterogeneity on trace gas retrievals of CO2 and CH4 for medium and lowresolution spectrometry. 4. Identify benefits from joint SWIR/TIR (ShortWave InfraRed/Thermal InfraRed ) data for trace gas detection and retrieval by comparison of MAMAP and AVIRIS/AVIRISNG NIR/SWIR data with MAKO (Aerospace Corp.)TIR data. The ability to derive emission source strength for a range of strong emitting targets by remote sensing will be evaluated from combined AVIRISNG and MAMAP data, adding significant value to the HyspIRI campaign AVIRISNG dataset. The data will be used to quantify anomalies in atmospheric CO2 and CH4 from strong local greenhouse gas sources e.g. localized industrial complexes, landfills, etc. and to derive CO2 and CH4 emissions estimates from atmospheric gradient measurements. The original campaign concept was developed by University of Bremen and BRI. The COMEX campaign is funded bilaterally by NASA and ESA (European Space Agency). Whereas NASA funds the US part of the project via a contract with Dr. Ira Leifer, BRI (Bubbleology Research International), the contribution of MAMAP to the COMEX campaign is funded by ESA within the COMEXE project and NASA with respect to a 50 percent contribution to the flight-related costs of flying MAMAP on a US aircraft. The Data Acquisition Report (RD9) describes the instrumentation used, the measurements made by the team during the COMEX campaign in May/June 2014 and August/September 2014 in California, and an initial assessment of the data quality

Evaluation of simulated CO₂ power plant plumes from six high-resolution atmospheric transport models

Global anthropogenic CO2 sources are dominated by power plants and large industrial facilities. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modelling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in-situ and remote sensing observations collected during the CoMet measurement campaign down-wind of the coal fired power plants at Bełchatów in Poland and Jaenschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models