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In order to improve our knowledge on emissions of the second most important anthropogenic greenhouse gas Methane (CH4), a coordinated measurement campaign in the Upper Silesian Coal Basin (USCB) in Poland, one of Europe's anthropogenic CH4 hot spots, was executed. The 4 weeks campaign in May/June 2018 was carried out by a team of scientists deploying in-situ and remote sensing instruments on aircraft as well as on ground. Two Cessna aircraft – one equipped with in-situ sensors for CH4 and related gases, another equipped with the CH4 remote sensing instrument MAMAP - concentrated on characterizing the CH4 distribution in the USCB with high horizontal and vertical detail. The German research aircraft HALO - equipped with the CH4 and CO2 detecting lidar CHARM-F, as well as with in-situ sensors – provided the larger scale picture about atmospheric CH4 distributions. These aircraft based measurements were complemented by ground based observations: several ground-based FTIR instruments as well as wind lidars were deployed. In addition, in-situ measurements from mobile vans and small drones provide near-surface information of CH4. In order to assess regional and local scale fluxes using the data set collected, a hierarchy of modelling approaches (mass balance/Gaussian plume modelling, regional inverse modelling, chemistry-climate modelling with regional refinement) is planned to be used. The paper will present initial findings from the CoMet campaign with a focus on CH4 emissions from coal mine venting shafts as well as a first top-down estimate of total CH4 emissions of the Upper Silesian Coal Basin. [ABSTRACT FROM AUTHOR]

Abstract: We have conducted a feasibility study for the geostationary monitoring of the diurnal variation of tropospheric NO2 over Tokyo. Using NO2 fields from a chemical transport model, synthetic spectra were created by a radiative transfer model, SCIATRAN, for summer and winter cases. We then performed a Differential Optical Absorption Spectroscopy (DOAS) analysis to retrieve NO2 slant column densities (SCDs), and after converting SCDs into vertical column densities (VCDs), we estimated the precision of the retrieved VCDs. The simulation showed that signal-to-noise ratio (SNR)⩾500 is needed to detect the diurnal variation and that SNR⩾1000 is needed to observe the local minimum occurring in the early afternoon (LT13–14) in summer. In winter, the detection of the diurnal variation during LT08–15 needs SNR⩾500, and SNR⩾1000 is needed if early morning (LT07) and early evening (LT16) are included. The currently discussed sensor specification for the Japanese geostationary satellite project, GMAP-Asia, which has a horizontal resolution of 10km and a temporal resolution of 1hr, has demonstrated the performance of a precision of several percent, which is approximately corresponding to SNR=1000–2000 during daytime and SNR⩾500 in the morning and evening. We also discuss possible biases caused by the temperature dependence of the absorption cross section utilized in the DOAS retrieval, and the effect of uncertainties of surface albedo and clouds on the estimation of precisions. [Copyright &y& Elsevier]

Stratospheric aerosols are of a great importance to the scientific community, predominantly because of their role in climate, but also because accurate knowledge of aerosol characteristics is relevant for trace gas retrievals from remote-sensing instruments. There are several data sets published which provide aerosol extinction coefficients in the stratosphere. However, for the instruments measuring in the limb-viewing geometry, the use of this parameter is associated with uncertainties resulting from the need to assume an aerosol particle size distribution (PSD) within the retrieval process. These uncertainties can be mitigated if PSD information is retrieved. While occultation instruments provide more accurate information on the aerosol extinction coefficient, in this study, it was shown that limb instruments are more sensitive to the smaller particles in the visible–near-infrared spectral range. However, the sensitivity of occultation instruments improves if the UV part of the wavelength spectrum is considered. A data set containing PSD information was recently retrieved from SCIAMACHY limb measurements and provides two parameters of the unimodal lognormal PSD for the SCIAMACHY operational period (2002–2012). In this study, the data set is expanded by aerosol extinction coefficients and Ångström exponents calculated from the retrieved PSD parameters. Parameter errors for the recalculated Ångström exponents and aerosol extinction coefficients are assessed using synthetic retrievals. For the extinction coefficient the resulting parameter error is within ±25 %, and for the Ångström exponent, it is better than 10 %. The SCIAMACHY aerosol extinction coefficients recalculated from PSD parameters are compared to those from SAGE II. The differences between the instruments vary from 0 % to 25 % depending on the wavelength. Ångström exponent comparison with SAGE II shows differences between 10 % at 31 km and 40 % at 18 km. Comparisons with SAGE II, however, suffer from the low number of collocated profiles. Furthermore, the Ångström exponents obtained from the limb-viewing instrument OSIRIS are used for the comparison. This comparison shows an average difference within 7 %. The time series of these differences do not show signatures of any remarkable events (e.g., volcanic eruptions or biomass burning events). In addition, the temporal behaviour of the Ångström exponent in the tropics is analyzed using the SCIAMACHY data set. It is shown that there is no trivial relation between the Ångström exponent value at a single wavelength pair and the PSD because the same value of Ångström exponent can be obtained from an infinite number of combinations of the PSD parameters. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR) combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with unprecedented level of detail on a global scale introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified DOAS (WFM-DOAS). We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine learning-based quality filter and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier Transform Spectrometer (FTS) measurements providing realistic error estimates of the satellite data: The XCO data set is characterised by a random error of 5.1 ppb (5.7 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.4 ppb (0.2 %). The natural XCO and XCH4 variations are well captured by the satellite retrievals, which is demonstrated by a high correlation to the reference data (R = 0.97 for XCO and R = 0.91 for XCH4 based on daily averages). We also present selected results from mission start until end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector. [ABSTRACT FROM AUTHOR]

Information about aerosols in the Earth's atmosphere is of a great importance in the scientific community. While tropospheric aerosol influences the radiative balance of the troposphere and affects human health, stratospheric aerosol plays an important role in atmospheric chemistry and climate change. In particular, information about the amount and distribution of stratospheric aerosols is required to initialize climate models, as well as validate aerosol microphysics models and investigate geoengineering. In addition, good knowledge of stratospheric aerosol loading is needed to increase the retrieval accuracy of key trace gases (e.g. ozone or water vapour) when interpreting remote sensing measurements of the scattered solar light. The most commonly used characteristics to describe stratospheric aerosols are the aerosol extinction coefficient and Ångström coefficient. However, the use of particle size distribution parameters along with the aerosol number density is a more optimal approach. In this paper we present a new retrieval algorithm to obtain the particle size distribution of stratospheric aerosol from space-borne observations of the scattered solar light in the limb-viewing geometry. While the mode radius and width of the aerosol particle size distribution are retrieved, the aerosol particle number density profile remains unchanged. The latter is justified by a lower sensitivity of the limb-scattering measurements to changes in this parameter. To our knowledge this is the first data set providing two parameters of the particle size distribution of stratospheric aerosol from space-borne measurements of scattered solar light. Typically, the mode radius and w can be retrieved with an uncertainty of less than 20%. The algorithm was successfully applied to the tropical region (20° N-20° S) for 10 years (2002-2012) of SCIAMACHY observations in limb-viewing geometry, establishing a unique data set. Analysis of this new climatology for the particle size distribution parameters showed clear increases in the mode radius after the tropical volcanic eruptions, whereas no distinct behaviour of the absolute distribution width could be identified. A tape recorder, which describes the time lag as the perturbation propagates to higher altitudes, was identified for both parameters after the volcanic eruptions. A quasi-biannual oscillation (QBO) pattern at upper altitudes (28-32 km) is prominent in the anomalies of the analysed parameters. A comparison of the aerosol effective radii derived from SCIAMACHY and SAGE II data was performed. The average difference is found to be around 30% at the lower altitudes, decreasing with increasing height to almost zero around 30 km. The data sample available for the comparison is, however, relatively small. [ABSTRACT FROM AUTHOR]

This paper describes the in-flight calibration of the polarization response of the SCIAMACHY polarization measurement devices (PMDs) and a selected region of its science channels. With the lack of polarized calibration sources it is not possible to obtain such a calibration from dedicated calibration measurements. Instead, the earthshine itself, together with a simplified radiative transfer model (RTM), is used to derive time-dependent and measurement-configuration-dependent polarization sensitivities. The results are compared to an instrument model that describes the degradation of the instrument as a result of a slow buildup of contaminant layers on its elevation and azimuth scan mirrors. This comparison reveals significant differences between the model prediction and the data, suggesting an unforeseen change between on-ground and in-flight calibration in at least one of the polarization-sensitive components of the optical bench. The possibility of mechanisms other than scan mirror contamination contributing to the degradation of the instrument will be discussed. The data are consistent with a polarization phase shift occurring in the beam split prism used to divert the light coming from the telescope to the different channels and polarization measurement devices. The extension of the instrument degradation model with a linear retarder enables the determination of the relevant parameters to describe this phase shift and ultimately results in a significant improvement of the polarization measurements as well as the polarization response correction of measured radiances. [ABSTRACT FROM AUTHOR]

Airborne in situ and remote sensing measurements of methane were performed over the marine seeps in the Santa Barbara Channel close to the Coal Oil Point in California on two days in June and August 2014 with the aim to re-assess their methane emissions. During this period, methane column averaged dry air mole fractions derived from airborne remote sensing measurements in the short-wave infrared and airborne in situ measurements of methane indicate that emissions are 2-6 ktCH4 y-1, significantly lower than expected from previous publications. This is also confirmed by the on ground in situ measurement time series recorded at the onshore West Campus Monitoring Station in Santa Barbara. Using a time series of methane data, a decline in methane concentrations between 2008 and 2015 of more than a factor of two was derived for air masses originating from the seep field direction. [ABSTRACT FROM AUTHOR]

The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (<3.7 ppb) to significantly improve our knowledge of methane sources from global to regional scales, with emphasis on poorly accessible regions in the tropics and at high latitudes. This paper presents the MERLIN objectives, describes the methodology and the main characteristics of the payload and of the platform, and proposes a first assessment of the error budget and its translation into expected uncertainty reduction of methane surface emissions. [ABSTRACT FROM AUTHOR]

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on Envisat (2002-2012) performed nadir, limb, solar/lunar occultation and various monitoring measurements. The pointing information of the instrument is determined by the attitude information of the Envisat platform with its star trackers together with the encoder readouts of both the azimuth and the elevation scanner of SCIAMACHY. In this work, we present additional sources of attitude information from the SCIAMACHY measurements itself. The basic principle is the same as used by the star tracker: we measure the viewing direction towards celestial objects, i.e. sun and moon, to detect possible mispointings. In sun over limb port observations, we utilise the vertical scans over the solar disk. In horizontal direction, SCIAMACHY's sun follower device (SFD) is used to adjust the viewing direction. Moon over limb port measurements use for both the vertical and the horizontal direction the adjustment by the SFD. The viewing direction is steered towards the intensity centroid of the illuminated part of the lunar disk. We use reference images from the USGS Robotic Lunar Observatory (ROLO) to take into account the inhomogeneous surface and the variations by lunar libration and phase to parameterise the location of the intensity centroid from the observation geometry. Solar observations through SCIAMACHY's so-called sub-solar port (with a viewing direction closely to zenith) also use the SFD in the vertical direction. In the horizontal direction the geometry of the port defines the viewing direction. [ABSTRACT FROM AUTHOR]

A new airborne remote sensing approach to estimate an upper limit of the direct sea-air methane emission flux was applied over the 22/4b blowout site located at N57.92°, E1.63° in the North Sea. Passive remote sensing data using sunglint/sunglitter geometry were collected during instrumental tests with the Methane Airborne MAPper – MAMAP – instrument installed aboard the Alfred Wegener Institute (AWI) Polar-5 aircraft on 3. June 2011. MAMAP is a passive short wave infrared (SWIR) remote sensing spectrometer for airborne measurements and retrieval of the atmospheric column-averaged dry air mole fractions of methane (XCH 4 ) and carbon dioxide (XCO 2 ). In addition to MAMAP a fast CH 4 in-situ analyzer (Los-Gatos Research Inc. RMT-200), two 5-hole turbulence probes and the Polar-5 basic sensor suite comprising different temperature, pressure, humidity and camera sensors were installed aboard the aircraft. The collected MAMAP remote sensing data acquired in the vicinity of the 22/4b blowout site showed no detectable increase in the derived XCH 4 (with respect to the atmospheric background). Based on the absence of a detectable XCH 4 column increase, an approximate top-down upper-limit for the direct atmospheric 22/4b blowout CH 4 emissions from the main bubble plume of less than 10 ktCH 4 /yr has been derived. The constraint has been determined by comparing XCH 4 information derived by the remote sensing measurements with results obtained from a Gaussian plume forward model simulation taking into account the actual flight track, the instrument sensitivity and measurement geometry, as well as the prevailing atmospheric conditions. [ABSTRACT FROM AUTHOR]

We show new results from an updated version of the Fast atmOspheric traCe gAs retrievaL (FOCAL) retrieval method applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL was originally developed for estimating the total column carbon dioxide mixing ratio (XCO2) from spectral measurements made by the Orbiting Carbon Observatory-2 (OCO-2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species and their uncertainties within one single retrieval. The main focus of the current paper is on methane (XCH4 ; full-physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2, it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. For XCO2 , the new FOCAL retrieval (v3.0) significantly increases the number of valid data compared with the previous FOCAL retrieval version (v1) by 50 % for GOSAT and about a factor of 2 for GOSAT-2 due to relaxed pre-screening and improved post-processing. All v3.0 FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with the Total Carbon Column Observing Network (TCCON) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb , which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb , which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are on the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data. Noting that only few XN2O measurements from satellites exist so far, the GOSAT-2 FOCAL product will be a valuable contribution in this context. [ABSTRACT FROM AUTHOR]

Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD). The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite and radiosondes facilitate the assessment of the significance of the observed changes in water vapor. In this manuscript, the authors have compared observed water vapor change and trends, derived from independent instruments, and assessed the statistical significance of their differences. This study deals with an example of the Behrens--Fisher problem, namely, the comparison of samples with different means and different standard deviations, applied to trends from time series. Initially the Behrens--Fisher problem for the derivation of the consolidated change and trends is solved using standard ( frequentist) hypothesis testing by performing the Welch test. Second, a Bayesian model selection is applied to solve the Behrens--Fisher problem by integrating the posterior probabilities numerically by using the algorithm Differential Evolution Markov Chain (DEMC). Additionally, an analytical approximative solution of the Bayesian posterior probabilities is derived by means of a quadratic Taylor series expansion applied in a computationally efficient manner to large datasets. The two statistical methods used in the study yield similar results for the comparison of the water vapor changes and trends from the different measurements, yielding a consolidated and consistent behavior. [ABSTRACT FROM AUTHOR]

Abstract: Carbon monoxide (CO) is an important air pollutant whose emissions and atmospheric concentrations need to be monitored. The measurements of the SCIAMACHY instrument on ENVISAT are sensitive to CO concentration changes at all atmospheric altitude levels including the boundary layer. The SCIAMACHY CO measurements therefore contain information on CO emissions. Until now no studies have been published where the SCIAMACHY CO measurements have been used to quantify CO emissions by applying, for example, inverse modelling approaches. Here we report about a step in this direction. We have analysed three years of CO columns to investigate if spatial gradients resulting from United Kingdom (UK) CO emissions can be observed from space. The UK is an interesting target area because the UK is a relatively well isolated CO source region. On the other hand, the UK is not the easiest target as its emissions are only moderate and because the surrounding water has low reflectivity in the 2.3μm spectral region used for CO retrieval. We determined horizontal CO gradients from seasonally and yearly averaged CO during 2003–2005 over the UK taking into account daily wind fields. We show that the measured CO longitudinal (downwind) gradients have the expected order of magnitude. The estimated 2σ error of the gradients depends on time period and applied filtering criteria (e.g., land only, cloud free) and is typically 10–20% of the total column. The gradients are barely statistically significant within the 2σ error margin. This is mainly because of the relatively high noise of the SCIAMACHY CO measurements in combination with a quite low number of measurements (∼100) mainly due to cloud cover. [Copyright &y& Elsevier]