Tropospheric aerosol profile information from high-resolution oxygen A-band measurements from space

Aerosols are an important factor in the Earth climatic system and they play a key role in air quality and public health. Observations of the oxygen A-band at 760 nm can provide information on the vertical distribution of aerosols from passive satellite sensors that can be of great interest for operational monitoring applications with high spatial coverage if the aerosol information is obtained with sufficient precision, accuracy and vertical resolution. To address this issue, retrieval simulations of the aerosol vertical profile retrieval from O[Subscript: 2] A-band observations by GOSAT, the upcoming Orbiting Carbon Observatory-2 (OCO-2) and Sentinel 5-P missions, and the proposed CarbonSat mission have been carried out. Precise retrievals of aerosol optical depth (AOD) within the boundary layer were found to favour low-resolution, high signal-to-noise instruments such as Sentinel-5 P, whereas higher-resolution instruments such as OCO-2 showed greater performance at higher altitudes and in information content above the boundary layer. Retrieval of the AOD in the 0–2 km range with precision appears difficult from all studied instruments and the retrieval errors typically exceed a value of 0.05 for AODs up to 0.3. Constraining the surface albedo is a promising and effective way of improving the retrieval of aerosol, but the accuracy of the required prior knowledge is very high. Due to the limited information content of the aerosol profile retrieval, the use of a parameterised aerosol distribution is assessed, and we show that the AOD and height of an aerosol layer can be retrieved well if the aerosol layer is uplifted to the free troposphere; however, errors are often large for aerosol layers in the boundary layer. Additional errors are introduced by incorrect assumptions on surface pressure and aerosol mixture, which can both bias retrieved AOD and height by up to 45%. In addition, assumptions of the boundary layer temperature are found to yield an additional error of up to 8%. We conclude that the aerosol profile retrievals from O[Subscript: 2] A-band using existing or upcoming satellite sensors will only provide limited information on aerosols in the boundary layer but such observations can be of great value for observing and mapping aerosol plumes in the free troposphere

Comparative multifractal analysis of methane gas concentration time series in India and regions within India

In the present study GOSAT CH4 has been used to analyze the methane gas concentration in India over the eight years from 2010 to 2017. The data have been analyzed using the multifractal detrended fluctuation analysis technique. Two different geographical regions within India have been selected and CH4 data for those regions are also analyzed. The generalized Hurst exponents for India and the two regions are 1.27, 0.74 and 0.91, which are significantly high from their shuffled data counterparts i.e. 0.50, 0.51 and 0.51 respectively. This finding reveals that methane gas concentration over time show multifractal nature which in turn establishes the presence of long-range temporal correlations in the data. The width of the Multifractal spectrum for India and the two regions are found to be 0.76, 1.38 and 1.01 respectively. This result shows that strength of correlation is different for the two regions selected, which we suggest may be due to the different methane emission process of the considered regions. Comparison of the results with that of shuffled data signify that the correlation is purely due to the methane production dynamics and not a result of mere statistics

Application of a PCA-Based Fast Radiative Transfer Model to XCO2 Retrievals in the Shortwave Infrared

In this work, we extend the principal component analysis (PCA)-based approach to accelerate radiative transfer (RT) calculations by accounting for the spectral variation of aerosol properties. Using linear error analysis, the errors induced by this fast RT method are quantified for a large number of simulated Greenhouse Gases Observing Satellite (GOSAT) measurements (N≈ 30,000). The computational speedup of the approach is typically 2 orders of magnitude compared to a line-by-line discrete ordinates calculation with 16 streams, while the radiance residuals do not exceed 0.01% for the most part compared to the same baseline calculations. We find that the errors due to the PCA-based approach tend to be less than ±0.06 ppm for both land and ocean scenes when two or more empirical orthogonal functions are used. One advantage of this method is that it maintains the high accuracy over a large range of aerosol optical depths. This technique shows great potential to be used in operational retrievals for GOSAT and other remote sensing missions

First satellite measurements of carbon dioxide and methane emission ratios in wildfire plumes

Using methane and carbon dioxide atmospheric mixing ratios retrieved using SWIR spectra from the Greenhouse Gases Observing SATellite (GOSAT), we report the first wildfire plume CH4 to CO2 emission ratios (ERCH4/CO2) determined from space. We demonstrate the approach’s potential using forward modeling and identify a series of real GOSAT spectra containing wildfire plumes. These show significantly changed total-column CO2 and CH4 mixing ratios, and from these we calculate ERCH4/CO2 for boreal forest, tropical forest, and savanna fires as 0.00603, 0.00527, and 0.00395 mol mol 1 , respectively. These ERs are statistically significantly different from each other and from the “normal” atmospheric CH4 to CO2 ratio and generally agree with past ground and airborne studies

Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission

The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for XCO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and XCO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved XCO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve XCO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the XCO2 average product for ingestion into data assimilation/inverse models. If biases in the XCO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 XCO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes

Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at ~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO2 retrievals work reasonably well. The XCO2 retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO2 retrievals of up to 2.5 ppm. The XCO2 retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78 ± 1.46 ppm and 1.06 ± 0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO2 retrievals, especially combined with an aerosol retrieval algorithm

Rain-fed pulses of methane from East Africa during 2018-2019 contributed to atmospheric growth rate

East Africa is a key location for wetland emissions of methane (CH4), driven by variations in rainfall that are in turn influenced by sea-surface temperature gradients over the Indian Ocean. Using satellite observations of CH4 and an atmospheric chemistry-transport model, we quantified East African CH4 emissions during 2018 and 2019 when there was 3-σ anomalous rainfall during the long rains (March-May) in 2018 and the short rains (October-December) in 2019. These rainfall anomalies resulted in CH4 emissions of 6.2 ± 0.3 Tg CH4 and 8.6 ± 0.3 Tg CH4, in each three month period, respectively, and represent a 10% and 37% increase compared to the equivalent season in the opposite year, when rainfall was close to the long-term seasonal mean. We find the additional short rains emissions were equivalent to over a quarter of the growth in global emissions in 2019, highlighting the disproportionate role of East Africa in the global CH4 budget

Global height-resolved methane retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp

This paper describes the global height-resolved methane (CH4) retrieval scheme for the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp, developed at the Rutherford Appleton Laboratory (RAL). The scheme precisely fits measured spectra in the 7.9 micron region to allow information to be retrieved on two independent layers centred in the upper and lower troposphere. It also uses nitrous oxide (N2O) spectral features in the same spectral interval to directly retrieve effective cloud parameters to mitigate errors in retrieved methane due to residual cloud and other geophysical variables. The scheme has been applied to analyse IASI measurements between 2007 and 2015. Results are compared to model fields from the MACC greenhouse gas inversion and independent measurements from satellite (GOSAT), airborne (HIPPO) and ground (TCCON) sensors. The estimated error on methane mixing ratio in the lower- and upper-tropospheric layers ranges from 20 to 100 and from 30 to 40 ppbv, respectively, and error on the derived column-average ranges from 20 to 40 ppbv. Vertical sensitivity extends through the lower troposphere, though it decreases near to the surface. Systematic differences with the other datasets are typically  < 10 ppbv regionally and  < 5 ppbv globally. In the Southern Hemisphere, a bias of around 20 ppbv is found with respect to MACC, which is not explained by vertical sensitivity or found in comparison of IASI to TCCON. Comparisons to HIPPO and MACC support the assertion that two layers can be independently retrieved and provide confirmation that the estimated random errors on the column- and layer-averaged amounts are realistic. The data have been made publically available via the Centre for Environmental Data Analysis (CEDA) data archive (Siddans, 2016)

Atmospheric CH₄ and CO₂ enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes

The 2015–2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015–2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH₄ (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon. Here, for the first time, we use satellite observations of CH₄ and CO₂ from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions. Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH₄ is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH₄ total column values typically exceeding 35 ppb above those of background “clean air” soundings. By examining the CH₄ and CO₂ excess concentrations in the fire-affected GOSAT observations, we determine the CH₄ to CO₂ (CH₄ ∕ CO₂) fire emission ratio for the entire 2-month period of the most extreme burning (September–October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH₄ to CO₂ emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm⁻¹. This is higher than that found over both the Amazon (5.1 ppb ppm⁻¹) and southern Africa (4.4 ppb ppm⁻¹), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellite-derived Indonesian ERs (6.18–13.6 ppb ppm⁻¹) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53–19.67 ppb ppm⁻¹), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation). The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood

GreenHouse gas Observations of the Stratosphere and Troposphere (GHOST): an airborne shortwave-infrared spectrometer for remote sensing of greenhouse gases

GHOST is a novel, compact shortwave-infrared grating spectrometer, designed for remote sensing of tropospheric columns of greenhouse gases (GHGs) from an airborne platform. It observes solar radiation at medium to high spectral resolution (better than 0.3nm), which has been reflected by the Earth's surface using similar methods to those used by polar-orbiting satellites such as the JAXA GOSAT mission, NASA's OCO-2, and the Copernicus Sentinel-5 Precursor. By using an original design comprising optical fibre inputs along with a single diffraction grating and detector array, GHOST is able to observe CO2 absorption bands centred around 1.61 and 2.06µm (the same wavelength regions used by OCO-2 and GOSAT) whilst simultaneously measuring CH4 absorption at 1.65µm (also observed by GOSAT) and CH4 and CO at 2.30µm (observed by Sentinel-5P). With emissions expected to become more concentrated towards city sources as the global population residing in urban areas increases, there emerges a clear requirement to bridge the spatial scale gap between small-scale urban emission sources and global-scale GHG variations. In addition to the benefits achieved in spatial coverage through being able to remotely sense GHG tropospheric columns from an aircraft, the overlapping spectral ranges and comparable spectral resolutions mean that GHOST has unique potential for providing validation opportunities for these platforms, particularly over the ocean, where ground-based validation measurements are not available. In this paper we provide an overview of the GHOST instrument, calibration, and data processing, demonstrating the instrument's performance and suitability for GHG remote sensing. We also report on the first GHG observations made by GHOST during its maiden science flights on board the NASA Global Hawk unmanned aerial vehicle, which took place over the eastern Pacific Ocean in March 2015 as part of the CAST/ATTREX joint Global Hawk flight campaign