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Microwave radiometer (MRM) is one of the important payloads on the Chang'e-2 (CE-2) Lunar satellite. In the Chang'e satellite's observation of the microwave radiation brightness temperature (TB) on the lunar surface, there are some "cold spots" of microwave thermal radiation at night containing the Jackson crater. In order to compare the diurnal radiation TB differences of "cold spots" on the lunar surface, two typical craters at similar latitudes on the northern hemisphere on the lunar farside were selected: Jackson, which represents the new craters with a large number of discrete rocks on their surfaces; and Morse, which no longer has a large number of rocks after long-term meteorite impact and lunar evolution. In this paper, the diurnal variation of CE-2 MRM data in the two craters is presented, and a comparative analysis is made with the (FeO + TiO 2 ) abundance (FTA) obtained by Clementine UV-VIS data and the rock abundance (RA) data of LRO Diviner. We find that the variation of the "cold spots" of lunar surface thermal radiation is closely related to the RA distribution in the newly formed craters on the lunar surface, and also has a certain correlation with the FTA. [ABSTRACT FROM AUTHOR]

In light of the proposed space segment of Europe's future CO2 monitoring system, we investigate the spectral resolution of the CO2 spectrometer, which measures earthshine radiance in the three relevant spectral bands at 0.76, 1.61, and 2.06 µm. The Orbiting Carbon Observatory-2 (OCO-2) mission covers these bands with fine spectral resolution but limited spatial coverage, which hampers the monitoring of localized anthropogenic CO2 emission. The future European CO2 monitoring constellation, currently undergoing feasibility studies at the European Space Agency (ESA), is targeting a moderate spectral resolution of 0.1, 0.3, and 0.3–0.55 nm in the three spectral bands with a high signal-to-noise ratio (SNR) as well as a spatial resolution of 4 km2 and an across-track swath width >250 km. This spectral and radiometric sizing is deemed to be favorable for large-swath imaging of point sources of CO2 emission. To assess this choice, we use real and synthetic OCO-2 satellite observations, which we spectrally degrade to the envisaged lower spectral resolution. We evaluate the corresponding CO2 retrieval accuracy by taking the Total Carbon Column Observing Network (TCCON) observations as reference. Here, a lower spectral resolution enhances the scatter error of the retrieved CO2 column mixing ratio (XCO2) but has little effect on the station-to-station variation in the biases. We show that the scatter error gradually increases with decreasing spectral resolution. Part of the scatter error increase can be attributed to the retrieval noise error which can be compensated for by a future instrument with improved SNR. Moreover, we consider the effect of the reduced spectral resolution on the capability to capture regional XCO2 variations and XCO2 plumes from selected OCO-2 orbits. The investigation using measurements from the Greenhouse gases Observing SATellite (GOSAT) and synthetic measurements confirms our finding and indicates that one major source of uncertainties regarding CO2 retrieval is the insufficient information on aerosol properties that can be inferred from the observations. We hence recommend the implementation of simultaneous, co-located measurements that have a larger information content on aerosols with an auxiliary instrument in the future European observing system. [ABSTRACT FROM AUTHOR]

Passive remote sensing of atmospheric carbon dioxide uses spectroscopic measurements of sunlight backscattered by the Earth's surface and atmosphere. The current state-of-the-art retrieval methods use three different spectral bands, the oxygen A band at 0.76 µ m and the weak and strong CO2 absorption bands at 1.61 and 2.06 µ m, respectively, to infer information on light scattering and the carbon dioxide column-averaged dry-air mole fraction XCO2. In this study, we propose a one-band XCO2 retrieval technique which uses only the 2.06 µ m band measurements from the Orbiting Carbon Observatory-2 (OCO-2) satellite. We examine the data quality by comparing the OCO-2 XCO2 with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON). Over land and ocean the OCO-2 one-band retrieval shows differences from TCCON observations with a standard deviation of ∼1.30 ppm and a station-to-station variability of ∼0.50 ppm. Moreover, we compare one-band and three-band retrievals over Europe, the Middle East, and Africa and see high correlation between the two retrievals with a SD of 0.93 ppm. Compared to the three-band retrievals, XCO2 retrievals using only the 2.06 µ m band have similar retrieval accuracy, precision, and data yield. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with goodness of fit, we applied an a posteriori bias correction to our OCO-2 retrievals. In overpass averaged results, XCO2 retrievals have an SD of ~1:30 ppm and a station-to-station variability of ~0:40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high-latitude areas and some specific regions like Japan. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI), due to its wide swath, performs observations over the ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the short-wave–infrared (SWIR) spectral band ocean surfaces are dark except for the specific sun glint geometry, for which the specular reflectance detected by the satellite provides a signal that is high enough to retrieve methane with high accuracy and precision. In this study, we build upon the RemoTeC full-physics retrieval algorithm for land measurements, and we retrieve 4 years of methane concentrations over the ocean from TROPOMI. We fully assess the quality of the dataset by performing a validation using ground-based measurements of the Total Carbon Column Observing Network (TCCON) from near-ocean sites. The validation results in an agreement of -0.5±0.3 % (-8.4±6.3 ppb) for the mean bias and station-to-station variability, which show that glint measurements comply with the mission requirement of precision and accuracy below 1 %. Comparison to ocean measurements from the Greenhouse gases Observing SATellite (GOSAT) results in a bias of -0.2±0.9 % (-4.4±15.7 ppb), equivalent to the comparison of measurements over land. The full-physics algorithm simultaneously retrieves the amount of atmospheric methane and the physical scattering properties of the atmosphere from measurements in the near-infrared (NIR) and SWIR spectral bands. Based on the scattering properties of the atmosphere and ocean surface reflection we further validate retrievals over the ocean. Using the "upper-edge" method, we identify a set of ocean glint observations where scattering by aerosols and clouds can be ignored in the measurement simulation to investigate other possible error sources such as instrumental errors, radiometric inaccuracies or uncertainties related to spectroscopic absorption cross-sections. With this ensemble we evaluate the RemoTeC forward model via the validation of the total atmospheric oxygen (O 2) column retrieved from the O 2 A-band, as well as the consistency of XCH4 retrievals using sub-bands from the SWIR band, which show a consistency within 1 %. We discard any instrumental and radiometric errors by a calibration of the O 2 absorption line strengths as suggested in the literature. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space Carbon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturised GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry–Pérot imaging interferometer with a 2.3×2.3 km 2 spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO 2 and CH 4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms and provide their systematic and random errors, column vertical sensitivities, and degrees of freedom as a function of five scattering-error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two-thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5–1 ppm and 4–6 ppb, respectively, compliant with their respective 1 ppm and 6 ppb precision objectives. Finally, these Level 2 performance results are parameterised as a function of the explored scattering-error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO 2 and CH 4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO 2) emissions during 2020 in large parts of the world. To investigate whether a regional-scale reduction of anthropogenic CO 2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO 2 , we have analysed a small ensemble of OCO-2 and GOSAT satellite retrievals of column-averaged dry-air mole fractions of CO 2 , i.e. XCO 2. We focus on East China and use a simple data-driven analysis method. We present estimates of the relative change of East China monthly emissions in 2020 relative to previous periods, limiting the analysis to October-to-May periods to minimize the impact of biogenic CO 2 fluxes. The ensemble mean indicates an emission reduction by approximately 10 % ± 10 % in March and April 2020. However, our results show considerable month-to-month variability and significant differences across the ensemble of satellite data products analysed. For example, OCO-2 suggests a much smaller reduction (∼ 1 %–2 % ± 2 %). This indicates that it is challenging to reliably detect and to accurately quantify the emission reduction with current satellite data sets. There are several reasons for this, including the sparseness of the satellite data but also the weak signal; the expected regional XCO 2 reduction is only on the order of 0.1–0.2 ppm. Inferring COVID-19-related information on regional-scale CO 2 emissions using current satellite XCO 2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO 2 surface fluxes. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH 4) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CH 4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH 4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH 4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH 4 data that show the good agreement of the updated TROPOMI CH 4 with TCCON (- 3.4 ± 5.6 ppb) and GOSAT (- 10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH 4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause. [ABSTRACT FROM AUTHOR]

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4 , respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4 , corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: -2.0 ppb (-2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/ , last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/ , last access: 10 January 2020). [ABSTRACT FROM AUTHOR]

Highlights • We introduce the SPEXone instrument for the NASA PACE mission. • We demonstrate that SPEXone has high capability for aerosol retrieval. • Polarimetric accuracy is the most important factor determining capability for aerosol retrieval. • Having many spectral measurements, it is sufficient to measure at a small number (5) of viewing angles for aerosol retrieval. • On the PACE mission, retrieval capability will be further enhanced by combining with HARP-2 and OCI. Abstract SPEXone is a Multi-Angle Polarimeter instrument that is baselined to fly on the NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, to be launched in 2022. It will perform hyper-spectral measurements of radiance and polarization in the spectral range 385–770 nm at 5 viewing angles (± 57 o , ± 20 o , 0 o ) with high accuracy (0.003) on the Degree of Linear Polarization (DoLP). Based on linear error analysis and retrievals on synthetic data, we conclude that SPEXone has the capability to significantly advance the accuracy of retrievals of optical and microphysical aerosol properties compared to past, present, and planned satellite instruments, as required for better quantification of the effect of aerosols on climate. The products that SPEXone will provide are Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), Aerosol Layer Height (ALH), effective radius, effective variance, complex refractive index, particle number column for both the fine and coarse mode as well as a shape parameter for the coarse mode. PACE will carry two other instrument: the Ocean Color Instrument (OCI) which is the main instrument and the Hyper-Angular Rainbow Polarimeter-2 (HARP-2). The synergistic use of SPEXone with these instruments will further increase retrieval accuracy, in particular for coarse mode parameters and absorption, and will provide unprecedented capability for aerosol above cloud retrievals. [ABSTRACT FROM AUTHOR]

There is growing consensus in the aerosol remote sensing community that Multi-Angle Photo-polarimetric (MAP) measurements are essential to unambiguously determine all aerosol properties relevant to quantify and understand the impact of aerosols on climate and air quality. This presentation will discuss the advanced algorithm development for aerosol and cloud retrieval from MAP instruments. This includes aerosol and cloud retrieval from the PARASOL mission (2004-2013) and its validation, estimation the Direct Radiative Effect of Aerosols (DREA) using PARASOL and other A-Train sensors, use of Neural Network techniques for cloud retrievals, and aerosol retrieval from airborne MAP instruments. Further, we will discuss the development of the SPEXone instrument for the NASA PACE mission (launch 2022) and its aerosol retrieval capabilities. SPEXone is a high accuracy MAP with 5 viewing angles with hyperspectral capability, that is expected to provide aerosol properties with unprecedented detail and accuracy. On the PACE mission SPEXone will be used in synergy with the Hyper-Angular Rainbow Polarimeter-2 (HARP-2) and the Ocean Color Instrument (OCI). [ABSTRACT FROM AUTHOR]

Previously, satellite-derived atmospheric carbon dioxide (CO2) and methane (CH4) Essential Climate Variable (ECV) data sets have been generated and made available via the GHG-CCI project of ESA's Climate Change Initiative (CCI, http://www.esa-ghg-cci.org/). Currently, the production and provision of these data sets is being continued operationally via the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/), which is implemented by ECMWF on behalf of the European Commission (EC). In this presentation an overview about these data sets will be given focusing on column-averaged dry-air mole fractions of CO2, denoted XCO2. Currently, this C3S data set covers the time period 2003-2017 but will be extended each year but one additional year. In addition, XCO2 from GOSAT is generated in quasi near-real-time for the Copernicus Atmosphere Monitoring Service (CAMS, https://atmosphere.copernicus.eu/) and the combined C3S/CAMS data set covering 2003-2018 has been used to provide up-to-date information on, for example, annual mean XCO2 growth rates. Main applications for the C3S data set are climate and carbon related applications such as comparisons with climate models and improving our knowledge of CO2 sources and sinks. Current satellites have however not been optimized to monitor anthropogenic emissions. To improve this situation in the future, ESA and the EC supported by other institutions and European and international scientist are working on the specification of a CO2 Monitoring (CO2M) mission, which will likely be a constellation of Sentinel satellites. In addition to high-resolution (approx. 2x2 km2) CO2 and CH4 observations, CO2M will also provide Solar-Induced-Fluorescence (SIF) and information on NO2 and aerosols probably via dedicated instruments on the same platform. In the presentation an overview about these current and future Copernicus CO2-related activities will be presented. [ABSTRACT FROM AUTHOR]