Development and Application of HECORA Cloud Retrieval Algorithm Based On the O2-O2 477 nm Absorption Band

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI.

Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions)

The ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240–4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° × 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999–2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the “Snow and ice” class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called “hot-spot” by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces.

NO2 Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and TROPOMI

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO2. Some first retrieval results of NO2 from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO2 show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO2 patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 1015 molec cm−2) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO2 from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO2 VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO2 monitoring.

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (meanR ¯ 2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (meanR ¯ 2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.

Global diffuse attenuation derived from vibrational Raman scattering detected in hyperspectral backscattered satellite spectra

Underwater light field characterization is of importance for understanding biogeochemical processes and heat budget of the global oceans, which are impacting and reacting to climate change. Vibrational Raman Scattering (VRS) was retrieved from backscattered radiances measured by three different hyperspectral satellite sensors, SCIAMACHY, GOME-2, and OMI, using Differential Optical Absorption Spectroscopy (DOAS). Diffuse attenuation coefficient (Kd) in the blue spectral range (390 to 426 nm) was derived from the VRS signal via a look-up-table established through ocean-atmosphere coupled radiative transfer modeling. We processed one year of data, representative of the overlapping period of optimal operation for all three sensors. Resulting data sets were evaluated by comparison with Kd at 490 nm from Ocean Colour Climate Change Initiative (OC-CCI) which was first converted to Kd at 390 to 426 nm. Good agreement with the OC-CCI Kd product was achieved for all three sensors when Kd was limited to below 0.15 m^-1. Differences among the hyperspectral sensors and to OC-CCI were attributed to particular instrumental effects on the DOAS retrieval leading to temporal and spatial biases. This is in addition to the fact that the spatial and temporal resolution of the hyperspectral sensors data differ among themselves and are much lower then for the OC-CCI Kd-product. Further corrections (e.g., empirical) are necessary before these data sets can be merged in order to obtain a long-term Kd product for the blue spectral range.

Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on Satellite Products

Formaldehyde (HCHO) is one of the most abundant hydrocarbons in the atmosphere. Its absorption features in the 320–360 nm range allow its concentration in the atmosphere to be retrieved from space. There are two versions of HCHO datasets derived from the Ozone Monitoring Instrument (OMI)—one provided by the Royal Belgian Institute for Space Aeronomy (BIRA-IASB) and one provided by the National Aeronautics and Space Administration (NASA)—referred to as OMI-BIRA and OMI-NASA, respectively. We conducted daily comparisons of OMI-BIRA and multi-axis differential optical absorption spectrometry (MAX-DOAS), OMI-NASA and MAX-DOAS, and OMI-BIRA and OMI-NASA and monthly comparisons of OMI-BIRA and MAX-DOAS and OMI-NASA and MAX-DOAS. Daily comparisons showed a strong impact of effective cloud fraction (eCF), and correlations were better for eCF < 0.1 than for eCF < 0.3. By contrast, the monthly and multi-year monthly mean values yielded correlations of R2 = 0.60 and R2 = 0.95, respectively, for OMI-BIRA and MAX-DOAS, and R2 = 0.45 and R2 = 0.78 for OMI-NASA and MAX-DOAS, respectively. Therefore, use of the monthly mean HCHO datasets is strongly recommended. We conducted a sensitivity test for HCHO air mass factor (AMF) calculations with respect to the HCHO profile, the aerosol extinction coefficient (AEC), the HCHO profile–AEC combination, the aerosol optical depth (AOD), and the single scattering albedo (SSA) to explicitly account for the aerosol optical effects on the HCHO AMF. We found that the combination of AEC and HCHO profiles can account for 23–39% of the HCHO AMF variation. Furthermore, a high load of absorptive aerosols can exert a considerable effect (−53%) on the AMF. Finally, we used the HCHO monthly mean profiles from Goddard Earth Observing System coupled to Chemistry (GEOS-Chem), seasonal mean AECs from Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and monthly climatologies of AOD and SSA from the OMAERUV (OMI level-2 near UV aerosol data product) dataset at Xianghe station to determine the aerosol correction. The results reveal that aerosols can account for +6.37% to +20.7% of the HCHO monthly change. However, the changes are greatest in winter and are weaker in summer and autumn, indicating that the aerosol correction is more applicable under high-AAOD conditions and that there may be other reasons for the significant underestimation between satellite and MAX-DOAS observations.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

Utilization of O4 slant column density to derive aerosol layer height from a spaceborne UV-Visible hyperspectral sensor: Sensitivity and case study

The sensitivities of oxygen-dimer (O₄) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the Linearlized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the Differential Optical Absorption Spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O₄ SCD by 10⁴⁰ molecules²cm^-5, to aerosol types and optical properties are also evaluated and compared. Among the O₄ absorption bands at 340, 360, 380, and 477 nm, the O₄ absorption band at 477 nm is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nm is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 m for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80% of retrieved aerosol effective heights are within the error range of 1 km compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.

Revising the slant column density retrieval of nitrogen dioxide observed by the Ozone Monitoring Instrument

Nitrogen dioxide retrievals from the Aura/Ozone Monitoring Instrument (OMI) have been used extensively over the past decade, particularly in the study of tropospheric air quality. Recent comparisons of OMI NO₂ with independent data sets and models suggested that the OMI values of slant column density (SCD) and stratospheric vertical column density (VCD) in both the NASA OMNO2 and Royal Netherlands Meteorological Institute DOMINO products are too large, by around 10-40%. We describe a substantially revised spectral fitting algorithm, optimized for the OMI visible light spectrometer channel. The most important changes comprise a flexible adjustment of the instrumental wavelength shifts combined with iterative removal of the ring spectral features; the multistep removal of instrumental noise; iterative, sequential estimates of SCDs of the trace gases in the 402-465 nm range. These changes reduce OMI SCD(NO₂) by 10-35%, bringing them much closer to SCDs retrieved from independent measurements and models. The revised SCDs, submitted to the stratosphere-troposphere separation algorithm, give tropospheric VCDs ∼10-15% smaller in polluted regions, and up to ∼30% smaller in unpolluted areas. Although the revised algorithm has been optimized specifically for the OMI NO₂ retrieval, our approach could be more broadly applicable.

Using dimers to measure biosignatures and atmospheric pressure for terrestrial exoplanets

We present a new method to probe atmospheric pressure on Earth-like planets using (O2-O2) dimers in the near-infrared. We also show that dimer features could be the most readily detectable biosignatures for Earth-like atmospheres and may even be detectable in transit transmission with the James Webb Space Telescope (JWST). The absorption by dimers changes more rapidly with pressure and density than that of monomers and can therefore provide additional information about atmospheric pressures. By comparing the absorption strengths of rotational and vibrational features to the absorption strengths of dimer features, we show that in some cases it may be possible to estimate the pressure at the reflecting surface of a planet. This method is demonstrated by using the O2 A band and the 1.06 μm dimer feature, either in transmission or reflected spectra. It works best for planets around M dwarfs with atmospheric pressures between 0.1 and 10 bar and for O2 volume mixing ratios above 50% of Earth's present-day level. Furthermore, unlike observations of Rayleigh scattering, this method can be used at wavelengths longer than 0.6 μm and is therefore potentially applicable, although challenging, to near-term planet characterization missions such as JWST. We also performed detectability studies for JWST transit transmission spectroscopy and found that the 1.06 and 1.27 μm dimer features could be detectable (SNR>3) for an Earth analogue orbiting an M5V star at a distance of 5 pc. The detection of these features could provide a constraint on the atmospheric pressure of an exoplanet and serve as biosignatures for oxygenic photosynthesis. We calculated the required signal-to-noise ratios to detect and characterize O2 monomer and dimer features in direct imaging-reflected spectra and found that signal-to-noise ratios greater than 10 at a spectral resolving power of R=100 would be required.

Influence of coal-based thermal power plants on the spatial-temporal variability of tropospheric NO2 column over India

The oxides of nitrogen--NO(x) (NO and NO(2))--are an important constituent of the troposphere. The availability of relatively higher spatial (0.25° grid) and temporal (daily) resolution data from ozone monitoring instrument (OMI) onboard Aura helps us to better differentiate between the point sources such as thermal power plants from large cities and rural areas compared to previous sensors. The annual and seasonal (summer and winter) distributions shows very high mean tropospheric NO(2) in specific pockets over India especially over the Indo-Gangetic plains (up to 14.2 × 10(15) molecules/cm(2)). These pockets correspond with the known locations of major thermal power plants. The tropospheric NO(2) over India show a large seasonal variability that is also observed in the ground NO(2) data. The multiple regression analysis show that the influence of a unit of power plant (in gigawatts) over tropospheric NO(2) (×10(15) molecules/cm(2)) is around ten times compared to a unit of population (in millions) over India. The OMI data show that the NO(2) increases by 0.794 ± 0.12 (×10(15) molecules/cm(2); annual) per GW compared to a previous estimate of 0.014 (×10(15) molecules/cm(2)) over India. The increase of tropospheric NO(2) per gigawatt is found to be 1.088 ± 0.18, 0.898 ± 0.14, and 0.395 ± 0.13 (×10(15) molecules/cm(2)) during winter, summer, and monsoon seasons, respectively. The strong seasonal variation is attributed to the enhancement or suppression of NO(2) due to various controlling factors which is discussed here. The recent increasing trend (2005-2007) over rural thermal power plants pockets like Agori and Korba is due to recent large capacity additions in these regions.