A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

Thin liquid water clouds: their importance and our challenge

Many clouds important to the Earth’s energy balance contain small amounts of liquid water, yet despite many improvements, large differences in retrievals of their liquid water amount and particle size still must be resolved

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O₂ A-band measurements

Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth's energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O₂) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O₂ A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale

A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O₂ A-band measurements

Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth's energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O₂) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O₂ A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale

Earth observations from DSCOVR EPIC instrument

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.The NASA GSFC DSCOVR project is funded by NASA Earth Science Division. We gratefully acknowledge the work by S. Taylor and B. Fisher for help with the SO2 retrievals and Marshall Sutton, Carl Hostetter, and the EPIC NISTAR project for help with EPIC data. We also would like to thank the EPIC Cloud Algorithm team, especially Dr. Gala Wind, for the contribution to the EPIC cloud products. (NASA Earth Science Division)Accepted manuscrip

Reducing the Uncertainties in Direct Aerosol Radiative Forcing

Airborne particles, which include desert and soil dust, wildfire smoke, sea salt, volcanic ash, black carbon, natural and anthropogenic sulfate, nitrate, and organic aerosol, affect Earth's climate, in part by reflecting and absorbing sunlight. This paper reviews current status, and evaluates future prospects for reducing the uncertainty aerosols contribute to the energy budget of Earth, which at present represents a leading factor limiting the quality of climate predictions. Information from satellites is critical for this work, because they provide frequent, global coverage of the diverse and variable atmospheric aerosol load. Both aerosol amount and type must be determined. Satellites are very close to measuring aerosol amount at the level-of-accuracy needed, but aerosol type, especially how bright the airborne particles are, cannot be constrained adequately by current techniques. However, satellite instruments can map out aerosol air mass type, which is a qualitative classification rather than a quantitative measurement, and targeted suborbital measurements can provide the required particle property detail. So combining satellite and suborbital measurements, and then using this combination to constrain climate models, will produce a major advance in climate prediction

Towards CO2 emission monitoring with passive air- and space-borne sensors

Coal-fueled power plants are responsible for 30 % of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ~50×50 m2 by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ~ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the air-borne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ~ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed