Estimates of Hyperspectral Surface and Underwater UV Planar and Scalar Irradiances from OMI Measurements and Radiative Transfer Computations

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

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.

A Cloud-Ozone Data Product from Aura OMI and MLS Satellite Measurements

Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low troposphere/boundary layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysics/transport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30°S to 30°N for October 2004 - April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of ~10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and India/east Asia; connections with tropical ENSO events; and intra-seasonal/Madden-Julian Oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary layer pollution and elevated ozone inside thick clouds over land-mass regions including southern Africa and India/east Asia.

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.

Accurate modeling of spectral fine-structure in Earth radiance spectra measured with the Global Ozone Monitoring Experiment

We present what we believe to be a novel approach to simulating the spectral fine structure (<1 nm) in measurements of spectrometers such as the Global Ozone Monitoring Experiment (GOME). GOME measures the Earth's radiance spectra and daily solar irradiance spectra from which a reflectivity spectrum is commonly extracted. The high-frequency structures contained in such a spectrum are, apart from atmospheric absorption, caused by Raman scattering and by a shift between the solar irradiance and the Earth's radiance spectrum. Normally, an a priori high-resolution solar spectrum is used to simulate these structures. We present an alternative method in which all the required information on the solar spectrum is retrieved from the GOME measurements. We investigate two approaches for the spectral range of 390-400 nm. First, a solar spectrum is reconstructed on a fine spectral grid from the GOME solar measurement. This approach leads to undersampling errors of up to 0.5% in the modeling of the Earth's radiance spectra. Second, a combination of the solar measurement and one of the Earth's radiance measurement is used to retrieve a solar spectrum. This approach effectively removes the undersampling error and results in residuals close to the GOME measurement noise of 0.1%.