Polar Mesospheric Clouds (PMCs) Observed by the Ozone Monitoring Instrument (OMI) on Aura

Backscattered ultraviolet (BUV) instruments designed for measuring stratospheric ozone profiles have proven to be robust tools for observing polar mesospheric clouds (PMCs). These measurements are available for more than 30 years, and have been used to demonstrate the existence of long-term variations in PMC occurrence frequency and brightness. The Ozone Monitoring Instrument (OMI) on the EOS Aura satellite provides new and improved capabilities for PMC characterization. OMI uses smaller pixels than previous BUV instruments, which increases its ability to identify PMCs and discern more spatial structure, and its wide cross-track viewing swath provides full polar coverage up to 90 latitude every day in both hemispheres. This cross-track coverage allows the evolution of PMC regions to be followed over several consecutive orbits. Localized PMC variations determined from OMI measurements are consistent with coincident SBUV/2 measurements. Nine seasons of PMC observations from OMI are now available, and clearly demonstrate the advantages of these measurements for PMC analysis

The Ozone Monitoring Instrument: Overview of 14 years in space

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain

Observation and integrated Earth-system science: a roadmap for 2016–2025

This report is the response to a request by the Committee on Space Research of the International Council for Science to prepare a roadmap on observation and integrated Earth-system science for the coming ten years. Its focus is on the combined use of observations and modelling to address the functioning, predictability and projected evolution of interacting components of the Earth system on timescales out to a century or so. It discusses how observations support integrated Earth-system science and its applications, and identifies planned enhancements to the contributing observing systems and other requirements for observations and their processing. All types of observation are considered, but emphasis is placed on those made from space. The origins and development of the integrated view of the Earth system are outlined, noting the interactions between the main components that lead to requirements for integrated science and modelling, and for the observations that guide and support them. What constitutes an Earth-system model is discussed. Summaries are given of key cycles within the Earth system. The nature of Earth observation and the arrangements for international coordination essential for effective operation of global observing systems are introduced. Instances are given of present types of observation, what is already on the roadmap for 2016–2025 and some of the issues to be faced. Observations that are organised on a systematic basis and observations that are made for process understanding and model development, or other research or demonstration purposes, are covered. Specific accounts are given for many of the variables of the Earth system. The current status and prospects for Earth-system modelling are summarized. The evolution towards applying Earth-system models for environmental monitoring and prediction as well as for climate simulation and projection is outlined. General aspects of the improvement of models, whether through refining the representations of processes that are already incorporated or through adding new processes or components, are discussed. Some important elements of Earth-system models are considered more fully. Data assimilation is discussed not only because it uses observations and models to generate datasets for monitoring the Earth system and for initiating and evaluating predictions, in particular through reanalysis, but also because of the feedback it provides on the quality of both the observations and the models employed. Inverse methods for surface-flux or model-parameter estimation are also covered. Reviews are given of the way observations and the processed datasets based on them are used for evaluating models, and of the combined use of observations and models for monitoring and interpreting the behaviour of the Earth system and for predicting and projecting its future. A set of concluding discussions covers general developmental needs, requirements for continuity of space-based observing systems, further long-term requirements for observations and other data, technological advances and data challenges, and the importance of enhanced international co-operation

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Simultaneous assimilation of ozone profiles from multiple UV-VIS satellite instruments

A three-dimensional global ozone distribution has been derived from assimilation of ozone profiles that were observed by satellites. By simultaneous assimilation of ozone profiles retrieved from the nadir looking satellite instruments Global Ozone Monitoring Experiment 2 (GOME-2) and Ozone Monitoring Instrument (OMI), which measure the atmosphere at different times of the day, the quality of the derived atmospheric ozone field has been improved. The assimilation is using an extended Kalman filter in which chemical transport model TM5 has been used for the forecast. The combined assimilation of both GOME-2 and OMI improves upon the assimilation results of a single sensor. The new assimilation system has been demonstrated by processing 4 years of data from 2008 to 2011. Validation of the assimilation output by comparison with sondes shows that biases vary between ĝ'5 and +10ĝ€̄% between the surface and 100ĝ€̄hPa. The biases for the combined assimilation vary between ĝ'3 and +3ĝ€̄% in the region between 100 and 10ĝ€̄hPa where GOME-2 and OMI are most sensitive. This is a strong improvement compared to direct retrievals of ozone profiles from satellite observations

Improvements to the OMI O₂-O₂ operational cloud algorithm and comparisons with ground-based radar-lidar observations

The OMI (Ozone Monitoring Instrument on board NASA's Earth Observing System (EOS) Aura satellite) OMCLDO2 cloud product supports trace gas retrievals of for example ozone and nitrogen dioxide. The OMCLDO2 algorithm derives the effective cloud fraction and effective cloud pressure using a DOAS (differential optical absorption spectroscopy) fit of the O2-O2 absorption feature around 477m. A new version of the OMI OMCLDO2 cloud product is presented that contains several improvements, of which the introduction of a temperature correction on the O2-O2 slant columns and the updated look-up tables have the largest impact. Whereas the differences in the effective cloud fraction are on average limited to 0.01, the differences of the effective cloud pressure can be up to 200hPa, especially at cloud fractions below 0.3. As expected, the temperature correction depends on latitude and season. The updated look-up tables have a systematic effect on the cloud pressure at low cloud fractions. The improvements at low cloud fractions are very important for the retrieval of trace gases in the lower troposphere, for example for nitrogen dioxide and formaldehyde. The cloud pressure retrievals of the improved algorithm are compared with ground-based radar-lidar observations for three sites at mid-latitudes. For low clouds that have a limited vertical extent the comparison yields good agreement. For higher clouds, which are vertically extensive and often contain several layers, the satellite retrievals give a lower cloud height. For high clouds, mixed results are obtained.Atmospheric Remote Sensin

Improving retrieval of volcanic sulfur dioxide from backscattered UV satellite observations L03102

Existing algorithms that use satellite measurements of solar backscattered ultraviolet (BUV) radiances to retrieve sulfur dioxide (SO2) vertical columns underestimate the large SO2 amounts encountered in fresh volcanic eruption clouds. To eliminate this underestimation we have developed a new technique, named the Iterative Spectral Fitting (ISF) algorithm, for accurate retrieval of SO2 vertical columns in the full range of volcanic emissions. The ISF algorithm is applied to Ozone Monitoring Instrument (OMI) BUV measurements of the Sierra Negra eruption (Galàpagos Islands, Ecuador) in October 2005. The results represent major improvements over the operational OMI SO2 products. Based on the ISF data, we report the largest SO2 vertical column amount (\u3e 1000 Dobson Units (DU), where 1 DU = 2.69 × 1016 molecules/cm2) ever observed by a space borne instrument, implying that very high concentrations of SO 2 can occur in the lower troposphere during effusive eruptions. Copyright 2009 by the American Geophysical Union

Minimizing aerosol effects on the OMI tropospheric NO₂ retrieval - An improved use of the 477nm O₂-O₂ band and an estimation of the aerosol correction uncertainty

Global mapping of satellite tropospheric NO2 vertical column density (VCD), a key gas in air quality monitoring, requires accurate retrievals over complex urban and industrialized areas and under any atmospheric conditions. The high abundance of aerosol particles in regions dominated by anthropogenic fossil fuel combustion, e.g. megacities, and/or biomass-burning episodes, affects the space-borne spectral measurement. Minimizing the tropospheric NO2 VCD biases caused by aerosol scattering and absorption effects is one of the main retrieval challenges from air quality satellite instruments. In this study, the reference Ozone Monitoring Instrument (OMI) DOMINO-v2 product was reprocessed over cloud-free scenes, by applying new aerosol correction parameters retrieved from the 477 nm O2-O2 band, over eastern China and South America for 2 years (2006 2007). These new parameters are based on two different and separate algorithms developed during the last 2 years in view of an improved use of the OMI 477 nm O2-O2 band: 1. the updated OMCLDO2 algorithm, which derives improved effective cloud parameters, 2. the aerosol neural network (NN), which retrieves explicit aerosol parameters by assuming a more physical aerosol model. The OMI aerosol NN is a step ahead of OMCLDO2 because it primarily estimates an explicit aerosol layer height (ALH), and secondly an aerosol optical thickness τ for cloud-free observations. Overall, it was found that all the considered aerosol correction parameters reduce the biases identified in DOMINO-v2 over scenes in China with high aerosol abundance dominated by fine scattering and weakly absorbing particles, e.g. from [-20% V -40%] to [0% V 20%] in summertime. The use of the retrieved OMI aerosol parameters leads in general to a more explicit aerosol correction and higher tropospheric NO2 VCD values, in the range of [0% V 40%], than from the implicit correction with the updated OMCLDO2. This number overall represents an estimation of the aerosol correction strategy uncertainty nowadays for tropospheric NO2 VCD retrieval from space-borne visible measurements. The explicit aerosol correction theoretically includes a more realistic consideration of aerosol multiple scattering and absorption effects, especially over scenes dominated by strongly absorbing particles, where the correction based on OMCLDO2 seems to remain insufficient. However, the use of ALH and τ from the OMI NN aerosol algorithm is not a straightforward operation and future studies are required to identify the optimal methodology. For that purpose, several elements are recommended in this paper. Overall, we demonstrate the possibility of applying a more explicit aerosol correction by considering aerosol parameters directly derived from the 477 nm O2-O2 spectral band, measured by the same satellite instrument. Such an approach can, in theory, easily be transposed to the new-generation of space-borne instruments (e.g. TROPOMI on board Sentinel- 5 Precursor), enabling a fast reprocessing of tropospheric NO2 data over cloud-free scenes (cloudy pixels need to be filtered out), as well as for other trace gas retrievals (e.g. SO2, HCHO).Atmospheric Remote Sensin