Retrieval Of Column Densities Research Articles

Atmospheric CO[formula omitted] retrieval from satellite spectral measurements by a two-step machine learning approach

In the present study, a two-step machine learning approach is proposed and tested based on the Greenhouse gases Observing SATllite (GOSAT) spectral measurements to efficiently retrieve the atmospheric CO2 column density. A simple one-dimensional line-by-line forward radiation model is developed to simulate the GOSAT observed spectra, which is used to generate the training data for the two-step machine learning model. By using the two-step machine learning model, the atmospheric spectral optical thickness can be retrieved first, and then the CO2 column density is retrieved from the optical thickness spectrum later. As a proof-of-concept study, the model has been applied to retrieve CO2 column density in Australia for clear sky conditions from the GOSAT observed 1.6 μm spectra. Results have shown that the accuracy of the proposed method for CO2 column density retrieval is about 3 ppm but the efficiency is found to be excellent. Further improvements for improving the retrieval accuracy are discussed.

UK Ammonia Emissions Estimated With Satellite Observations and GEOS‐Chem

AbstractAgricultural emissions of ammonia (NH3) impact air quality, human health, and the vitality of aquatic and terrestrial ecosystems. In the UK, there are few direct policies regulating anthropogenic NH3 emissions and development of sustainable mitigation measures necessitates reliable emissions estimates. Here, we use observations of column densities of NH3 from two space‐based sensors (IASI and CrIS) with the GEOS‐Chem model to derive top‐down NH3 emissions for the UK at fine spatial (∼10 km) and time (monthly) scales. We focus on March‐September when there is adequate spectral signal to reliably retrieve NH3. We estimate total emissions of 272 Gg from IASI and 389 Gg from CrIS. Bottom‐up emissions are 27% less than IASI and 49% less than CrIS. There are also differences in seasonality. Top‐down and bottom‐up emissions agree on a spring April peak due to fertilizer and manure application, but there is also a comparable summer July peak in the top‐down emissions that is not in the bottom‐up emissions and appears to be associated with dairy cattle farming. We estimate relative errors in the top‐down emissions of 11%–36% for IASI and 9%–27% for CrIS, dominated by column density retrieval errors. The bottom‐up versus top‐down emissions discrepancies estimated in this work impact model predictions of the environmental damage caused by NH3 emissions and warrant further investigation.

First Measurements of Gas Flux with a Low-Cost Smartphone Sensor-Based UV Camera on the Volcanoes of Northern Chile

UV cameras have been used for over a decade in order to remotely sense SO2 emission rates from active volcanoes, and to thereby enhance our understanding of processes related to active and passive degassing. Whilst SO2 column density retrievals can be more accurate/sophisticated using alternative techniques (e.g., Differential Optical Absorption Spectrometer (DOAS), Correlation Spectrometer (COSPEC)), due to their higher spectral resolutions, UV cameras provide the advantage of high time-resolution emission rates, a much greater spatial resolution, and the ability to simultaneously retrieve plume speeds. Nevertheless, the relatively high costs have limited their uptake to a limited number of research groups and volcanic observatories across the planet. One recent intervention in this regard has been the introduction of the PiCam UV camera, which has considerably lowered instrumental cost. Here we present the first data obtained with the PiCam system from seven persistently degassing volcanoes in northern Chile, demonstrating robust field operation in challenging conditions and over an extended period of time, hence adding credence to the potential of these units for more widespread dissemination to the international volcanic gas measurement community. Small and weak plumes, as well as strongly degassing plumes were measured at distances ranging 0.6–10.8 km from the sources, resulting in a wide range of SO2 emission rates, varying from 3.8 ± 1.8 to 361 ± 31.6 td−1. Our acquired data are discussed with reference to previously reported emission rates from other ground-based remotely sensed techniques at the same volcanoes, in particular considering: resolution of single plume emissions in multi-plume volcanoes, light dilution, plume geometry, seasonal effects, and the applied plume speed measurement methodology. The main internal/external factors that influence positive/negative PiCam measurements include camera shake, light dilution, and the performance of the OpenCV and control points post processing methods. A simple reprocessing method is presented in order to correct the camera shake. Finally, volcanoes were separated into two distinct groups: low and moderate SO2 emission rates systems. These groups correlate positively with their volcanological characteristics, especially with the fluid compositions from fumaroles.

Uncertainties of SO 2 Vertical Column Density Retrieval from Ground-based Hyper-spectral UV Sensor Based on Direct Sun Measurement Geometry

Uncertainties of SO 2 Vertical Column Density Retrieval from Ground-based Hyper-spectral UV Sensor Based on Direct Sun Measurement Geometry

Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8 × 1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12 × 1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase < 1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to < 3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼ 65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼ 35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

An improved constraint method in optimal estimation of CO2 from GOSAT SWIR observations

An improved Optimal Estimation (OE) method is presented for methane (CH 4 ) column density retrieval from satellite observations in short-wave infrared band (SWIR), to avoid non-convergence of iteration process for CH 4 retrieval caused by the singularity or non-positivity of the hessian matrix. We add a constraining factor γ and a step factor α to the OE iteration algorithm. Then, total column averaged CH 4 dry air mole fraction, X CH4 is retrieved using GOSAT Level 1b data. Retrievals are validated by comparisons with ground-based FTIR measurements from TCCON stations. Comparison shows good agreement and the correlation coefficient is more than 0.55. Preliminary validations approve the utility of proposed retrieval algorithm.

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.

Improved spectral fitting of nitrogen dioxide from OMI in the 405–465 nm window

Abstract. An improved nitrogen dioxide (NO2) slant column density retrieval for the Ozone Monitoring Instrument (OMI) in the 405–465 nm spectral region is presented. Since the launch of OMI on board NASA's EOS-Aura satellite in 2004, differential optical absorption spectroscopy (DOAS) retrievals of NO2 slant column densities have been the starting point for the KNMI DOMINO and NASA SP NO2 vertical column data as well as the OMI NO2 data of some other institutes. However, recent intercomparisons between NO2 retrievals from OMI and other UV/Vis and limb spectrometers, as well as ground-based measurements, suggest that OMI stratospheric NO2 is biased high. This study revises and, for the first time, fully documents the OMI NO2 retrieval in detail. The representation of the OMI slit function to convolve high-resolution reference spectra onto the relevant spectral grid is improved. The window used for the wavelength calibration is optimised, leading to much-reduced fitting errors. Ozone and water vapour spectra used in the fit are updated, reflecting the recently improved knowledge of their absorption cross section in the literature. The improved spectral fit also accounts for absorption by the O2–O2 collision complex and by liquid water over clear-water areas. The main changes in the improved spectral fitting result from the updates related to the wavelength calibration: the RMS error of the fit is reduced by 23% and the NO2 slant column by 0.85 × 1015 molec cm−2, independent of latitude, solar zenith angle and NO2 value. Including O2–O2 and liquid water absorption and updating the O3 and water vapour cross-section spectra further reduces NO2 slant columns on average by 0.35 × 1015 molec cm−2, accompanied by a further 9% reduction in the RMS error of the fit. The improved OMI NO2 slant columns are consistent with independent NO2 retrievals from other instruments to within a range that can be explained by photochemically driven diurnal increases in stratospheric NO2 and by small differences in fitting window and approach. The revisions indicate that current OMI NO2 slant columns suffered mostly from an additive positive offset, which is removed by the improved wavelength calibration and representation of the OMI slit function. It is therefore anticipated that the improved NO2 slant columns are most important to retrievals of spatially homogeneous stratospheric NO2 rather than to heterogeneous tropospheric NO2.

Assessing hazards to aviation from sulfur dioxide emitted by explosive Icelandic eruptions

AbstractVolcanic eruptions take place in Iceland about once every 3 to 5 years. Ash emissions from these eruptions can cause significant disruption to air traffic over Europe and the North Atlantic as is evident from the 2010 eruption of Eyjafjallajökull. Sulfur dioxide (SO2) is also emitted by volcanoes, but there are no criteria to define when airspace is considered hazardous or nonhazardous. However, SO2 is a well‐known ground‐level pollutant that can have detrimental effects on human health. We have used the United Kingdom Met Office's NAME (Numerical Atmospheric‐dispersion Modelling Environment) model to simulate SO2 mass concentrations that could occur in European and North Atlantic airspace for a range of hypothetical explosive eruptions in Iceland with a probability to occur about once every 3 to 5 years. Model performance was evaluated for the 2010 Eyjafjallajökull summit eruption against SO2 vertical column density retrievals from the Ozone Monitoring Instrument and in situ measurements from the United Kingdom Facility for Airborne Atmospheric Measurements research aircraft. We show that at no time during the 2010 Eyjafjallajökull eruption did SO2 mass concentrations at flight altitudes violate European air quality standards. In contrast, during a hypothetical short‐duration explosive eruption similar to Hekla in 2000 (emitting 0.2 Tg of SO2 within 2 h, or an average SO2 release rate 250 times that of Eyjafjallajökull 2010), simulated SO2 concentrations are greater than 1063 µg/m3 for about 48 h in a small area of European and North Atlantic airspace. By calculating the occurrence of aircraft encounters with the volcanic plume of a short‐duration eruption, we show that a 15 min or longer exposure of aircraft and passengers to concentrations ≥500 µg/m3 has a probability of about 0.1%. Although exposure of humans to such concentrations may lead to irritations to the eyes, nose and, throat and cause increased airway resistance even in healthy individuals, the risk is very low. However, the fact that volcanic ash and sulfur species are not always collocated and that passenger comfort could be compromised might be incentives to provide real‐time information on the presence or absence of volcanic SO2. Such information could aid aviation risk management during and after volcanic eruptions.

Global column density retrievals of mesospheric and thermospheric Mg I and Mg II from SCIAMACHY limb and nadir radiance data

The Scanning Imaging Absorption Spectrometer for Atmospheric Chartography instrument (SCIAMACHY) instrument exhibits useful observational abilities to separate meteoric metal total content within different levels of the atmosphere. In this work, we present an algorithm to give the total content and to separate the total column into two partial columns in the thermosphere and the mesosphere, using measurements of resonant radiation by the SCIAMACHY instrument. Limb and nadir measurements are used. The results presented here constitute the first simultaneous measurements of mesospheric and thermospheric column densities including the successful separation of same. We present column density measurements of Mg II and Mg I in the mesosphere and the thermosphere and the total content. The data set considered here covers the latitude range from 80°S to 80°N and covers the time range from 2002 to 2006. Measurements are taken on a global scale to investigate seasonal and spatial variations. Total columns range from 107 to 1010 cm−2, in good agreement with previous satellite measurements and model calculations. The temporal evolution of both Mg I species is investigated over the time range from mid‐2002 until the end of 2006. A pronounced minimum in 2003 and a subsequent increase until 2006 are observed for the ionized species. The global distribution of both Mg I and Mg II is found to be seasonally varying, exhibiting maximum column densities within the summer hemisphere. No significant correlation with the solar activity is found. Increased abundances of Mg II are observed within regions of high geomagnetic activity (i.e., the northern and southern auroral ovals) only if the background abundance of Mg II is low. It is concluded that the increase of Mg II due to particle impact is a minor effect.

Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements

Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) but, also, with cloud information from HICRU (Heidelberg Iterative Cloud Retrieval Utilities), SACURA (SemiAnalytical CloUd Retrieval Algorithm) and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using the GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

Retrieval methods of effective cloud cover from the GOME instrument: an intercomparison

Abstract. The radiative scattering by clouds leads to errors in the retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites. Moreover, the presence of clouds changes the UV actinic flux and the photo-dissociation rates of various species significantly. The Global Ozone Monitoring Experiment (GOME) instrument on the ERS-2 satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. Four cloud fraction retrieval methods for GOME data that have been developed are discussed in this paper (the Initial Cloud Fitting Algorithm, the PMD Cloud Recognition Algorithm, the Optical Cloud Recognition Algorithm (an in-house version and the official implementation) and the Fast Retrieval Scheme for Clouds from the Oxygen A-band). Their results of cloud fraction retrieval are compared to each-other and also to synoptic surface observations. It is shown that all studied retrieval methods calculate an effective cloud fraction that is related to a cloud with a high optical thickness. Generally, we found ICFA to produce the lowest cloud fractions, followed by our in-house OCRA implementation, FRESCO, PC2K and finally the official OCRA implementation along four processed tracks (+2%, +10%, +15% and +25% compared to ICFA respectively). Synoptical surface observations gave the highest absolute cloud fraction when compared with individual PMD sub-pixels of roughly the same size.

A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment

The Global Ozone Monitoring Experiment (GOME) on board the ERS‐2 is designed to measure trace gas column densities in the Earth's atmosphere. Such retrievals are hindered by the presence of clouds. The most important cloud parameters that are needed to correct trace gas column density retrievals for the disturbing effects of clouds are the (effective) cloud fraction and cloud top pressure. At present, in the operational GOME data processor an effective cloud fraction is derived for each pixel, but cloud top pressure is assumed a priori and is deduced from a climatological database. Here we report an improved cloud retrieval scheme, which simultaneously retrieves the effective cloud fraction and cloud top pressure from GOME data. This algorithm, called Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO), makes use of reflectivities as measured by GOME inside and outside the oxygen A band (758–778 nm). For validation, the results of FRESCO are compared to effective cloud fractions and cloud top pressures derived with standard methods from colocated measurements made by the Along Track Scanning Radiometer‐2 (ATSR‐2). The brightness temperatures of the cloudy pixels as measured by ATSR‐2 are related to cloud top pressures using temperature profiles from the European Center for Medium‐range Weather Forecasts model. Generally, the results from FRESCO and ATSR‐2 agree reasonably well. For the effective cloud fractions the average difference (based on a comparison of 322 points) is 0.04; the standard deviation is 0.09. For the cloud top pressures, only points with an effective cloud fraction larger than 0.1 have been compared. For these 236 points the average difference in cloud top pressure is 65 hPa, and the standard deviation is 92 hPa.

Tropospheric carbon monoxide column density retrieval during Pre-launch MOPITT Validation exercise

The tropospheric carbon monoxide (CO) column density over the United States Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) Central Facility near Lamont, Oklahoma (latitude 36°37′N, longitude 97°30′W) during the Pre-launch MOPITT Validation Exercise (Pre-MOVE) is retrieved from infrared spectra obtained by the ground-based Atmospheric Emitted Radiance Interferometer (AERI). This paper reports the first tropospheric CO time series retrieved from an AERI. With spectra measured every 8 min, CO retrieved from AERI spectra has a much higher temporal resolution than from any other ground-based instrument measuring atmospheric emission. The retrieved CO column density time series is examined using local meteorological data. Synoptic atmospheric conditions are found to have a controlling effect on tropospheric CO. During 2–4 March, 1998, a southerly to easterly surface wind brought an airmass with higher CO column density over the SGP Central Facility (SGPCF), whereas northerly and/or westerly surface winds tended to reduce the CO column density.