The origins of slaty cleavage

Imperial Users onl

A new scheme for sulphur dioxide retrieval from IASI measurements: application to the Eyjafjallajökull eruption of April and May 2010

A new optimal estimation algorithm for the retrieval of sulphur dioxide (SO<sub>2</sub>) has been developed for the Infrared Atmospheric Sounding Interferometer (IASI) using the channels between 1000–1200 and 1300–1410 cm<sup>−1</sup>. These regions include the two SO<sub>2</sub> absorption bands centred at about 8.7 and 7.3 μm (the ν<sub>1</sub> and ν<sub>3</sub> bands respectively). The retrieval assumes a Gaussian SO<sub>2</sub> profile and returns the SO<sub>2</sub> column amount in Dobson units and the altitude of the plume in millibars (mb). Forward modelled spectra (against which the measurements are compared) are based on the Radiative Transfer for TOVS (RTTOV) code. In our implementation RTTOV uses atmospheric profiles from European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological data. The retrieval includes a comprehensive error budget for every pixel derived from an error covariance matrix that is based on the SO<sub>2</sub>-free climatology of the differences between the IASI and forward modelled spectra. The IASI forward model includes the ability to simulate a cloud or ash layer in the atmosphere. This feature is used to illustrate that: (1) the SO<sub>2</sub> retrieval is not affected by underlying cloud but is affected if the SO<sub>2</sub> is within or below a cloud layer; (2) it is possible to discern if ash (or other atmospheric constituents not considered in the error covariance matrix) affects the retrieval using quality control based on the fit of the measured spectrum by the forward modelled spectrum. In this work, the algorithm is applied to follow the behaviour of SO<sub>2</sub> plumes from the Eyjafjallajökull eruption during April and May 2010. From 14 April to 4 May (during Phase I and II of the eruption) the total amount of SO<sub>2</sub> present in the atmosphere, estimated by IASI measurements, is generally below 0.02 Tg. During the last part of the eruption (Phase III) the values are an order of magnitude higher, with a maximum of 0.18 Tg measured on the afternoon of 7 May

Diagnosing air quality changes in the UK during the COVID-19 lockdown using TROPOMI and GEOS-Chem

The dramatic and sudden reduction in anthropogenic activity due to lockdown measures in the UK in response to the COVID-19 outbreak has resulted in a concerted effort to estimate local and regional changes in air quality, though changes in underlying emissions remain uncertain. Here we combine satellite observations of tropospheric NO_{2} from TROPOspheric Monitoring Instrument and the Goddard Earth Observing System (GEOS)-Chem 3D chemical transport model to estimate that NO_{x} emissions declined nationwide by ~20% during the lockdown (23 March to 31 May 2020). Regionally, these range from 22% to 23% in the western portion of the country to 29% in the southeast and Manchester, and >40% in London. We apply a uniform 20% lockdown period emission reduction to GEOS-Chem anthropogenic emissions over the UK to determine that decline in lockdown emissions led to a national decline in PM_{2.5} of 1.1 μg m^{−3}, ranging from 0.6 μg m^{−3} in Scotland to 2 μg m^{−3} in the southwest. The decline in emissions in cities (>40%) is greater than the national average and causes an increase in ozone of ~2 ppbv in London and Manchester. The change in ozone and PM_{2.5} concentrations due to emission reductions alone is about half the total change from 2019 to 2020. This emphasizes the need to account for emissions and other factors, in particular meteorology, in future air pollution abatement strategies and regulatory action

Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean

The Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) project has produced a global data-set of cloud and aerosol properties from the Along Track Scanning Radiometer-2 (ATSR-2) instrument, covering the time period 1995�2001. This paper presents the validation of aerosol optical depths (AODs) over the ocean from this product against AERONET sun-photometer measurements, as well as a comparison to the Advanced Very High Resolution Radiometer (AVHRR) optical depth product produced by the Global Aerosol Climatology Project (GACP). The GRAPE AOD over ocean is found to be in good agreement with AERONET measurements, with a Pearson's correlation coefficient of 0.79 and a best-fit slope of 1.0±0.1, but with a positive bias of 0.08±0.04. Although the GRAPE and GACP datasets show reasonable agreement, there are significant differences. These discrepancies are explored, and suggest that the downward trend in AOD reported by GACP may arise from changes in sampling due to the orbital drift of the AVHRR instruments

Validation of SCIAMACHY top-of-atmosphere reflectance for aerosol remote sensing using MERIS L1 data

Aerosol remote sensing is very much dependent on the accurate knowledge of the top-of-atmosphere (TOA) reflectance measured by a particular instrument. The status of the calibration of such an instrument is reflected in the quality of the aerosol retrieval. Current data of the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument (operated with the data processor version 5 and earlier) give too small values of the TOA reflectance, compared e.g. to data from MERIS (Medium Resolution Imaging Spectrometer), both operating on ENVISAT (ENVIronmental SATellite). This effect causes retrievals of wrong aerosol optical thickness and disables the processing of aerosol parameters. <br><br> From an inter-comparison of MERIS and SCIAMACHY TOA reflectance, for collocated scenes correction factors are derived to improve the insufficient SCIAMACHY L1 data calibration for data obtained with the processor 5 for the purpose of aerosol remote sensing. The corrected reflectance has been used for tests of remote sensing of the aerosol optical thickness by the BAER (Bremen AErosol Retrieval) approach using SCIAMACHY data