Cloud clearing techniques over land for land surface temperature retrieval from the Advanced Along Track Scanning Radiometer

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along Track Scanning Radiometer (AATSR) instrument and compare these with the standard threshold based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8-9 % over SADIST (maximum score of 77.93 % compared to 69.27 %). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3x3 pixel domain. We find an increase of 5-7 % in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02 % compared to 85.69 %). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014)

MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP

Satellite infrared emission instruments require efficient systems that can separate and flag observations which are affected by clouds and aerosols. This paper investigates the identification of cloud and aerosols from infrared, limb sounding spectra that were recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), a high spectral resolution Fourier transform spectrometer on the European Space Agency's (ESA) ENVISAT (Now inoperative since April 2012 due to loss of contact). Specifically, the performance of an existing cloud and aerosol particle detection method is simulated with a radiative transfer model in order to establish, for the first time, confident detection limits for particle presence in the atmosphere from MIPAS data. The newly established thresholds improve confidence in the ability to detect particle injection events, plume transport in the upper troposphere and lower stratosphere (UTLS) and better characterise cloud distributions utilising MIPAS spectra. The method also provides a fast front-end detection system for the MIPClouds processor; a processor designed for the retrieval of macro- and microphysical cloud properties from the MIPAS data. <br><br> It is shown that across much of the stratosphere, the threshold for the standard cloud index in band A is 5.0 although threshold values of over 6.0 occur in restricted regimes. Polar regions show a surprising degree of uncertainty at altitudes above 20 km, potentially due to changing stratospheric trace gas concentrations in polar vortex conditions and poor signal-to-noise due to cold atmospheric temperatures. The optimised thresholds of this study can be used for much of the time, but time/composition-dependent thresholds are recommended for MIPAS data for the strongly perturbed polar stratosphere. In the UT, a threshold of 5.0 applies at 12 km and above but decreases rapidly at lower altitudes. The new thresholds are shown to allow much more sensitive detection of particle distributions in the UTLS, with extinction detection limits above 13 km often better than 10<sup>−4</sup> km<sup>−1</sup>, with values approaching 10<sup>−5</sup> km<sup>−1</sup> in some cases. <br><br> Comparisons of the new MIPAS results with cloud data from HIRDLS and CALIOP, outside of the poles, establish a good agreement in distributions (cloud and aerosol top heights and occurrence frequencies) with an offset between MIPAS and the other instruments of 0.5 km to 1 km between 12 km and 20 km, consistent with vertical oversampling of extended cloud layers within the MIPAS field of view. We conclude that infrared limb sounders provide a very consistent picture of particles in the UTLS, allowing detection limits which are consistent with the lidar observations. Investigations of MIPAS data for the Mount Kasatochi volcanic eruption on the Aleutian Islands and the Black Saturday fires in Australia are used to exemplify how useful MIPAS limb sounding data were for monitoring aerosol injections into the UTLS. It is shown that the new thresholds allowed such events to be much more effectively derived from MIPAS with detection limits for these case studies of 1 × 10<sup>−5</sup> km<sup>−1</sup> at a wavelength of 12 μm

ULIRS, an optimal estimation retrieval scheme for carbon monoxide using IASI spectral radiances: sensitivity analysis, error budget and simulations

This paper presents a new retrieval scheme for tropospheric carbon monoxide (CO), using measured radiances from the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite. The University of Leicester IASI Retrieval Scheme (ULIRS) is an optimal estimation retrieval scheme, which utilises equidistant pressure levels and a floating pressure grid based on topography. It makes use of explicit digital elevation and emissivity information, and incorporates a correction for solar surface reflection in the daytime with a high resolution solar spectrum. The retrieval scheme has been assessed through a formal error analysis, via the simulation of surface effects and by an application to real IASI data over a region in Southern Africa. The ULIRS enables the retrieval of between 1 and 2 pieces of information about the tropospheric CO vertical profiles, with peaks in the sensitivity at approximately 5 and 12 km. Typical errors for the African region relating to the profiles are found to be ~20% at 5 and 12 km, and on the total columns to range from 18 to 34%. Finally the performance of the ULIRS is shown for a range of simulated geophysical conditions

Educational supervision and the impact of workplace-based assessments: a survey of psychiatry trainees and their supervisors

<p>Abstract</p> <p>Background</p> <p>Educational supervision (ES) is considered to be an essential component of basic specialist training in psychiatry in the UK. However, previous studies have indicated variation in its provision, and uncertainty about structure and content. Workplace-based assessments (WPBAs) were introduced in 2007 as part of major postgraduate medical training reform. Placing considerable time demands on trainees and supervisors alike, the extent to which WPBAs should utilise ES time has not been specified. As ES and WPBAs have discrete (although complementary) functions, there is the potential for this increased emphasis on assessment to displace other educational needs.</p> <p>Methods</p> <p>All junior doctors and their educational supervisors in one UK psychiatry training scheme were surveyed both before and after the introduction of WPBAs. Frequency and duration of ES were established, and structure, content and process were ascertained. Opinions on usefulness and responsibility were sought. The usage of ES for WPBAs was also assessed.</p> <p>Results</p> <p>The response rate of 70% showed general agreement between trainees and supervisors, but some significant discrepancies. Around 60% reported 1 hour of ES taking place weekly or 3 times per month. Most agreed that responsibility for ES should be shared equally between trainees and supervisors, and ES was largely seen as useful. Around 50% of trainees and supervisors used 25–50% of ES time for WPBAs, and this did not appear to affect the usefulness of ES or the range of issues covered.</p> <p>Conclusion</p> <p>ES continues to be an important component of psychiatric training. However, using ES for WPBAs introduces the potential for tension between trainees' education and their assessment by emphasising certain training issues at the expense of others. The impact of reduced training time, WPBAs and uncertainties over ES structure and content should be monitored to ensure that its benefits are maximised by remaining tailored to individual trainees' needs.</p

A measurement-based verification framework for UK greenhouse gas emissions: an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project

We describe the motivation, design, and execution of the Greenhouse gAs Uk and Global Emissions (GAUGE) project. The overarching scientific objective of GAUGE was to use atmospheric data to estimate the magnitude, distribution, and uncertainty of the UK greenhouse gas (GHG, defined here as CO₂, CH₄, and N₂O) budget, 2013–2015. To address this objective, we established a multi-year and interlinked measurement and data analysis programme, building on an established tall-tower GHG measurement network. The calibrated measurement network comprises ground-based, airborne, ship-borne, balloon-borne, and space-borne GHG sensors. Our choice of measurement technologies and measurement locations reflects the heterogeneity of UK GHG sources, which range from small point sources such as landfills to large, diffuse sources such as agriculture. Atmospheric mole fraction data collected at the tall towers and on the ships provide information on sub-continental fluxes, representing the backbone to the GAUGE network. Additional spatial and temporal details of GHG fluxes over East Anglia were inferred from data collected by a regional network. Data collected during aircraft flights were used to study the transport of GHGs on local and regional scales. We purposely integrated new sensor and platform technologies into the GAUGE network, allowing us to lay the foundations of a strengthened UK capability to verify national GHG emissions beyond the project lifetime. For example, current satellites provide sparse and seasonally uneven sampling over the UK mainly because of its geographical size and cloud cover. This situation will improve with new and future satellite instruments, e.g. measurements of CH₄ from the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P. We use global, nested, and regional atmospheric transport models and inverse methods to infer geographically resolved CO₂ and CH₄ fluxes. This multi-model approach allows us to study model spread in a posteriori flux estimates. These models are used to determine the relative importance of different measurements to infer the UK GHG budget. Attributing observed GHG variations to specific sources is a major challenge. Within a UK-wide spatial context we used two approaches: (1) Δ¹⁴CO₂ and other relevant isotopologues (e.g. δ¹³CCH₄) from collected air samples to quantify the contribution from fossil fuel combustion and other sources, and (2) geographical separation of individual sources, e.g. agriculture, using a high-density measurement network. Neither of these represents a definitive approach, but they will provide invaluable information about GHG source attribution when they are adopted as part of a more comprehensive, long-term national GHG measurement programme. We also conducted a number of case studies, including an instrumented landfill experiment that provided a test bed for new technologies and flux estimation methods. We anticipate that results from the GAUGE project will help inform other countries on how to use atmospheric data to quantify their nationally determined contributions to the Paris Agreement