Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

In this study, the distributed catchment-scale model, DiCaSM, was applied on five catchments across the UK. Given its importance, river flow was selected to study the uncertainty in streamflow prediction using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology at different timescales (daily, monthly, seasonal and annual). The uncertainty analysis showed that the observed river flows were within the predicted bounds/envelope of 5% and 95% percentiles. These predicted river flow bounds contained most of the observed river flows, as expressed by the high containment ratio, CR. In addition to CR, other uncertainty indices – bandwidth B, relative bandwidth RB, degrees of asymmetry S and T, deviation amplitude D, relative deviation amplitude RD and the R factor – also indicated that the predicted river flows have acceptable uncertainty levels. The results show lower uncertainty in predicted river flows when increasing the timescale from daily to monthly to seasonal, with the lowest uncertainty associated with annual flows

Corrigendum to "Trend analysis of aerosol optical thickness and Ångström exponent derived from the global AERONET spectral observations" published in Atmos. Meas. Tech., 5, 1271–1299, 2012

No abstract available

The semianalytical cloud retrieval algorithm for SCIAMACHY II. The application to MERIS and SCIAMACHY data

International audienceThe SemiAnalytical CloUd Retrieval Algorithm (SACURA) is applied to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data. In particular, we derive simultaneously cloud optical thickness (COT) and cloud top height (CTH), using SCIAMACHY measurements in the visible (442 nm, COT) and in the oxygen A-band (755?775 nm, CTH). Some of the results obtained are compared with those derived from the Medium Resolution Imaging Spectrometer (MERIS), which has better spatial resolution and observes almost the same scene as SCIAMACHY. The same cloud algorithm is applied to both MERIS and SCIAMACHY data. In addition, we perform the vicarious calibration of SCIAMACHY at the wavelength 442 nm, using MERIS measurements at the same wavelength. Differences in the retrieved COT for the same cloud field obtained using MERIS and SCIAMACHY measurements are discussed

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

Systemic amyloidosis presenting as constrictive pericarditis. A case studied with cardiac catheterization

A case of systemic amyloidosis and plasmacytosis is presented. Both on clinical observation and cardiac catheterization, the features of this case simulated those of constrictive pericarditis. The pertinent literature is reviewed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32142/1/0000196.pd

Retrieval of aerosol optical thickness for desert conditions using MERIS observations during the SAMUM campaign

This journal is published under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported LicenseApproximately 30% of the land surface is arid, having desert or semi-desert conditions. Aerosol originating from these regions plays a significant role in climate and atmospheric chemistry of the atmosphere. Retrieving aerosol properties from space-borne platforms above desert conditions, where the surface reflectance is usually very bright, is a challenging task. The proportion of the surface to top of atmosphere (TOA) reflectance can reach values over 90%, especially for wavelength above 500 nm. For these reasons detailed knowledge of aerosol and surface optical properties from these regions is required to separate atmosphere from intrinsically bright surfaces. An approach to retrieve aerosol properties over arid and semi-arid regions based on the Bremen Aerosol Retrieval (BAER) has been developed and validated within the Dust Aerosol Retrievals from Space-Born Instruments (DREAMS) Project, which is part of the Saharan Mineral Dust Experiment (SAMUM, 2006). Combining measurements of the backscattered radiation from the Medium Resolution Imaging Spectrometer (MERIS) instrument aboard Environmental Satellite (ENVISAT) and ground-based measurements in Morocco in radiation closure experiments yields the aerosol optical properties of mineral dust at selected locations.Peer reviewe

Retrieval of aerosol mass load (PM10) from MERIS/Envisat top of atmosphere spectral reflectance measurements over Germany

Results of a new methodology for retrievals of surface particulate matter concentration (PM₁₀) from satellite reflectance measurements over Germany are presented in this paper. The retrieval derives effective radii from Ångström-α exponents and benefits from the fitting of a smooth spectral slope from seven MERIS spectrometer channels. Comparisons with ground measurements from the air quality surveillance show standard deviations of 33.9% with −18.9% bias over Hamburg. Over rural sites a standard deviation of 17.9% (bias 12.9%) is reached. <br><br> We discuss critically limitations and potential applications of the retrieval. Additionally, we talk about the aspects at comparing of retrieved particulate matter with ground station measurements