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  • 1
    Online Resource
    Online Resource
    Combining Photo‐Organo Redox‐ and Enzyme Catalysis Facilitates Asymmetric C‐H Bond Functionalization
    Wiley ; 2019
    In:  European Journal of Organic Chemistry Vol. 2019, No. 1 ( 2019-01-10), p. 80-84
    Details
    In: European Journal of Organic Chemistry, Wiley, Vol. 2019, No. 1 ( 2019-01-10), p. 80-84
    Abstract: In this study, we combined photo‐organo redox catalysis and biocatalysis to achieve asymmetric C–H bond functionalization of simple alkane starting materials. The photo‐organo catalyst anthraquinone sulfate (SAS) was employed to oxyfunctionalise alkanes to aldehydes and ketones. We coupled this light‐driven reaction with asymmetric enzymatic functionalisations to yield chiral hydroxynitriles, amines, acyloins and α‐chiral ketones with up to 99 % ee. In addition, we demonstrate functional group interconversion to alcohols, esters and carboxylic acids. The transformations can be performed as concurrent tandem reactions. We identified the degradation of substrates and inhibition of the biocatalysts as limiting factors affecting compatibility, due to reactive oxygen species generated in the photocatalytic step. These incompatibilities were addressed by reaction engineering, such as applying a two‐phase system or temporal and spatial separation of the catalysts. Using a selection of eleven starting alkanes, one photo‐organo catalyst and 8 diverse biocatalysts, we synthesized 26 products and report for the model compounds benzoin and mandelonitrile 〉 97 % ee at gram scale.
    Type of Medium: Online Resource
    ISSN: 1434-193X , 1099-0690
    URL: Issue
    URL: Article
    DOI: 10.1002/ejoc.v2019.1
    DOI: 10.1002/ejoc.201801692
    RVK:
    VA 4073
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1475010-7
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  • 2
    Online Resource
    Online Resource
    Local Anesthetic Actions on Thromboxane-Induced Platelet Aggregation
    Ovid Technologies (Wolters Kluwer Health) ; 2001
    In:  Anesthesia & Analgesia Vol. 93, No. 5 ( 2001-11), p. 1240-1245
    Details
    In: Anesthesia & Analgesia, Ovid Technologies (Wolters Kluwer Health), Vol. 93, No. 5 ( 2001-11), p. 1240-1245
    Type of Medium: Online Resource
    ISSN: 0003-2999
    URL: Article
    DOI: 10.1097/00000539-200111000-00040
    RVK:
    XA 22250
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2001
    detail.hit.zdb_id: 2018275-2
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  • 3
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    An Overview of Using Weather Radar for Climatological Studies: Successes, Challenges, and Potential
    American Meteorological Society ; 2019
    In:  Bulletin of the American Meteorological Society Vol. 100, No. 9 ( 2019-09), p. 1739-1752
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 100, No. 9 ( 2019-09), p. 1739-1752
    Abstract: Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-18-0166.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 4
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    Closing the Water Cycle from Observations across Scales: Where Do We Stand?
    American Meteorological Society ; 2021
    In:  Bulletin of the American Meteorological Society Vol. 102, No. 10 ( 2021-10), p. E1897-E1935
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 10 ( 2021-10), p. E1897-E1935
    Abstract: Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth’s climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0316.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 5
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    Online Resource
    The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach
    Copernicus GmbH ; 2018
    In:  Atmospheric Measurement Techniques Vol. 11, No. 6 ( 2018-06-13), p. 3397-3431
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 11, No. 6 ( 2018-06-13), p. 3397-3431
    Abstract: Abstract. The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    DOI: 10.5194/amt-11-3397-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2505596-3
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  • 6
    Online Resource
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    Uncertainty information in climate data records from Earth observation
    Copernicus GmbH ; 2017
    In:  Earth System Science Data Vol. 9, No. 2 ( 2017-07-25), p. 511-527
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 9, No. 2 ( 2017-07-25), p. 511-527
    Abstract: Abstract. The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    DOI: 10.5194/essd-9-511-2017
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2475469-9
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  • 7
    Online Resource
    Online Resource
    A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements
    Copernicus GmbH ; 2019
    In:  Atmospheric Measurement Techniques Vol. 12, No. 2 ( 2019-02-18), p. 1059-1076
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 12, No. 2 ( 2019-02-18), p. 1059-1076
    Abstract: Abstract. The accurate identification of the presence of cloud in the ground scenes observed by remote-sensing satellites is an end in itself. The lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of aerosol optical thickness (AOT) is the knowledge of the presence of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared (TIR), coupled with solar extraterrestrial irradiance, are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the reflectance observations of the Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA) addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud-free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson correlation coefficient (PCC) parameter is used to measure the “stability” of the atmosphere–surface system observed by satellites. The cloud-free surface is classified by analysing the PCC values on the block scale 25×25 km2. Subsequently, the reflection at 3.7 µm is used for accurate cloud identification at scene level: with areas of either 1×1 or 0.5×0.5 km2. The ASCIA data product has been validated by comparison with independent observations, e.g. surface synoptic observations (SYNOP), the data from AErosol RObotic NETwork (AERONET) and the following satellite products: (i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the product from a method based on a clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate Resolution Imaging Spectroradiometer (MODIS) products. In comparison to ground-based SYNOP measurements, we achieved a promising agreement better than 95 % and 83 % within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for the identification of clouds in a ground scene observed at high latitudes.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    DOI: 10.5194/amt-12-1059-2019
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2505596-3
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  • 8
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    Online Resource
    CLARA-A2: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data -- Edition 2, Satellite Application Facility on Climate Monitoring
    Copernicus GmbH ; 2017
    In:  Atmospheric Chemistry and Physics Vol. 17, No. 9 ( 2017-05-11), p. 5809-5828
    Details
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 17, No. 9 ( 2017-05-11), p. 5809-5828
    Abstract: Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    URL: Article
    DOI: 10.5194/acp-17-5809-2017
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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  • 9
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    Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters
    Copernicus GmbH ; 2018
    In:  Atmospheric Measurement Techniques Vol. 11, No. 3 ( 2018-03-29), p. 1793-1815
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 11, No. 3 ( 2018-03-29), p. 1793-1815
    Abstract: Abstract. Latent heat flux (LHF) is one of the main contributors to the global energy budget. As the density of in situ LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology (version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground reference to be bias-free, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50 W m−2 with a global mean of 25 W m−2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are on the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m−2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Regional and seasonal analyses suggest that largest total LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. The demonstrated approach can easily be transferred to other satellite retrievals, which increases the significance of the present work.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    DOI: 10.5194/amt-11-1793-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2505596-3
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  • 10
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    Online Resource
    Trends and Variability of Surface Solar Radiation in Europe Based On Surface‐ and Satellite‐Based Data Records
    American Geophysical Union (AGU) ; 2018
    In:  Journal of Geophysical Research: Atmospheres Vol. 123, No. 3 ( 2018-02-16), p. 1735-1754
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 123, No. 3 ( 2018-02-16), p. 1735-1754
    Abstract: CMSAF's satellite climate data record of surface solar radiation have high accuracy and stability Surface solar radiation trends given by the satellite data and the station data agree well for Europe, except for the Mediterranean summer The main reason for the observed trends of surface solar radiation in Europe is changes in clouds
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    URL: Issue
    URL: Article
    DOI: 10.1002/jgrd.v123.3
    DOI: 10.1002/2017JD027418
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2018
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016800-7
    detail.hit.zdb_id: 2969341-X
    SSG: 16,13
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