Other language confidence: 0.989691408258878
This product is based on Vaisala RS92 radiosonde measurements of temperature, humidity, wind and pressure that have been processed following the requirements of the GCOS Reference Upper Air Network (GRUAN) that were described in Immler et al. [2010]. The GRUAN data product file comply to the requirements of GRUAN in particular by providing a full uncertainty analysis. The uncertainty is calculated according to the recommendations of the “Guide for expressing uncertainty in measurement” [GUM2008]. The total uncertainty is assessed from estimates of the calibration uncertainty, the uncertainty of corrections and statistical standard deviations. Corrections are applied such that the data is bias free according to current knowledge.
This product is based on Vaisala RS92 radiosonde measurements of temperature, humidity, wind and pressure that have been processed following the requirements of the GCOS Reference Upper Air Network (GRUAN) that were described in Immler et al. [2010]. The GRUAN data product file comply to the requirements of GRUAN in particular by providing a full uncertainty analysis. The uncertainty is calculated according to the recommendations of the “Guide for expressing uncertainty in measurement” [GUM2008]. The total uncertainty is assessed from estimates of the calibration uncertainty, the uncertainty of corrections and statistical standard deviations. Corrections are applied such that the data is bias free according to current knowledge.
This data set contains CTD data collected during the DAUNE experiment using the given sensor. The goal of this experiment was to reach a common understanding of how measurement uncertainty can be derived initially focusing on temperature data. Data collection was performed using the AWI O2A infrastructure (https://epic.awi.de/id/eprint/37171/) which performs automatized near real time quality control. During the data ingest and archival process, the hereby assigned quality flags used by the O2A system have been transformed into the pangaea flagging scheme as follows, flagging symbols are shown in brackets: O2A Flag ->PANGAEA Flag No quality control (0) ->unknown (*) Good data (1) ->valid () Probably good (2) ->questionable (?) Probably bad (3) ->questionable (?) Bad (4) ->not valid (/)
This data set contains CTD data collected during the DAUNE experiment using the given sensor. The goal of this experiment was to reach a common understanding of how measurement uncertainty can be derived initially focusing on temperature data. Data collection was performed using the AWI O2A infrastructure (https://epic.awi.de/id/eprint/37171/) which performs automatized near real time quality control. During the data ingest and archival process, the hereby assigned quality flags used by the O2A system have been transformed into the pangaea flagging scheme as follows, flagging symbols are shown in brackets: O2A Flag ->PANGAEA Flag No quality control (0) ->unknown (*) Good data (1) ->valid () Probably good (2) ->questionable (?) Probably bad (3) ->questionable (?) Bad (4) ->not valid (/)
This data set contains CTD data collected during the DAUNE experiment using the given sensor. The goal of this experiment was to reach a common understanding of how measurement uncertainty can be derived initially focusing on temperature data. Data collection was performed using the AWI O2A infrastructure (https://epic.awi.de/id/eprint/37171/) which performs automatized near real time quality control. During the data ingest and archival process, the hereby assigned quality flags used by the O2A system have been transformed into the pangaea flagging scheme as follows, flagging symbols are shown in brackets: O2A Flag ->PANGAEA Flag No quality control (0) ->unknown (*) Good data (1) ->valid () Probably good (2) ->questionable (?) Probably bad (3) ->questionable (?) Bad (4) ->not valid (/)
CO-MICC is a data portal for freshwater-related climate change risk assessment at multiple spatial scales. It is named after the research project during which it was developed, i.e. the CO-MICC (CO-development of Methods to utilize uncertain multi-model-based Information on freshwater-related hazards of Climate Change) project (2017-2021). The aim of CO-MICC is to support decision making in the public and private spheres dealing with future availability of freshwater resources. This climate service is operated and maintained by the International Centre for Water Resources and Global Change (ICWRGC), and more broadly by the German Federal Institute of Hydrology. The portal comprises data of over 80 indicators of freshwater-related hazards of climate change, which can be visualized in the form of global maps or interactive graphs. The indicators are dynamically calculated based on modelled annual and monthly gridded (0.5°) data sets of climate and hydrological variables. These data sets were computed by a multi-model ensemble comprising four Representative Concentration Pathways (RCPs), four General Circulation Models (GCMs), three Global Hydrological Models (GHMs) and two variants per hydrological model, which amounts to 96 ensemble members in total. They were provided by three European research modelling teams that are part of the ISIMIP consortium. The indicator data correspond to absolute or relative changes averaged over future 30-year periods, as compared to the reference period 1981-2010.
Paleo±Dust is an updated compilation of bulk and <10-µm paleo-dust deposition rate with quantitative 1-σ uncertainties that are inter-comparable among archive types (lake sediment cores, marine sediment cores, polar ice cores, peat bog cores, loess samples). Paleo±Dust incorporates a total of 285 pre-industrial Holocene (pi-HOL) and 209 Last Glacial Maximum (LGM) dust flux constraints from studies published until December 2022. We also recalculate previously published dust fluxes to exclude data from the last deglaciation and thus obtain more representative constraints for the last pre-industrial interglacial and glacial end-member climate states. Metadata include all components necessary to derive dust deposition rate, including: age range, thickness, density, eolian content. We also include 1-sigma uncertainties on each of these components, and on the final bulk and <10-µm dust deposition rates. Specific notes for each site and a list of references are also included.
Objective: WISER will support the implementation of the Water Framework Directive (WFD) by developing tools for the integrated assessment of the ecological status of European surface waters (with a focus on lakes and coastal/transitional waters), and by evaluating recovery processes in rivers, lakes and coastal/transitional waters under global change constraints. The project will (1) analyse existing data from more than 90 databases compiled in previous and ongoing projects, covering all water categories, Biological Quality Elements (BQEs) and stressor types and (2) perform targeted field-sampling exercises including all relevant BQEs in lakes and in coastal/transitional waters. New assessment systems will be developed and existing systems will be evaluated for lakes and coastal/transitional waters, with special focus on how uncertainty affects classification strength, to complete a set of assessment methodologies for these water categories. Biological recovery processes, in all water categories and in different climatic conditions, will be analysed, with focus on mitigation of hydromorphological and eutrophication pressures. Large-scale data will be used to identify linkages between pressure variables and BQE responses. Specific case studies, using a variety of modelling techniques, will address selected pressure-response relationships and the efficacy of mitigation measures. The responses of different BQEs and different water categories to human-induced degradation and mitigation will be compared, with special focus on response signatures of BQEs within and among water categories. Guidance for the next steps of the intercalibration exercise will be given by comparing different intercalibration approaches. Stakeholders will be included from the outset, by building small teams of stakeholders and project partners responsible for a group of deliverables, to ensure the applicability and swift implementation of results.
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