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  • Cloud properties derived from a synergetic retrieval from MERIS and AATSR on ENVISAT by the ESA Cloud CCI project. The L3C dataset consists of data combined (averaged) into a global space-time grid, with a spatial resolution of 0.5 degrees lat/lon and a temporal resolution of 1 month. This dataset is version 2.0 data from Phase 1 of the CCI project.

  • UTLS-OZONE was a NERC directed mode programme funding projects to study the upper troposphere and lower stratosphere. The particular emphasis was on the processes determining the distribution of ozone and any subsequent climate impacts. Two UTLS Ozone projects were based on airborne campaigns using the FAAM aircraft, namely ITOP-UK and CIRRUS. This dataset contains ECMWF meteorological images.

  • The images in this dataset show the mixing of two liquid solutions in a random bead pack as a function of time and in three-dimensions. The working fluids used in this study are solutions of methanol and ethylene-glycol (MEG, fluid 1) and brine (fluid 2). In particular, three mixtures of ethylene-glycol and methanol were prepared that differ in wt% ethylene-glycol, namely 55 wt% (MEG55), 57 wt% (MEG57) and 59 wt% (MEG59). Measurements are conducted using in the regime of Rayleigh numbers, Ra = 2000-5000. X-ray Computed Tomography is applied to image the spatial and temporal evolution of the solute plume non -invasively. The tomograms are used to compute macroscopic quantities including the rate of dissolution and horizontally averaged concentration profiles, and enable the visualisation of the ow patterns that arise upon mixing at a spatial resolution of about (2x2x2) mm3. We observe that the mixing process evolves systematically through three stages, starting from pure diffusion, followed by convection-dominated and shutdown. A modified diffusion equation is applied to model the convective process with an onset time of convection that compares favourably with literature data and an effective diffusion coefficient that is almost two orders of magnitude larger than the molecular diffusivity of the solute. The comparison of the experimental observations of convective mixing against their numerical counterparts of the purely diffusive scenario enables the estimation of a non-dimensional convective mass flux in terms of the Sherwood number, Sh = 0.025Ra. We observe that the latter scales linearly with Ra, in agreement with observations from both experimental and numerical studies on thermal convection over the same Ra regime.

  • This data set is part of the ESA Sea Ice Climate Change Initiative (CCI) project. The dataset provides sea ice concentration for the Arctic region, derived from the SSMI satellite instrument. It consists of daily gridded SIC fields based on Passive Microwave Radiometer measurements from the SSMI instrument with a 25km grid spacing, along with the total standard error (uncertainty) and quality control flags. It has been built upon the algorithms and processing software originally developed at the EUMETSAT OSI SAF for their SIC dataset. Please note, in the sea ice concentration data set - on purpose - no weather filter has been applied to eliminate weather-induced spurious ice in the open ocean along the ice edge in order to avoid discarding regions with a real sea ice cover. Users are advised to read the product user guide and the publication by Ivanova et al. [2015] (see documentation section). A second sea ice dataset has also been produced from the AMSR-E instrument, and these should be regarded as individual datasets and not combined without further investigations about the compatibility.

  • Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Autumn and Winter Experiments (AUTEX / WINTEX) project.

  • Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS SRPR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). This proxy version of the product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section. The GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link:

  • Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Microwave Emission Validation over sub-Arctic Lake Ice (MEVALI) and Methane and other greenhouse gases in the Arctic - Measurements, process studies and Modelling (MAMM) as part of the NERC Arctic Research Programme (ARP) projects.

  • The data provided here are the numerical simulation data for the multi-decadal experiment (1960 – 2013 inclusive) for the validation of the upgraded Met Office HadGEM3-A based operational event attribution system for EUCLEIA (European Climate and weather Events: Interpretation and Attribution). Improvements include higher horizontal and vertical resolution (N216 L85) and the latest dynamical core (ENDGame) and land surface model (JULES). External forcings are historical natural variability of solar irradiance and volcanic aerosol optical depth as well as historical anthropogenic prescriptions of GHGs, ozone, aerosols and land use change. SST and SIC lower boundary conditions are provided from the HadISST observational dataset. The experiment comprises a 15 member stochastic physics ensemble using kinetic energy backscatter and randomly perturbed physics schemes. All ensemble members share identical initialisation of the atmospheric state from ERA-40 reanalysis at 0000Z December 1st 1959. Atmospheric data are provided at temporal output resolutions of 3-hourly, 6-hourly, daily and monthly; land data are provided at daily and monthly resolutions.

  • Cloud properties derived from the AVHRR instrument on the NOAA-17 satellite by the ESA Cloud CCI project. The L3C dataset consists of data combined (averaged) from a single instrument into a global space-time grid, with a spatial resolution of 0.5 degrees lat/lon and a temporal resolution of 1 month. This dataset is version 1.0 data from Phase 1 of the CCI project.

  • This is version of HadISD the Met Office Hadley Centre's global sub-daily data, extending v1.0.1.2012p to span 1/1/1973 - 31/12/2013. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code" The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.