(My organization)
Continents, countries, sea regions of the world.
2014, ConocoPhillips, Judy Platform Undershoot UKCS 30/07a, Platform Survey, DECC Reference Number GS_50
An oil and gas industry platform site survey acquired between March and April 2014. The block number traversed was 30/7.
The copyright of materials derived from the British Geological Survey's work is vested in the Natural Environment Research Council [NERC]. No part of this work may be reproduced or transmitted in any form or by any means, or stored in a retrieval system of any nature, without the prior permission of the copyright holder, via the BGS Intellectual Property Rights Manager. Use by customers of information provided by the BGS, is at the customer's own risk. In view of the disparate sources of information at BGS's disposal, including such material donated to BGS, that BGS accepts in good faith as being accurate, the Natural Environment Research Council (NERC) gives no warranty, expressed or implied, as to the quality or accuracy of the information supplied, or to the information's suitability for any use. NERC/BGS accepts no liability whatever in respect of loss, damage, injury or other occurence however caused.
The metadata is freely available for general disclosure. The actual survey data is restricted.
This dataset was collected on behalf of ConocoPhillips and reported by the licensee through the PON 14 regulation. Data was collected using an air gun array.
2004, Kerr McGee North Sea (UK) Ltd, UKCS 21/4b Muness, Semi-Sub Rig, BGS Reference Number KM04SS0002
An oil and gas industry site survey acquired in June 2004 to investigate potential hazards for the siting and drilling of a semi-submersible rig. The block number traversed was 21/4.
This dataset was collected by Gardline Geosurvey Ltd on behalf of Kerr McGee North Sea (UK) Ltd. Information about the survey was provided to the British Geological Survey in a legacy site survey report. Data was collected using hull-mounted pinger, high resolution seismic, multibeam echo sounder, sub bottom profiler, single beam echo sounder, side scan sonar.
Land Cover Map 2000 (1km percentage target class, GB)
This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Great Britain. The raster consists of 27 bands. Within each band, each 1km pixel represents a percentage cover value for one of 27 target (or 'sub') classes, roughly representing Broad Habitats. [Note subclass codes are numbered 0-26 but the raster bands are numbered 1-27; therefore each band relates to the one below it in the subclass code list (e.g. band 1 = unclassified, 2 = Sea/Estuary, 3 = inland water, etc] The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB). Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/d5ee5360-12c5-448c-9d2b-f5c941fe3948
If you reuse this data, you should cite: Fuller, R.M., Smith, G.M., Sanderson, J.M., Hill, R.A., Thomson, A.G., Cox, R., Brown, N.J., Clarke, R.T., Rothery, P., Gerard, F.F. (2002). Land Cover Map 2000 (1km percentage target class, GB). NERC Environmental Information Data Centre https://doi.org/10.5285/d5ee5360-12c5-448c-9d2b-f5c941fe3948
Registration is required to access this data
In a major development of the 1990 methods, LCM2000 used image segmentation to identify land parcels in the pixel-based images. An automated procedure grouped the image pixels into spectrally 'uniform' segments. These were built around 'seedpoints' selected to avoid parcel boundaries. The segmentation recorded a field-by-field pattern, also separating land use zones within urban and suburban areas, and subdividing mosaics of semi-natural vegetation. Generalisation gave minimum mappable units of a half hectare. A 'vector' version was then created in the geographical information system (GIS), where the boundaries between the pixel-based segments were represented by digital linework. The resultant land parcels were treated as GIS objects to which attribute data could then be attached covering such topics as land cover class, parcel area, length of boundary, processing history, knowledge-based correction and identification of the original satellite scene. The raster products have been derived from the vector dataset. LCM20o0 was produced at CEH Monks Wood. For further information on the methodology developed and used to create Land Cover Map 2000, see online supporting documentation.
Single nucleotide polymorphism (SNP) genotypes and phenotypes for marker-trait association analysis of three pine species grown in a glasshouse
This dataset contains details of the phenotypes (height, bud set and budburst) and genotypes (via SNP array) of trees from a common garden multi-species pine (Pinus sylvestris, Pinus mugo and Pinus uncinata) glasshouse trial between 2010 and 2013. Full details about this dataset can be found at https://doi.org/10.5285/55118e26-cf5c-41d6-9157-738fce6bdddf
If you reuse this data, you should cite: Perry, A., Wachowiak, W., Donnelly, K., Cavers, S. (2021). Single nucleotide polymorphism (SNP) genotypes and phenotypes for marker-trait association analysis of three pine species grown in a glasshouse. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/55118e26-cf5c-41d6-9157-738fce6bdddf
This resource is available under the terms of the Open Government Licence
A common garden glasshouse trial was established in 2010 using seed from natural populations from three species of pine: Pinus sylvestris (SY), Pinus mugo (MU), Pinus uncinata (UN). Open-pollinated seeds were collected from three to five trees per population from twenty-eight natural populations in Europe covering the geographic range of each species. The collection consisted of thirteen populations of SY, nine MU, and six UN. The trial was divided into 25 randomized blocks with up to five families per population, of which the first 18 blocks were analysed by Wachowiak et al. (2018a). Phenotypes (height, bud set and budburst) were assessed in the glasshouse during spring (budburst), autumn (bud set) and winter (height) between 2010 and 2013. A subset of trees were selected to be genotyped using a multi-species SNP array. Trees were genotyped using a multi-species SNP array (Perry et al., 2020). DNA was extracted from needles using a Qiagen DNeasy 96 kit following the manufacturer’s instructions. Needles were dried on silica gel prior to extraction and DNA was quantified using a Qubit spectrophotometer to ensure a minimum standardized concentration of 35ng/µl. The quality of genomic DNA was also checked visually for fragmentation on 1 % agarose gel. Genotyping was done at Edinburgh Genomics following DNA amplification, fragmentation, chip hybridisation, single-base extension through DNA ligation and signal amplification performed according to the Affymetrix Axiom® Assay protocol. Genotyping was performed in 384-well format on a GeneTitan according to the manufacturer’s procedure. Genotype calls were performed using Axiom Analysis Suite software as recommended by the manufacturer (ThermoFisher). Samples were assigned to an analysis group based on their call rate (CR) and dish QC (DQC: a metric provided by ThermoFisher which is generated by measuring signals at multiple sites in the genome known not to vary among individuals), using the following thresholds: DQC ‘high’ ≥ 0.82; DQC ‘low’ < 0.82; CR ‘high’ ≥ 96; CR ‘low’ < 96. Analyses: 1) DQC high + CR high; 2) DQC high + CR low; 3) DQC low + CR low. High quality samples (N=529), with high CR and DQC, were used to set thresholds for allele calls. Posteriors for allele calls were subsequently used as priors for analyses 2 (N = 753) and 3 (N = 251).
Map based index (GeoIndex) offshore hydrocarbon wells
This layer of the map based index (GeoIndex) shows the location, with basic metadata, of selected DECC (Department of Energy & Climate Change) offshore hydrocarbon wells. The source data for this layer was downloaded from the DECC website at www.og.decc.gov.uk/information/maps_offshore.htm. These are DECC owned data and information and supply of the actual data is through the DECC release agents.
Either: (i) the dataset is made freely available, e.g. via the Internet, for a restricted category of use (e.g. educational use only); or (ii) the dataset has not been formally approved by BGS for access and use by external clients under licence, but its use may be permitted under alternative formal arrangements; or (iii) the dataset contains 3rd party data or information obtained by BGS under terms and conditions that must be consulted in order to determine the permitted usage of the dataset. Refer to the BGS staff member responsible for the creation of the dataset if further advice is required. He / she should be familiar with the composition of the dataset, particularly with regard to 3rd party IPR contained in it, and any resultant use restrictions. This staff member should revert to the IPR Section (ipr@bgs.ac.uk) for advice, should the position not be clear.
Free access. The usual BGS licensing terms and conditions apply to external use of the data.
GeoIndex is a publically-accessible map-based index to the datasets that BGS have collected or have obtained from other sources. All datasets presented are derived from the BGS corporate data holdings.
Land Cover Map 2000 (1km dominant target class, GB)
This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Great Britain. Each 1km pixel represents the dominant target class (or 'sub class') across the 1km area. The target classes broadly represent Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB). Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/abff8409-0995-48d2-9303-468e1a9fe3df
If you reuse this data, you should cite: Fuller, R.M., Smith, G.M., Sanderson, J.M., Hill, R.A., Thomson, A.G., Cox, R., Brown, N.J., Clarke, R.T., Rothery, P., Gerard, F.F. (2002). Land Cover Map 2000 (1km dominant target class, GB). NERC Environmental Information Data Centre https://doi.org/10.5285/abff8409-0995-48d2-9303-468e1a9fe3df
In a major development of the 1990 methods, LCM2000 used image segmentation to identify land parcels in the pixel-based images. An automated procedure grouped the image pixels into spectrally 'uniform' segments. These were built around 'seedpoints' selected to avoid parcel boundaries. The segmentation recorded a field-by-field pattern, also separating land use zones within urban and suburban areas, and subdividing mosaics of semi-natural vegetation. Generalisation gave minimum mappable units of a half hectare. A 'vector' version was then created in the geographical information system (GIS), where the boundaries between the pixel-based segments were represented by digital linework. The resultant land parcels were treated as GIS objects to which attribute data could then be attached covering such topics as land cover class, parcel area, length of boundary, processing history, knowledge-based correction and identification of the original satellite scene. The raster products have been derived from the vector dataset. LCM2000 was produced at CEH Monks Wood. For further information on the methodology developed and used to create Land Cover Map 2000, see the on-line supporting documentation.
https://catalogue.ceda.ac.uk/uuid/799c2bbf16bf4ae984df7c70aa755719 799c2bbf16bf4ae984df7c70aa755719 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.clt.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.huss.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.pr.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.prsn.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.rlds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.rlus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.rsds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.rsdsdiff.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.rsus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.tas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.uas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.3hr.vas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.6hrLev.hus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.6hrLev.ta.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.6hrLev.ua.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.6hrLev.va.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.6hrPlev.psl.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.clt.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.evspsbl.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.hfls.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.hfss.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.hur.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.hurs.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.hus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.huss.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.pr.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.prsn.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.psl.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rlds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rlus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rlut.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rlutcs.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rsds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rsdt.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rsus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rsut.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.rsutcs.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.sfcWind.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.ta.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.tas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.tasmax.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.tasmin.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.tauu.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.tauv.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.ts.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.ua.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.uas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.va.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.vas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Amon.zg.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Omon.sos.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.Omon.tos.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.clt.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.hurs.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.hus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.huss.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.mrro.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.mrsos.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.pr.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.prsn.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.psl.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.rlds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.rlus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.rlut.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.rsds.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.rsus.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.sfcWind.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.ta.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.tas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.tasmax.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.tasmin.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.ua.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.uas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.va.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.vas.gn.v20201112.v20201112 CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126.r1i1p1f1.day.zg.gn.v20201112.v20201112
WCRP CMIP6: Indian Institute of Tropical Meteorology Pune (CCCR-IITM) IITM-ESM model output for the "ssp126" experiment
The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Indian Institute of Tropical Meteorology Pune (CCCR-IITM) IITM-ESM model output for the "update of RCP2.6 based on SSP1" (ssp126) experiment. These are available at the following frequencies: 3hr, 6hrLev, 6hrPlev, Amon, Omon and day. The runs included the ensemble member: r1i1p1f1. CMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.
Public data: access to these data is available to both registered and non-registered users.
Under the following licence https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf, appropriate use of these data may fall under unclassified use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data were produced and verified by Indian Institute of Tropical Meteorology Pune (CCCR-IITM) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).
Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]
1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2019. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. Full details about this dataset can be found at https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c
© Database Right/Copyright UK Centre for Ecology & Hydrology. All rights reserved.
Contains material based on Met Éireann data © Met Éireann and Met Office data © Crown copyright.
Users should be aware that the quality of the rainfall data will decrease when the minimum distance (i.e. distance to the closest gauge) increases. The value of the minimum distance is provided alongside rainfall values within the dataset. For values of minimum distance greater than 100 km, rainfall values are not derived.
If you reuse this data, you should cite: Tanguy, M., Dixon, H., Prosdocimi, I., Morris, D.G., Keller, V.D.J. (2021). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2019) [CEH-GEAR]. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/dbf13dd5-90cd-457a-a986-f2f9dd97e93c
The rainfall estimates are derived from the Met Office national database of observed precipitations. To derive the monthly estimates, monthly and daily (when complete month available) precipitation totals from the UK raingauge network are used. An additional quality control step (data already quality controlled by the Met Office) was introduced to check events greater than the 200 year return period. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (SAAR61-90), was used to generate the daily and monthly rainfall grids. The daily grids are adjusted so that the monthly totals calculated from those are in agreement with the monthly grids. As an indicator of the quality of the estimates, the dataset also contains for each grid generated a grid containing the distance to the closest raingauge used to interpolate the grid point. A grid containing the number of missing estimates for each year is also provided. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012. This version of the CEH-GEAR dataset differs from the previous version (1890-2017) only by temporal coverage. That is, previous years' data have not been modified.
WATCH forcing data (WFD) - 20th Century LWdown (surface incident longwave radiation) 1901-2001
The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. LWdown or surface incident longwave radiation (also known as downwards long-wave radiation flux ) is the surface incident longwave radiation averaged over the next six hours, measured in W/m2 at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.
Please note that these data are based on climate reanalysis data, plus a spatial interpolation (from one-degree to half-degree), elevation correction and monthly-correction based on gridded observations. As such the WATCH Forcing data are designed for use in modelling (e.g. hydrological modelling) and for where good meteorological observations are not readily available.
Please reference the use of the WATCH forcing data in any work published using http://journals.ametsoc.org/doi/pdf/10.1175/2011JHM1369.1
These data were created as part of the EU FP6 project WATCH which was coordinated by NERC-CEH and ran from 2007-2011. The data were generated in two tranches according to time interval. The data from 1958-2001 were based on ECMWF (European Centre for Medium Range Weather Forecasting) "ERA-40" reanalysis data via interpolation, elevation correction and monthly bias correction from gridded observations. The data from 1901-1957 were created via re-ordering of the ERA-40 reanalysis one year at a time. For full details of the process see the WATCH technical report No 22 and Weedon (2011, J. Hydrometeorology).
UKCCSRC Call 2 Project final report: UK demonstration of Enhanced Calcium looping, and first Global Demonstration of Advanced Doping Techniques
Final report for UKCCSRC Call 2 project, UK demonstration of Enhanced Calcium looping, and first Global Demonstration of Advanced Doping Techniques. Grant number: UKCCSRC-C2-209.
Either : (i) the dataset has not been formally approved by BGS for access and use by external clients under license; and / or (ii) the dataset contains 3rd party data or information obtained by BGS under terms and conditions that must be consulted before the dataset can be provided to, or accessed by, BGS staff or external clients. Refer to the BGS staff member responsible for the creation of the dataset if further advice is required. He / she should be familiar with the composition of the dataset, particularly with regard to 3rd party IPR contained in it, and any resultant access restrictions. This staff member should revert to the IPR Section (IPR@bgs.ac.uk) for advice, should the position not be clear.
UKCCSRC Call 2 project, grant number: UKCCSRC-C2-209, Lead institution: Cranfield University
CMIP6 ES-Doc ensemble member r1i1p1f1 entry for the ssp126 run with IITM-ESM model.
Detailed experiment description for: "update of RCP2.6 based on SSP1" (ssp126)
Official CMIP6 DOI Citation: Narayanasetti, Sandeep; Panickal, Swapna; Gopinathan, Prajeesh A.; Choudhury, Ayantika Dey; Singh, Manmeet; Raghavan, Krishnan (2020). CCCR-IITM IITM-ESM model output prepared for CMIP6 ScenarioMIP ssp126. Version YYYYMMDD*.Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.14747
NERC EDS Environmental Information Data Centre
NERC EDS Centre for Environmental Data Analysis
Indian Institute of Tropical Meteorology Pune (CCCR-IITM)
Land Cover Map 2007 (1km percentage aggregate class, GB)
[THIS DATASET HAS BEEN WITHDRAWN]. Land Cover Map 2007 (LCM2007) is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors. It also covers Northern Ireland and incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible, incorporating land cover classes sought by other users. LCM2007 is produced in both vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. This dataset consists of the 1km raster, percentage Aggregate Class. Great Britain only. Full details about this dataset can be found at https://doi.org/10.5285/3f3fa303-d019-4b28-ad79-9a45f8ee04d0
If you reuse this data, you should cite: Morton, R.D., Rowland, C., Wood, C., Meek, L., Marston, G., Smith, G., Wadsworth, R., Simpson, I. (2011). Land Cover Map 2007 (1km percentage aggregate class, GB). NERC Environmental Information Data Centre https://doi.org/10.5285/3f3fa303-d019-4b28-ad79-9a45f8ee04d0
LCM2007 uses a spatial framework based on OS MasterMap (R). MasterMap was generalised to remove unnecessary detail, then the framework was segmented according to the underlying satellite data to split areas of non-uniform landscape. The data was classified according to a parcel-based supervised maximum likelihood classification procedure. The raster products are derived from the vector products.
https://catalogue.ceda.ac.uk/uuid/f3c7e70b155f4bad85432cf52c2b563c f3c7e70b155f4bad85432cf52c2b563c
Snow-Vegetation-Atmosphere Interactions over Heterogeneous Landscapes: Sodankyla Above and Below-Canopy Radiation data
Vegetation and meteorological observations (snow and radiation) were collected by various ground instruments in an area of forest near Abisko (Sweden) and Sodankylä (Finland) during measurement campaigns in March 2011 and March 2012. This dataset contains the radiation data collected at Abisko site in March 2011. Above-canopy radiation: An open area was selected at each study site (“plot O”) for measurements assumed to be representative of incoming radiation above the nearby forest canopy. A Delta-T Devices BF3 sunshine sensor and a Kipp & Zonen CGR3 pyrgeometer were connected to a Campbell Scientific CR1000 data logger recording 5-minute averages of measurements made every 5 seconds. The BF3 measures total and diffuse incoming shortwave radiation, and the CGR3 measures thermal longwave radiation. Below-canopy radiation: In the forest plots, two arrays of ten Kipp & Zonen CM3 shortwave pyranometers and four Kipp & Zonen CGR3 longwave pyrgeometers were connected to AM16/32B multiplexers and Campbell Scientific CR1000 data loggers recording 5-minute averages of measurements made every 5 seconds. One array was set up in a “continuity plot” C for the entire duration of each field campaign, while the other array was moved between four “roving plots” R1 to R4, providing at least 5 complete days of data at each plot. All radiometers were placed on small plywood platforms on the snow surface and were levelled and cleared of snow every morning. Radiometer positions were recorded using differential GPS at Abisko and averages of repeated handheld GPS measurements at Sodankylä. This was a NERC funded project.
Access to these data is available to any registered CEDA user. Please Login or Register for a CEDA account to gain access.
Under the following licence https://artefacts.ceda.ac.uk/licences/missing_licence.pdf, appropriate use of these data may fall under unstated use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
https://catalogue.ceda.ac.uk/uuid/bac493ecc5f14b4d809f35f89eceb834 bac493ecc5f14b4d809f35f89eceb834
MYD35_L2 - MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
These data are a copy of MODIS data from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC). The copy is potentially only a subset. Below is the description from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MYD35_L2 " The MODIS Cloud Mask product is a Level-2 product generated at 1-km and 250-m (at nadir) spatial resolutions. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on visible channel data only. Radiometrically-accurate radiances are required, thus holes in the Cloud Mask will appear wherever the input radiances are incomplete or of poor quality assurance. There are two MODIS Cloud Mask data product files: MOD35_L2, containing data collected from the Terra platform; and MYD35_L2, containing data collected from the Aqua platform. The MODIS cloud mask algorithm employs a battery of spectral tests, which use methodology applied in the AVHRR Processing scheme Over cLoudy Land and Ocean (APOLLO), International Satellite Cloud Climatology Project (ISCCP), CLoud Advanced Very high resolution Radiometer (CLAVR), and the Support of Environmental Requirements for Cloud Analysis and Archive (SERCAA) algorithms to identify cloudy FOVs. From these, a clear-sky confidence level (high confident clear, probably clear, undecided, cloudy) is assigned to each FOV. For inconclusive results, spatial- and temporal-variability tests are applied. The spectral tests rely on radiance (temperature) thresholds in the infrared and reflectance thresholds in the visible and near-infrared. Thresholds vary with surface type, atmospheric conditions (moisture, aerosol, etc.), and viewing geometry. In addition to the MYD02 calibrated radiances, a 1-km land/water mask, DEM, ecosystem analysis, snow/ice cover map, NCEP analysis of surface temperature and wind speed, and an estimate of precipitable water are required as inputs. A determination of the presence of global cloudiness is essential to the MODIS mission for two reasons. First, clouds play a critical role in the radiative balance of the Earth and must be accurately described to assess climate and potential climate change. Second, the presence of cloudiness must be accurately determined to properly retrieve many atmospheric and surface parameters. For many of these retrieval algorithms even thin cirrus represents contamination. Cloud mask validation will be conducted using MODIS Airborne Simulator (MAS) data from several field campaigns, all-sky cameras, and comparison with NOAA operational instruments and possibly ASTER. For additional details see the MODIS Atmospheres web site page onCollection 6.1 Updates. " Shortname: MYD35_L2 , Platform: Aqua , Instrument: MODIS , Processing Level: Level-2 , Spatial Resolution: 250 m, 1 km , Temporal Resolution: 5 minute , ArchiveSets: 61 , Collection: MODIS Collection 6.1 - Level 1, Atmosphere, Land (ArchiveSet 61) , PGE Number: PGE03 , File Naming Convention: MYD35_L2.AYYYYDDD.HHMM.CCC.YYYYDDDHHMMSS.hdf YYYYDDD = Year and Day of Year of acquisition HHMM = Hour and Minute of acquisition CCC = Collection number YYYYDDDHHMMSS = Production Date and Time AYYYYDDD = Year and Day of Year of acquisition , Citation: Ackerman, S., et al., 2017. MODIS Atmosphere L2 Cloud Mask Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: http://dx.doi.org/10.5067/MODIS/MYD35_L2.061 , Keywords: Cloud Fraction, Climate Change, Atmospheric Correction
Under the following licence https://modaps.modaps.eosdis.nasa.gov/services/faq/LAADS_Data-Use_Citation_Policies.pdf, appropriate use of these data may fall under unclassified use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data were downloaded from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/ to support UK research groups, especially NCEO.
https://catalogue.ceda.ac.uk/uuid/21f2f35df04841ab9323e4d0eca286e7 21f2f35df04841ab9323e4d0eca286e7
EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from FMI's Vaisala CL31 instrument deployed at Mikkeli Lentoasema Awos, Finland
Daily concatenated files of ceilometer cloud base height and aerosol profile data from Finnish Meteorological Institution (FMI)'s Vaisala CL31 deployed at Mikkeli Lentoasema Awos, Finland. These data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide. The site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-246-0-855522. Details for this WIGOS station are presently unavailable in the Observing Systems Capability Analysis and Review (OSCAR) Tool. EUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.
Under the following licence https://artefacts.ceda.ac.uk/licences/cuncgl, appropriate use of these data may fall under personal, academic and policy use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.
LAADS DAAC DOI: 10.5067/MODIS/MYD35_L2.061
EUMETNET activities web page detailing the range of activities undertaken by EUMETNET.
NCAS British Atmospheric Data Centre (NCAS BADC)
National Aeronautics and Space Administration (NASA)
Finnish Meteorological Institution (FMI)