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Department of Meteorology

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  • These data are high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. This includes: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates on a high-resolution (1/360x1/360 degree) grid, produced by the Department of Meteorology at the University of Reading. Data was derived using the ESA CCI Land Cover Map (see linked documentation). Datasets containing information to locate and identify water bodies have been generated from high-resolution (1/360x1/360 degree, about 300mx300m) data locating static-water-bodies recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The new datasets provide: distance to land, distance to water, water body identifiers and lake centre locations. The lake identifiers (IDs) are from the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. The LC CCI water bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR (Advanced Synthetic Aperture Radar) on Envisat between 2005 and 2010. Temporal change in water body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated. The paper associated with this dataset is: L.Carrea O. Embury C.J. Merchant "High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32

  • The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project Sea and Lake Surface Temperature Climate Data Record core retrieved quantity is the skin (radiometric) temperature of the Earth’s water surfaces (sea and large lakes). This is provided as a best estimate, plus an ensemble of 10 perturbations capturing known uncertainties. The CDR contains grid-cell instantaneous averagesof retrieved surface temperature over ice-free oceans and 300 large lakes. The FIDUCEO Surface Temperature CDR differs from the ESA Sea Surface Temperature Climate Change Initiative CDRs ; which were generated using in the using the same cloud detection and SST retrieval methodology in the following points: - The calibration of the brightness temperatures used is revised for the FIDUCEO ST CDR. The first step in this has been multi-sensor harmonisation to obtain baseline calibration coefficients (Giering et al., 2019). For specific ST application, these coefficients were adjusted such that SSTs had lower bias, using a method of cross-referencing to matched drifting buoys (Merchant et al., 2019) - Perturbations to the obtained ST and quality level determination are provided for an ensemble of 10 members, for the purpose of propagating uncertainty in ST in complex (large scale, non-linear) applications. - The FIDUCEO ST CDR includes retrievals over the world’s 300 largest lakes, unlike the SST-only product. (Lakes, including much smaller lakes,are addressed in other CDRs requiring significantly different methodsto cope with the difficulties of small target water bodies.) Full documentation including product user guide, tutorials, the scientific basis and relevant publications are available in the documentation.

  • This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes. • Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide. • Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.

  • The MVIRI Aerosol Optial depth demonstration dataset contains the aerosol optical thickness (AOT) as retrieved from the visible channel of the Meteosat Visible and Infrared Imager (MVIRI) operated on board Meteosat First Generation (MFG) spacecrafts. The channel is centred around 0.7 µm but the spectral coverage of this channel is very broad. The dataset is produced for 2 of the 7 Meteosat satellites, Meteosat -5 and Meteosat-7, that were operated during the period between 1991 and 2007. While Meteosat-7 was, during the considered period, positioned above 0° longitude, Meteosat-5 was moved from 0° to 63° longitude in support of the INDOEX Experiment in 1998, with continued service in the course of the Indian Ocean Data Coverage (IODC) mission. The aerosol optical thickness (AOT) was retrieved from the MVIRI fundamental climate data record (FCDR) using the Combined Inversion of Surface and AeRosol (CISAR) Algorithm. Both datasets were produced as part of the FIDUCEO (Fidelity and uncertainty in climate data records from Earth Observations) EU Horizon 2020 project. The primary objective of this data record is to assess and demonstrate how the recalibrated and uncertainty-quantified MVIRI FCDR can support improved retrieval of geophysical parameters. Of particular interest is the impact of in-flight reconstructed and spectrally degrading spectral response functions. More information is available in the MVIRI Report and Release Note in the documentation

  • An ensemble of simulations made using the Unified Model version 6.6 (HadGEM2) in AMIP (atmosphere only) configuration for the SAPRISE (South Asian PRecIpitation: A SEamless assessment) project. The simulations are used to investigate the impacts of aerosols on the South Asian Monsoon. The four-member ensemble of simulations are forced with anthropogenic-only aerosols i.e. sulphur dioxide, black carbon and biomass burning aerosols. The simulations cover the period from 1850-2000. Since aerosol-only simulation is not compulsory in CMIP5, these four runs are complements to other CMIP5 simulations conducted by Met Office using the HadGEM2-ES (vn 6.6).

  • The MVIRI Albedo and Uncertainties demonstration dataset contains the broadband surface albedo as retrieved from the visible channel of the Meteosat Visible and Infrared Imager (MVIRI) operated on board Meteosat First Generation (MFG) spacecrafts. The channel is centered around 0.7 µm but the spectral coverage of this channel is very broad. The dataset is produced for 2 of the 7 Meteosat satellites, Meteosat -5 and Meteosat-7, that were operated during the period between 1991 and 2007. While Meteosat-7 was, during the considered period, positioned above 0° longitude, Meteosat-5 was moved from 0° to 63° longitude in support of the INDOEX Experiment in 1998, with continued service in the course of the Indian Ocean Data Coverage (IODC) mission. The albedo data was retrieved from the MVIRI fundamental climate data record; both were produced as part of the FIDUCEO (Fidelity and uncertainty in climate data records from Earth Observations) EU Horizon 2020 project.

  • Global Observatory of Lake Responses to Environmental Change (GloboLakes) was a project funded by the Natural Environment Research Council (NERC) with the following grant references: NE/J023345/2, NE/J02211X/1, NE/J023396/1, NE/J021717/1 and NE/J022810/1. This dataset contains the GloboLakes LSWT v4.0 of daily observations of Lake Surface Water Temperature (LSWT), its uncertainty and quality levels. The LSWTs are obtained by combining the orbit data from the AVHRR (Advanced Very High Resolution Radiometer) on MetOpA, AATSR (Advanced Along Track Scanning Radiometer) on Envisat and ATSR-2 (Along Track Scanning Radiometer) on ERS-2 (European Remote Sensing Satellite). The temperatures from the different instruments have been derived with the same algorithm and harmonised to insure consistency for the period 1995-2016. The GloboLakes LSWT v4.0 was produced by the University of Reading in 2018 for long term observations of surface water temperature for about 1000 lakes globally. The dataset consist of two sets of files: 1) a single file per day on a 0.05° regular latitude- longitude grid covering the period from June 1995 to December 2016 (folder = daily), 2) a file per lake which contains the time series (daily) of the lake on a 0.05° regular grid (folder = per-lake). The list of the GloboLakes lakes is included as a CSV file and it contains name, GLWD identifier, coordinate of the lake centre and a set of coordinates that can be used to locate the lake in the daily-file dataset. The LSWTs consists of the daily observations of the temperature of the water (skin temperature). Uncertainty estimates and quality levels are provided for each value.

  • The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project Fundamental Climate Data Record of recalibrated brightness temperatures for the High-resolution Infrared Radiation Sounder (HIRS) contains recalibrated brightness temperatures for HIRS for all editions of HIRS/2, HIRS/2I, HIRS/3, and HIRS/4, with metrologically traceable uncertainty estimates. This version is harmonised and anchored to infrared atmospheric sounding interferometer IASI via MetopA satellite. It contains 40 years worth of data covering the period period 1985-03-10 to 2016-12-31. Each file contains: Basic telemetry: longitude, latitude, time, satellite and solar angles; Brightness temperatures for channels1--19; Independent and structured uncertainty for channels 1--19; A lookup table to convert between radiances and brightness temperatures for channels 1—19; A channel error correlation matrix; Two bitfields indicating identified problems with the data. For any data field that varies across the channels (such as brightness temperatures and their uncertainties). Full documentation including product user guide, tutorials, the scientific basis and relevant publications are available in the documentation.

  • This Fundamental Climate Data Record (FDCR) of recalibrated brightness temperatures for the Advanced Very-High-Resolution Radiometer (AVHRR) AVHRR/1, AVHRR/2 andAVHRR/3 with metrologically-traceable uncertainty estimates. Error covariance information is also provided.In this data set , in addition to relative reflectance for channels 1, 2 and 3A( when available) together with estimated independent, common and structured uncertainties are also provided. The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project AVHRR FCDR improves on existing AVHRR level-1B data (such as that processed by NOAA or EUMETSAT): the calibration has been improved with a measurement function approach such that the data is of better quality (noise has been reduced, outliers have been filtered) the metrologically traceable uncertainties have been derived together with their associated effects cross-channel correlations and long-term correlation structures have now been calculated from the processed data and are being understood and used to improve data quality and consistency all the sensors are calibrated to a common reference (AATSR series).The products have been harmonised across the satellite series using Simultaneous (Nadir) Overpasses (SNOs). Full documentation including product user guide, tutorials, the scientific basis and relevant publications are available in the documentation.

  • This Fundamental Climate Data Record (FDCR) ensemble product contains both recalibrated AVHRR/3 MetOp-A Radiance/Brightness Temperature data with associated metrologically traceable uncertainties in the FIDUCEO FCDR format. It also contains files containing an Ensemble dataset consisting of perturbations to the associated FIDUCEO FCDR radiances and brightness temperatures. By applying the 10 perturbations to the baseline FCDR radiances and brightness temperatures a user is able to generate 10 sets of new measurements whose variance capture the associated underlying uncertainty distributions contained in the Easy FCDR itself. The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project AVHRR FCDR improves on existing AVHRR level-1B: in the infrared the calibration has been improved with a measurement function approach such that the data is of better quality (noise has been reduced, outliers have been filtered) the metrologically traceable uncertainties have been derived together with their associated effects, cross-channel correlations and long-term correlation structures have now been calculated from the processed data and are being understood and used to improve data quality and consistency. For the Ensemble product the sensors have been calibrated against the Advanced Along-Track Scanning Radiometer (AATSR) sensor with additional corrections to calibration parameters which make the data better able to derive sea surface temperature estimates that are consistent with theInternational Comprehensive Ocean-Atmosphere Data Set (ICOADS) drifting buoy network. Because the Ensemble has been tuned for Sea Surface Temperature retrieval it should only be used over ocean scenes.