The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied. The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global 90m (GLO-90) DSM through the public AWS S3 bucket established by Sinergise. Note: In the original datasets, the longitudinal spacing of cells increases as a function of latitude for latitudes north of 50N and south of 50S. See the original documentation for details. In order to keep the pixel dimensions uniform, OpenTopography interpolates data north of 50 degrees latitude and south of -50 degrees latitude in order to output a consistent 90m product. GLO-90 is available on a free basis for the general public under the terms and conditions of the License found here

The Copernicus DEM is a Digital Surface Model (DSM) which represents the bare-Earth surface and all above ground natural and built features. It is based on WorldDEM™ DSM that is derived from TanDEM-X and is infilled on a local basis with the following DEMs: ASTER, SRTM90, SRTM30, SRTM30plus, GMTED2010, TerraSAR-X Radargrammetric DEM, ALOS World 3D-30m. Copernicus Programme provides Copernicus DEM in 3 different instances: COP-DEM EEA-10, COP-DEM GLO-30 and COP-DEM GLO-90 where "COP-DEM GLO-90" tiles and most of the "COP-DEM GLO-30 " tiles are available worldwide with free license. Sentinel Hub provides two instances named COPERNICUS_90 which uses "COP-DEM GLO-90" and COPERNICUS_30 which uses "COP-DEM GLO-30 Public" and "COP-DEM GLO-90" in areas where "COP-DEM GLO-30 Public" tiles are not yet released to the public by Copernicus Programme. Copernicus DEM provides elevation data and can also be used for the orthorectification of satellite imagery (e.g Sentinel 1).

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied.

The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global 30m (GLO-30) DSM through the public AWS S3 bucket established by Sinergise.

• GLO-30 coverage is not entirely global because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. See the AWS landing page for details on which countries are excluded.
• In the original datasets (available via OpenTopography bulk download or the Sinergise aws bucket), the longitudinal spacing of cells increases as a function of latitude for latitudes north of 50N and south of 50S. See the original documentation for details. However, in order to keep the pixel dimensions uniform, OpenTopography resamples data north of 50 degrees latitude and south of -50 degrees latitude in order to output a consistent 30m or 90m product for data accessed through the web-interface or API.
• GLO-30 is available on a free basis for the general public under the terms and conditions of the License found here

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions. The selected data records in this data set are regularly extended with time as ERA5 forcing data become available. This dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service.

The Sentinel-2 satellites provide images in the visible and infrared spectrum. Its 13 channels are optimised for observation of land surfaces. The high resolution of up to 10 m and the sampling width of 290 km are ideal for detecting changes in vegetation and, for example, creating harvest forecasts, mapping forest stocks or determining the growth of wild and crops. The instrument is also used on coasts and inland waters to observe algae growth or to track sediment input in river deltas. All data is accessible free of charge. On the basis of the data base of the Sentinel-2 satellite Digital Orthophoto (DOP) mosaics of Mecklenburg Vorpommern, the Office for Geoinformation, Surveying and Catastrophic Engineering Mecklenburg Vorpommern creates mosaics. Depending on the data situation, a mosaic is sought for each month. If necessary, pictures of the previous month are also used. These mosaics are offered as RGB and CIR images. This download service provides this Sentinel2 satellite image mosaic with a soil resolution of 10 m via an atomic feed. The following monthly mosaics are assigned to the numbers for download: 1 — mosaik_2018-02-08_2018-02-16 2 — mosaik_2018-03-18_2018-03-18 3 — mosaik_2018-04-02_2018-04-02 4 — mosaik_2018-04-09_2018-04-22 5 — mosaik_2018-05-07_2018-05-07 6 — mosaik_2018-06-06_2018-06-06 7 — mosaik_2018-07-04_2018-07-31 8 — mosaik_2018-08-03_2018-08-23 9 — mosaik_2018-09-06_2018-09-19 10 — mosaik_2018-10-14_2018-10-10-14 11 — mosaik_2018-11-16_2018-11-28 12 — mosaik_2019-02-14_2019-02-02-24 13 — mosaik_2019-04-02_2019-04-02 14 — mosaik_2019-06-26_2019-06-29 15 — mosaik_2019-07-26_2019-07-26 16 — mosaik_2019-08-20_2019-09-22 17 — mosaik_2019-12-10_2020-01-17 18 — mosaik_2020-04-04_2020-04-24 19 — mosaik_2020-05-03_2020-05-31 20 — mosaik_2020-06-02_2020-06-27 21 — mosaik_2020-09-18_2020-09-23 22 — mosaik_2021-02-25_2021-02-02-25 23 — mosaik_2021-03-05_2021-03-30 24 — mosaik_2021-05-01_2021-05-31 25 — mosaik_2021-06-05_2021-06-18 26 — mosaik_2021-09-01_2021-09-10 27 — mosaik_2021-10-06_2021-10-13 28 — mosaik_2021-11-22_2021-11-22 29 — mosaik_2022-01-06_2022-01-06 30 — mosaik_2022-03-07_2022-03-07 31 — mosaik_2022-04-08_2022-04-28

This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.

This dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.0 extends from 2017-2021.

A historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalgoue and through the ESA CCI Open Data Portal.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied.

The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global 90m (GLO-90) DSM through the public AWS S3 bucket established by Sinergise.

• In the original datasets (available via OpenTopography bulk download or the Sinergise aws bucket), the longitudinal spacing of cells increases as a function of latitude for latitudes north of 50N and south of 50S. See the original documentation for details. However, in order to keep the pixel dimensions uniform, OpenTopography resamples data north of 50 degrees latitude and south of -50 degrees latitude in order to output a consistent 30m or 90m product for data accessed through the web-interface or API.
• GLO-90 is available on a free basis for the general public under the terms and conditions of the License found here

This data collection contains two datasets over the region of Mumbai (India):

- Long-term statistics of NDVI computed over 1999-2019

- NDVI values for 2021 (10-days)

The original NDVI datasets can be downloaded from the Copernicus Global Land Service portal (registration is mandatory but free of charge).
Data can be downloaded as well directyl from Copernicus Open Access Hub but data are not based on atmospherically and BRDF corrected data.

This dataset provides gridded modelled daily hydrological time series forced with meteorological reanalysis data. The data set is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of key hydrological variables across the global domain including:

River discharge Soil wetness index (root zone) Snow water equivalent Runoff water equivalent (surface plus subsurface)

Also provided are two ancillary files for interpretation, one containing upstream area data and the other containing elevation data (see the table of related variables and the associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with ERA5 meteorological reanalysis data, interpolated to the GloFAS resolution, produced at a 24-hourly timestep. Two variations of the ERA5 forcing data are used, resulting in two types of hydrological data: intermediate and consolidated. Intermediate hydrological data is produced using ERA5 Near Real Time (ERA5T) data and is updated daily, whilst consolidated hydrological data is produced using the consolidated ERA5 reanalysis and is updated monthly. Companion datasets, also available through the EWDS, are forecasts for users who are looking for medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users specifically interested in European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All the GloFAS and EFAS datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre.

The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Dates of Images:7/8/21 - PresentDate of Next Image:Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass.Summary:The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites and surface water extent products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event.Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas).To identify the date of an image either set the time slider to a 1 day interval or zoom in to AOI, set time slider to desired range and click on the imagery. The name will contain a full date and time in a format like this example:S1A_IW_20210705T231453_DVR_RTC30_G_gpufed_27F4The date and time would be 07/05/2021 at 23:14:53 UTC.Additional SAR InformationSuggested Use:In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A/B satelliteNOTE: Sentinel-1B is no longer acquiring data and is only available into December 2021Resolution:30 metersCredits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2019-2022), processed by ESA, Alaska Satellite Facility.Esri REST Endpoint:RGB Product:https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_RGB/ImageServerRTC Product:https://asf.img.arcgis.com/arcgis/rest/services/ASF_RTC/ASF_S1_RTC/ImageServerSurface Water Extent Product:https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_WM/ImageServerData Download: Data can be downloaded via the Pop-Ups in the Web Map or through each service's attribute table.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

The Water Level is defined as the height, in meters above the geoid, of the reflecting surface of continental water bodies. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only water bodies located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the water body, but also on the reflecting targets in its surroundings such as topography or vegetation. Water Level is computed as time series: over lakes ; over rivers, at the intersections of the river network with the satellite ground tracks, so-called Virtual Stations. The Water Level of lakes is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS).

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

This dataset provides global ocean and sea-ice reanalysis (ORAS5: Ocean Reanalysis System 5) monthly mean data prepared by the European Centre for Medium-Range Weather Forecasts (ECMWF) OCEAN5 ocean analysis-reanalysis system. This system comprises 5 ensemble members from which one member is published in this catalogue entry. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset taking into account the laws of physics. The reanalysis provides information without temporal and spatial gaps, i.e. the data are continuous in time, and the assimilation system provides information on every model grid point independently of whether observations are available nearby or not. The OCEAN5 reanalysis system uses the Nucleus for European Modelling of the Ocean (NEMO) ocean model and the NEMOVAR ocean assimilation system. NEMOVAR uses the so-called 3D-Var FGAT (First Guess at Appropriate Time) assimilation technique, which assimilates sub-surface temperature, salinity, sea-ice concentration and sea-level anomalies. The ORAS5 data is forced by either global atmospheric reanalysis (for the consolidated product) or the ECMWF/IFS operational analysis (for the operational product) and is also constrained by observational data of sea surface temperature, sea surface salinity, sea-ice concentration, global-mean-sea-level trends and climatological variations of the ocean mass. The consolidated product (referred to as "Consolidated" in the download form) uses reanalysis atmospheric forcing (ERA-40 until 1978 and ERA-Interim from 1979 to 2014) and re-processed observations. The near real-time (referred to as "Operational" in the download form) ORAS5 product is available from 2015 onwards and is updated on a monthly basis 15 days behind real time. It uses ECMWF operational atmospheric forcing and near real time observations. The consolidated data benefits from atmospheric forcing consistency. The operational data benefits from near real-time latency. ORAS5 data are also available at the Copernicus Marine Environment Monitoring Service (CMEMS) and at the Integrated Climate Data Centre (ICDC), Hamburg University. The present dataset, at the time of publication, provides more variables than the others and has regular updates with near real-time data. For the period from 2015 to the present, the operational ORAS5 data provided in the CDS is different from the dataset provided by CMEMS, because different atmospheric forcings and ocean observation data were used in the generation of the two products. The ORAS5 dataset is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S).

OFFL/L3_O3 This dataset provides offline high-resolution imagery of total column ozone concentrations. See also COPERNICUS/S5P/OFFL/L3_O3_TCL for the tropospheric column data. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also …

OFFL/L3_CO This dataset provides offline high-resolution imagery of CO concentrations. Carbon monoxide (CO) is an important atmospheric trace gas for understanding tropospheric chemistry. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. Whereas fossil fuel combustion is the main source of CO at northern mid-latitudes, the oxidation of isoprene and biomass burning play an important role in the tropics. TROPOMI on the Sentinel 5 Precursor (S5P) satellite observes the CO global abundance exploiting clear-sky and cloudy-sky Earth radiance measurements in the 2.3 μm spectral range of the shortwave infrared (SWIR) part of the solar spectrum. TROPOMI clear sky observations provide CO total columns with sensitivity to the tropospheric boundary layer. For cloudy atmospheres, the column sensitivity changes according to the light path. More information. OFFL L3 Product To make our OFFL L3 products, we find areas within the product's bounding box with data using a command like this: harpconvert --format hdf5 --hdf5-compression 9 -a 'CO_column_number_density_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_CO_20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc grid_info.h5 We then merge all the data into one large mosaic (area-averaging values for pixels that may have different values for different times). From the mosaic, we create a set of tiles containing orthorectified raster data. Example harpconvert invocation for one tile: harpconvert --format hdf5 --hdf5-compression 9 -a 'CO_column_number_density_validity>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(CO_column_number_density,H2O_column_number_density,cloud_height, sensor_altitude,sensor_azimuth_angle, sensor_zenith_angle, solar_azimuth_angle,solar_zenith_angle)' S5P_OFFL_L2_CO_20181031T060643_20181031T074813_05432_01_010200_20181106T052542.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor is a satellite launched on 13 October 2017 by the European Space Agency to monitor air pollution. The onboard sensor is frequently referred to as Tropomi (TROPOspheric Monitoring Instrument). All of the S5P datasets, except CH4, have two versions: Near Real-Time (NRTI) and Offline (OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL assets, but appear more quickly after acquisition. The OFFL assets contain data from a single orbit (which, due to half the earth being dark, contains data only for a single hemisphere). Because of noise in the data, negative vertical column values are often observed in particular over clean regions or for low SO2 emissions. It is recommended not to filter these values except for outliers, i.e. for vertical columns lower than -0.001 mol/m^2. The original Sentinel 5P Level 2 (L2) data is binned by time, not by latitude/longitude. To make it possible to ingest the data into Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping a single grid per orbit (that is, no aggregation across products is performed). Source products spanning the antimeridian are ingested as two Earth Engine assets, with suffixes _1 and _2. The conversion to L3 is done by the harpconvert tool using the bin_spatial operation. The source data is filtered to remove pixels with QA values less than: 80% for AER_AI 75% for the tropospheric_NO2_column_number_density band of NO2 50% for all other datasets except for O3 and SO2 The O3_TCL product is ingested directly (without running harpconvert).

Maximum air temperature calculated at a height of 2 metres above the surface. Unit: K. The Maximum air temperature variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

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This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing.

Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice:

• Generated using Copernicus Climate Change Service information [Year]

and/or

• Generated using Copernicus Atmosphere Monitoring Service information [Year]

More information on Copernicus License in PDF version at: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Data download from original source