copernicus_atmosphere.json

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        "id": "cams-global-greenhouse-gas-forecasts",
        "title": "CAMS global greenhouse gas forecasts",
        "description": "CAMS produces global forecasts for the two main long-lived greenhouse gases once a day. This dataset consists of 5-day high-resolution forecasts of carbon dioxide (CO2) and methane (CH4). Additionally, carbon monoxide (CO) and meteorological parameters relevant to the CAMS greenhouse gas forecast are included.\nThe initial conditions of each forecast are obtained by combining a previous forecast with current satellite observations through a process called data assimilation. This best estimate of the state of the atmosphere at the initial forecast time step, called the analysis, provides a globally complete and consistent dataset allowing for estimates at locations where observation data coverage is low.\nThe analysis has a resolution of approximately 25km and it is produced 4 days behind real-time due to the current latency of satellite retrievals of CO2 and CH4. As this analysis of greenhouse gases is not available close to real time, it is not provided. Instead, the high-resolution forecast with a resolution of approximately 9km is run a few hours behind real time, with initial conditions based on a 4-day forecast of the analysis experiment. Additionally, because some meteorological fields in the forecast do not fall within the general CAMS data licence, they are only available with a delay of 5 days.\nThe forecast itself uses a model of the atmosphere based on the laws of physics and prescribed chemical loss rates or simplified chemistry to determine the evolution of the concentrations of all species over time for the next five days. Apart from the required initial state, it also uses inventory-based, observation-based and modelled emission estimates as a boundary condition at the surface.\nThe CAMS global forecasting system is upgraded about once a year resulting in technical and scientific changes. The horizontal or vertical resolution can change, new species can be added, and more generally the accuracy of the forecasts can be improved. Details of these system changes can be found in the documentation.\nUsers looking for a more consistent long term data set should consider using the CAMS greenhouse gas reanalysis instead, which is available through the Atmosphere Data Store (ADS) and spans the period from 2003 onwards, or the CAMS global inversion-optimised greenhouse gas fluxes and concentrations which provides a longer dataset that is suitable for trend analysis and also available through the ADS.",
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        "description": "This data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios  for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20).\nNatural and anthropogenic surface fluxes of greenhouse gases are key drivers of the evolution of Earth\u2019s climate, so their monitoring is essential. Such information has been used in particular as part of the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand. This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface. By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the surface fluxes for the last few decades. For CH4 and N2O, the flux inversions account also for the chemical loss of these greenhouse gases. The net fluxes include contributions from the natural biosphere (e.g., vegetation, wetlands) as well anthropogenic contributions (e.g., fossil fuel emissions, rice fields).\nThe data sets for the three species are updated once or three times per year adding the most recent year to the data record, while re-processing the original data record for consistency. This is reflected by the different version numbers. In addition, fluxes for methane are available based on surface air samples only or based on a combination of surface air samples and satellite observations (reflected by an 's' in the version number).",
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        "description": "EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting 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 to allow for the provision of a dataset spanning back more than a decade. 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.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nAlthough the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.",
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        "description": "This dataset provides annual air quality reanalyses for Europe based on both unvalidated and validated observations.\nCAMS produces annual air quality reanalyses for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global reanalyses. The production is currently based on an ensemble of eleven air quality data assimilation systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the eleven models can be used to provide an estimate of the analysis uncertainty. \nThe reanalysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. Additional sources of observations can complement the in-situ data assimilation, like satellite data. \nAn interim reanalysis is provided each year for the year before based on the unvalidated near-real-time observation data stream that has not undergone full quality control by the data providers yet. Once the fully quality-controlled observations are available from the data provider, typically with an additional delay of about 1 year, a final validated annual reanalysis is provided. Both reanalyses are available at hourly time steps at height levels.",
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        "title": "CAMS European air quality forecasts optimised at observation sites",
        "description": "This dataset provides daily air quality forecasts at European observation\nstations after optimisation using a statistical post-processing method called\nModel Output Statistics (MOS). The unoptimised \"raw\" forecasts are also\nprovided in the same format.\nThe MOS method uses machine learning with predictive variables including\nbackground air quality observation datasets, ECMWF meteorological forecasts and\nthe \"raw\" CAMS European air quality ensemble median forecast. The result is\ndaily time series forecasts at many hundreds of European observation stations\nand for four species: ozone, nitrogen dioxide, PM10 and PM2.5. Please note that\nnot all species are available at all stations.\nThe data files contain EIONET (European Environment Information and Observation\nNetwork) station identifiers but not the associated station metadata such as\nlatitude, longitude, altitude and environment type (urban, surburban,\nrural). These are provided in a separate file that can be optionally added to\nthe download. The file contains latitude and longitude in degrees north and east\nrespectively, and altitude in metres. Observation stations can occasionally\nchange position so a given identifier may have more than one row. In this case\neach row will have a unique position number (1, 2, 3, etc.) and contain the\nstart and end date of the period for which the station had this position.",
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        "description": "This dataset provides historical values of global, direct and diffuse solar\nirradiation, as well as direct normal irradiation, on a latitude/longitude grid\ncovering land surfaces and coastal areas of Europe, Africa, Oceania, Eastern\nSouth America, the Middle East and South-East Asia. It is created from 15 minute\nresolved timeseries at each grid point. These timeseries were calculated by the\nCAMS Solar Radiation Time Series Service and use information on aerosol, ozone\nand water vapour from the CAMS global forecasting system. Other properties, such\nas ground albedo and ground elevation, are also taken into account. Data is\nprovided for both clear-sky and observed cloud conditions. For cloudy conditions\nhigh-resolution cloud information is directly inferred from satellite\nobservations provided by the Meteosat Second Generation (MSG) and Himawari 8\nsatellites. It is the Himawari satellite that provides the Asian coverage, which\nis only available from 2016 and v4.6 (rev2) onwards.\nThe aim of the dataset is to fulfil the needs of European and national policy\ndevelopment and the requirements of both commercial and public downstream\nservices, e.g. for planning, monitoring, efficiency improvements and the\nintegration of solar energy systems into energy supply grids.\nData is offered in monthly netCDF formatted files.",
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            "Product type: Analysis",
            "Variable domain: Solar radiation",
            "Spatial coverage: Africa",
            "Spatial coverage: South America"
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