{"collections":[{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1","title":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ExtractLocationValues.ipynb","title":"DT Tutorial: Is it going to rain in the next 3 weekends?","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Land","generation:1","activity:ScenarioMIP","Decision Making","High Performance Computing","Sea Ice","Earth","Europe","model:IFS-NEMO","Snow","Soil","type:fc","Meteorology","stream:clte","dataset:climate-dt","Digital Twins","expver:0001","Climate Change","class:d1","Atmosphere","resolution:standard,high","Ocean","realization:1","experiment:SSP3-7.0"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2025-11-06T17:27:26Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(hl)":{"attrs":{"levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(hl)":{"attrs":{"levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(sol)":{"attrs":{"levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_X-component_of_sea_ice_velocity(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean X-component of sea ice velocity","parameter_ID":"263021","product_type":"forecast","shortName":"avg_six","standard_name":"Time-mean_X-component_of_sea_ice_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263021"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_Y-component_of_sea_ice_velocity(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean Y-component of sea ice velocity","parameter_ID":"263022","product_type":"forecast","shortName":"avg_siy","standard_name":"Time-mean_Y-component_of_sea_ice_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263022"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(o3d)":{"attrs":{"levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(o3d)":{"attrs":{"levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(o3d)":{"attrs":{"levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(o3d)":{"attrs":{"levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(o3d)":{"attrs":{"levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(o2d)":{"attrs":{"levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_long-wave_(thermal)_radiation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(sfc)":{"attrs":{"levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(pl)":{"attrs":{"levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1","title":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Land","activity:projections","Decision Making","stream:clmn,clte","High Performance Computing","Sea Ice","Earth","levtype:hl,o2d,o3d,pl,sfc,sol","Europe","model:IFS-NEMO","Snow","Soil","type:fc","Meteorology","dataset:climate-dt","Digital Twins","generation:2","expver:0001","Climate Change","class:d1","Atmosphere","resolution:standard,high","Ocean","realization:1","experiment:SSP3-7.0"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-17T13:10:40Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area 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flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model."}],"links":[{"rel":"root","type":"application/json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/","title":"DEDL HDA STAC API"},{"rel":"self","type":"application/json","href":"https://hda-staging.lumi.data.destination-earth.eu/stac/v2/collections?q=%22Climate+Change+Adaptation+Digital+Twin%22%2C%22Future+Projection%22%2C%22+IFS-NEMO%22","title":"Current Page"}]}