subject to appropriate attribution.

Weather Source offers the full European Centre for Medium-Range Weather Forecasts (ECMWF) suite which is known as the best forecast model in the world. The products include (i) historical data back to 2000; (ii) short/mid-range forecast (i.e., up to 360-hour or 15 days); (iii) sub-seasonal forecast out to 46 days (iv) and a seasonal forecast in monthly format out to 7 months. We also offer historical forecasts in pristine format.

In addition, we also have the raw and statistically analyzed ensembles and we summarize the ensemble members by deciles and quartiles which are incredibly valuable to understand the potential of forecast variance (i.e., are the ensemble members tightly wound around the forecast mean which tells me the skill score of the forecast is very high or do they expose a bi-modal distribution which indicates I should plan for possible variance in the forecast.).

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

Single prediction that uses

observations
prior information about the Earth-system
ECMWF's highest-resolution model

HRES Direct model output Products offers "High Frequency products"

4 forecast runs per day (00/06/12/18) (see dissemination schedule for details)
Hourly steps to step 90 for all four runs.

Not all post-processed Products are available at 06/18 runs or in hourly steps.

This dataset contains model data for SNAPSI experiment 'nudged-full' produced by scientists at ECMWF (European Centre for Medium-Range Weather Forecasts, United Kingdom). This dataset contains all ensemble members by the ECMWF IFS model.

The SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.

The nudged-full experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, stratospheric temperatures and horizontal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.

Sources of additional information

The following web links are provided in the Details/Docs section of this catalogue record: - Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts - New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) - ECMWF IFS model reference publication

European Centre for Medium-Range Weather Forecasts (ECMWF)-IFS-MR model output prepared for Climate Model Intercomparison Project 6 (CMIP6) _NCProperties=version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.17 activity_id=HighResMIP branch_method=Initialized directly from parent restart files branch_time_in_child=0.0 branch_time_in_parent=18262.0 cdm_data_type=Grid cmor_version=3.2.4 contact=chris.roberts@ecmwf.int Conventions=CF-1.7 CMIP-6.0, COARDS, ACDD-1.3 creation_date=2018-11-13T20:24:26Z data_specs_version=01.00.23 Easternmost_Easting=359.0 experiment=coupled historical 1950-2014 experiment_id=hist-1950 external_variables=areacella forcing_index=1 frequency=mon further_info_url=https://furtherinfo.es-doc.org/CMIP6.ECMWF.ECMWF-IFS-MR.hist-1950.none.r1i1p1f1 geospatial_lat_max=90.0 geospatial_lat_min=-90.0 geospatial_lat_resolution=1.0 geospatial_lat_units=degrees_north geospatial_lon_max=359.0 geospatial_lon_min=0.0 geospatial_lon_resolution=1.0 geospatial_lon_units=degrees_east grid=Data interpolated onto 1.0x1.0 regular grid from native Tco199 cubic octahedral reduced Gaussian grid; 91 levels; top level 0.01 hPa grid_label=gr history=2018-11-13T20:24:26Z CMOR rewrote data to be consistent with CF standards and CMIP6 requirements.; 2019-12-23T17:32:04Z ChildBranchTimeDoubleFix, DataSpecsVersionAdd, EcmwfInstitution, EcmwfReferences, FurtherInfoUrlToHttps, ParentBranchTimeDoubleFix infoUrl=??? initialization_index=1 institution=European Centre for Medium-Range Weather Forecasts, Reading RG2 9AX, UK institution_id=ECMWF keywords_vocabulary=GCMD Science Keywords member_id=r1i1p1f1 min_number_yrs_per_sim=65 mip_era=CMIP6 nominal_resolution=50 km Northernmost_Northing=90.0 parent_activity_id=HighResMIP parent_experiment_id=spinup-1950 parent_mip_era=CMIP6 parent_source_id=ECMWF-IFS-MR parent_time_units=days since 1950-01-01 00:00:00 parent_variant_label=r1i1p1f1 physics_index=1 product=model-output realization_index=1 realm=atmos references=Roberts, C. D., Senan, R., Molteni, F., Boussetta, S., Mayer, M., and Keeley, S. P. E.: Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP, Geosci. Model Dev., 11, 3681-3712, https://doi.org/10.5194/gmd-11-3681-2018, 2018. source=ECMWF-IFS-MR (2017): aerosol: none atmos: IFS (IFS CY43R1, Tco199, cubic octahedral reduced Gaussian grid equivalent to 800 x 400 longitude/latitude; 91 levels; top level 0.01 hPa) atmosChem: none land: HTESSEL (as implemented in IFS CY43R1) landIce: none ocean: NEMO3.4 (NEMO v3.4; ORCA025 tripolar grid; 1442 x 1021 longitude/latitude; 75 levels; top grid cell 0-1 m) ocnBgchem: none seaIce: LIM2 (LIM v2; ORCA025 tripolar grid; 1442 x 1021 longitude/latitude) source_id=ECMWF-IFS-MR source_type=AOGCM sourceUrl=(local files) Southernmost_Northing=-90.0 standard_name_vocabulary=CF Standard Name Table v70 table_id=Amon table_info=Creation Date:(12 July 2017) MD5:6f543be94b857ea44f7fb1086ef08b79 tier=2 time_coverage_end=2014-12-16T12:00:00Z time_coverage_start=1950-01-16T12:00:00Z tracking_id=hdl:21.14100/0c6ba9ab-c159-4a6c-95d2-54928a809eb9 variable_id=clt variant_info=Coupled historical integration with external forcings for 1950-2014. Ensemble spread is generated by stochastic physics. variant_label=r1i1p1f1 Westernmost_Easting=0.0

This dataset contains model data for SNAPSI experiment 'free' produced by scientists at ECMWF (European Centre for Medium-Range Weather Forecasts, United Kingdom). This dataset contains all ensemble members by the ECMWF IFS model.

The SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.

The free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.

Sources of additional information

The following web links are provided in the Details/Docs section of this catalogue record: - Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts - New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) - ECMWF IFS model reference publication

Weather Source offers European Centre for Medium-Range Weather Forecast (ECMWF) including the ECMWF Long-Range forecast back to January 1, 2014.

Weather Source is known worldwide for its industry leading data and novel weather solutions. Our data products and solutions are the most reliable on the market and provide businesses with properly collocated and actionable data enabling them to identify and quantify the impact of weather on any KPI at their locations of interest. Our curated continuum of weather data was built for analytics and machine learning. Weather Source data is delivered via its high-resolution OnPoint grid, which ensures your location of interest is never more than 2.2 miles away from a grid point.

Utilize Weather Source weather and climate data to reveal meaningful observations for a variety of industries. By leveraging hyper-local weather & climate information, businesses are able to create business intelligence and models around sales and footfall traffic forecasts, advertising and marketing, logistics and supply chain, inventory, staffing, management, and more.

ECMWF is the European Centre for Medium-Range Weather Forecasts. Both a research institute and a 24/7 operational service, producing global numerical weather predictions and other data for our Member and Co-operating States and the broader community. The Centre has one of the largest supercomputer facilities and meteorological data archives in the world. Other strategic activities include delivering advanced training and assisting the WMO in implementing its programmes. A key player in Copernicus, the Earth Observation component of the European Union’s Space programme, offering quality-assured information on climate change (Copernicus Climate Change Service), atmospheric composition (Copernicus Atmosphere Monitoring Service), flooding and fire danger (Copernicus Emergency Management Service), and through the EU's Destination Earth initiative, we are developing prototype digital twins of the Earth. The organisation was established in 1975 and now employs around 450 staff from more than 35 countries. ECMWF is one of the six members of the Co-ordinated Organisations, which also include the North Atlantic Treaty Organisation (NATO), the Council of Europe (CoE), the European Space Agency (ESA), the Organisation for Economic Co-operation and Development (OECD), and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). This page contains information how to access data of the ECMWF.

This monthly ozone climatology dataset was produced from a combination of satellite and ozonesonde data from 1966 to 1993. It was produced following the method described by Fortuin and Langematz (1994) and is used by the radiation code within the ECMWF IFS to generate the output from from ERA-Interim reanalysis model runs (see Dee et al. 2011 for further details). The ozone values are given in ppmv on 36 vertical pressure levels and 19 latitudinal bands. These are then internally interpolated to the grid used by the model before entering the radiation scheme. The 19 latitudinal bands the data are present are at: -90, -80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 degrees. The climatology was originally produced by Fortuin and Langematz (1994) in response to the many GCMs at that time that used a prescribed ozone field. The aim was to generate a reference field with the required spatial and temporal coverage for use within modelling studies.

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

A meso-scale ensemble system focusing on short range probabilistic forecasts and profiting from advanced multi-scale ALARO physics. Its main purpose is to provide probabilistic forecast on daily basis for the national weather services of RC LACE partners. It also serves as a reliable source of probabilistic information applied to downstream hydrology and energy industry.

This dataset is used to showcase in Jupyter notebooks the usage of the Pythie postprocessing software available on GitHub.

We use the ERA5 reanalysis over a large area in Europe from 1997 to 2016 as gridded observations. These reanalysis have been downloaded from the Copernicus Data Store in GRIB format and converted to the NetCDF file format.

The reforecasts files have been download from ECMWF and converted to NetCDF files.

The observation data of the WMO-compliant DWD meteorological station of Soltau from 1997 to 2016. The station is located at the point 52°57'37.5"N, 9°47'35.0"E. The data have been downloaded from the DWD Climate Data Center.

Gridded reforecast data source

Source www.ecmwf.int

Creative Commons Attribution 4.0 International (CC BY 4.0) Copyright © 2021 European Centre for Medium-Range Weather Forecasts (ECMWF).

Copernicus ERA5 gridded reanalysis data source

Source https://cds.climate.copernicus.eu/

Copyright © 2021 European Union.

Generated using Copernicus Climate Change Service information 2021.

Hersbach et al. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 21-04-2021 >), doi:10.24381/cds.adbb2d47.

Observation data source

Source: Deutscher Wetterdienst, DWD CDC portal

This entry covers single-level data post-processed for bias adjustment on a monthly time resolution. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The variables available in this data set are listed in the table below. The data includes forecasts created in real-time each month starting from the publication of this entry.

The Sub-seasonal to Seasonal (S2S) prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community. The Documentation section provided with this entry is a crucial resource for understanding this dataset, it provides all the necessary information, including data sources, model configurations, and limitations. Such information is vital in avoiding misuses of the data, which can result in lost time and efficiency, and prevent the production of harmful content. By fully understanding the dataset, users can utilize the data to its full potential and make informed decisions.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per month. Unit: mm month-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

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

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

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

Resource Contact: ECMWF Support Portal

Data lineage:

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

Resource constraints:

License Permission This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing. Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: • 'Generated using Copernicus Climate Change Service information [Year]' and/or • 'Generated using Copernicus Atmosphere Monitoring Service information [Year]'

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

Online resources:

If you're looking for weather datasets, there are several reputable sources where you can access comprehensive weather data for various applications, including research, machine learning, and more. Here are some popular options:

1. National Centers for Environmental Information (NCEI):

   • The NCEI, part of NOAA, offers a wide range of climate and weather data. You can find historical weather data, global climate data, and more.
   • NCEI Weather Data

2. OpenWeatherMap:

   • Provides current weather data, forecasts, and historical data. They offer free and paid plans depending on the level of access and detail needed.
   • OpenWeatherMap API

3. Weather Underground:

   • Offers a rich set of weather data including current conditions, forecasts, and historical weather data.
   • Weather Underground API

4. European Centre for Medium-Range Weather Forecasts (ECMWF):

   • ECMWF provides datasets including ERA-Interim, ERA5, and seasonal forecasts. They focus on global weather and climate data.
   • ECMWF Data

5. The Weather Company (IBM):

   • Offers a range of weather data services, including historical weather data, forecasts, and more through their APIs.
   • The Weather Company API

6. NASA Earth Observing System Data and Information System (EOSDIS):

   • Provides access to a vast array of global climate data, satellite imagery, and other environmental data.
   • NASA EOSDIS

7. Global Surface Summary of the Day (GSOD):

   • A dataset that includes daily weather summaries from global stations, available through the National Centers for Environmental Information.
   • GSOD Data

8. Climate Data Online (CDO):

   • Another resource from NOAA, offering access to a variety of climate data, including daily and monthly summaries, storm data, and more.
   • NOAA CDO

9. Meteostat:

   • Provides free access to historical weather and climate data, focusing on quality-controlled and easy-to-use datasets.
   • Meteostat

The Sub-seasonal to Seasonal (S2S) prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community. The Documentation section provided with this entry is a crucial resource for understanding this dataset, it provides all the necessary information, including data sources, model configurations, and limitations. Such information is vital in avoiding misuses of the data, which can result in lost time and efficiency, and prevent the production of harmful content. By fully understanding the dataset, users can utilize the data to its full potential and make informed decisions.

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

ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".

Content:

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Storm Friederike:

ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format.

ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format.

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Storm Vladiana:

ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format.

========================================================================================

Storm Egon:

ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.