Abstract: This data shows the model nodes, indicating water level only and/or flow and water levels along the centre-line of rivers that have been modelled to generate the CFRAM flood maps. The nodes estimate maximum design event flood flows and maximum flood levels. Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time. The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed, expressed in terms of Annual Exceedance Probability (AEP), and identifies their parallels under other forms of expression: 10% (High Probability) Annual Exceedance Probability which can also be expressed as the 10 Year Return Period and as a 10:1 odds of occurrence in any given year. 1% (Medium Probability - Fluvail/River Flood Maps) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.5% (Medium Probability - Coastal Flood Maps) Annual Exceedance Probability which can also be expressed as the 200 Year Return Period and as 200:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year. The Mid-Range Future Scenario extents where generated taking in in the potential effects of climate change using an increase in rainfall of 20% and sea level rise of 500mm (20 inches). Data has been produced for the 'Areas of Further Assessment' (AFAs), as required by the EU 'Floods' Directive [2007/60/EC] and designated under the Preliminary Flood Risk Assessment, and also for other reaches between the AFAs and down to the sea that are referred to as 'Medium Priority Watercourses' (MPWs). River reaches that have been modelled are indicated by the CFRAM Modelled River Centrelines dataset. Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources). The purpose of the Flood Maps is not to designate individual properties at risk of flooding. They are community-based maps. Lineage: Fluvial and coastal flood map data is developed using hydrodynamic modelling, based on calculated design river flows and extreme sea levels, surveyed channel cross-sections, in-bank / bank-side / coastal structures, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods for model calibration, etc.). The process may vary for particular areas or maps. Technical Hydrology and Hydraulics Reports set out full technical details on the derivation of the flood maps. For fluvial flood levels, calibration and verification of the models make use of the best available data, including hydrometric records, photographs, videos, press articles and anecdotal information. Subject to the availability of suitable calibration data, models are verified in so far as possible to target vertical water level accuracies of approximately +/-0.2m for areas within the AFAs, and approximately +/-0.4m along the MPWs. For coastal flood levels, the accuracy of the predicted annual exceedance probability (AEP) of combined tide and surge levels depends on the accuracy of the various components used in deriving these levels i.e. accuracy of the tidal and surge model, the accuracy of the statistical data and the accuracy for the conversion from marine datum to land levelling datum. The output of the water level modelling, combined with the extreme value analysis undertaken as detailed above is generally within +/-0.2m for confidence limits of 95% at the 0.1% AEP. Higher probability (lower return period) events are expected to have tighter confidence limits. Purpose: The data has been developed to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods.

CFRAM Coastal Flood Extents - Mid-Range Future Scenario. Published by Office of Public Works. Available under the license Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (cc-by-nc-nd).Abstract: This data shows the modelled extent of land that might be flooded by the sea (coastal flooding) during a theoretical or ‘design’ flood event with an estimated probability of occurrence, rather than information for actual floods that have occurred in the past. The extents have been developed taking account of effective flood defences.

Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time.

The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed, expressed in terms of Annual Exceedance Probability (AEP), and identifies their parallels under other forms of expression: 10% (High Probability) Annual Exceedance Probability which can also be expressed as the 10 Year Return Period and as a 10:1 odds of occurrence in any given year. 1% (Medium Probability - Fluvial/River Flood Maps) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.5% (Medium Probability - Coastal Flood Maps) Annual Exceedance Probability which can also be expressed as the 200 Year Return Period and as 200:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year.

The Mid-Range Future Scenario extents where generated taking in in the potential effects of climate change using an increase in rainfall of 20% and sea level rise of 500mm (20 inches).

Data has been produced for the 'Areas of Further Assessment' (AFAs), as required by the EU 'Floods' Directive [2007/60/EC] and designated under the Preliminary Flood Risk Assessment, and also for other reaches between the AFAs and down to the sea that are referred to as 'Medium Priority Watercourses' (MPWs). River reaches that have been modelled are indicated by the CFRAM Modelled River Centrelines dataset.

Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources).

The purpose of the Flood Maps is not to designate individual properties at risk of flooding. They are community-based maps.

Lineage: Fluvial and coastal flood map data is developed using hydrodynamic modelling, based on calculated design river flows and extreme sea levels, surveyed channel cross-sections, in-bank / bank-side / coastal structures, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods for model calibration, etc.).

The process may vary for particular areas or maps. Technical Hydrology and Hydraulics Reports set out full technical details on the derivation of the flood maps.

For coastal flood levels, the accuracy of the predicted annual exceedance probability (AEP) of combined tide and surge levels depends on the accuracy of the various components used in deriving these levels i.e. accuracy of the tidal and surge model, the accuracy of the statistical data and the accuracy for the conversion from marine datum to land levelling datum.

The output of the water level modelling, combined with the extreme value analysis undertaken as detailed above is generally within +/-0.2m for confidence limits of 95% at the 0.1% AEP. Higher probability (lower return period) events are expected to have tighter confidence limits.

Flood levels, depths and velocities are derived from the hydrodynamic models for the various event probabilities and scenarios. Flood extents are derived from the raster flood depth maps and vectorised to produce the final vector outputs.

Purpose: The data has been developed to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods....

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.

CFRAM River Flood Extents - Mid-Range Future Scenario. Published by Office of Public Works. Available under the license Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (cc-by-nc-nd).Abstract: This data shows the modelled extent of land that might be flooded by rivers (fluvial flooding) during a theoretical or ‘design’ flood event with an estimated probability of occurrence, rather than information for actual floods that have occurred in the past. The extents have been developed taking account of effective flood defences.

Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time.

The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed, expressed in terms of Annual Exceedance Probability (AEP), and identifies their parallels under other forms of expression: 10% (High Probability) Annual Exceedance Probability which can also be expressed as the 10 Year Return Period and as a 10:1 odds of occurrence in any given year. 1% (Medium Probability - Fluvial/River Flood Maps) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.5% (Medium Probability - Coastal Flood Maps) Annual Exceedance Probability which can also be expressed as the 200 Year Return Period and as 200:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year.

The Mid-Range Future Scenario extents where generated taking in in the potential effects of climate change using an increase in rainfall of 20% and sea level rise of 500mm (20 inches).

Data has been produced for the 'Areas of Further Assessment' (AFAs), as required by the EU 'Floods' Directive [2007/60/EC] and designated under the Preliminary Flood Risk Assessment, and also for other reaches between the AFAs and down to the sea that are referred to as 'Medium Priority Watercourses' (MPWs). River reaches that have been modelled are indicated by the CFRAM Modelled River Centrelines dataset.

Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources).

The purpose of the Flood Maps is not to designate individual properties at risk of flooding. They are community-based maps.

Lineage: Fluvial and coastal flood map data is developed using hydrodynamic modelling, based on calculated design river flows and extreme sea levels, surveyed channel cross-sections, in-bank / bank-side / coastal structures, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods for model calibration, etc.).

The process may vary for particular areas or maps. Technical Hydrology and Hydraulics Reports set out full technical details on the derivation of the flood maps.

For fluvial flood levels, calibration and verification of the models make use of the best available data, including hydrometric records, photographs, videos, press articles and anecdotal information. Subject to the availability of suitable calibration data, models are verified in so far as possible to target vertical water level accuracies of approximately +/-0.2m for areas within the AFAs, and approximately +/-0.4m along the MPWs.

All fluvial models are run, and maps produced, assuming clear flow through culverts and bridges, and the models and flood maps do not account for blockage of such structures.

Flood levels, depths and velocities are derived from the hydrodynamic models for the various event probabilities and scenarios. Flood extents are derived from the raster flood depth maps and vectorised to produce the final vector outputs.

Purpose: The data has been developed to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods....

This dataset contains annotations for 5000 music files on the following music properties:

Melodiousness Articulation Rhythmic stability Rhythmic complexity Dissonance Tonal stability Modality

The annotations were given by musicians and collected through a crowd-sourcing platform (Toloka).

The Maternity Indicators Data Set captures data relating to the woman at initial assessment and to mother and baby (or babies) for all births. This relates to initial assessment and birth activity undertaken in Wales only. Each Health Board must make available data in relation to the initial assessments and/or birth events which they managed.

For example, if they only carried out the initial assessment the Health Board would only be required to provide the initial assessment data. This is further detailed in the technical specification (see ‘return submission details’).

Where the initial assessment and birth events take place in different Health Boards, data will be linked nationally by the NHS Wales Informatics Service.

Velindre NHS Trust are excluded from this requirement, as they do not provide any maternity services.

Monthly activity data must include only initial assessment and birth activity that took place in the previous month.

This dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.

subject to appropriate attribution.

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. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

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 144 for all four runs

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

three of these models are available:

The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires. It is based on the Fire Weather Index but more accurately reflects the expected efforts required for fire suppression.

This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.

Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).

This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).

Details:

• File format: netcdf4
• Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).
• Longitude range: [-180, +180]
• Latitude range: [-90, +90]
• Temporal resolution: 1 day
• Spatial resolution: 0.7 degrees (~80 Km)
• Spatial coverage: Global
• Time span: from 1980-01-01 to 2018-12-31

developed by C3S at ECMWF

The ECMWF (European Center for Medium range Weather Forecasts) ERA-40 model uses a variational data assimilation system to make a new synthesis of the in-situ and remotely-sensed measurements made worldwide over the period since mid-1957. It produces the basic analysed conventional meteorological wind, temperature and humidity variables as well as stratospheric ozone, ocean-wave and soil conditions. The CSIRO archive contains monthly data for variables concatenated into a single contiguous netcdf file for the time period 1957-2002 with a spatial resolution of 2.5° x 2.5° and has also been further processed to include calculated Anomaly, Climatology and Seasonal forms of this dataset. Trend analysis data is available for specific variables.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

06

Full metadata for this service can be found at http://gisportal.msl.mt.gov/geoportal/catalog/search/resource/details.page?uuid=%7B9285b4c0-db9e-424d-8639-c138b8a27aed%7D .

16 potential temperature levels

06

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States. The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page. For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below. No filters have been applied to the raw WRF output.

including aerosols