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

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.

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.

Note: a new time-series dataset from ERA5 has been published — this one won't be updated/maintained anymore

Country averages of meteorological variables generated using the R routines available in the package panas based on the Copernicus Climate Change ERA5 reanalyses. The time-series are at hourly resolution and the included variables are:

• 2-meter temperature (t2m),
• snow depth (snow_depth),
• mean sea-level pressure (mslp),
• runoff,
• surface solar radiation (ssrd),
• surface solar radiation with clear-sky (ssrdc),
• temperature at 850hPa (t850),
• total precipitation (total_prec),
• zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),
• meridional (north-sud) wind speed at 10m (v10) and 100m (v100),
• dew point temperature (dew)

The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.

The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview

The script used to generate the files is available on github here

ERA5-Land เป็นชุดข้อมูลการวิเคราะห์ใหม่ซึ่งให้มุมมองที่สอดคล้องกันเกี่ยวกับการเปลี่ยนแปลงของตัวแปรบนบกในช่วงหลายทศวรรษที่ผ่านมาโดยมีความละเอียดที่ดีขึ้นเมื่อเทียบกับ ERA5 ERA5-Land สร้างขึ้นโดยการเล่นซ้ำองค์ประกอบพื้นดินของการวิเคราะห์สภาพอากาศอีกครั้งของ ECMWF ERA5 การวิเคราะห์ใหม่จะรวมข้อมูลแบบโมเดลเข้ากับข้อมูลที่สังเกตได้ทั่วโลกให้เป็นชุดข้อมูลที่สมบูรณ์และสอดคล้องกันทั่วโลกโดยใช้กฎของฟิสิกส์ การวิเคราะห์ใหม่จะสร้างข้อมูลที่ย้อนหลังไปหลายทศวรรษ ซึ่งให้คำอธิบายสภาพภูมิอากาศในอดีตที่แม่นยำ ชุดข้อมูลนี้ประกอบด้วยตัวแปรทั้งหมด 50 รายการที่มีอยู่ใน CDS ข้อมูลที่แสดงที่นี่คือชุดข้อมูลย่อยของชุดข้อมูล ERA5-Land ทั้งหมดที่ ECMWF ประมวลผลในภายหลัง ระบบได้คํานวณค่าเฉลี่ยรายเดือนไว้ล่วงหน้าเพื่ออำนวยความสะดวกให้กับแอปพลิเคชันจํานวนมากที่จําเป็นต้องเข้าถึงข้อมูลได้อย่างรวดเร็วและง่ายดาย เมื่อไม่จําเป็นต้องใช้ช่องย่อยรายเดือน โปรดทราบว่ารูปแบบการสะสมที่ใช้ใน ERA5-Land แตกต่างจาก ERA5 ระบบจะจัดการการสะสมเช่นเดียวกับใน ERA-Interim หรือ ERA-Interim/Land กล่าวคือ ระบบจะสะสมตั้งแต่ช่วงเริ่มต้นของการคาดการณ์จนถึงช่วงสิ้นสุดของขั้นตอนการคาดการณ์ เหตุการณ์เช่นนี้จะเกิดขึ้นภายในทุกวันและรีเซ็ตตอนเที่ยงคืน ทีมข้อมูล Earth Engine ได้เพิ่มอีก 19 ย่านความถี่ โดยเพิ่ม 1 ย่านความถี่สำหรับแต่ละย่านความถี่ของปริมาณน้ำฝนสะสม โดยค่ารายชั่วโมงจะคํานวณจากส่วนต่างระหว่างขั้นตอนการคาดการณ์ 2 ขั้นตอนติดต่อกัน

ERA5-Land to zestaw danych z ponownym przeanalizowaniem, który zapewnia spójny obraz ewolucji zmiennych dotyczących lądu w ciągu kilku dekad w ulepszonej rozdzielczości w porównaniu z ERA5. Dane ERA5-Land zostały wygenerowane przez odtworzenie komponentu lądowego w ramach ponownej analizy klimatu ECMWF ERA5. Reanalysis łączy dane modelu z obserwacjami z całego świata…

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

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

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

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

Resource Contact: ECMWF Support Portal

Data lineage:

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

Resource constraints:

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:

Data download from original source

Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Unit: m s-1. The Wind Speed 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:

Data download from original source

ERA5-Land 是一种再分析数据集，与 ERA5 相比，其分辨率更高，可提供几十年来陆地变量演变的一贯视图。ERA5-Land 是通过重放ECMWF ERA5 气候再分析的陆地组件而生成的。重分析会将模型数据与来自世界各地的观测数据相结合，

ERA5-Land là một tập dữ liệu phân tích lại, cung cấp thông tin nhất quán về sự phát triển của các biến trên đất trong vài thập kỷ ở độ phân giải cao hơn so với ERA5. ERA5-Land được tạo bằng cách phát lại thành phần đất của quá trình phân tích lại khí hậu ERA5 của ECMWF. Phân tích lại kết hợp dữ liệu mô hình với dữ liệu quan sát từ khắp nơi trên thế giới thành một tập dữ liệu hoàn chỉnh và nhất quán trên toàn cầu bằng cách sử dụng các định luật vật lý. Phân tích lại tạo ra dữ liệu từ vài thập kỷ trước, cung cấp thông tin mô tả chính xác về khí hậu trong quá khứ. Tập dữ liệu này bao gồm tất cả 50 biến có trên CDS. Dữ liệu được trình bày ở đây là một tập hợp con của tập dữ liệu ERA5-Land đầy đủ do ECMWF xử lý sau. Giá trị trung bình hằng tháng đã được tính toán trước để tạo điều kiện cho nhiều ứng dụng yêu cầu truy cập dễ dàng và nhanh chóng vào dữ liệu, khi không cần các trường hằng tháng phụ. Xin lưu ý rằng quy ước tích luỹ dùng trong ERA5-Land khác với quy ước dùng trong ERA5. Các giá trị tích luỹ được xử lý giống như các giá trị trong ERA-Interim hoặc ERA-Interim/Land, tức là các giá trị này được tích luỹ từ đầu thời gian dự báo đến cuối bước dự báo. Quá trình này diễn ra trong mỗi ngày và được đặt lại vào lúc nửa đêm. Nhóm Dữ liệu Earth Engine đã thêm 19 dải bổ sung, một dải cho mỗi dải tích luỹ, với các giá trị hằng giờ được tính là chênh lệch giữa hai bước dự báo liên tiếp.

Yearly timeseries (.csv) of basic-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.

We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.

Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:

Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac

Acknowledgement also to:

DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac)

SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

Overview: 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. Surface temperature: 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. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the spatial resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Data available is the daily average, minimum and maximum of surface temperature. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

ERA5-Land は、ERA5 と比較して解像度が向上した数十年にわたる陸地変数の推移の一貫したビューを提供する再解析データセットです。ERA5-Land は、ECMWF ERA5 気候再解析の陸地コンポーネントを再生して作成されました。再解析では、モデルデータと世界中の観測データを組み合わせて、

Reference evapotranspiration per dekade with a spatial resolution of 0.1 degree. Unit: mm dekad-1. The dataset contains dekadal values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are 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: 2021-10-30

Contact points:

Metadata Contact: AQUASTAT

Resource Contact: AQUASTAT

Data lineage:

Copernicus 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:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

• The dataset contains modified Copernicus Climate Change Service information [1979-to date]; Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. 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:

Download Reference Evapotranspiration - AgERA5 derived (Dekadal - ~10km)

The presented database is a set of hydrological, meteorological, environmental and geometric values for Russia Federation for the period from 2008 to 2020.

Database consist of next items:

• Point geometry for hydrological observation stations from Roshydromet network across Russia
• Geometry of the catchment for correspond observation station point
• Daily hydrological values
  • Water level
    • In relative representation (sm)
    • In meters of Baltic system (m)
  • Water discharge
    • as an observed value (qms/s)
    • as a layer (mm/day)
• Daily meteorological values
  • Maximum and minimum daily temperatures (°C) from ERA5 and ERA5-Land
  • Total precipitation (mm/day) from ERA5, ERA5-Land, IMERG v06, GPCP v3.2 and MSWEP
  • Different kind of evaporation (mm/day) corresponded to each variable calculated in GLEAM model
• Set of hydro-environmental characteristics derived from HydroATLAS database

Each variable derived from the grid data was calculated for each watershed, taking into account the intersection weights of the watershed contour geometry and grid cells.

Coordinates of hydrological stations were obtained from resource of Federal Agency for Water Resources of Russia Federation—AIS GMVO

To calculate the contours of the catchment areas, a script was developed that builds the contours in accordance with the rasters of flow directions from MERIT Hydro. To assess the quality of the contour construction, the obtained value of the catchment area was compared with the archival value from the corresponded table from AIS GMVO. The average error in determining the area for 2080 catchments is approximately 2%

To derive values for different hydro-environmental values from HydroATLAS were developed approach which calculate aggregated values for catchment, leaning on type of variable: qualitative (Land cover classes, Lithological classes etc.) Or quantitive (Air temperature, Snow cover extent etc.). Every quantitive variable were calculated as mode value for intersected sub-basins and target catchment, e.g. most popular attribute from sub-basins will describe whole catchment which are they relating. Quantitative values were calculated as mean value of attribute from each sub-basin. More detail could be found in publication.

Files are distributed as follows:

Each file has some connection with the unique identifier of the hydrological observation post. Files in netcdf format (hydrological and meteorological series) are named in response to identifier.

Every file which describe geometry (point, polygon, static attributes) has and column named gauge_id with same correspondence.

• attributes/static_data.csv – results from HydroATLAS aggregation
• geometry/russia_gauges.gpkg – coordinates of hydrological observation stations

  • 	gauge_id	name_ru	name_en	geometry
    0	49001	р. Ковда – пос. Софпорог	r.Kovda - pos. Sofporog	POINT (31.41892 65.79876)
    1	49014	р. Корпи-Йоки – пос. Пяозерский	r.Korpi-Joki - pos. Pjaozerskij	POINT (31.05794 65.77917)
    2	49017	р. Тумча – пос. Алакуртти	r.Tumcha - pos. Alakurtti	POINT (30.33082 66.95957)

• geometry/russia_ws.gpkg – catchments polygon for each hydrological observation stations

  • 	gauge_id	name_ru	name_en	new_area	ais_dif	geometry
    0	9002	р. Енисей – г. Кызыл	r.Enisej - g.Kyzyl	115263.989	0.230	POLYGON ((96.87792 53.72792, 96.87792 53.72708...
    1	9022	р. Енисей – пос. Никитино	r.Enisej - pos. Nikitino	184499.118	1.373	POLYGON ((96.87792 53.72708, 96.88042 53.72708...
    2	9053	р. Енисей – пос. Базаиха	r.Enisej - pos.Bazaiha	302690.417	0.897	POLYGON ((92.38292 56.11042, 92.38292 56.10958...

  • Column ais_diff is corresponded to % error in area definition
• nc_all_q
  • netcdf files for hydrological observation stations which has no missing values on discharge for 2008-2020 period
• nc_all_h
  • netcdf files for hydrological observation stations which has no missing values on level for 2008-2020 period
• nc_all_q_h
  • netcdf files for hydrological observation stations which has no missing values on discharge and level for 2008-2020 period
• nc_concat
  • data for all available geometry provided in dataset

More details on processing scripts which were used for development of this database can be found in folder of GitHub repository where I store results for my PhD dissertation

05.04.2023 – Significant data changes. Removed catchments and related files that have more than ±15% absolute error in calculated area relative to AIS GMVO information. Now these are data for 1886 catchments across the Russia.

17.05.2023 – Significant data changes. Major review of parsing algorithm for AIS GMVO data. Fixed the way of how 0.0xx values were read. Use previous versions with caution.

11.10.2023 – Significant data changes. Added 278 catchments for CIS region from GRDC resource. Calculate meteorological and environmental attributes for each catchment. New folder /nc_all_q_h with no missing observations on discharge and level. Now these are data for 2164 catchments across CIS.

This 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. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. 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. The data was produced on behalf of the Copernicus Climate Change Service.

Reference evapotranspiration per month with a spatial resolution of 0.1 degree. Unit: mm month-1. The dataset contains monthly values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are 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: 2021-10-30

Contact points:

Metadata Contact: AQUASTAT

Resource Contact: AQUASTAT

Data lineage:

Copernicus 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:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

• The dataset contains modified Copernicus Climate Change Service information [1979-to date];

Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

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:

Download Reference Evapotranspiration - AgERA5 derived (Monthly - ~10km)

This resource includes two Jupyter Notebooks as a quick start tutorial for the ERA5 Data Component of the PyMT modeling framework (https://pymt.readthedocs.io/) developed by Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/).

The bmi_era5 package is an implementation of the Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) for the ERA5 dataset (https://confluence.ecmwf.int/display/CKB/ERA5). This package uses the cdsapi (https://cds.climate.copernicus.eu/api-how-to) to download the ERA5 dataset and wraps the dataset with BMI for data control and query (currently support 3 dimensional ERA5 dataset). This package is not implemented for people to use and is the key element to help convert the ERA5 dataset into a data component for the PyMT modeling framework.

The pymt_era5 package is implemented for people to use as a reusable, plug-and-play ERA5 data component for the PyMT modeling framework. This package uses the BMI implementation from the bmi_era5 package and allows the ERA5 datasets to be easily coupled with other datasets or models that expose a BMI.

HydroShare users can test and run the Jupyter Notebooks (bmi_era5.ipynb, pymt_era5.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps: - For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub. - Click on the "Open with" button. (on the top right corner of the page) - Select "CUAHSI JupyterHub". - Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If there is any question or suggestion about the ERA5 data component, please create a github issue at https://github.com/gantian127/bmi_era5/issues

ERA5-Land monthly averaged data January 2019

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

Product type:Monthly averaged reanalysis

Variable:

10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth

Year:

2019

Month:

January

Time:

00:00

Format:

NetCDF (experimental)

ECMWF has announced that the Copernicus Climate Change Service (C3S) has begun the release of the ERA5 back extension data covering the period 1950-1978 on the Climate Data Store (CDS).

Although in many other respects the quality of this dataset is quite satisfactory, the current back extension appears to suffer from tropical cyclones that are sometimes unrealistically intense. This is in contrast with the ERA5 product from 1979 onwards (also available from the CDS and RDA ds633.0). For more details see the article, ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense [https://confluence.ecmwf.int/display/CKB/ERA5+back+extension+1950-1978+(Preliminary+version):+tropical+cyclones+are+too+intense].

For this reason the current release of the back extension is preliminary.

It is therefore available from separate CDS catalogue entries (hourly, monthly, single level and pressure levels), and this RDA dataset. Around the end of 2021 an updated version of the back extension is to be made available which will be added to the ERA5 catalogue entries that currently reach back to 1979. After an overlap period (the duration of which is not yet decided), the preliminary back extension will be deprecated.

The full back extension preliminary dataset is expected to be made available near the end of 2020/early 2021.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters, e.g. mean rates and accumulations, that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

Please note: DECS produces a CF 1.6 compliant netCDF-4/HDF5 version of ERA5 for the CISL RDA at NCAR. The netCDF-4/HDF5 version is the de facto RDA ERA5 online data format. The GRIB1 data format is also available online. There is a one-to-one correspondence between the netCDF-4/HDF5 and GRIB1 files, with as much GRIB1 metadata as possible incorporated into the attributes of the netCDF-4/HDF5 counterpart.