contact User Support.

In support of the COAPEC Thematic Programme the BADC has extracted seasonal forecast ensemble data from the ECMWF MARS (Meteorological Archive and Retrieval System) archive. These data are also known as "Hindcasts" as they are forecasts run retrospectively. Since the data is part of the ECMWF Operational system BADC users must successfully apply for access to this dataset before they can obtain the data. The ECMWF produced two sets of runs, System 1 and System 2. The data archived at the BADC are the System 2 runs which use the atmospheric component Cy23r4 of the IFS (Integrated Forecasting System) with a horizontal resolution of TL95 at 40 levels in the vertical. This is the same cycle of the IFS used for the ERA-40 re-analysis. A detailed description of the ECMWF Seasonal Forecasting system can be found on the ECMWF web site. Products: The BADC has extracted monthly means, maxima, minima and standard deviations for the available surface variables from 1987 to 6 months before the present date. Atmospheric variables are only currently available as monthly means. The data is held as part of the main BADC Operational ECMWF archive. For each month there are six forecast months archived, with 5 ensemble members for 10 months of the year, and 40 ensemble members in May and November of each year from 1987-2001. From 2002 onwards there are 40 ensemble members per month. There are 33 parameters held on surface or single levels and 6 parameters available on pressure levels. The data is held on a regular 1.875 x 1.875 degree grid in GRIB format.

Atmospheric Model high resolution 15-day forecast (HRES)

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) 
Hourly steps to step 144 for all four runs

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

the Twentieth Century Reanalysis Project is generating a six-hourly

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 single levels from 1940 to present".

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.

This data accompanies the submitted article Shirin Ermis et al. (under review): Event attribution of a midlatitude cyclone using ensemble weather forecasts. Description Please note that the data in this dataset is post-processed and not raw data to make storage more efficient. The study correcponding to this dataset compares the operational forecast (curr) as well as a future (fut or incr) and pre-industrial (pi) scenario of storm Eunice which hit the UK on February 18, 2022. Forecasts are run using the ECMWF ensemble prediction system (IFS EPS, CY47R3) with 51 ensemble members. We use forecasts at 8, 4, and 2 days lead time to the storm hitting the UK. Acordindly, the forecasts were initialised at 00:00UTC on Febryary 10th, 14th and 18th, 2022. For the counterfactual scenarios of the storm, we adjusted ocean temperatures in 3d, including sea surface temperatures (SST) as well as CO2 concentrations. The concentrations of CO2 were 285ppm, 421ppm, and 625ppm for pi, curr, and fut respectively. The dataset aslo contains data from the operational analysis and forecast initialised at 00:00UTC on February 18th, 2022. Raw data can be downloaded by ECMWF MARS users under the United Kingdom memberstate data and experiment codes b2nn, b2ns, b2nq (all for pi), and b2no, b2nr, b2nt (for incr). The curr simulations can be found in the operational ensemble prediction system archive for the respective initialisation dates. For any further information, please refer to the article and references therein. Usage Each of the data files is needed to run the code which is publicly available here. The notebooks folder contains the python notebooks PAPER1, PAPER2, PAPER3, and PAPER4 which produce the plots as shown in the paper. To be able to run the code fully with all aspects of the figures, reanalysis data from ERA5 (Hersbach et al. 2020) is also necesary. This data is freely available online for research purposes. We use the variables mean sea level pressure (msl) and wind gusts at 10m (fg10) for February 2022. To run our code, please create a conda environment with the environment.yml file in the docs folder. In the PAPERx notebooks, please import the necessary packages at the start and then skip to the sectio "Save or load data". In most cases this will load all the necessary data for the plotting cell below, although in some cases loading and processing ERA5 data might be neceessary. Acknowledgements S.E. was supported by the Natural Environment Research Council (NERC) under Grant NE/S0074747/1 and a Graduate Scholarship from St Cross College Oxford. N.J.L. was supported by the Natural Environment Research Council under Grant NE/L002612/1 and the European Union's Horizon 2020 project FORCeS under Grant GA 821205. A.W. was supported by the NERC CANARI project (NE/W004984/1) and by European Union’s Horizon Europe research and innovation programme under grant agreement No. 101081460. F.C.L. was supported by the Met Office Hadley Centre Climate Programme funded by DSIT. S.N.S. was supported by the NERC Centre for Greening Finance and Investment (CGFI) under Grant NE/V017756/1. The results contain modified Copernicus Climate Change Service information [2022]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Acknowledgement is made for the use of ECMWF’s computing and archive facilities in this research under the special project spgbleac.

running daily at ECMWF.

ERA5-Land ist ein Reanalyse-Datensatz, der eine konsistente Sicht auf die Entwicklung von Landvariablen über mehrere Jahrzehnte hinweg in einer im Vergleich zu ERA5 verbesserten Auflösung bietet. ERA5-Land wurde durch die erneute Ausführung der Landkomponente der ECMWF ERA5-Klimareanalyse erstellt. Bei der Reanalyse werden Modelldaten mit Beobachtungen aus aller Welt kombiniert.

This dataset contains wind speed forecast and observation data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) MARS archive and ERA5 for 18 offshore sites in Northern Europe for the year 2019.

The original data from MARS and from ERA5 were modified by (i) changing its data format to json and (ii) extracting the wind speed for 18 specific locations.

Each json file has 5 entries:

• "time" - represents the time step of the forecast issued time or of the observation data, expressed as datetime64[ns]
• "lat" - Latitude of the location
• "lon" - Longitude of the location
• "steps" - lead time of the issued forecasts, expressed in hours
• "u100" - wind speed component u, in m/s
• "v100" - wind speed component v, in m/s

The data in "u100" and "v100" are stored as a three-dimensional array, where the first dimension corresponds to the time step of the forecast issue or of the observation data and matches the matches the dimension of "time", the second dimension is the forecast scenario (here always equal to one), and the third dimension is the lead-time and matches the dimension of "steps". For the hourly data, since there is no forecast, the data stored in the third dimension of the array is always a scalar.

The forecast files are named as "mars_ptf_2019_60h_xxx.json". The observation data files are named "data_era5_2019_xxx.json". The "xxx" corresponds to the three letter identifier for each site (e.g. "hkn", "sea", etc.).

The research results presented in the associated publication can be reproduced with the open-source code SHIPP: https://github.com/jennaiori/shipp

All datasets are made available under the CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/).

‫ERA5-Land هي مجموعة بيانات لإعادة التحليل تقدّم عرضًا متسقًا لتطوّر المتغيّرات الأرضية على مدى عدة عقود بدقة محسّنة مقارنةً بمجموعة بيانات ERA5. تم إنتاج ERA5-Land من خلال إعادة تشغيل مكوّن الأرض في إعادة تحليل المناخ ERA5 الصادر عن "المركز الأوروبي للتنبؤات الجوية المتوسطة المدى". تجمع عملية إعادة التحليل بين بيانات النماذج والملاحظات من جميع أنحاء العالم في مجموعة بيانات متكاملة ومتسقة على مستوى العالم باستخدام قوانين الفيزياء. تنتج إعادة التحليل بيانات تعود إلى عدة عقود من الزمن، ما يوفّر وصفًا دقيقًا لمناخ الماضي. تتضمّن مجموعة البيانات هذه جميع المتغيرات الـ 50 المتوفّرة على CDS. تتوفّر بيانات ERA5-Land من عام 1950 إلى ثلاثة أشهر من الوقت الفعلي. يُرجى الرجوع إلى قسم "المشاكل المعروفة" في ERA5-Land. على وجه الخصوص، يُرجى العِلم بأنّه تم تبديل قيم ثلاثة مكوّنات من إجمالي التبخّر والنتح على النحو التالي: المتغير "التبخّر من التربة العارية" (رمز مَعلمة نظام MARS‏ 228101 (evabs)) يتضمّن القيم المقابلة لـ "التبخّر من النتح النباتي" (مَعلمة نظام MARS‏ 228103 (evavt))، المتغيّر "التبخّر من أسطح المياه المفتوحة باستثناء المحيطات" (رمز مَعلمة نظام MARS‏ 228102 (evaow)) يتضمّن القيم المتوافقة مع "التبخّر من التربة العارية" (رمز مَعلمة نظام MARS‏ 228101 (evabs))، يحتوي المتغير "التبخّر من النتح النباتي" (رمز مَعلمة mars‏ 228103 (evavt)) على القيم المقابلة لـ "التبخّر من أسطح المياه المفتوحة باستثناء المحيطات" (رمز مَعلمة mars‏ 228102 (evaow)). يُرجى العِلم أنّ اصطلاح عمليات التجميع المستخدَمة في ERA5-Land يختلف عن ذلك المستخدَم في ERA5. يتم التعامل مع التراكمات بالطريقة نفسها كما في ERA-Interim أو ERA-Interim/Land، أي يتم تجميعها من بداية التوقّع إلى نهاية خطوة التوقّع. يحدث ذلك خلال كل يوم، وتتم إعادة ضبطه عند منتصف الليل. أضاف فريق بيانات Earth Engine 19 نطاقًا إضافيًا، نطاقًا واحدًا لكل نطاق من نطاقات التراكم، مع احتساب القيم كل ساعة كفرق بين خطوتَي توقع متتاليتَين.

In this folder we gathered some additional material to our paper on UNSEEN applied to heatwaves in Northern Europe. You will find: - Codes: - Jupyter code used to compute region-averaged timeseries of both UNSEEN ensemble and reanalysis data. For downloading data from Copernicus CDS please refer to Timo Kelder's codes: https://unseen-open.readthedocs.io/en/latest/index.html - R markdown file including all the statistical analysis of the timeseries obtained from the jupyter code. - Data: - Gridded netCDF files of 3day running mean Tmax for both SEAS5 ensemble and MET Nordic LTC (based on NORA3 reanalysis). Archived SEAS5 seasonal forecast data are available at ECMWF MARS archive: https://apps.ecmwf.int/mars-catalogue/ . Description and availability of the MET Nordic long-term consistent product can be found at MET Norway's Github pages: https://github.com/metno/NWPdocs/wiki/MET-Nordic-dataset . - Gridded MJJ 2m temperature, mean for each year (also SEAS5). - Modified NUTS regions. Shapefiles of the NUTS dataset are available from Eurostat's webpages: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units - Figures: The figures included in the paper as well as supplementary figures in high resolution. - Results: - Notebooks for all regions containing the detailed analysis with all the 'usual' (from UNSEEN-open) UNSEEN plots, return period plots, probability calculations etc. - An Excel-spreadsheet containing many of the resulting values. The maps were plotted on this basis.

MPI

ERA5-Land là một tập dữ liệu phân tích lại, cung cấp thông tin nhất quán về sự phát triển của các biến số trên đất liền trong nhiều thập kỷ ở độ phân giải cao hơn so với ERA5. ERA5-Land được tạo ra bằng cách phát lại thành phần đất của phân tích lại khí hậu ERA5 của ECMWF. Phân tích lại kết hợp dữ liệu mô hình với các quan sát trên toàn thế giới thành một tập dữ liệu hoàn chỉnh và nhất quán trên toàn cầu bằng cách sử dụng các quy luật vật lý. Phân tích lại tạo ra dữ liệu từ nhiều 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 ERA5-Land có sẵn từ năm 1950 đến 3 tháng kể từ thời gian thực. Vui lòng tham khảo phần "Các vấn đề đã biết" của ERA5-Land. Cụ thể, hãy lưu ý rằng 3 thành phần của tổng lượng thoát hơi nước có các giá trị được hoán đổi như sau: biến "Evaporation from bare soil" (Sự bốc hơi từ đất trống) (mã tham số mars 228101 (evabs)) có các giá trị tương ứng với "Evaporation from vegetation transpiration" (Sự bốc hơi từ quá trình thoát hơi nước của thực vật) (tham số mars 228103 (evavt)), biến "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) có các giá trị tương ứng với "Evaporation from bare soil" (mars parameter code 228101 (evabs)), biến "Evaporation from vegetation transpiration" (Mã tham số mars 228103 (evavt)) có các giá trị tương ứng với "Evaporation from open water surfaces excluding oceans" (Mã tham số mars 228102 (evaow)). Xin lưu ý rằng quy ước về lượng tích luỹ được dùng trong ERA5-Land khác với quy ước về lượng tích luỹ được dùng trong ERA5. Lượng tích luỹ được xử lý giống như trong ERA-Interim hoặc ERA-Interim/Land, tức là được tích luỹ từ đầu đến cuối bước dự báo. Việc này diễn ra hằng ngày và được đặt lại vào nửa đêm. Nhóm Dữ liệu Earth Engine đã thêm 19 dải tần bổ sung, mỗi dải tần cho một trong các dải tần tích luỹ, với các giá trị hằng giờ được tính là chênh lệch giữa hai bước dự báo liên tiếp.

climate variability and change can be better understood and predicted.

This dataset contains the evaporative sources of moisture supplying precipitation across Australia each day between 1979 and 2013. Evaporative sources were estimated using a Lagrangian back-trajectory approach based on Dirmeyer and Brubaker (1999; Journal of Geophysical Research). Evaporative sources were estimated by tracking all forms of atmospheric water backward through the atmosphere using 3d wind fields and a 10 minute time step. Precipitation falling anywhere on the Australian continent of more than 2mm/day was tracked.

The back-trajectory model was driven by three-hourly, 3d atmospheric fields of wind, temperature, precipitable water and pressure, and 2d fields of precipitation and latent heat flux, produced by a WRFv3.6.1 simulation driven by ERA-Interim reanalysis (Dee D. et al., 2011). The simulation was performed over the CORDEX Australasia region with a spatial horizontal resolution of approximately 50km and 30 vertical levels.

The dataset contains the following variables:

- water vapour contribution of domain grid cells to each cell where it rained

- precipitation at each grid cell where it rained

- x and y index of precipitation in number of cells from [0,0]

The water vapour contribution is dimensioned [gridcell_wvc,i_cross,j_cross] where gridcell_wvc is the record identifier of grid cells where precipitation occurred on that day; i_cross and j_cross are the coordinates of the curvilinear grid.

References

ERA5-Land 是一种再分析数据集，与 ERA5 相比，其分辨率更高，可提供一致的视图来查看几十年来陆地变量的演变。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 è un set di dati di reanalisi che fornisce una visione coerente dell'evoluzione delle variabili relative alla terra nell'arco di diversi decenni a una risoluzione migliorata rispetto a ERA5. ERA5-Land è stato prodotto riproducendo il componente terrestre della rianalisi climatica ECMWF ERA5. La nuova analisi combina i dati del modello con le osservazioni di tutto il mondo …

Italy

A pre-operational Data Targeting System (DTS) was developed to assess the feasibility of operational adaptive control of the observing system and as a facility to aid research projects using data targeting. The DTS was developed and hosted at ECMWF. The work was jointly funded by EUCOS and the EC as part of the PREVIEW Integrated Project (work package WP3320) of the EU 6th Framework Programme.