ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty. ERA5 data are available in the Climate Data Store on regular latitude-longitude grids at 0.25o x 0.25o resolution, with atmospheric parameters on 37 pressure levels.

2022年4月13日 · This dataset contains ERA5 surface level invariant parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details.

2022年10月19日 · It reminds me of the XArray logo, which is a nice homage due to the parallels between our ARCO data and XArray/Dask/Zarr chunking. Are you down for making a v2 of the logo? If so, I would nudge this design to make the chunking theme more explicit.

2025年1月6日 · ERA5是一个由欧洲中期天气预报中心（ECMWF）提供的再分析数据集，它结合了观测数据和数值天气预测模型来提供全球范围内大气、陆地表面以及海洋波浪的状态估计。而ERA5 Ensemble则是基于集合预报系统的一种扩展，用于...

Reanalysis datasets like ERA5, the fifth generation of ECMWF atmospheric reanalyses, combine models with data from satellites and ground sensors to build a consistent long-term record of meteorological conditions, usually going back several decades into the past.

ERA5 is the 5 th generation of E CMWF re analysis. It’s the result of the efforts of dozens of scientists since the 1980s when ECMWF started producing its first reanalyses. In order to fulfill its mission, C3S created the ERA5 dataset as an evolution from ERA-Interim, started in 2006 by ECMWF and covering a period starting in 1975.

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What is the recommended workflow to acquire ERA5 data for a region of interest (ROI) for a few surface variables (precip, soilmoisutre, tempmin, solar rad)? Is there an API that connects me (R, Python, Curl) to the ERA5 database?

2022年1月21日 · Time series of monthly and globally averaged ERA5 ensemble spread from 1979 to 2018 at indicated pressure levels for (a) temperature ($K$), (b) zonal wind ($m s^{−1}$), (c) ozone (partial pressure in $mPa$) and (d) specific humidity (in percent of the 1981 to 2010 mean value at the pressure level in question).

2020年1月20日 · For ERA5 the "deaccumulation" is already done for you. With ERA-Interim you need to difference consecutive timesteps to convert from an accumulated field, and there is a post here that shows how to do this with CDO or python: converting an accumulated variable to timestep values in a netcdf file with CDO )