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.

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.

Congo, The Democratic Republic of the CD: Deposit Rate data was reported at 4.185 % pa in 2017. This records an increase from the previous number of 3.375 % pa for 2016. Congo, The Democratic Republic of the CD: Deposit Rate data is updated yearly, averaging 7.719 % pa from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 16.773 % pa in 2010 and a record low of 3.375 % pa in 2016. Congo, The Democratic Republic of the CD: Deposit Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

Congo, The Democratic Republic of the CD: Lending Rate data was reported at 20.621 % pa in 2017. This records an increase from the previous number of 19.046 % pa for 2016. Congo, The Democratic Republic of the CD: Lending Rate data is updated yearly, averaging 35.800 % pa from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 65.418 % pa in 2009 and a record low of 18.692 % pa in 2014. Congo, The Democratic Republic of the CD: Lending Rate data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

Congo, The Democratic Republic of the CD: Literacy Rate: Adult: % of People Aged 15 and Above data was reported at 77.043 % in 2016. This records an increase from the previous number of 75.017 % for 2012. Congo, The Democratic Republic of the CD: Literacy Rate: Adult: % of People Aged 15 and Above data is updated yearly, averaging 71.095 % from Dec 2001 (Median) to 2016, with 4 observations. The data reached an all-time high of 77.043 % in 2016 and a record low of 61.206 % in 2007. Congo, The Democratic Republic of the CD: Literacy Rate: Adult: % of People Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This catalogue entry provides monthly climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP5 simulations:

Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005. Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300.

In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.

This dataset provides estimates of surface elevation change over the Greenland and Antarctic ice sheets since 1992, utilizing satellite radar altimetry from five missions: ERS-1, ERS-2, ENVISAT, CryoSat-2, and Sentinel-3A. The surface elevation change is modelled over successive, overlapping periods and reported monthly. The dataset production method is an evolution of those employed by the European Space Agency (ESA)'s Greenland and Antarctic Ice Sheet Climate Change Initiatives and is guided by the Global Climate Observing System targets for the Ice Sheets Essential Climate Variable. An annual Climate Data Record (CDR), and monthly intermediate CDRs (ICDRs) are issued. Each monthly record includes all previous data, from 1992 onwards, as well as that month's update. This product is designed to provide data stability, so changes in the historic data, eg. if a satellite's elevation data is reprocessed or if inter-satellite cross-calibration is revised, are only introduced in the annual CDR. Each annual CDR is given a version number. The differences in the geographical location of the two sheets result in site-specific processing: Greenland: Data consists of surface elevation change rate and its uncertainty in a five-year (for the early satellites: ERS-1, ERS-2, and ENVISat) or three-year (for CryoSat-2 and Sentinel-3A) moving window. The moving window is advanced at one-month steps. Elevation measurements from satellite radar altimetry are used to build time-series of elevation change by the most optimal combination of the crossover-, repeat-track- and plane-fitting methods. The timeseries is derived for each cell on a 25km by 25km polar stereographic grid, covering the main Greenland ice sheet, and not including peripheral glaciers and ice caps. Data gaps have been filled using an ordinary Kriging interpolation method, and the distance to the nearest observational point is provided as utility information. The distance can be used to flag filled data. Antarctica: Data consists of surface elevation change rate over a five-year moving window that advances in one-month steps. It covers the Antarctic ice sheet, ice shelves and associated ice rises and islands on a 25km by 25km polar stereographic grid. Elevation measurements from five satellite radar altimetry missions, ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A, are used to produce timeseries of surface elevation change by the crossover method for each grid cell. The mission timeseries are cross-calibrated into a consistent record, which is used to derive surface elevation change rates and their uncertainty estimates in each cell and time-window. Data gaps are flagged but not filled.

Deposit Interest Rate in Guatemala increased to 4.93 percent in 2024 from 4.21 percent in 2023. This dataset includes a chart with historical data for Deposit Interest Rate in Guatemala.

Deposit Interest Rate in Russia decreased to 21.10 percent in January from 21.57 percent in December of 2024. This dataset includes a chart with historical data for Deposit Interest Rate in Russia.

Deposit Interest Rate in Honduras increased to 8.21 percent in 2024 from 5.94 percent in 2023. This dataset includes a chart with historical data for Deposit Interest Rate in Honduras.

Deposit Interest Rate in Zimbabwe increased to 8.87 percent in February from 8.15 percent in January of 2025. This dataset includes a chart with historical data for Deposit Interest Rate in Zimbabwe.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.