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 dataset presented here is a regridded subset of the full ERA5 data set on native resolution that is stored in a format designed for retrieving long time-series for a single point. When the requested location does not match the exact location of a grid point then the nearest grid point is used instead. It is this source of ERA5 data that is used by the ERA-Explorer to ensure response times required for the interactive web-application. 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.

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 (monthly means are available around the 6th of each month). 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 monthly mean data on single levels from 1940 to present".

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). 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 (monthly means are available around the 6th of each month). 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. So far this has not been the case and when this does occur users will be 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 monthly mean data on single levels from 1979 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.

ERA-NUTS (1980-2021)

This dataset contains a set of time-series of meteorological variables based on Copernicus Climate Change Service (C3S) ERA5 reanalysis. The data files can be downloaded from here while notebooks and other files can be found on the associated Github repository.

This data has been generated with the aim of providing hourly time-series of the meteorological variables commonly used for power system modelling and, more in general, studies on energy systems.

An example of the analysis that can be performed with ERA-NUTS is shown in this video.

Important: this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us sending an email or opening an Issue in the associated Github repository.

Data

The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS 2016 classification. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).

This dataset contains NUTS0/1/2 time-series for the following variables obtained from the ERA5 reanalysis data (in brackets the name of the variable on the Copernicus Data Store and its unit measure):

• t2m: 2-meter temperature (2m_temperature, Celsius degrees)
• ssrd: Surface solar radiation (surface_solar_radiation_downwards, Watt per square meter)
• ssrdc: Surface solar radiation clear-sky (surface_solar_radiation_downward_clear_sky, Watt per square meter)
• ro: Runoff (runoff, millimeters)
• sd: Snow depth (sd, meters)

There are also a set of derived variables: - ws10: Wind speed at 10 meters (derived by 10m_u_component_of_wind and 10m_v_component_of_wind, meters per second) - ws100: Wind speed at 100 meters (derived by 100m_u_component_of_wind and 100m_v_component_of_wind, meters per second) - CS: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky) - RH: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996) - HDD/CDD: Heating/Cooling Degree days (derived by 2-meter temperature the EUROSTAT definition.

For each variable we have 367 440 hourly samples (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for 34/115/309 regions (NUTS 0/1/2).

The data is provided in two formats:

• NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as int16 type using a scale_factor to minimise the size of the files.
• Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)

All the CSV files are stored in a zipped file for each variable.

Methodology

The time-series have been generated using the following workflow:

1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset
2. The data is read in R with the climate4r packages and aggregated using the function /get_ts_from_shp from panas. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.
3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R
4. The NetCDF are created using xarray in Python 3.8.

Example notebooks

In the folder notebooks on the associated Github repository there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in xarray and how to visualise them in several ways by using matplotlib or the enlopy package.

There are currently two notebooks:

• exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.
• ERA-NUTS-explore-with-widget: explorer interactively the datasets with jupyter and ipywidgets.

The notebook exploring-ERA-NUTS is also available rendered as HTML.

Additional files

In the folder additional fileson the associated Github repository there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.

License

This dataset is released under CC-BY-4.0 license.

Changelog

2022-04-08 Added Relative Humidity (RH) 2022-03-07 Added the missing month in CDD/HDD 2022-02-08 Updated the wind speed and temperature data due to missing months.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 DAILY provides aggregated values for each day for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, daily minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Daily total precipitation values are given as daily sums. All other parameters are provided as daily averages. ERA5 data is available from 1979 to three months from real-time. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Daily aggregates have been calculated based on the ERA5 hourly values of each parameter.

This dataset contains ERA5 model level analysis 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. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Surface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

ERA5 hourly gridded data on single levels from 1979 to present. ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. 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. 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. This dataset is not intended for general public access since the original row dataset is freely distributed by ECMWF

Raster of hourly estimates of climate variables for Greece, 2017-2018. The estimates, which form the ERA5 Reanalysis data, are based on vast amounts of historical observations. From ECMWF through the Copernicus Climate Change Service. Prepared by Athena Research Center for use in the i4SEA project.

The data was downloaded from ERA5 hourly data on single levels from 1979 to present on May 16, 2019. It covers the years 2017-2018 and a rectangular region encompassing Greece. The climate variables contained are:

10m u-component of wind (10u)

10m v-component of wind (10v)

2m dewpoint temperature (2d)

2m temperature (2t)

Mean sea level pressure (msl)

Mean wave direction (mwd)

Mean wave period (mwp)

Sea surface temperature (sst)

Significant height of combined wind waves and swell (swh)

Surface pressure (sp)

Total precipitation (tp)

Generated using Copernicus Climate Change Service information 2019. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

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.

Perdigao wind research dataset including ERA5 data, high resolution and low resolution Large Eddy Simulations for the year 2020 at 1 hour frequency. Generated using Copernicus Climate Change Service information 2022[1] [1] 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. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 05-04-2022), 10.24381/cds.adbb2d47

EOOffshore is a Sustainable Energy Authority of Ireland (SEAI) funded project, which commenced in June 2020 in the School of Physics in University College Dublin (UCD). It presents a case study that demonstrates the utility of the Pangeo software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the Irish Continental Shelf (ICS) region. It has involved the creation of a new wind data catalog for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.

ERA5 is the fifth generation global reanalysis data set produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It is a component of the Copernicus Climate Change Service (C3S), where data products are publicly available in the C3S Climate Data Store. This particular catalog data set (eooffshore_ics_era5_single_level_hourly_wind.zarr.tar.gz) contains 2001-2021 products for the ICS region from the ERA5 hourly data on single levels from 1979 to present data set, which provides hourly data from 1979 to the present day, at single levels (atmospheric, ocean-wave and land surface quantities). Wind speed and direction have been calculated from the uX and vX variables, where X = 10 m and 100 m above sea level. This ERA5 data set was used in the EOOffshore project outputs presented (Scalable Offshore Wind Analysis With Pangeo) at the Meeting Exascale Computing Challenges with Compression and Pangeo 2022 EGU General Assembly session.

Description and example usage of the ERA5 data set in EOOffshore:

ERA5 Wind Data for Irish Continental Shelf region

Offshore Wind in Irish Areas Of Interest

Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation

As requested by the ECMWF - Licence to Use Copernicus Products, this Zarr store was:

Generated using Copernicus Climate Change Service information [2001 - 2021]

This dataset contains ERA5.1 surface level analysis parameter data for the period 2000-2006 from 10 member ensemble runs. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). Ensemble means and spreads are calculated from these 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.

The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

This dataset contains ERA5.1 surface level forecast parameter data for the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. Surface and Model level analysis data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset. The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F892e473ab10901ddcf4a45e92a94aca4%2Fharhar.jpeg?generation=1729615360333284&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of 137 Major Philippine Cities from https://open-meteo.com/ from January 01, 2010 to Dec 31, 2019.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2010-01-01&end_date=2019-12-31
• Datetime is in PHL timezone of GMT+8

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• 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 [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• 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 [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

Philippine Weather Series Datasets

• 🇵🇭 Philippine Cities Weather Data (1960-1969)
• 🇵🇭 Philippine Cities Weather Data (1970-1979)
• 🇵🇭 Philippine Cities Weather Data (1980-1989)
• 🇵🇭 Philippine Cities Weather Data (1990-1999)
• 🇵🇭 Philippine Cities Weather Data (2000-2009)
• 🇵🇭 Philippine Cities Weather Data (2010-2019)
• 🇵🇭 Philippine Cities Weather Data (2020-2023)
• 🇵🇭 Philippine Cities Weather Data (YTD 2024)

Note

Image generated with Bing Image Generator

The negative of the vertical component of the atmospheric direct current (DC) electric field is referred to as the atmospheric electric potential gradient (PG). The PG depends on the actual ionospheric potential, local electric fields, and the electrical conductivity of the air at the place of the measurement. These factors are more or less directly connected to meteorological conditions. The overall state of the global network of large-scale electrical currents in the Earth-ionosphere system can be inferred from the PG when the weather is locally calm. This meteorological condition is traditionally referred to as “fair weather” and is characterized by allowed ranges of specified meteorological parameters. This is why information on the actual weather condition is supposed to be an inherent supplement of PG datasets. This dataset contains PG data recorded in the Széchenyi István Geophysical Observatory (NCK, 47.632°N, 16.718°E), Hungary from 1962, when the regular measurements were started, up to 2009. Throughout this time period, data were collected using practically the same instrument at the same location. The PG was measured by a locally developed radioactive apparatus which equalizes the atmospheric potential over the lowest 1 m thick air layer so that the potential difference between the sensing and grounded electrodes at ground level is the PG itself. Zero signal offset was determined daily and the instrument was calibrated in the ±250 V/m range weekly whenever it was possible. The instrument has a measuring range of −300 V/m to 300 V/m. The data were recorded photographically by a sensitive galvanometer. Hourly averages were then obtained from the photographical records via manual evaluation with an uncertainty of ±10 V/m. Hourly averaged PG data was included in this dataset when valid records from more than 30 minutes from a given hour were available. Records in the original dataset marked as unreliable or saturated have been omitted. Detailed characteristics of the instruments and the applied calibration technique as well as links to original data publications can be found in Bór et al., 2020 and the references therein. This dataset also contains hourly PG averages which have been corrected for the time-dependent bias caused by the electrostatic shielding effect of trees that were growing up not far from the measuring instrument over the decades. Note that this shielding effect largely dominates the long-term trends in the uncorrected data, so the original PG data must be interpreted with care. The uncertainty of the conversion is also provided. This uncertainty arises from unexact information on the age and growth rate of different trees near the measuring instrument. Detailed explanation of the correction can be found in Buzás et al., 2021. On-site measurements of temperature, total rainfall, relative humidity, resultant wind direction and speed, and global solar radiation are available after 2000. These parameters were measured by a Campbell meteorological station. The measured data was compiled using the factory calibration of the sensors throughout the included time interval. The original time resolution of the data is 10 minutes. The data was converted to hourly time resolution to comply with the PG records. In order to provide a possibility for examining the relation of PG to meteorology on the full time span of the PG records, meteorological parameters obtained in hourly time resolution from the ERA5 reanalysis framework are attached. The two horizontal components of wind speed at 10 m (to calculate the resultant wind speed and direction; see reference ERA5WindCalculation), temperature at 2 m, dew point temperature at 2 m (to calculate relative humidity according to Tetens (1930)), total precipitation, surface pressure, downwards surface solar radiation, snow cover, and snow depth were extracted from the ERA5-Land hourly data 1950 to present dataset (Muñoz Sabater, 2019). Total cloud cover, low cloud cover, and cloud base height between 1962 and 1978 were compiled from the ERA5 hourly data on single levels 1950-1978 (preliminary version) dataset (Bell et al., 2020). The latter parameter set in the 1979–2009 time period was compiled from the ERA5 hourly data on single levels 1979 to present dataset (Herschbach et al., 2018). Note that, due to the finite spatial resolution of the ERA5 framework, these values correspond to the whole area 47.6-47.7 °N latitude and 16.7-16.8 °E longitude (11 km x 11 km) in the case of the ERA5-Land hourly data dataset and 47.5-47.75 °N latitude and 16.5-16.75 °E longitude (28 km x 28 km) in the cases of ERA5 hourly data on single levels datasets. The observatory is located 4 km and 13 km from the center of the corresponding area in the case of higher and lower spatial resolution ERA5 datasets, respectively.

For RDA ERA5 monthly mean data prior to 1979, please see ds633.5: ERA5 monthly mean back extension 1950-1978 (Preliminary version) [https://rda.ucar.edu/datasets/ds633.5/] 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.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F53a3fc918b9aa2adf567e9b3403d1729%2F_18b3a14a-a8f8-40b1-8834-7441c14b00cc-small.jpeg?generation=1745057200601804&alt=media" alt="">

Context

Coordinates of ASEAN cities from https://simplemaps.com/, hourly and daily Weather Dataset from https://open-meteo.com/ from January 01, 1970 to Dec 31, 1979.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=1970-01-01&end_date=1979-12-31
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• 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 [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• 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 [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

NOTE

🇸🇬 Singapore and 🇵🇭 Philippine cities are not in this dataset, because I've extracted and placed them in other datasets. I don't want to re-extract what I've already done. You can find the data of the same time period below.

• 🇵🇭 Philippine Cities Weather Data (1970-1979)
• 🌍Forbes Top 100 Cities Weather Data🏙️(1970-1979)

Image

Image generated with Bing Image Generator

This dataset is used to showcase in Jupyter notebooks the usage of the Pythie postprocessing software available on GitHub.

We use the ERA5 reanalysis over a large area in Europe from 1997 to 2016 as gridded observations. These reanalysis have been downloaded from the Copernicus Data Store in GRIB format and converted to the NetCDF file format.

The reforecasts files have been download from ECMWF and converted to NetCDF files.

The observation data of the WMO-compliant DWD meteorological station of Soltau from 1997 to 2016. The station is located at the point 52°57'37.5"N, 9°47'35.0"E. The data have been downloaded from the DWD Climate Data Center.

Gridded reforecast data source

Source www.ecmwf.int

Creative Commons Attribution 4.0 International (CC BY 4.0) Copyright © 2021 European Centre for Medium-Range Weather Forecasts (ECMWF).

Copernicus ERA5 gridded reanalysis data source

Source https://cds.climate.copernicus.eu/

Copyright © 2021 European Union.

Generated using Copernicus Climate Change Service information 2021.

Hersbach et al. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < 21-04-2021 >), doi:10.24381/cds.adbb2d47.

Observation data source

Source: Deutscher Wetterdienst, DWD CDC portal

Summary

This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.

Main model characteristics

The modeling approach consists of two steps called Model Sublimation and Model Air Parcel. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: Run E assumes that sublimation is associated with equilibrium fractionation while Run N assumes that sublimation occurs without isotopic fractionation.

Model Sublimation Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.

Model Air Parcel Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.

References

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi: 10.24381/cds.bd0915c6

Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). 10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017. Zenodo. doi: 10.5281/zenodo.4031705