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 (for example, 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.

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.

This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 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 member and ensemble mean data.

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.

This dataset contains ERA5 surface level forecast 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. Model and surface 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 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.

This dataset contains ERA5 surface 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. Model level analysis and surface 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.

These datasets come from Google Earth Engine and are used in ACEA challenge

The first is daily time series from Copernicus ECMWF ERA5 Daily aggregates, extracted using weather station geolocations. Time series range from 1998 to 2020. 48 different stations are located in Italy.
The extraction have been done with this script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series(lat, lon, start, end, product_name, sf):
  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .filterDate(start, end)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  return df

if _name_ == "_main_":
  ee.Initialize()
  for i in locations.keys(): # locations is a dictionnary containing latitude and longitude
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    while True:
      try: 
        output = extract_time_series(latitude,
                 longitude,
                 '1998-01-01',
                 '2020-01-01',
                 'ECMWF/ERA5/DAILY',
                 1)
        break
      except: 
        print(i + " 1 fail")
        continue              
    name =PATH + i + "_1.csv"        
    output.to_csv(name, index=True)

The second dataset is Forecasted Weather from Global Forecast System.
The purpose of this dataset is to provide forecasted rainfall and temperature for the 16 coming days. Creation_time column is the released date while forecast_hours is forecasted weather for horizon : creation_time + forecast_hours. Time series are daily and range from 2015 to 2020. Unfortunately, there are missing values.
Python script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series_gfs(lat, lon, start, end, product_name, sf, h):

  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .select(['total_precipitation_surface','temperature_2m_above_ground'])\
    .filterDate(start, end)\
    .filterMetadata('forecast_hours', 'equals', h)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  df=df[df["system:footprint"] == "LinearRing"]

  return df
if _name_ == "_main_":

  ee.Initialize()
  horizon = [i*24 for i in range(1,17)]
  for i in locations.keys():
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    
    for j in horizon:
      while True:
        try:
          output = extract_time_series_gfs(latitude,
                 longitude,
                 '2015-07-01',
                 '2020-08-01',
                 'NOAA/GFS0P25',
                 1,
                 j)
          break
        except:
          print(i + " " + str(j) +" 1 fail")
          continue
      name = PATH + i + "_" + str(j) +"_1.csv"
           
      output.to_csv(name, index=True)

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.

This dataset contains ERA5 initial release (ERA5t) model level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. 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.

This dataset contains a set of stations located in the South Pacific region, obtained from the PACRAIN (Pacific Rainfall Database). Similarly, the TRMM (Tropical Rainfall Measuring Mission) satellite precipitation series and the ERA5 reanalysis precipitation series have been extracted for the aforementioned stations. For the stations corresponding to IDs NZ75400, NZ82400, NZ84317, NZ99701, SP00646, US14000 and US14690, the following daily variables are included:

• TRMM raw
• ERA5 raw
• PACRAIN
• Weather type associated
• TRMM calibrated (scaling)
• TRMM calibrated (empirical quantile mapping)
• TRMM calibrated (scaling conditioned)
• TRMM calibrated (empirical quantile mapping conditioned)

where the last four are a consequence of the application of 4 different calibration methods. A summary of the stations is shown in the following table:

Station Id	Station Name	Longitude	Latitude	Start	End	% Missing Data	Altitude
NZ75400	Kolopelu (Wallis and Futuna)	-178.12	-14.32	1998-01-01	2012-01-01	9.74	36
NZ82400	Alofi (Niue)	-163.93	-19.07	1998-01-01*	2010-09-02	2.68	59
NZ84317	Rarotonga (Cook Islands)	-159.8	-21.2	1999-09-28	2012-01-12	11.36	4
NZ99701	Raoul Island (New Zealand)	-177.93	-29.23	1998-01-01*	2012-01-01	0.72	49
SP00646	Port Vila (Vanuatu)	168.3	-17.72	2000-01-26	2013-06-01	18.13	24
US14000	Aoloau (American Samoa)	-170.77	-14.3	1998-01-01*	2019-12-31*	21.72	408
US14690	Nu'uuli (American Samoa)	-170.70	-14.32	1998-01-01*	2019-12-31*	0.037	3

This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 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 member and ensemble mean data. 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.

This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:

ERA5: Total Precipitation

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole world
This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2022.

Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2022 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.

Process:

Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2010 to 2022. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84) .The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.

This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

File names:

The wd at the start of each file name indicates that the image covers the whole world in the E4warning and is in geographic projection. 22 refers to the year timeline of 2010-2022.

The next two characters identify the channel:
20 - Monthly Total Precipitation

The last two characters of each file name denote the output from Fourier processing:
a0 - mean
mn - minimum
mx - maximum
a1 - amplitude of annual cycle
a2 - amplitude of bi-annual cycle
a3 - amplitude of tri-annual cycle
p1 - phase of annual cycle
p2 - phase of bi-annual cycle
p3 - phase of tri-annual cycle
d1 - variance in annual cycle
d2 - variance in bi-annual cycle
d3 - variance in tri-annual cycle
da - combined variance in annual, bi-annual, and tri-annual cycles
vr - variance in raw data

Parameter Fourier Variable Image values are
ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm
ALL D1,D2,D3,Da Percentages
ALL E1,E2,E3 Percentages
ALL P1,P2.P3 Months*100. (Jan=100)

This is a set of images produced by temporal Fourier analysis of Monthly precipitation provided by the ERA5 dataset for 2001-2019 from the European Centre for Medium-Range Weather Forecasting. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area.

Abstract:

Monthly precipitation values were extracted from ERA5 files for the years 2019 through 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility

File naming scheme:
The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection.

The next two characters identify the channel:
20 for precipitation and 19 refers to the year timeline of 2001-2019.

The last two characters of each file name denote the output from Fourier processing:
a0 - mean
mn - minimum
mx - maximum
a1 - amplitude of annual cycle
a2 - amplitude of bi-annual cycle
a3 - amplitude of tri-annual cycle
p1 - phase of annual cycle
p2 - phase of bi-annual cycle
p3 - phase of tri-annual cycle
d1 - variance in annual cycle
d2 - variance in bi-annual cycle
d3 - variance in tri-annual cycle
da - combined variance in annual, bi-annual, and tri-annual cycles
vr - variance in raw data

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)
Spatial extent:
Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716
Spatial resolution:
0.0083333 deg (approx. 1000 m)
Temporal resolution:
2001-2019
Pixel values:

Parameter Fourier Variable Image values are
A0, A1, A2, A3, Index Value * 10
ALL D1,D2,D3,Da Percentages
ALL E1,E2,E3 Percentages
ALL P1,P2.P3 Months*100. (Jan=100)

Source:
Monthly Precipitation for ERA5 from the European Centre for Medium-Range Weather Forecasting (ECMWF)
Software used:
Codes for modelling are in Python and C++
The software used for map production is ESRI ArcMap 10.8

License: CC-BY-SA 4.0
Processed by:
ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

This dataset refers to a newly constructed daily drought index termed as Daily Evapotranspiration Deficit Index (DEDI). DEDI is calculated based on daily actual evapotranspiration and potential evapotranspiration, deriving from ERA5 reanalysis data on a grid of 0.25°×0.25° for the period 1979–2020 provided by European Centre for Medium-Range Weather Forecasts (ECMWF).DEDI was applied to analyze the spatial and temporal evolution characteristics of four drought events that occurred over Southwest, North, Northeast and eastern Northwest China in the spring and summer of 2019. Comparisons with the operationally used Meteorological drought Composite Index (MCI) and another commonly used Standardized Precipitation Evapotranspiration Index (SPEI) indicate that, DEDI can reasonably well characterize the spatiotemporal evolutions of the four drought events and has the superiority in depicting the onset and ending time of drought event as well as the multiple peaks of drought intensity. Additionally, DEDI takes into account land surface conditions such as vegetation, which enables potential application not only on meteorological but also in agricultural drought monitoring and warning.NOTICE: We have reevaluated the daily DEDI index via using different metadata (i.e., ERA5 and GLEAM) and performing comparisons between the DEDI index and the other multiple indices (i.e., standardized precipitation evapotranspiration index, evaporative demand drought index, soil moisture, and vapor pressure deficit) over the global land during 1979–2022. Thus, we provide further clarifications about the advantages and limitations for the usage of the daily DEDI dataset over the global land scale (Zhang et al., 2023, Scientific Data, https://doi.org/10.1038/s41597-023-02756-1). We have also updated the daily DEDI dataset based on the latest ERA5 datasets over the global land during 1979–2022 (available at https://doi.org/10.5281/zenodo.7768534). The DEDI values derived from the updated DEDI dataset are different from the old DEDI datasets that were used to assess regional drought events over China sometimes.

This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.

Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";

Creator_name = "Valerio Capecchi";

Contact = "capecchi@lamma.toscana.it";

Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";

Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";

Grid spacing = "7 km";

Grid = "890x482"

The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread.

This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs.

The dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately on Zenodo.

Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset.

Details:

• File format: netcdf4
• Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)
• Longitude range: [-180, +180]
• Latitude range: [-90, +90]
• Temporal resolution: 1 day (at 12 local noon)
• Spatial resolution: 0.28 degrees (~31 Km)
• Spatial coverage: Global
• Time span: from 1980-01-01 to 2019-06-30
• Stream: Deterministic forecasts

This dataset contains ERA5.1 surface level analysis parameter data ensemble means over 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). The ensemble means are calculated from the ERA5.1 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. See linked datasets for ensemble member and spread data. 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). 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.

The Level 4 merged microwave wind product is a complete set of hourly global 10-m wind maps on a 0.25x0.25 degree latitude-longitude grid, spanning 1 Jan 2010 through the end of 2020. The product combines background neutral equivalent wind fields from ERA5, daily surface current fields from CMEMS, and stress equivalent winds obtained from several microwave passive and active sensors to produce hourly surface current relative stress equivalent wind analyses. The satellite winds include those from recently launched L-band passive sensors capable of measuring extreme winds in tropical cyclones, with little or no degradation from precipitation. All satellite winds used in the analyses have been recalibrated using a large set of collocated satellite-SFMR wind data in storm-centric coordinates. To maximize the use of the satellite microwave data, winds within a 24-hour window centered on the analysis time have been incorporated into each analysis. To accomodate the large time window, satellite wind speeds are transformed into deviations from ERA5 background wind speeds interpolated to the measurement times, and then an optical flow-based morphing technique is applied to these wind speed increments to propagate them from measurement to analysis time. These morphed wind speed increments are then added to the background wind speed at the analysis time to yield a set of total wind speeds fields for each sensor at the analysis time. These individual sensor wind speed fields are then combined with the background 10-m wind direction to yield vorticity and divergence fields for the individual sensor winds. From these, merged vorticity and divergence fields are computed as a weighted average of the individual vorticity and divergence fields. The final vector wind field is then obtained directly from these merged vorticity and divergence fields. Note that one consequence of producing the analyses in terms of vorticity and divergence is that there are no discontinuities in the wind speed fields at the (morphed) swath edges. There are two important points to be noted: the background ERA5 wind speed fields have been rescaled to be globally consistent with the recalibrated AMSR2 wind speeds. This rescaling involves a large increase in the ERA5 background winds beyond about 17 m/s. For example, an ERA5 10 m wind speed of 30 m/s is transformed into a wind speed of 41 m/s, and a wind speed of 34 m/s is transformed into a wind speed of about 48 m/s. Besides the current version of the product is calibrated for use within tropical cyclones and is not appropriate for use elsewhere. This dataset was produced in the frame of ESA MAXSS project. The primary objective of the ESA Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project is to provide guidance and innovative methodologies to maximize the synergetic use of available Earth Observation data (satellite, in situ) to improve understanding about the multi-scale dynamical characteristics of extreme air-sea interaction.

The GraphCast Global Forecast System (GraphCastGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.

The GraphCastGFS system is an experimental weather forecast model built upon the pre-trained Google DeepMind’s GraphCast Machine Learning Weather Prediction (MLWP) model. The GraphCast model is implemented as a message-passing graph neural network (GNN) architecture with “encoder-processor-decoder” configuration. It uses an icosahedron grid with multiscale edges and has around 37 million parameters. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The GraphCastGFSl takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 degree GDAS analysis data and runs GraphCast (37 levels) and GraphCast_operational (13 levels) with a pre-trained model provided by GraphCast. Unit conversion to the GDAS data is conducted to match the input data required by GraphCast and to generate forecast products consistent with GFS from GraphCastGFS’ native forecast data.

The GraphCastGFS version 2 made the following changes from the GraphcastCastGFS version 1.

1. The 37 vertical levels model is removed due to the storage restriction and limited accuracy.
   
2. The 13 levels graphcast ML model was fine-tuned with NCEP’s GDAS data as inputs and ECMWF ERA5 data as ground truth from 20210323 to 20220901, validated from 20220901 to 20230101. Evaluation is done with forecasts from 20230101-20240101. The new weights created from the training are used to create global forecasts. It is important to note that the GraphCastGFS v1 model weights obtained from Google’s DeepMInd were provided based on 12 timesteps training with ERA5 data, while the GraphCastGFS v2 model weights resulted from training with 14 timesteps with GDAS and ERA5 data that significantly increased the accuracy of the forecasts compared with GraphCastGFS V1.
   
   The input data generated from the GDAS data as GraphCast input is provided under input/ directory. An example of file names is shown below
   
   source-gdas_date-2024022000_res-0.25_levels-13_steps-2.nc
   
   The files are under forecasts_13_levels/. There are 40 files under each directory covering a 10 day forecast. An example of file name is listed below
   
   graphcastgfs.t00z.pgrb2.0p25.f006
   
   

The GraphCastGFS version 2.1 change log:

1. Starting from 06 cycle on 20240710, the forecast length is increased from 10 days to 16 days.
   
   Please note that this NOAA GraphCastGFS Model was produced using a code package released by Google DeepMind. For information on Google DeepMind, please visit their github page listed in the documentation and license sections of this page.

This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.

Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";

Creator_name = "Valerio Capecchi";

Contact = "capecchi@lamma.toscana.it";

Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";

Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";

Grid spacing = "2.5 km";

Grid = "506x626"

This dataset provides globally continuous, daily snow and ice cover information at a high spatial resolution (0.1° latitude/longitude grid) for the period from January 1, 1980, to June 30, 1987. It extends the Global Automated Snow and Ice Mapping System (GMASI) dataset, which begins in July 1987. For access to GMASI snow and ice cover data starting in 1987, users can visit: https://www.star.nesdis.noaa.gov/pub/smcd/emb/snow/gmasi_reprocessing/dailymaps/data/.The extended dataset was developed using advanced machine learning techniques, specifically a Random Forest algorithm, applied to ERA5 reanalysis data. This dataset is designed to support diverse applications, including climate studies, hydrological modeling, and long-term precipitation analyses.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu