Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

As a subset of the Japanese 55-year Reanalysis (JRA-55) project, the Meteorological Research Institute of the Japan Meteorological Agency has conducted a global atmospheric reanalysis that assimilates only conventional surface and upper air observations, with no use of satellite observations, using the same data assimilation system as the JRA-55. The project, named the JRA-55 Conventional (JRA-55C), aims to produce a more homogeneous dataset over a long period, unaffected by changes in historical satellite observing systems. The dataset is intended to be suitable for studies of climate change or multidecadal variability. The reanalysis period of JRA-55C is from November 1972 to December 2012. The JMA recommends the use of JRA-55 to extend JRA-55C back to January 1958. The Data Support Section at NCAR has downloaded all JRA-55C data. The entire archive has been reorganized into single parameter time series, and model resolution data has been transformed to a regular Gaussian grid. The JRA-55C products are currently being made accessible to RDA registered users of JRA-55, and will appear incrementally via the Data Access tab.

Himawari-9, stationed at 140.7E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-8 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volcanoes, and general weather systems. Archive data back to July 2015 is available for Full Disk (AHI-L1b-FLDK) products in the bucket. For questions regarding Himawari-9 imagery specifications, visit the JMA site at https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/spsg_ahi.html. For examples of Full Disk Himawari-9 imagery coverage, visit the NOAA Himawari-9 data page at https://www.ospo.noaa.gov/Products/imagery/himawari.html.

The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25. Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet...

Important Notice: Update of JRA-55 data will terminate at the end of January 2024. Please use Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) [https://rda.ucar.edu/datasets/d640000/] at that time.

The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis.

Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25.

Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively.

The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet download, including complete subsetting and data format conversion services.

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary. The following data sources are taken into account: * AAD: Australian Antarctic Division (https://data.aad.gov.au/aws) * ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division * AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/) * BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/) * CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en) * IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph * JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html) * NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/) * Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication * Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020) * PANGAEA: World Data Center (e.g. König-Langlo, 2012) Important notes * Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions. * Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset. * No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.

Acknowledgements

We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.

References

Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020 Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted. König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA. 775173

Data from the Meteorological Institute of the University of Bonn ECHO-G simulations

Daily (1990-2014) reference evapotranspiration (FAO-56) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data processing: Daily reference evapotranspiration calculated following Allen (1998; FAO-56). Daily saturation vapour pressure calculated using the August-Roche-Magnus equation (Alduchov & Eskridge 1996). B) Input datasets: 1) Gridded daily solar radiation data produced using hourly satellite images as part of the AWAP project were obtained from the Australian Bureau of Meteorology (http://www.bom.gov.au/). Imagery from the Geostationary Meteorological Satellite series operated by Japan Meteorological Agency (JMA) and from GOES-9 operated by the National Oceanographic & Atmospheric Administration (NOAA) for the JMA is used by the BoM to process each daily scene. Analysis of the data series between January 1990 and July 2014 indicated that 4.8% of the scenes were not available (due to data quality issues, technical interruptions, etc.). In order to ensure a continuous data series, gaps for any given day were filled by taking the long-term average across all scenes within 5 days of the target date. 2) Near-surface wind speed (McVicar 2011; https://doi.org/10.25919/5c5106acbcb02) 3) Minimum temperature (Stewart & Nitschke 2018; https://doi.org/10.25919/5c183912b47c7) 4) Maximum temperature (Stewart & Nitschke 2018; https://doi.org/10.25919/5c182fd156dd9) 5) Vapour pressure (Stewart et al. 2020; https://doi.org/10.25919/5e3be4e3511bf)

The most valuable function of meteorological satellites is their ability to monitor atmospheric phenomena globally and uniformly over various areas such as seas, deserts and mountains where surface-based observation is difficult. World Weather Watch (WWW; a core World Meteorological Organization (WMO) program) is supported by multiple geostationary and polar-orbiting meteorological satellites that form space-based observation networks, and the satellite missions JMA started in 1978 have long contributed to the program for the East Asia and Western Pacific region. With their new sensors, Himawari-8/9 will further support and lead to the improvement of meteorological services in a variety of fields including weather forecasting, climate monitoring, natural disaster prevention and safe transportation. Both of JMA’s Himawari-8/9 geostationary meteorological satellites (the successors to the MTSAT series) are equipped with highly improved Advanced Himawari Imagers (AHIs). JMA aims to establish a stable and continuous satellite observation system with redundancy based on twin satellite operation involving Himawari-8 and -9, which is expected to contribute to disaster risk reduction in Asia and the western Pacific until 2029. Himawari data can be found in the GCS buckets: Himawari 8 Himawari 9 Use cases Geostationary satellite data can be used in many different applications, including: Real-time weather monitoring and forecasting including storm and severe weather tracking, tropical cyclone monitoring Climate research and monitoring including spatially dense information about temperature, cloud cover, etc. Monitoring environmental events such as wildfires or floods Research and Development for weather modeling including data needed for ML/AI pipelines Data analytics for businesses where weather plays an important role like energy, logistics, transportation, agriculture, finance, etc. About Google Cloud Storage This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

The input data are Himawari-8 AHI retrievals v1.3, freely available from the Japanese Meteorological Agency. Meteorological Satellite Centre of JMA: https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/sample_hisd.html" rel="nofollow">https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/sample_hisd.html

Ancillary data come from

• OISST Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, W. Hankins, T. Smith, and H.-M. Zhang, 2020: Improvements of the Daily Optimum Sea Surface Temperature (DOISST) - Version 2.1, http://dx.doi.org/10.1175/JTECH-D-19-0177.1" rel="nofollow">doi:10.1175/JTECH-D-19-0177.1
• GFS https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00634" rel="nofollow">Global Forecast System (GFS) [0.5 Deg.] Landing Page
• NOAA daily 4km snow and Ice Cover

  HDF file containing merged NISE and IMS files on a daily gridded product.

  NISE - Brodzik, M. J. and J. S. Stewart. 2016. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5. [Indicate subset used]. Boulder, Colorado, USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://protect-au.mimecast.com/s/YKzMCmOxBVhnMQywHNqafx?domain=doi.org" rel="nofollow">https://doi.org/10.5067/3KB2JPLFPK3R. [2020-10-01].

  IMS - U.S. National Ice Center. 2008, updated daily. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. [Indicate subset used]. Boulder, Colorado, USA. NSIDC: National Snow and Ice Data Center. doi: https://protect-au.mimecast.com/s/LK7BCnxyDVHoWR90u0jafO?domain=doi.org" rel="nofollow">https://doi.org/10.7265/N52R3PMC. [2020-10-01].

L2 product includes the following variables:

• Cloud albedo
• Cloud Optical Depth
• Cloud Phase
• Cloud Type
• Low Cloud and Fog
• Cloud Particle Size Distribution
• Cloud Top Height
• Cloud Top Pressure
• Cloud Top Temperature
• Cloud Liquid Water Path
• Cloud Ice Water Path
• Clear Sky Masks

These variables are derived using the following algorithms:

• ACHA - ABI Cloud Height Algorithm

• DCOMP - Daytime Cloud Optical and Microphysical Properties

• NCOMP - Nighttime Cloud Optical and Microphysical Properties

This dataset is a product of the Centre of Excellence for Climate Extremes (CLEX) Extreme Rainfall research project

NB Some images were not available for processing at the time of the dataset release, we listed all the missing timesteps in the file L2_file_gaps_20210225.txt

More images might become available and then new lists will be created accordingly with the timestamp indicating when the files where added.

We ask users to specify the date they access the datasets when they cite them as indicated in the citation field.

Concerns about widespread human-induced declines in insect populations are mounting, yet little is known about how land-use change modifies the dynamics of insect communities, particularly in understudied regions. Here, we examine how the seasonal activity patterns of ants—key drivers of terrestrial ecosystem functioning—vary with anthropogenic land-cover change on a subtropical island landscape, and whether differences in temperature or species composition can explain observed patterns. Using trap captures sampled biweekly over two years from a biodiversity monitoring network covering Okinawa Island, Japan, we processed 1.2 million individuals and reconstructed activity patterns within and across habitat types. Forest communities exhibited greater temporal variability of activity than those in more developed areas. Using time-series decomposition to deconstruct this pattern, we found that sites with greater human development exhibited ant communities with diminished seasonality, reduce..., The ant activity data used in the analysis was collected with Sea, Land, and Air Malaise (SLAM) traps on Okinawa Island in Japan from 2016-2018, and was processed using the code provided in the Zenodo archive (see README for links and the paper for references). Other datasets come from the Japan Meterological Agency (JMA), in situ climate variables for sampling stations measured on-site, and land-cover data for Okinawa developed by our team. The data used in the analysis is included in the data upload (with JMA and land-cover data files in a Zenodo supplemental information under the CC-By 4 license), and all the analysis code is included in the R package provided in a separate Zenodo software archive. NOTE: All datasets (from Dryad and Zenodo) must be put into a single folder called /data within the main directory of the R analysis package before running any code. Please consult the README for further details on the data and code., All analysis were conducted in R., # Data and code for: Breakdown in seasonal dynamics of subtropical ant communities with land-cover change

Access these datasets on Dryad

This archive contains the data files used in the analyses for "Breakdown in seasonal dynamics of subtropical ant communities with land-cover change", published in Proceedings of the Royal Society B: Biological Sciences (DOI: 10.1098/rspb.2023.1185).

NOTE: All datasets (from Dryad and Zenodo) must be put into a single folder called /data within the main directory of the R analysis package before running any code.

activity_orig.rds: A tibble with the ant activity data (before processing), collected by the Okinawa Environmental Observation Network (OKEON) with Sea, Land, and Air Malaise (SLAM) traps on Okinawa Island in Japan from 2016-2018 at 24 sites sampling different environments across the island (data downloaded from OKEON database in November 2022). The field "cou...

JMA is currently conducting the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q), which covers the period from September 1947 onward to extend the current period of data coverage and improve the quality of long-term reanalysis. The project involves a sophisticated data assimilation system (based on the operational set-up as of December 2018) incorporating development results from the operational NWP system and sea surface temperature analysis achieved since JRA-55 (based on the operational set-up as of December 2009). New datasets of past observations are also assimilated, including rescued historical observations and reprocessed satellite data supplied by meteorological and satellite centers worldwide. Many of the deficiencies of JRA-55 are alleviated in JRA-3Q, providing a high-quality homogeneous reanalysis dataset that covers the previous 75 years.