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Gridded 20-Year Parameterization of a Stochastic Weather Generator (CLIGEN) for South American and African Continents at 0.25 Arc Degree Resolution

CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. The resulting timeseries are statistically similar to observed timeseries considering various temporal scales and climate factors. This dataset consisting of CLIGEN inputs may be used to generate timeseries at any point in a 0.25 arc degree resolution grid covering South American and African continents. Estimated parameter values at each grid point are based on 20-year records taken from global climate datasets. Precipitation parameters are statistically downscaled from grid-scale to point-scale based on observations from globally distributed ground networks representing >10,000 stations. This dataset is intended for use in climate-related research in ungauged areas where observed climate records are unavailable.

The data are formatted as CLIGEN *.par files, which are the only required input for CLIGEN. The files are separated into Africa and South America folders containing n=40936 and n=24588 files, respectively. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign.

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FieldValue
Tags
Climate Change
climatologyMeteorologyAtmosphere
climate models
soil erosion
hydrology
Modified
2023-01-17
Release Date
2022-03-11
Identifier
0edb3d6d-c764-46bb-b605-a2d5f0f43ccc
Spatial / Geographical Coverage Area
POLYGON ((-93.234347105026 -57.279042764978, -93.234347105026 16.720385051694, -24.046872854233 16.720385051694, -24.046872854233 -57.279042764978))
POLYGON ((-24.046872854233 -41.337219333843, -24.046872854233 41.469068342309, 56.953135728836 41.469068342309, 56.953135728836 -41.337219333843))
Publisher
Ag Data Commons
Spatial / Geographical Coverage Location
0.25 arc degree resolution grid covering South America and Africa
Temporal Coverage
January 1, 2000
License
Creative Commons CCZero
Contact Name
Goodrich, Dave
Contact Email
dave.goodrich@usda.gov
Public Access Level
Public
Program Code
005:040 - Department of Agriculture - National Research
Bureau Code
005:18 - Agricultural Research Service
Extended Metadata: 
FieldValue
Authors
Fullhart, Andrew T. (ORCID)
Goodrich, David C. (ORCID)
Ponce-Campos, Guillermo E. (ORCID)
Meles, Menberu B. (ORCID)
McGehee, Ryan P. (ORCID)
Armendariz, Gerardo (ORCID)
Oliveira, Paulo T. (ORCID)
Almeida, Cristiano N. (ORCID)
de Araújo, José C. (ORCID)
Nel, Werner (ORCID)
Peer Reviewed
Yes
Intended Use
Soil erosion modeling, hydrologic modeling, climate change impact studies.
Use Limitations
Applications for this dataset should consider the spatial variability of climate within the resolution of the grid. Strong climate gradients may be poorly represented in some cases, such as in mountainous areas, coastal areas, and other situations. Sub-daily precipitation timeseries produced by CLIGEN generally have higher uncertainty than daily timeseries.
Equipment or Software Used
CLIGEN v5.3
Funding Sources
Agricultural Research Service
2022-13610-012-000D
Primary Article

Fullhart, A., Ponce-Campos, G. E., Meles, M. B., McGehee, R. P., Armendariz, G., Oliveira, P. T. S., Das Neves Almeida, C., de Araújo, J. C., Nel, W., & Goodrich, D. C. (2022). Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN). Big Earth Data, 1-26. https://doi.org/10.1080/20964471.2022.2136610
Related Articles

Fullhart, A., Ponce-Campos, G. E., Meles, M. B., McGehee, R. P., Armendariz, G., Oliveira, P. T. S., Das Neves Almeida, C., de Araújo, J. C., Nel, W., & Goodrich, D. C. (2022). Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN). Big Earth Data, 1-26. https://doi.org/10.1080/20964471.2022.2136610

Fullhart, A., Goodrich, D. C., Meles, M. B., Oliveira, P. T. S., Almeida, C. das N., de Araújo, J. C., & Burns, S. (2023). Atlas of precipitation extremes for South America and Africa based on depth-duration-frequency relationships in a stochastic weather generator dataset. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2023.01.004
Method Citation

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5(1). https://doi.org/10.1038/sdata.2017.191

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. https://doi.org/10.1002/qj.3803

Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S. H. (2015). NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4, 26.

Rui, H., Beaudoing, H., and Loeser, C.: README Document for NASA GLDAS Version 2 Data Products, Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA Goddard Space Flight Center, https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/GLDAS_NOAH025_3H.2.0/d..., last access: 6 March 2022.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Dataset DOI (digital object identifier)
10.15482/USDA.ADC/1524754
Product Type
Dataset
Cites Other Datasets
ERA5
TerraClimate
GPM-IMERG
GLDAS
NOAA GHCN-Daily
NOAA ASOS
International Climate Benchmarks and Input Parameters for a Stochastic Weather Generator, CLIGEN
FieldValue
OSTI Identifier
ISO Topic(s)
climatologyMeteorologyAtmosphere
National Agricultural Library Thesaurus Term
climate models
time series analysis
weather
climatic factors
data collection
climate change
climatology
soil erosion
hydrology
South America
Africa
Ag Data Commons Keywords
  • Agroecosystems & Environment
  • Weather and Climate
  • Agroecosystems & Environment
  • Water
  • Agroecosystems & Environment
Program
Long-Term Agroecosystem Research - LTAR

Suggested Citations:

(original article) Fullhart, A., Ponce-Campos, G. E., Meles, M. B., McGehee, R. P., Armendariz, G., Oliveira, P. T. S., Das Neves Almeida, C., de Araújo, J. C., Nel, W., & Goodrich, D. C. (2022). Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN). Big Earth Data, 1-26. https://doi.org/10.1080/20964471.2022.2136610

(dataset) Fullhart, Andrew T.; Goodrich, David C.; Ponce-Campos, Guillermo E.; Meles, Menberu B.; McGehee, Ryan P.; Armendariz, Gerardo; Oliveira, Paulo T.; Almeida, Cristiano N.; de Araújo, José C.; Nel, Werner (2022). Gridded 20-Year Parameterization of a Stochastic Weather Generator (CLIGEN) for South American and African Continents at 0.25 Arc Degree Resolution. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1524754. Accessed 2023-06-02.