chirps: API Client for the CHIRPS Precipitation Data in R
The chirps package provides functionalities for reproducible analysis in R (R Core Team, 2020) using the CHIRPS (Funk et al., 2015) data. CHIRPS is daily precipitation data set developed by the Climate Hazards Group (Funk et al., 2015) for high resolution precipitation gridded data. Spanning 50◦ S t...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Open Journal
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/108710 |
| _version_ | 1855525136826892288 |
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| author | Sousa, Kauê de Sparks, Adam H. Ashmall, William Etten, Jacob van Solberg, Svein Ø. |
| author_browse | Ashmall, William Etten, Jacob van Solberg, Svein Ø. Sousa, Kauê de Sparks, Adam H. |
| author_facet | Sousa, Kauê de Sparks, Adam H. Ashmall, William Etten, Jacob van Solberg, Svein Ø. |
| author_sort | Sousa, Kauê de |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The chirps package provides functionalities for reproducible analysis in R (R Core Team, 2020) using the CHIRPS (Funk et al., 2015) data. CHIRPS is daily precipitation data set developed by the Climate Hazards Group (Funk et al., 2015) for high resolution precipitation gridded data. Spanning 50◦ S to 50◦ N (and all longitudes) and ranging from 1981 to nearpresent (normally with a 45 day lag), CHIRPS incorporates 0.05 arc-degree resolution satellite imagery, and in-situ station data to create gridded precipitation time series for trend analysis and seasonal drought monitoring (Funk et al., 2015). Additionally, the package provides the API client for the IMERG (Huffman et al., 2014) and ESI (SERVIR Global, 2019a) data. The Integrated Multi-satelliE Retrievals for GPM (IMERG) data provides near-real time global observations of rainfall at 0.5 arc-degree resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall and flood impacts from tropical cyclones and other storm systems. IMERG is a daily precipitation dataset available from 2015 to near-present. The evaporative stress index (ESI) data describes temporal anomalies in evapotranspiration produced weekly at 0.25 arc-degree resolution for the entire globe (Anderson et al., 2011). The ESI data is based on satellite observations of land surface temperature, which are used to estimate water loss due to evapotranspiration (the sum of evaporation and plant transpiration from the Earth’s land and ocean surface to the atmosphere). The ESI data is available from 2001 to near-present. When using these data sets in publications please cite Funk et al. (2015) for CHIRPS, Huffman et al. (2014) |
| format | Journal Article |
| id | CGSpace108710 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Open Journal |
| publisherStr | Open Journal |
| record_format | dspace |
| spelling | CGSpace1087102025-11-12T05:42:02Z chirps: API Client for the CHIRPS Precipitation Data in R Sousa, Kauê de Sparks, Adam H. Ashmall, William Etten, Jacob van Solberg, Svein Ø. data management data analysis precipitation equipment gestión de datos análisis de datos precipitación atmosférica The chirps package provides functionalities for reproducible analysis in R (R Core Team, 2020) using the CHIRPS (Funk et al., 2015) data. CHIRPS is daily precipitation data set developed by the Climate Hazards Group (Funk et al., 2015) for high resolution precipitation gridded data. Spanning 50◦ S to 50◦ N (and all longitudes) and ranging from 1981 to nearpresent (normally with a 45 day lag), CHIRPS incorporates 0.05 arc-degree resolution satellite imagery, and in-situ station data to create gridded precipitation time series for trend analysis and seasonal drought monitoring (Funk et al., 2015). Additionally, the package provides the API client for the IMERG (Huffman et al., 2014) and ESI (SERVIR Global, 2019a) data. The Integrated Multi-satelliE Retrievals for GPM (IMERG) data provides near-real time global observations of rainfall at 0.5 arc-degree resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall and flood impacts from tropical cyclones and other storm systems. IMERG is a daily precipitation dataset available from 2015 to near-present. The evaporative stress index (ESI) data describes temporal anomalies in evapotranspiration produced weekly at 0.25 arc-degree resolution for the entire globe (Anderson et al., 2011). The ESI data is based on satellite observations of land surface temperature, which are used to estimate water loss due to evapotranspiration (the sum of evaporation and plant transpiration from the Earth’s land and ocean surface to the atmosphere). The ESI data is available from 2001 to near-present. When using these data sets in publications please cite Funk et al. (2015) for CHIRPS, Huffman et al. (2014) 2020-07-01 2020-07-07T10:59:16Z 2020-07-07T10:59:16Z Journal Article https://hdl.handle.net/10568/108710 en Open Access application/pdf Open Journal de Sousa, K.; Sparks, A.H.; Ashmall, W.; van Etten, J.; Solberg, S.Ø. (2020) chirps: API Client for the CHIRPS Precipitation Data in R. Journal of Open Source Software, 5(51), 2419 ISSN: 2475-9066 |
| spellingShingle | data management data analysis precipitation equipment gestión de datos análisis de datos precipitación atmosférica Sousa, Kauê de Sparks, Adam H. Ashmall, William Etten, Jacob van Solberg, Svein Ø. chirps: API Client for the CHIRPS Precipitation Data in R |
| title | chirps: API Client for the CHIRPS Precipitation Data in R |
| title_full | chirps: API Client for the CHIRPS Precipitation Data in R |
| title_fullStr | chirps: API Client for the CHIRPS Precipitation Data in R |
| title_full_unstemmed | chirps: API Client for the CHIRPS Precipitation Data in R |
| title_short | chirps: API Client for the CHIRPS Precipitation Data in R |
| title_sort | chirps api client for the chirps precipitation data in r |
| topic | data management data analysis precipitation equipment gestión de datos análisis de datos precipitación atmosférica |
| url | https://hdl.handle.net/10568/108710 |
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