Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia
Canonical correlation analysis (CCA) is used to improve the skill of seasonal forecasts in the Orinoquía region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet days are predicted, as rice yields simulated by a calibrated crop model are better correlated wit...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
American Meteorological Society
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/106601 |
| _version_ | 1855521452857491456 |
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| author | Fernandes, Katia Muñoz, Angel Ramírez Villegas, Julián Armando Agudelo, Diego Llanos Herrera, Lizeth Esquivel, Alejandra Rodríguez Espinoza, Jeferson Prager, Steven D. |
| author_browse | Agudelo, Diego Esquivel, Alejandra Fernandes, Katia Llanos Herrera, Lizeth Muñoz, Angel Prager, Steven D. Ramírez Villegas, Julián Armando Rodríguez Espinoza, Jeferson |
| author_facet | Fernandes, Katia Muñoz, Angel Ramírez Villegas, Julián Armando Agudelo, Diego Llanos Herrera, Lizeth Esquivel, Alejandra Rodríguez Espinoza, Jeferson Prager, Steven D. |
| author_sort | Fernandes, Katia |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Canonical correlation analysis (CCA) is used to improve the skill of seasonal forecasts in the Orinoquía region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet days are predicted, as rice yields simulated by a calibrated crop model are better correlated with wet-day frequency than with precipitation amounts in June–August (JJA). Prediction of the frequency of wet days, using as predictors variables from the NCEP Climate Forecast System, version 2 (CFSv2), results in a forecast with higher skill than models predicting seasonal precipitation amounts. Using wet-day frequency as an alternative climate variable reveals that the distribution of daily rainfall is both more relevant for rice yield variability and more skillfully predicted than seasonal precipitation amounts. Forecast skill can also be improved by using the Climate Hazards Infrared Precipitation with Stations (CHIRPS) merged satellite–station JJA precipitation as the predictand in a CCA model, especially if the predictor is CFSv2 vertically integrated meridional moisture flux (VQ). The probabilistic hindcast derived from the CCA model using CHIRPS as the predictand can successfully discriminate above-normal, normal, and below-normal terciles of over 80% of the stations in the region. This is particularly relevant for stations that, due to discontinuity in their time series, are not included in station-only CCA models but are still in need of probabilistic seasonal forecasts. Finally, CFSv2 VQ performs better than precipitation as the predictor in CCA, which we attribute to CFSv2 being more internally consistent in regards to sea surface temperature (SST)-forced VQ variability than to SST-forced precipitation variability in the Orinoquía region. |
| format | Journal Article |
| id | CGSpace106601 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | American Meteorological Society |
| publisherStr | American Meteorological Society |
| record_format | dspace |
| spelling | CGSpace1066012025-03-13T09:44:07Z Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia Fernandes, Katia Muñoz, Angel Ramírez Villegas, Julián Armando Agudelo, Diego Llanos Herrera, Lizeth Esquivel, Alejandra Rodríguez Espinoza, Jeferson Prager, Steven D. forecasting agriculture climate change Canonical correlation analysis (CCA) is used to improve the skill of seasonal forecasts in the Orinoquía region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet days are predicted, as rice yields simulated by a calibrated crop model are better correlated with wet-day frequency than with precipitation amounts in June–August (JJA). Prediction of the frequency of wet days, using as predictors variables from the NCEP Climate Forecast System, version 2 (CFSv2), results in a forecast with higher skill than models predicting seasonal precipitation amounts. Using wet-day frequency as an alternative climate variable reveals that the distribution of daily rainfall is both more relevant for rice yield variability and more skillfully predicted than seasonal precipitation amounts. Forecast skill can also be improved by using the Climate Hazards Infrared Precipitation with Stations (CHIRPS) merged satellite–station JJA precipitation as the predictand in a CCA model, especially if the predictor is CFSv2 vertically integrated meridional moisture flux (VQ). The probabilistic hindcast derived from the CCA model using CHIRPS as the predictand can successfully discriminate above-normal, normal, and below-normal terciles of over 80% of the stations in the region. This is particularly relevant for stations that, due to discontinuity in their time series, are not included in station-only CCA models but are still in need of probabilistic seasonal forecasts. Finally, CFSv2 VQ performs better than precipitation as the predictor in CCA, which we attribute to CFSv2 being more internally consistent in regards to sea surface temperature (SST)-forced VQ variability than to SST-forced precipitation variability in the Orinoquía region. 2020-04-01 2020-01-16T16:07:21Z 2020-01-16T16:07:21Z Journal Article https://hdl.handle.net/10568/106601 en Open Access American Meteorological Society Fernandes, Katia; Muñoz, Angel; Ramirez-Villegas, Julian; Agudelo, Diego; Llanos-Herrera, Lizeth; Esquivel, Alejandra; Rodriguez-Espinoza , Jeferson & Prager, Steven D. (2020). Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia. Weather and Forecasting, 35(2) p. 437-449. |
| spellingShingle | forecasting agriculture climate change Fernandes, Katia Muñoz, Angel Ramírez Villegas, Julián Armando Agudelo, Diego Llanos Herrera, Lizeth Esquivel, Alejandra Rodríguez Espinoza, Jeferson Prager, Steven D. Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title | Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title_full | Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title_fullStr | Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title_full_unstemmed | Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title_short | Improving seasonal precipitation forecast for agriculture in the Orinoquía Region of Colombia |
| title_sort | improving seasonal precipitation forecast for agriculture in the orinoquia region of colombia |
| topic | forecasting agriculture climate change |
| url | https://hdl.handle.net/10568/106601 |
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