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--- tools:sunset_sunset [2023/07/12 06:31]
nperez created
+++ tools:sunset_sunset [2025/05/12 07:59] (current)
abatalla [Materials and documentation]
@@ Line 1: / Line 1: @@
 ===== SUNSET =====
-=== SUbseasoNal to decadal climate forecast post-processIng and asSEsmenT suite ===
+=== SUbseasoNal to decadal climate forecast post-processing and asSEsmenT suite ===
+SUNSET is an R-based tool that provides climate services for sub-seasonal, seasonal and decadal climate forecast horizons. The tool post-processes climate forecast outputs by applying state-of-the-art methodologies to tailor climate products for each application and sector (e.g.: agriculture, energy, water management, or health). Its modular design allows the technicians and researchers to decide on the post-processing required steps, such as regridding, anomalies, downscaling, bias-adjustment methods, as well as the products definition by deciding on the forecast system and reference datasets, variables, and forecast horizon among others.
+The tool also allows the creation and visualization of climate forecast products, such as maps for the most likely terciles, and performs the verification of the products using user-defined metrics, which can be visualized on maps and scorecards.
+The integration of Autosubmit in the tool allows users to easily parallelize the computation in HPC machines.
+You can join the [[http://mailman3.bsc.es/postorius/lists/ess-verification-suite.bsc.es/|SUNSET mailing list]] to receive the latest updates about news and information on the next internal meetings.
+For more information, visit its **GitLab project**: https://earth.bsc.es/gitlab/es/sunset
+We use issues to identify and address bugs and propose new developments in the codebase. If you find a problem in our tools, please open an issue in the GitLab project.
+===== Past trainings =====
+  * {{ :tools:day2_04_handson_r_tools.pdf | BSC Training (PATC) 2023 - Climate Forecast Analysis hands-on tutorial: R tools}}
+===== Materials and documentation =====
+  * For detailed documentation and usage guidelines, please refer to the [[https://earth.bsc.es/gitlab/es/sunset/-/wikis/home|SUNSET Wiki]] available on the GitLab project.
+  * {{ :tools:ddt_presentation_-_multimodel_autosubmit.pptx.pdf | How SUNSET uses Autosubmit for multi-model forecast verification workflows}}
+===== Related works and publications =====
+==== 2024 ====
+    - CERISE General Assembly 2024 (Reading, UK, 17-19 January 2024), SUNSET: the SUbseasoNal to decadal climate forecast post-processiNg and asSEssmenT suite, V. Agudetse et al. (CERISE-101082139). {{{{ :library:external:sunset_poster_cerise.pdf | PDF}}
+    - Project for Computing Programming and its Aplications (CPIA) class (4th year of Physics Engineering), Provenance Integration in R seasonal-to-decadal verification workflow, A. Puiggrós et al.  {{ :library:external:provenance_mini-project.pdf | PDF}}
+    - Final Degree Project for Physics Engineering, Provenance Integration in R seasonal-to-decadal verification workflow, A. Puiggrós et al.{{ :tools:memoria_albert_puiggros.pdf | PDF}}
+    - Poster, Provenance Integration in R seasonal-to-decadal verification workflow, A. Puiggrós et al.{{ :tools:provenance_for_sunset_poster.pdf | PDF}}
-SUNSET is an R-based tool that provides climate services for sub-seasonal, seasonal and decadal climate forecast horizons. The tool post-processes climate forecast outputs by applying state-of-the-art methodologies to tailor climate products for each application and sector (e.g.: agriculture, energy, water management, or health). Its modular design allows the technicians and researchers to decide on the post-processing required steps, such as regridding, anomalies, downscaling, bias-adjustment methods, as well as the products definition by deciding on the forecast system and reference datasets, variables, and forecast horizon among others. The tool also allows the creation and visualization of climate forecast products, such as maps for the most likely terciles, and performs the verification of the products using user-defined metrics, which can be visualized on maps and scorecards. The integration of Autosubmit in the tool allows users to parallelize the computation in HPC machines.

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