{"query_echo":{"q":"","filters":{"analysis_type":["technology transfer"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'technology transfer' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=technology+transfer","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"sei-001388","url":"https://climateinsightsdirectory.org/document/sei-001388","title":"GMET in Bolivia: building gridded climate datasets for water resource studies","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"briefing","language":"en","date_published":"2019-02","source_url":"https://www.sei.org/wp-content/uploads/2019/03/gmet-in-bolivia.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The Stockholm Environment Institute (SEI), in collaboration with the National Center for Atmospheric Research (NCAR) and Bolivian partners, applied the Gridded Meteorological Ensemble Tool (GMET) to generate daily climate grids for Bolivia and parts of neighbouring countries from 1980 to 2016. Using a database of over 400 stations, the project demonstrated GMET's ability to incorporate prediction errors and uncertainty into spatial precipitation and temperature data, though it found that satellite-based data remained necessary to fill gaps in data-sparse regions. The project included a technology transfer to train local experts and the release of the tool as open-source software.","ai_generated":true,"unit":"sei-001388#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Gridded Meteorological Ensemble Tool (GMET) addresses the challenge of sparse and unevenly distributed meteorological stations by creating continuous gridded products that incorporate data and prediction errors into the analysis.","ai_generated":true,"unit":"sei-001388#insight-1","date_modified":"2026-10-23"},{"text":"In its first application outside the US, GMET used a database of more than 400 stations to produce daily climate grids for Bolivia and parts of Argentina, Brazil, Chile, Paraguay, and Peru for the period 1980–2016.","ai_generated":true,"unit":"sei-001388#insight-2","date_modified":"2026-10-23"},{"text":"The application of GMET in Bolivia showed that while the tool realistically represents spatial patterns and quantity in areas with sufficient station density, satellite-based data is still required to fill gaps in data-sparse regions to complete water balance calculations.","ai_generated":true,"unit":"sei-001388#insight-3","date_modified":"2026-10-23"},{"text":"The project focused on local capacity building and technology transfer, training Bolivian experts via the Laboratory of Atmospheric Physics (LFA) to use R software and Linux to run and update the open-source GMET tool.","ai_generated":true,"unit":"sei-001388#insight-4","date_modified":"2026-10-23"}],"climate_tags":["precipitation","temperature"],"geography_discussed":["United States","Peru","Brazil","Paraguay","Argentina","Chile"],"frame_perspective":[],"analysis_type":["case study","technology transfer"]}]}