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Multispectral Satellite Application Course

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Presentation on theme: "Multispectral Satellite Application Course"— Presentation transcript:

1 Multispectral Satellite Application Course
BMKG Multispectral Satellite Application Course by Asteria S. Handayani Research & Development Center of BMKG -to be presented at the WMO Training Development Workshop, Melbourne, 28 August 2017-

2 Course Description Audience: Training Goals:
Training Goals: to strengthen operational forecaster capacity in multispectral satellite application using RGB compositing technique  to strengthen BMKG performance and confidence as a whole with improved monitoring of the rapidly chang ing weather in Indonesia. forecaster Job competencies: researcher analyze and monitor continually the evolving meteorological feature forecast meteorological phenomena using multispectral satellite application in integration with other data effectively communicate the information to users trainer

3 Learning Outcomes & Contents
access data from Himawari-8 geostationary satellite data server select the most appropriate channels in Himawari-8 display and manipulate the imagery apply enhancement to imagery use generated RGB product integrate other data deliver the information   Learning outcomes: access data from Himawari-8 geostationary satellite data server at BMKG Database Center select the most appropriate channels in Himawari-8, combination of channels and RGB products for tropical region (using currently developed tropical RGB products recipes) effectively display and manipulate the imagery as combined channels, singly or animation apply enhancement to imagery use generated RGB product to analyze current meteorological phenomena in the Maritime Continent with confidence integrate the appropriate other data (radar, NWP, and observation data) into the analysis deliver the whole package of information to the users in clear and concise manner Himawari-8 satellite RGB concept Weather information Data integration Course contents:

4 Key Design Decisions Learning Solutions: blended learning
(asynchronous & synchronously) self-directed learning working in teams learning from colleagues online learning / webinar Learning Activities: discussion forums team assignments case studies quizzes simulator exercise Learning Assessment: pre-assessment: open-ended questions on the nominees’ skill backgrounds formative: quizzes - case studies - simulator exercise - webinar summative assessment Training evaluations : online questionnaires post-course discussion during the last webinar ask report from Regional Office managers and BMKG Headquat er Units managers 3 months after (Evaluation Level 3) Training evaluations : online questionnaires consist of close-ended and open-ended questions in the middle of the course and at the end of the course (before the last webinar is held) post-course discussion during the last webinar at the end of the course to evaluate the whole learning process together and to gather feedbacks for the improvement of similar course in the future ask Regional Offices managers and BMKG Headquater Units managers to give reports on participants’ performance 3 months after the course, whether participants can apply their learning to their work according to the goal of the course and whether their skills and performances have improved afterward.

5 Challenges & Bucket list
time management skills and experience of training staff nomination of participant lack of reference in Indonesian language to have an external expert (WMO VLab /BMTC/EUMETSAT / JMA) to have more modules & references in satellite applicati on in Indonesian language - Most participants of the course are operational forecasters who work in shift. This will be a challenge for them to manage their work and their participation to the course at the same time, since mostly routine work requires them to be focus and prepared for any sudden interviews coming from the media. -Only forecasters in BMKG Headquarter who has already familiar with Himawari-8 data processing with multispectral analysis. It is included in their daily duties. Forecasters in regional offices have less skills and experience in the same data and application, since it is not obligatory for them to use it. This is also the case for participants from research and training centers. They are not exposed to this topic often. - Often there were cases where nominations of participation in courses from regional offices were given to the wrong employee who has less prerequisite knowledge and skill in the course topic. THANK YOU


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