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Application: how to find the students who are struggling

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What kind of artefacts may be produced?

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Below you can access nine exercises, which teach forecasters how to identify various synoptic scale and mesoscale systems and features and their characteristics in satellite imagery and products.

These exercises are prepared in partnership with ASMET team, and the cases are representative for African regions.

Les Exercices en langue française

For each one of the these topics, students work through a series of guided exercises on their own. These are supplemented by group discussions of related questions. The concluding activity for the course enables participants to demonstrate what they have learned by finding a case and applying the relevant questions to it.

Note that the cases only address system/feature identification in satellite imagery and do not cover forecasting.

Disclaimer:

Please be aware that the exercises are been reviewed and may contain minor inconsistencies.
You can use these exercises within your meteorology courses, either online or as a part of classroom instruction.
If you want to use the exercises locally, you can download each exercise as a zipped file.

# data[].attributes.attachment_link.description
Below you can access nine exercises, which teach forecasters how to identify various synoptic scale and mesoscale systems and features and their characteristics in satellite imagery and products. These exercises are prepared in partnership with ASMET team, and the cases are representative for African regions.

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The structure of the training course 'Satellite meteorology' at OSEU

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The structure of the training course 'Satellite meteorology' at OSEU

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Demonstrate: showing a work flow in video and screen capture

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Demonstration: Students experience full working examples

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LTPy - Learning tool for Python on Atmospheric Composition Data is a Python-based training course on Atmospheric Composition Data. The training course covers modules on data access, handling and processing, visualisation as well as case studies.

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LTPy - Learning tool for Python on Atmospheric Composition Data is a Python-based training course on Atmospheric Composition Data. The training course covers modules on data access, handling and processing, visualisation as well as case studies.

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Application: students post examples from their work as they go

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Application: students work through a real case in fast time

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Application: get students to work through small analysis cases

# data[].attributes.attachment_link.readable_text
Below you can access nine exercises, which teach forecasters how to identify various synoptic scale and mesoscale systems and features and their characteristics in satellite imagery and products.

These exercises are prepared in partnership with ASMET team, and the cases are representative for African regions.

Les Exercices en langue française

For each one of the these topics, students work through a series of guided exercises on their own. These are supplemented by group discussions of related questions. The concluding activity for the course enables participants to demonstrate what they have learned by finding a case and applying the relevant questions to it.

Note that the cases only address system/feature identification in satellite imagery and do not cover forecasting.

Disclaimer:

Please be aware that the exercises are been reviewed and may contain minor inconsistencies.
You can use these exercises within your meteorology courses, either online or as a part of classroom instruction.
If you want to use the exercises locally, you can download each exercise as a zipped file.

# data[].attributes.attachment_link.description
Below you can access nine exercises, which teach forecasters how to identify various synoptic scale and mesoscale systems and features and their characteristics in satellite imagery and products. These exercises are prepared in partnership with ASMET team, and the cases are representative for African regions.

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Activation: getting users to show what they o now by annotating images