Difference between revisions of "HABSIM"

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'''High Altitude Balloon Simulator''' (HABSIM) is a web server ([https://predict.stanfordssi.org/ link]) developed by the SSI [[Balloons Team]] for customizable, ensemble-based, long range trajectory predictions. The aims of the prediction server, in comparison to existing ones, are as follows:
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'''High Altitude Balloon Simulator''' (HABSIM) is a web server ([https://predict.stanfordssi.org/ link]) developed by the SSI [[Balloons]] Team for customizable, ensemble-based, long range trajectory predictions. The aims of the prediction server, in comparison to existing ones, are as follows:
  
 
*Offer probabilistic predictions based on the Monte-Carlo GEFS run set.
 
*Offer probabilistic predictions based on the Monte-Carlo GEFS run set.

Revision as of 07:26, 12 August 2019

High Altitude Balloon Simulator (HABSIM) is a web server (link) developed by the SSI Balloons Team for customizable, ensemble-based, long range trajectory predictions. The aims of the prediction server, in comparison to existing ones, are as follows:

  • Offer probabilistic predictions based on the Monte-Carlo GEFS run set.
  • Offer predictions out to the maximum time window permitted by GEFS (+378 hrs).
  • Offer finer and broader control over simulation parameters, including ascent rate, duration, simulation step, and drift coefficient.
  • Export an intuitive prediction API which can be used to simulate arbitrary flight profiles.
  • Offer a web-based UI which captures the functionality and flexibility of the API.

Primary clients of HABSIM include Cycloon and Spaceshot. The project source code may be viewed on GitHub. To get involved, please contact SlackLogo.png@{{{display-name}}}  and SlackLogo.png@{{{display-name}}}  and join SlackLogo.png#balloons-habsim !

PyHABSIM is a Python package which exploits the HABSIM API to offer an intuitive, object-oriented, highly customizable prediction interface, wind and elevation data accession utilities, and trajectory analysis and optimization methods in Python. The package home pages may be found at PyPI or GitHub. Please contact SlackLogo.png@{{{display-name}}}  to get involved!