A Python API for Dakotamiracle network dance marathon Hydrotrend: 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 recurrence interval distribution RI 10 12 1.0 0.8 0.6 0.4 0.2 0.0 14

A Python API for DakotaMark Piper, Eric Hutton, and James Syvitski

CSDMSUniversity of Colorado Boulder

Agenda

● Uncertainty quantification● Dakota● The CSDMS Dakota Interface (a.k.a. Dakotathon)● An experiment● Summary and future work

(image courtesy J. Adam Stephens and Laura Swiler, SNL)

dakotathon

Given uncertain T and P, what’s the likelihood of the Waipaoa producing hyperpycnal plumes?

Mulder et al. (2003)

An experiment

● 1000-yr Hydrotrend runs with defaults, except for T and P, which are uniformly distributed about ±10% from default values, and L = 3.0

● 100 samples from T-P parameter space are selected using LHS

● Count of daily output Cs > 40 kg m-3 is the response statistic

● Use moments, correlations, PDF, and CDF to assess RI

https://github.com/mdpiper/AGU-2016

RI = 8.4 ± 0.4 yr

https://github.com/csdms/dakota

Summary

● Uncertainty quantification is vital for communicating model predictions to policymakers and to the public

● Dakota is powerful, but it requires user code to interface with a model

● Dakotathon presents an easier-to-use Python interface

Future work

● Expose more Dakota analysis techniques

● Incorporate Dakotathon into the CSDMS Web Modeling Tool, WMT

● Perform a sensitivity study on Hydrotrend’s L

Thank you!

Dakotathon

Experiments

Email

GitHub, Twitter

https://github.com/csdms/dakota

https://github.com/mdpiper/AGU-2016

[email protected]

@mdpiper