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Tana Page

Adjunct Faculty - Teaching
Public Health

2148 LSB
Provo, UT 84602

Biography

Courses Taught

Winter 2019

  • IAS 369R: International Internship Prep Section 018

Fall 2018

  • HLTH 381: Methods & Curriculum A Section 001

Fall 2017

  • HLTH 381: Methods & Curriculum A Section 001

Fall 2016

  • HLTH 381: Methods & Curriculum A Section 001

Courses Taught

2020

  • HLTH 381 : Section 001

2019

  • HLTH 381 : Section 001
  • IAS 369R: Section 018

2018

  • HLTH 381 : Section 001

2017

  • HLTH 381 : Section 001

2016

  • HLTH 381 : Section 001
  • HLTH 276R: Section 001

2015

  • HLTH 276R: Section 1
  • HLTH 381 : Section 001
  • HLTH 276R: Section 001

2014

  • HLTH 276R: Section 001
  • HLTH 381 : Section 001
  • HLTH 276R: Section 001

2013

  • HLTH 276R: Section 001
  • HLTH 381 : Section 001
  • HLTH 276R: Section 001

2012

  • HLTH 276R: Section 001
  • HLTH 381 : Section 001
  • HLTH 276R: Section 001
  • HLTH 381 : Section 001
  • HLTH 361 : Section 001

2011

  • HLTH 276R: Section 001
  • HLTH 381 : Section 001
  • HLTH 361 : Section 001
  • HLTH 361 : Section 001
  • HLTH 276R: Section 001
  • HLTH 361 : Section 002

2010

  • HLTH 276R: Section 001
  • HLTH 361 : Section 001
  • HLTH 361 : Section 002
  • HLTH 276R: Section 001
  • HLTH 361 : Section 002

2009

  • HLTH 276R: Section 001
  • HLTH 361 : Section 001
  • HLTH 361 : Section 002
  • HLTH 276R: Section 001
  • HLTH 361 : Section 003

2008

  • HLTH 361 : Section 001
  • HLTH 361 : Section 002
  • HLTH 421 : Section 001
  • HLTH 361 : Section 003
  • HLTH 421 : Section 001

2007

  • HLTH 361 : Section 001
  • HLTH 361 : Section 002
  • HLTH 421 : Section 001
  • HLTH 361 : Section 003

2006

  • HLTH 361 : Section 001
  • HLTH 361 : Section 002

Publications

  • Page RM, Page TS. 2015. Promoting Health and Emotional Well-Being in Your Classroom, 6th Edition . Sudbury, MA: Jones and Bartlett.

Presentations

  • Long DG, Lindsley R, Madsen N, Page TS. Improved ASCAT and OSCAT Spatial Response Functions for Enhanced Resolution Processing. International Ocean Vector Wind Science Team. In reconstruction processing, the surface backscatter is determined at high spatial resolution using the spatial response function (SRF) of the measurements, a dense sampling of the surface, and ground-based signal processing The surface backscatter can be used for wind retrieval or land/ice studies It has long been believed that accurate reconstruction requires a precisely detailed description of the SRF and the sampling geometry since the reconstruction processing has the effect of inverting the SRF However, there is a tradeoff between resolution and noise in the reconstructed surface backscatter To reduce the sensitivity of the reconstruction to noise, mathematical techniques for “regularization” of the inverse are often employed Regularization can be explicitly included as an additive term in the reconstruction inverse, or implicitly included in iterative reconstruction algorithm by truncating the number of iterations in iterative reconstruction Studies of the reconstruction process for scatterometer data have revealed that regularization has the side effect of reducing the requirements on the knowledge and accuracy of the SRF used in the reconstruction processing To date, the sensitivity of the backscatter field to accuracy of the SRF employed in the reconstruction when regularization is employed has not been explored in much detail To support such a study in this paper we develop improved descriptions of the SRF for OSCAT and ASCAT and use these in studying the sensitivity of the reconstruction to SRF accuracy We very briefly describe our SRF modeling techniques, and use simulation to evaluate the effects of approximations in the SRF on the reconstructed surface backscatter Sample reconstruction results are provided for both the ASCAT and OSCAT sensors For OSCAT SRF modeling we use a detailed model of the onboard signal processing, the antenna pattern, and the measurement geometry to generate the SRF for each of the measurement slices for each pulse similar to the proven approach for QuikSCAT For ASCAT, a slightly different approach is used since multiple pulses are averaged into a single fine resolution sigma-0 measurement in reported datasets (SZF) We extend our previous single-pulse ASCAT SRF models to be weighted averages of the individual single-pulse SRFs, and compare the resulting SRF with previous models To evaluate the reconstruction performance, a number of synthetic images are used to generate simulated sigma-0 measurements using the full SRF models based on the observation geometry of actual data collections Then various approximations to the SRF are used to reconstruct the synthetic “truth” image using a variety of regularization values The differences between the “truth” and estimated backscatter images are then evaluated to understand the tradeoffs in the accuracy of the SRF, the regularization, and the precision of the reconstructed signal For land imaging we combine data from multiple passes and assume a temporally stable surface backscatter to generate enhanced resolution images For ultra-high resolution (UHR) ocean wind retrieval, we cannot assume temporal stability and so use only single pass data The latter has coarser resolution than the former, but still provides significantly higher resolution wind measurements than conventional techniques The higher resolution can provide insights into mesoscale processes and provide wind estimates closer to land than conventional techniques We find that for the range of regularization values desired to minimize noise effects, the key requirement on the SRF description accuracy is the size of the antenna footprint, and that the reconstructed results have limited sensitivity to errors in the SRF roll-off characteristics This means that high precision descriptions of the SRF are not required for accurate scatterometer reconstruction, while still ensuring accurate wind retrieval and land backscatter measurements . June, 2014.