Combining spatial information sources while accounting for systematic errors in proxies

Statistics – Methodology

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

5 figures, 2 tables

Scientific paper

Environmental research increasingly uses high-dimensional remote sensing and numerical model output to help fill space-time gaps between traditional observations. Such output is often a noisy proxy for the process of interest. Thus one needs to separate and assess the signal and noise (often called discrepancy) in the proxy given complicated spatio-temporal dependencies. Here I extend a popular two-likelihood hierarchical model using a more flexible representation for the discrepancy. I employ the little-used Markov random field approximation to a thin plate spline, which can capture small-scale discrepancy in a computationally efficient manner while better modeling smooth processes than standard conditional auto-regressive models. The increased flexibility reduces identifiability, but the lack of identifiability is inherent in the scientific context. I model particulate matter air pollution using satellite aerosol and atmospheric model output proxies. The estimated discrepancies occur at a variety of spatial scales, with small-scale discrepancy particularly important. The examples indicate little predictive improvement over modeling the observations alone. Similarly, in simulations with an informative proxy, the presence of discrepancy and resulting identifiability issues prevent improvement in prediction. The results highlight but do not resolve the critical question of how best to use proxy information while minimizing the potential for proxy-induced error.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Combining spatial information sources while accounting for systematic errors in proxies does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Combining spatial information sources while accounting for systematic errors in proxies, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Combining spatial information sources while accounting for systematic errors in proxies will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-502465

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.