Tim Hoar Associate Scientist Institute for Mathematics Applied to Geosciences National Center for Atmospheric Research Boulder, CO 80307 thoar .at. ucar.edu 303.497.1708 (voice) 303.497.1298 (fax)

Projects    Publications    MySCD    links

I have two main duties as an Associate Scientist in IMAGe at the National Center for Atmospheric Research. Essentially, my job is to ensure that the Geophysical Statistics Project (GSP) visitors, post-docs, and collaborators have everything they need -- be it data, code, hardware, methodologies, programming, programming advice, home repair ... AND that anyone, anywhere, on any architecture -- can download, install, and run the Data Assimilation Research Testbed (DART) software developed by the Data Assimilation Research Section (DAReS). Easy.

Select Projects

Data Assimilation Research Testbed

The Data Assimilation Research Testbed (DART) is an effort led by Jeff Anderson to develop a suite of software to explore data assimilation methodologies instead of data assimilation programming. Our intent is to develop a very modular suite of models and observations to facilitate research in data assimilation and forecasting. My presentation at the "Workshop on Ensemble Methods" in Exeter (Oct 2004) was titled: An Operational-Quality Ensemble Assimilation System for NCAR's CAM Climate Model" [pdf]

Stochastic Multiresolution Models for Turbulence

This project is a collaboration with Brandon Whitcher, Jeff Weiss (University of Colorado, Boulder), Thomas Lee (CSU), Doug Nychka and myself. We are attempting to quantitatively describe the coherent structures (eddies, vortices) in turbulent fluids using stochastic multiresolution models. The source of the "data" is the result of a high-resolution numerical integration of the Navier-Stokes equation. The method will result in the ability to automatically detect and separate "target" features (ones that resemble some generic template) from a 2D image, allowing examination of either the background or the target features.

Gibbs Sampling/Wind Blending

This is work with Chris Wikle (U of Missouri), Ralph Milliff (CoRA), Doug Nychka (GSP), and Mark Berliner (Ohio State). We combine the information from satellite observations of surface winds over the oceans with the best-guess estimate of the surface winds from the operational weather centers. The result is a suite of spatially- and temporally-complete winds that have realistic variance at all spatial scales. Our domain is the entire Tropical Pacific at 6hourly and half-degree resolution. The winds are modeled with a Bayesian Heirarchical model which incorporates propagation of large-scale modes according to the laws of physics. The models are estimated with a gigantic Gibbs Sampler that runs in parallel on NCAR's 1600-processor SMP IBM RS/6000 (as well as many other) platform(s).

Royal Arch Hike

I have some pictures of a hike from NCAR to 'the quarry' and then up a VERY STEEP (didn't know it was that steep) social trail to Royal Arch. We didn't lose anyone, which was good. [pic 1] [pic 2] [pic 3] [pic 4] [pic 5] [pic 6] [pic 7] [pic 8] [pic 9] I'm saving all the incriminating photos of people 'running out of puff' (I just learned that one from an Aussie friend) for my retirement fund. Because we're a bunch of statisticians, I thought it would be fun to collect some data on the hike. I had two GPS units - a Garmin Forerunner 305, and a Garmin 60CSX. The breadcrumbs (tracks) from the units are: [forerunner tracks], and [60CSX tracks]. They have different sample rates, and the last line of each file has a different number of items, so you may want to delete that one before trying to ingest it. The best graphics will get posted here, the winner receiving another invite to the next hike. The consolation prizes will also be invitations to the next hike, so get your entry in now!

Publications (reverse chronologically, sort-of)

Whitcher, B., T.C.M. Lee, J.B. Weiss, T.J. Hoar, and D.W. Nychka, 2008

A Multiresolution Census Algorithm for Calculating Vortex Statistics in Turbulent Flows. Journal of the Royal Statistical Society: Series C (Applied Statistics), 57 (3), 293-321.

Hoar, T.J., R.F. Milliff, D. Nychka, C.K. Wikle, and L. Mark Berliner, 2003

Winds from a Bayesian Heirarchical Model: Computation for Atmosphere-Ocean Research. Journal of Computational and Graphical Statistics, Volume 4, Number 12, 781-807

Berliner, L.M, Nychka, D. and Hoar, T. (Eds.), 2000:

Studies in the Atmospheric Sciences, Springer-Verlag, New York.

Madden, R.A., Hoar, T.J., and Milliff, R.F., 1999:

Scatterometer winds composited according to the phase of the Tropical Interseasonal Oscillation. Tellus, 51A, 263-272.

Hoar, T., 1999:

Bayes meets Big Iron. Proceedings of the 1999 Joint Statistical meetings, Journal of the American Statistical Association.

Milliff, R.F., Hoar, T.J., van Loon, H., and Raphael, M., 1999:

Quasi-stationary wave variability in NSCAT winds. J. Geophys. Res., 104, 11,425-11,436.

Milliff, R.F., Hoar, T.J., and Madden, R.A., 1998:

Fast, eastward moving disturbances in the surface winds of the equatorial Pacific. Tellus, 50A, 26-41.

Jones, R.H., Y. Zhang, and T.J.Hoar, 1997:

Constructing surface wind space-time maps from scatterometer data. manuscript

Trenberth, K.E., and Hoar, T.J., 1997:

El Niño and climate change. Geophys. Res. Lett., 24, No. 23, 3057-3060.

Trenberth, K.E., and Hoar, T.J., 1996:

The 1990-1995 El Niño -Southern Oscillation Event: Longest on record. Geophysical Research Letters, 23, No. 1, 57-60.

Shea, D.J., Worley, S.J. Stern, I.R. and Hoar, T.J., 1994:

An Introduction to Atmospheric and Oceanographic Data. NCAR/TN-404+IA, 132 pp.

Hoar, T.J., and Wilson, C.R., 1994:

Geosat Observations of Sea Level Response to Barometric Pressure Forcing. Geophysical Research Letters, 21, No. 23, 2515-2518

Various useful (to me) links.

• My SVN cheat-sheet
• My CVS cheat-sheet
• Instructions for blending winds with the Gibbs Sampler
• My Matlab instructional demo