CISL

CISL Seminar Series

Overview of the DoD HPCMP, PETTT, Select Projects, and Upcoming Interests
Sean Ziegeler
Engility Corporation

CISL SIParCS Final presentations July 27 - 29

Please join us for the CISL Summer Internships in Parallel Computational Science (SIParCS) final presentations next week.

Every morning from Wednesday July 27 until Friday July 29 starting at 9:00am

Location: Mesa Laboratory, Main Seminar Room

For full program and abstracts please visit www2.cisl.ucar.edu/siparcs/presentations-2016

CISL Seminar - Introduction to Cheyenne HPC System at the Center Green Lab

“Introduction to Cheyenne”

The new 5.34-petaflops Cheyenne HPC system is the subject of a presentation scheduled for 11 am, Friday, July 8, Room 2126 at the NCAR Center Green Lab in Boulder.

CISL Seminar - Introduction to Cheyenne HPC System at the Foothills Lab

“Introduction to Cheyenne”

The new 5.34-petaflops Cheyenne HPC system is the subject of a presentation scheduled for 11 am, Friday, July 1, Room 1001 at the NCAR Foothills Lab in Boulder.

CISL Seminar- Introduction to Cheyenne, HPC System

“Introduction to Cheyenne”

The new 5.34-petaflops Cheyenne HPC system is the subject of a presentation scheduled for 1 p.m. Friday, June 24, in the Main Seminar Room at the NCAR Mesa Lab in Boulder.

Bayes in Space! — NASA’s CO2 Measurements and Uncertainty Quantification

NASA's Orbiting Carbon Observatory-2 (OCO-2) mission is now actively collecting space-based measurements of atmospheric carbon dioxide (CO2). Data are collected with high spatial and temporal resolution and the data product includes both an estimate of column averaged CO2 dry air mole fraction (XCO2) and an estimate of uncertainty. In this talk we will take a look at how these estimates are obtained. As with any remote sensing method, the measurements are indirect. The OCO-2 instrument measures reflected sunlight in three spectral bands that make a single "sounding”.

Semiparametric Inference Via Sparsity-Induced Kriging for Massive Spatial Datasets

With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This manuscript proposes a semiparametric approach to modeling and inference for massive spatial datasets.

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