Getting to know Anke Kamrath

September 21, 2017 | As part of a series of interviews with new leaders at NCAR/UCAR, Staff News recently chatted with Anke Kamrath. Anke was named the director of CISL earlier this summer after serving as interim director since September 2016. 

Staff News: You came to NCAR in 2009 after 22 years at the San Diego Supercomputer Center.  How did you end up in the field of supercomputing? 

SIParCS End of Summer Talks

Please join us for the last day of the SIParCS program's end of summer talks:
Friday, August 4
9:05 - Anderson Banihirwe, University of Arkansas - Little Rock: PySpark for "big" atmospheric and oceanic data analysis
9:25 - Marcin Jurek, Texas A&M University: Parallel implementation of the multiresolution approximation method

SIParCS End of Summer Talks

Please join us for the SIParCS program's end of summer talks:
Thursday, August 3:
9:05 - Nicolas Rodriguez Jeangros, Colorado School of Mines: Informing the prediction of compression method and level for climate models using variable features
9:25 - Yun Joon Soh, Stony Brook University - SUNY: Accelerating statistical analysis through parallel computations

SIParCS End of Summer Talks

Please join us Wednesday, August 2nd through Friday, August 4th for the SIParCS program's end of summer talks:
Wednesday, August 2:
9:05 - Ananta Thapaliya, Claflin University: Supercomputer infiniband fabric analysis
9:25 - Supreeth Suresh, University of Wyoming: Vectorizing and parallelizing NCL routines for improved perfor

CISL Seminar Series: A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting

A Novel Recurrent Convolutional Neural Network for Ocean and Weather Forecasting
Robert Firth

Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon.

UCAR collaboration with The Weather Company to improve weather forecasts worldwide

BOULDER, Colo. — The University Corporation for Atmospheric Research (UCAR) today announced a new collaboration with The Weather Company, an IBM business, to improve global weather forecasting. The partnership brings together cutting-edge computer modeling developed at the National Center for Atmospheric Research (NCAR) with The Weather Company's meteorological science and IBM's advanced compute equipment."This is a major public-private partnership that will advance weather prediction and generate significant benefits for businesses making critical decisions based on weather forecasts," said UCAR President Antonio J. Busalacchi. "We are gratified that taxpayer investments in the development of weather models are now helping U.S. industries compete in the global marketplace."UCAR, a nonprofit consortium of 110 universities focused on research and training in the atmospheric and related Earth system sciences, manages NCAR on behalf of the National Science Foundation.With the new agreement, The Weather Company will develop a global forecast model based on the Model for Prediction Across Scales (MPAS), an innovative software platform developed by NCAR and the Los Alamos National Laboratory.The Model for Prediction Across Scales (MPAS) enables forecasters to combine a global view of the atmosphere with a higher-resolution view of a particular region, such as North America. (@UCAR. This image is freely available for media & nonprofit use.)MPAS offers a unique way of simulating the global atmosphere while providing users with more flexibility when focusing on specific regions of interest. Unlike traditional three-dimensional models that calculate atmospheric conditions at multiple points within a block-shaped grid, it uses a hexagonal mesh resembling a honeycomb that can be stretched wide in some regions and compressed for higher resolution in others. This enables forecasters to simultaneously capture far-flung atmospheric conditions that can influence local weather, as well as small-scale features such as vertical wind shear that can affect thunderstorms and other severe weather.Drawing on the computational power of GPUs — graphics processing units — such as those being used in a powerful new generation of IBM supercomputers, and on the expertise of NCAR and The Weather Company, the new collaboration is designed to push the capabilities of MPAS to yield more accurate forecasts with longer lead times. The results of NCAR's work will be freely available to the meteorological community. Businesses, from airlines to retailers, as well as the general public, stand to benefit.Mary Glackin, head of weather science and operations for The Weather Company, said, "As strong advocates for science, we embrace strong public-private collaborations that understand the value science brings to society, such as our continued efforts with UCAR to advance atmospheric and computational sciences.""Thanks to research funded by the National Science Foundation and other federal agencies, society is on the cusp of a new era in weather prediction, with more precise short-range forecasts as well as longer-term forecasts of seasonal weather patterns," Busalacchi said. "These forecasts are important for public health and safety, as well as enabling companies to leverage economic opportunities in ways that were never possible before."About The Weather CompanyThe Weather Company, an IBM Business, helps people make informed decisions and take action in the face of weather. The company offers weather data and insights to millions of consumers, as well as thousands of marketers and businesses via Weather’s API, its business solutions division, and its own digital products from The Weather Channel ( and Weather Underground ( webpage was last updated on July 5, 2017.

CISL Seminar Series - "Opportunities and Challenges: Diversifying Your Workforce" - Toni Collis

CISL Seminar Series

 Opportunities and Challenges: Diversifying Your Workforce
Toni Collis
Co-Founder of Women in HPC & Applications Consultant in HPC Research & Industry, EPCC,


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