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Mark W. Govett authored and/or contributed to the following articles/publications.
The Parallel Pre-Processor: a Compiler for Distributed and Shared Memory Computers
The goal in developing a robust parallelization tool is that it is easy to use, it requires minimal modifications to the original serial code, it is extensible to a wide variety of applications, and that it provide good portable performance. A directive-based parallelization tool is described called the Parallel Pre-processor (PPP) that meets mo...
Interactive Graphical Access to Real time and Retrospective Precipitation Data
A single software framework is introduced to evaluate numerical accuracy of the A-grid (NICAM) versus C-grid (MPAS) shallow-water model solvers on icosahedral grids. The C-grid staggering scheme excels in numerical noise control and total energy conservation, which results in exceptional stability for long time integration. Its weakness lies in ...
An Optimal 4D-Var Data Assimilation for Coupled Model–Air Quality and Weather Forecasting
Both atmospheric meteorology and chemistry models provide valuable societal and research services, from forecasting of weather to development of strategies for addressing the challenges of ambient air pollution and climate change. A specific atmospheric constituent of great concern is black carbonaceous aerosol (BC), a byproduct of both natural ...
Institution National Oceanic and Atmospheric Administration - NOAA
An Update on the Parallelization of the FV3 Model for cpu , gpu , and MIC Processors
NOAA's Earth System Research Laboratory (ESRL) has been working on the parallelization of the FV3 dynamical core toward fine-grain GPU and MIC processors. Initial work focused on modifying the code to expose more loop level parallelism needed to run efficiently on GPU processors containing over 3000 processing cores. Code changes have been quite...
Institution National Oceanic and Atmospheric Administration - NOAA
Machine Learning for Targeted Assimilation of Satellite Data
Optimizing the utilization of huge data sets is a challenging problem for weather prediction. To a significant degree, prediction accuracy is determined by the data used in model initialization, assimilated from a variety of observational platforms. At present, the volume of weather data collected in a given day greatly exceeds the ability of as...
Institution National Oceanic and Atmospheric Administration - NOAA
Position paper on high performance computing needs in Earth system prediction
The United States experiences some of the most severe weather on Earth. Extreme weather or climate events—such as hurricanes, tornadoes, flooding, drought, and heat waves—can devastate communities and businesses, cause loss of life and property, and impact valuable infrastructure and natural resources. The number and severity of extreme weather ...
Institutions National Oceanic and Atmospheric Administration - NOAA National Center for Atmospheric Research - NCAR
Tropical and Extratropical Cyclone Detection Using Deep Learning
Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive...
Institution National Oceanic and Atmospheric Administration - NOAA
Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors
The design and performance of the Non-Hydrostatic Icosahedral Model (NIM) global weather prediction model is described. NIM is a dynamical core designed to run on central processing unit (CPU), graphics processing unit (GPU), and Many Integrated Core (MIC) processors. It demonstrates efficient parallel performance and scalability to tens of thou...
Institutions Earth System Research Laboratory - ESRL National Oceanic and Atmospheric Administration - NOAA
One of the National Oceanic and Atmospheric Administration (NOAA) goals is to provide timely and reliable weather forecasts to support important decisions when and where people need it for safety, emergencies, planning for day-to-day activities. Satellite data is essential for areas lacking in-situ observations for use as initial conditions in N...
Machine Learning: Defining Worldwide Cyclone Labels for Training
In this paper we present a procedure for labeling both tropical and extratropical cyclones. The procedure is developed based off of a set of strict heuristics for the purpose of creating a worldwide labeled dataset for cyclones. The heuristics are defined from time, pressure, vorticity, and gradient thresholds without an explicit terrain cut-off...
Performance Comparison of the A-grid and C-grid Shallow-Water Models on Icosahedral Grids
Different methods of solving shallow water models on icosahedral grids were compared. Specifically, researchers examined the differences in accuracy from using unstaggered variables (A-grid, with mass and velocity at cell centers) compared to staggered variables (C-grid, with mass and velocity on cell edges). It was found that C-grid performs be...
Institution National Oceanic and Atmospheric Administration - NOAA
A new software framework using a well-established high-order spectral element discretization is presented for solving the compressible Navier–Stokes equations for purposes of research in atmospheric dynamics in bounded and unbounded limited-area domains, with a view toward capturing spatiotemporal intermittency that may be particularly challengi...
Institution National Oceanic and Atmospheric Administration - NOAA
The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A recent WMO report on exascale computing recommends “urgency in ...
Institution National Oceanic and Atmospheric Administration - NOAA