Tag Archives: Casey Dietrich
Conference: NCBIWA 2016
News: Hurricane Matthew
2016/10/17 – DHS CRCoE
Coastal Resilience Center researchers, partners aid in Hurricane Matthew preparation and recovery
Coastal Resilience Center researchers, partners aid in Hurricane Matthew preparation and recovery
Dr. Casey Dietrich of NCSU, whose CRC project focuses on improving the speed of ADCIRC modeling, visited the North Carolina Emergency Management (NCEM) State Emergency Operations Center to see NCEM’s operation and workflow during storm response. Dietrich said emergency managers were excited about the data provided by ADCIRC predictions.
“They are using both the CERA site and the shapefiles we are generating,” Dietrich said. “The shapefiles are being combined manually with other datasets to determine the potential flood damages, in terms of both number and cost of buildings and infrastructure.”
Dietrich said that ADCIRC predictions have compared favorably to post-storm high-water marks and U.S. Geological Survey measurements of storm surge.
“Their comparisons after Hermine showed matches within a foot to the peak water levels,” Dietrich said. “They described ADCIRC as their eyes on the coast.”
Dietrich’s work with ADCIRC to provide more accurate storm surge estimates for North Carolina is also partially funded by the North Carolina Sea Grant and the National Consortium for Data Science.
The CERA website is used during Hurricane Matthew preparations at the NCEM Emergency Operations Center.
2016/10/06 – CCEE
Dietrich Aiding Efforts to Forecast Flooding during Hurricane Matthew
As Hurricane Matthew approaches Florida and prepares to move up the U.S. east coast, researchers in North Carolina are running models to forecast the storm surge and coastal flooding. Dr. Casey Dietrich is working with collaborators at the University of North Carolina, the Renaissance Computing Institute, and Seahorse Coastal Consulting to generate and share guidance during the storm. The models are run every 6 hours, and they provide high-resolution forecasts of possible flooding throughout the NC coast. The forecasts can be found at: http://nc-cera.renci.org/. Dietrich is providing forecast guidance to NC Emergency Management, for use in decisions about evacuation and resource deployment. This real-time forecasting is part of a research project to downscale the model results and provide them in formats tailored to the needs of emergency managers.
Forecast of coastal flooding due to Hurricane Matthew (2016).
How to Run Kalpana
Kalpana converts ADCIRC output files in netCDF format to Google Earth (kmz) or GIS shapefiles for use with conventional GIS software. The latest version of the code is maintained at our GitHub repository: https://github.com/ccht-ncsu/Kalpana.
Command line arguments control the way it produces output, including the number of contour levels, their values, and the color scale. When these specifications are absent from the command line, it uses reasonable default settings so in many cases only a few of the available command line options will be used for any particular plot.
Visualization of Maximum Water Levels along the North Carolina coast during Hurricane Arthur (2014) using polygon shapefiles created by Kalpana with ArcGIS satellite imagery.
Recent Improvements to Kalpana
This post was contributed by CCHT collaborator Jason Fleming. He revised the Kalpana script to improve its efficiency and utility.
In the process of working on Kalpana, one of the deliverables was to clean the code, where possible. The following improvements were made as part of this deliverable.
Notes on the Development of Kalpana
This document describes the methodology followed in kalpana.py for the creation of shape file and kml files. The sequence of steps involved has been grouped under related sub-headings for better comprehension. The terms in fixed width font refer to variable names in the code.
Installing Python Modules for Kalpana
This post was contributed by CCHT collaborator Jason Fleming. This is a LONG post, but as he notes, it is a stream of consciousness about how he compiled the modules needed by Kalpana. Hopefully his experience will be helpful to new users. After the modules have been compiled, then Kalpana is very easy to use. So please don’t be discouraged!
The actual script kalapana.py does not require installation, it is merely executed. However, it relies on several Python modules that most users (or their IT supporters) will need to install before working with Kalpana. These modules are:
matplotlib– main python module used for data visualization.pylab– Imports plotting and numerics libraries in a single name space.shapely– used to construct geometric objects like Points, Polygons and LineStrings.fiona– for writing .shp files.netCDF4– reading and writing netCDF files.datetime– dates and time calculation, manipulation, and formatting.time– contains time related functions for measuring the performance of the code itself.numpy– to facilitate scientific computing; used primarily in Kalpana for working with n-dimensionalnumpyarrays, which are ideal for storing large amounts of data.collections– accessing OrderedDict, which is a dictionary subclass that remembers the order in which entries were added, whereas an ordinary dictionary does not do so).simplekml– writing kml (Google Earth) files.
In the process of installing Python modules on a desktop platform, as well as an HPC platform, I’ve found that the process can be very challenging. The biggest issue is conflicting module version dependencies, which can be handled by the use of virtual execution environments for Python (explained below).