FOSS4G North America Conference 2013 Preliminary Program

future atmosphere and ocean. However, major barriers exist to publishing geospatiotemporal
climate data in an open and transparent manner. This is primarily due to the sheer volume of
information that these data represent. A handful of variables for a handful of model realizations
by dozens of models for scores of scenarios over centuries of time on tens to hundreds of
thousands of grid points create big data. Secondary challenges include the novelty of
high­resolution climate data and the complexity and sometimes enigmatic nature of climate data
interpretation. Finally, scientific teams often lack either the background, the resources, or the
mandate required to facilitate strategic data sharing, aside from dropping their output files onto
an FTP site.
In the spirit of open data and open science, the Pacific Climate Impacts Consortium (PCIC), a
regional climate services provider in British Columbia, Canada, has been making a concerted
effort to use geospatial FOSS in order to expand the availability, comprehensibility and
transparency of big climate data sets from the Coupled Model Intercomparison Project (CMIP5)
experiment. This presentation will describe some of the technical challenges to serving large
geospatiotemporal climate data sets over the web, including some of the nuances of climate
data. Additionally, I will explain the requirements of the geospatial climate community, and
outline the FOSS solutions that we have employed to serve big climate data sets over the web.
Presenting National Weather Service Digital Forecasts Using Open Source
Geospatial Technology
David T. Miller, National Weather Service, Meteorological Development Laborartory, Wyle
Science, Technology & Engineering, Marc Saccucci and Tim Kempisty, National Weather
Service, Meteorological Development Laborartory
National Weather Service (NWS) field offices, working in collaboration with the National Centers
for Environmental Prediction (NCEP), feed data into the National Digital Forecast Database
(NDFD) to produce a seamless mosaic of digital forecasts. Data from NDFD is available to the
public in raw format as well as web­based displays. Currently, the public display provides only
static images with minimal user­interaction, i.e. no pan/zoom or data probe capabilities. In
addition, the images are created for different sectors of the United States, each containing
hundreds of static images. The need for
custom sectors requires hundreds more static images, often overlapping pieces of existing
sectors.
In order to overcome these limitations, a development team at the NWS Meteorological
Development Laboratory (MDL) has created an updated version of the web­based NDFD data
display by combining several Open Source software packages: OpenLayers, ExtJS/GeoExt,
MapServer, jQuery, PostgreSQL/PostGIS, and GDAL. The updated display dramatically reduces
the number of images needed per United States region (resulting in a pre­processing time
savings) and increases user interactive capabilities with the data as well via a map window on
the web page. The updated display was recently released on an experimental basis to the
general public for comment.