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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=climate">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=climate]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for climate]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/427/High-Resolution-Decadal-Gridding-of-NASA-Atmospheric-Infrared-Sounder-AIRS-Earth-Monitoring-Instrument-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/241/A-General-Algorithm-for-Gridding-Earth-Sensing-Scanning-Instruments"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/230/Provenance-Tracking-in-Climate-Science-Data-Processing-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/229/Gridded-Outgoing-Longwave-Radiation-using-the-Atmospheric-Infrared-Sounder"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/202/Using-an-RDF-Framework-to-Carry-Metadata-for-Climate-Datasets"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/100/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/101/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/23/UMBC-and-IBM-collaborate-on-autonomic-computing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1034/Re-imagining-the-Power-of-Priming-and-Framing-Effects-in-the-Context-of-Political-Crowdfunding-Campaigns"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports"/>
    </rdf:Seq>
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 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/427/High-Resolution-Decadal-Gridding-of-NASA-Atmospheric-Infrared-Sounder-AIRS-Earth-Monitoring-Instrument-">
  <title><![CDATA[High Resolution Decadal Gridding of NASA Atmospheric Infrared Sounder (AIRS) Earth Monitoring Instrument.]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/427/High-Resolution-Decadal-Gridding-of-NASA-Atmospheric-Infrared-Sounder-AIRS-Earth-Monitoring-Instrument-</link>
  <description><![CDATA[This week's ebiquity lab meeting will comprise of presentation by PhD candidate, David Chapman.


David will talk on - High Resolution Decadal Gridding of the NASA Atmospheric Infrared Sounder (AIRS) Earth Monitoring Instrument.



Abstract: 
The NASA Atmospheric Infrared Sounder (AIRS) has been monitoring sun synchronous hyperspectral infrared radiation from Earth's surface and atmosphere operationally since September 2002, making AIRS one of the longest running IR sounders. AIRS has...]]></description>
  <dc:date>2012-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/241/A-General-Algorithm-for-Gridding-Earth-Sensing-Scanning-Instruments">
  <title><![CDATA[A General Algorithm for Gridding Earth Sensing Scanning Instruments]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/241/A-General-Algorithm-for-Gridding-Earth-Sensing-Scanning-Instruments</link>
  <description><![CDATA[Gridding in remote sensing must re-project observations from their
original coordinate system based on satellite orbit and attitude to a grid
defined by Earth coordinates.  Primitive methods assume that observations
are located at points on Earth and typically average observations in grid
cells, or interpolate geolocated observations.  These approaches are
inaccurate, because they do not make use of the instrument’s footprint
geometry, and spatial response.  Observation Coverage (Obsc...]]></description>
  <dc:date>2008-05-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/230/Provenance-Tracking-in-Climate-Science-Data-Processing-Systems">
  <title><![CDATA[Provenance Tracking in Climate Science Data Processing Systems]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/230/Provenance-Tracking-in-Climate-Science-Data-Processing-Systems</link>
  <description><![CDATA[NASA, NOAA, ESA and other organizations involved with climate research
have captured huge archives of earth observations.  Over time, the
sensors, spacecraft, science algorithms for transforming and analyzing
the data and the processing frameworks have all evolved.  Tracking the
complete provenance information in concert with the science data used
in research and ultimately, policy decisions is a tremendously
complicated problem.  Data are stored in multiple archives across
multiple ag...]]></description>
  <dc:date>2008-03-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/229/Gridded-Outgoing-Longwave-Radiation-using-the-Atmospheric-Infrared-Sounder">
  <title><![CDATA[Gridded Outgoing Longwave Radiation using the Atmospheric Infrared Sounder]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/229/Gridded-Outgoing-Longwave-Radiation-using-the-Atmospheric-Infrared-Sounder</link>
  <description><![CDATA[Outgoing Longwave Radiation(OLR) is an infrared satellite measurement of earth, and is used successfully to observe climate processes such as the Madden Julian Oscillation.  The Atmospheric Infrared Sounder(AIRS) is a five year old high-tech satellite instrument to measure OLR.  AIRS is 500x more accurate in spectral resolution than the popular 30 year old AVHRR instrument.  Unfortunately, AIRS OLR is not frequently used in climate studies, because no processing system exists to reproject and...]]></description>
  <dc:date>2008-02-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure">
  <title><![CDATA[Empowering Scientific Discovery by Distributed Data Mining on the Grid Infrastructure]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure</link>
  <description><![CDATA[The grid-based computing paradigm has attracted much attention in recent years. The sharing of distributed computing resources (such as software, hardware, data, sensors, etc) is an important aspect of grid computing. Computational Grids focus on methods for handling compute intensive tasks while Data Grids are geared toward data-intensive computing. Grid-based computing has been put to use in several scientific disciplines such as astronomy, engineering, climate studies, ecology, biology and...]]></description>
  <dc:date>2007-09-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/202/Using-an-RDF-Framework-to-Carry-Metadata-for-Climate-Datasets">
  <title><![CDATA[Using an RDF Framework to Carry Metadata for Climate Datasets]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/202/Using-an-RDF-Framework-to-Carry-Metadata-for-Climate-Datasets</link>
  <description><![CDATA[The standards underlying the Semantic Web -- Resource Description
Framework (RDF) and Web Ontology Language (OWL) -- show great
promise in addressing some of the basic problems in earth science
metadata. They provide a framework for explicitly describing the
data models implicit in programs that display and manipulate
data. They also provide a framework where multiple metadata
standards can be described. Most importantly, these data models
and metadata standards can be interrelated, a ...]]></description>
  <dc:date>2007-04-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/100/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future">
  <title><![CDATA[Women in Science and Engineering at Research Universities:  Lessons of the Past, Prospects for the Future]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/100/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future</link>
  <description><![CDATA[In 1991, the Harvard Faculty of Arts and Sciences Standing Committee on the Status of Women (which I chaired at the time) released a report entitled, "Women in the Sciences at Harvard: Part I: Junior Faculty and Graduate Students." The report drew attention to the fact that Harvard was having difficulty in attracting women to the sciences at all levels, from graduate students through senior faculty. A companion memo reported on discussions with undergraduate women in science. These reports id...]]></description>
  <dc:date>2005-04-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/101/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future">
  <title><![CDATA[Women in Science and Engineering at Research Universities:  Lessons of the Past, Prospects for the Future]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/101/Women-in-Science-and-Engineering-at-Research-Universities-Lessons-of-the-Past-Prospects-for-the-Future</link>
  <description><![CDATA[In 1991, the Harvard Faculty of Arts and Sciences Standing Committee on the Status of Women (which I chaired at the time) released a report entitled, "Women in the Sciences at Harvard: Part I: Junior Faculty and Graduate Students." The report drew attention to the fact that Harvard was having difficulty in attracting women to the sciences at all levels, from graduate students through senior faculty. A companion memo reported on discussions with undergraduate women in science. These reports id...]]></description>
  <dc:date>2005-04-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/23/UMBC-and-IBM-collaborate-on-autonomic-computing">
  <title><![CDATA[UMBC and IBM collaborate on autonomic computing]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/23/UMBC-and-IBM-collaborate-on-autonomic-computing</link>
  <description><![CDATA[University of Maryland, Baltimore County and IBM Collaborate on 
Autonomic Computing Research

BALTIMORE, February 24, 2005 - IBM today announced a new Shared University Research (SUR) grant awarded a group of faculty researchers of the eBiquity research group at the University of Maryland, Baltimore County to help build a major new center for high performance computational research. 

This SUR grant is part of the latest series of Shared University Research (SUR) awards, bringing IBM's ...]]></description>
  <dc:date>2005-02-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research">
  <title><![CDATA[Modelling the evolution of climate change research]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/108/Modelling-the-evolution-of-climate-change-research</link>
  <description><![CDATA[We are developing algorithms using dynamic topic modeling to understand influence and predict future trends in a scientific discipline. As an initial use case, we are applying this to climate change and use assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, ...]]></description>
  <dc:date>2015-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting">
  <title><![CDATA[DUNE: A Machine Learning Deep UNET++ based ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1203/DUNE-A-Machine-Learning-Deep-UNET-based-ensemble-Approach-to-Monthly-Seasonal-and-Annual-Climate-Forecasting</link>
  <description><![CDATA[Capitalizing on the recent availability of ERA5 monthly averaged, long-term data records of mean atmospheric and climate fields derived from the high-resolution reanalysis, deep learning architectures provide an alternative to physics-based daily numerical weather predictions for subseasonal to seasonal (S2S) and annual forecasts. A novel deep U-Net++-based ensemble (DUNE) neural architecture is introduced, incorporating encoder–decoder structures with residual blocks. When initialized with...]]></description>
  <dc:date>2025-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model">
  <title><![CDATA[Towards a Dynamic Data Driven AI Regional  Weather Forecast Model]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model</link>
  <description><![CDATA[The advent of long-term reanalysis datasets such as ECMWF
ERA 4/5 has enabled the development of AI-driven machine learning
models for weather forecasting. The major benefit of AI as an approach
is its ability to reduce computational forecast time from tens of hours

to tens of seconds, thereby enabling a variety of new applications rang-
ing from extreme regional weather event forecasting to first responder

aid for wildfires, severe storms, floods, oil spills, tornadoes, and other
...]]></description>
  <dc:date>2024-11-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1034/Re-imagining-the-Power-of-Priming-and-Framing-Effects-in-the-Context-of-Political-Crowdfunding-Campaigns">
  <title><![CDATA[Re-imagining the Power of Priming and Framing Effects in the Context of Political Crowdfunding Campaigns]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1034/Re-imagining-the-Power-of-Priming-and-Framing-Effects-in-the-Context-of-Political-Crowdfunding-Campaigns</link>
  <description><![CDATA[through which politicians raise money to fund their election campaigns.  Divisive issues discussed in these campaigns may not only motivate donations but also could have a broader priming effect on people’s social opinions. In the U.S., more than one-third of the population with moderate opinions show a tendency to swing their opinion based on recent and more accessible events. In this paper, we ask: can such campaigns further prime people’s responses to partisan topics, even when we disc...]]></description>
  <dc:date>2022-04-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing">
  <title><![CDATA[Variational Autoencoders using D-Wave Quantum Annealing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/869/Variational-Autoencoders-using-D-Wave-Quantum-Annealing</link>
  <description><![CDATA[Exploring the use of deep learning algorithms on the quantum computer will provide insight into how the quantum computer, in particular quantum annealing, can be applied to climate related research to accelerate the learning process. Current research has explored using Restricted Boltzmann Machines (RBM) using D-Wave's quantum annealer. This work has explored problems such as MNIST image recognition tasks. In addition, another body of research has explored variational inference methods using ...]]></description>
  <dc:date>2018-12-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research">
  <title><![CDATA[Ontology-Grounded Topic Modeling for Climate Science Research]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/831/Ontology-Grounded-Topic-Modeling-for-Climate-Science-Research</link>
  <description><![CDATA[In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize, and exploit the research is invaluable.  Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases,...]]></description>
  <dc:date>2018-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling">
  <title><![CDATA[Discovering Scientific Influence using Cross-Domain Dynamic Topic Modeling]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/810/Discovering-Scientific-Influence-using-Cross-Domain-Dynamic-Topic-Modeling</link>
  <description><![CDATA[We describe an approach using dynamic topic
modeling to model influence and predict future trends in
a scientific discipline. Our study focuses on climate change
and uses assessment reports of the Intergovernmental Panel
on Climate Change (IPCC) and the papers they cite. Since
1990, an IPCC report has been published every five years
that includes four separate volumes, each of which has many
chapters. Each report cites tens of thousands of research
papers, which comprise a correlated ...]]></description>
  <dc:date>2017-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-">
  <title><![CDATA[Dynamic Data Assimilation for Topic Modeling (DDATM)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/815/Dynamic-Data-Assimilation-for-Topic-Modeling-DDATM-</link>
  <description><![CDATA[Understanding how a particular discipline such as climate science evolves over time has received renewed interest. By understanding this evolution, predicting the future direction of the discipline becomes more achievable. Dynamic Topic Modeling (DTM) has been applied to a number of disciplines to model topic evolution as a means to learn how a particular scientific discipline and its underlying concepts are changing. Understanding how a discipline evolves, and its internal and external influ...]]></description>
  <dc:date>2017-07-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps">
  <title><![CDATA[Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/770/Modeling-the-Evolution-of-Climate-Change-Assessment-Research-Using-Dynamic-Topic-Models-and-Cross-Domain-Divergence-Maps</link>
  <description><![CDATA[Climate change is an important social issue and the subject of much research, both to understand the history of the Earth's changing climate and to foresee what changes to expect in the future. Approximately every five years, starting in 1990, the Intergovernmental Panel on Climate Change (IPCC) publishes a set of reports that cover the current state of climate change research, how this research will impact the world, risks, and approaches to mitigate the effects of climate change. Each repor...]]></description>
  <dc:date>2017-03-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports">
  <title><![CDATA[Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/769/Advanced-Large-Scale-Cross-Domain-Temporal-Topic-Modeling-Algorithms-to-Infer-the-Influence-of-Recent-Research-on-IPCC-Assessment-Reports</link>
  <description><![CDATA[One way of understanding the evolution of science within a particular scientific discipline is by studying the temporal influences that research publications had on that discipline. We provide a methodology for conducting such an analysis by employing cross-domain topic modeling and local cluster mappings of those publications with the historical texts to understand exactly when and how they influenced the discipline. We apply our method to the Intergovernmental Panel on Climate Change (IPCC)...]]></description>
  <dc:date>2016-12-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports">
  <title><![CDATA[Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/768/Dynamic-Topic-Modeling-to-Infer-the-Influence-of-Research-Citations-on-IPCC-Assessment-Reports</link>
  <description><![CDATA[A common Big Data problem is the need to integrate large temporal data sets from various data sources into one comprehensive structure. Having the ability to correlate evolving facts between data sources can be especially useful in supporting a number of desired application functions such as inference and influence identification. As a real world application we use climate change publications based on the Intergovernmental Panel on Climate Change, which publishes climate change assessment rep...]]></description>
  <dc:date>2016-12-05</dc:date>
 </item>
</rdf:RDF>
