author = {Jitendra Kumar and E. Downey Brill and S. Ranji Ranjithan and G.
	Mahinthakumar and J. Uber},
  title = {Source Identification for Contamination Events Involving Reactive
  booktitle = {World Environmental and Water Resources Congress 2008, Honolulu,
  year = {2008},
  editor = {Roger W. Babcock and Jr and Raymond Walton},
  volume = {316},
  number = {40976},
  pages = {504-504},
  publisher = {ASCE},
  doi = {10.1061/40976(316)504},
  file = {:Publications/Kumar2008.pdf:PDF},
  keywords = {Water pollution; Water distribution systems},
  location = {Honolulu, Hawaii},
  owner = {jkumar},
  timestamp = {2009.09.19},
  url = {}
@comment{{jabref-meta: selector_review:}}
@comment{{jabref-meta: selector_publisher:}}
@comment{{jabref-meta: selector_author:}}
@comment{{jabref-meta: selector_journal:}}
@comment{{jabref-meta: selector_keywords:}}
  author = {David J. {Erickson III} and Richard T. Mills and Jay Gregg and T. J. Blasing and Forrest M. Hoffman and Robert J. Andres and Matthew Devries and Z. Zhu and S. R. Kawa},
  title = {An Estimate of Monthly Global Emissions of Anthropogenic {CO$_2$}:  Impact on the Seasonal Cycle of Atmospheric {CO$_2$}},
  journal = jgr,
  volume = 113,
  number = {G1},
  pages = {G01023},
  doi = {10.1029/2007JG000435},
  day = 15,
  month = mar,
  year = 2008,
  abstract = {Monthly estimates of the global emissions of anthropogenic CO$_2$ are presented. Approximating the seasonal CO$_2$ emission cycle using a 2-harmonic Fourier series with coefficients as a function of latitude, the annual fluxes are decomposed into monthly flux estimates based on data for the United States and applied globally. These monthly anthropogenic CO$_2$ flux estimates are then used to model atmospheric CO$_2$ concentrations using meteorological fields from the NASA GEOS-4 data assimilation system. We find that the use of monthly resolved fluxes makes a significant difference in the seasonal cycle of atmospheric CO$_2$ in and near those regions where anthropogenic CO$_2$ is released to the atmosphere. Local variations of 2--6~ppmv CO$_2$ in the seasonal cycle amplitude are simulated; larger variations would be expected if smaller source-receptor distances could be more precisely specified using a more refined spatial resolution. We also find that in the midlatitudes near the sources, synoptic scale atmospheric circulations are important in the winter and that boundary layer venting and diurnal rectifier effects are more important in the summer. These findings have implications for inverse-modeling efforts that attempt to estimate surface source/sink regions especially when the surface sinks are colocated with regions of strong anthropogenic CO$_2$ emissions.}
  author = {Forrest M. Hoffman and James T. Randerson and Inez Y. Fung and Peter E. Thornton and Yen-Huei ``Jeff'' Lee and Curtis C. Covey and Gordon B. Bonan and Steven W. Running},
  title = {The {C}arbon-{L}and {M}odel {I}ntercomparison {P}roject ({C-LAMP}):  A Protocol and Evaluation Metrics for Global Terrestrial Biogeochemistry Models},
  booktitle = {Proceedings of the {iEMSs} {F}ourth {B}iennial {M}eeting:  {I}nternational {C}ongress on {E}nvironmental {M}odelling and {S}oftware {S}ociety ({iEMSs} 2008)},
  editor = {Miquel S\`anchez-Marr\`e and Javier B\'ejar and Joaquim Comas and Andrea E. Rizzoli and Giorgio Guariso},
  dates = {7--10 July 2008},
  location = {Barcelona, Catalonia, Spain},
  pages = {1039--1046},
  isbn = {978-84-7653-074-0},
  day = 7,
  month = jul,
  year = 2008,
  abstract = {Described here is a protocol and accompanying metrics for evaluation of scientific model performance of global terrestrial biogeochemistry models. Developed under the guise of the NCAR Community Climate System Model (CCSM) Biogeochemistry Working Group, the Carbon-Land Model Intercomparison Project (C-LAMP) experimental protocol improves and expands upon the Coupled Carbon Cycle-Climate Model Intercomparison Project (C4MIP) Phase 1 protocol. However, unlike traditional model intercomparisons, C-LAMP has established scientific model performance metrics based upon comparison against best-available satellite- and ground-based measurements. Moreover, C-LAMP has partnered with the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison (PCMDI) to collect, archive, and distribute---via the Earth System Grid (ESG)---model results from C-LAMP experiments performed by international modeling groups in the same fashion as was done for the model results used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4).  In addition, because future IPCC Assessment Reports are expected to be based on results from integrated Earth System Models (ESMs), C-LAMP is helping to establish the metadata standards for model output from terrestrial biogeochemistry components of ESMs. Proposed as an extension to the netCDF Climate and Forecast (CF) 1.1 Convention, these metadata standards will facilitate future model-model and model-measurement intercomparisons. A prototype diagnostics tool has been developed for C-LAMP that summarizes model results, produce graphical representations of these results as compared with observational data sets, and score models on their scientific performance.}
  author = {Forrest M. Hoffman and William W. Hargrove and Richard T. Mills and Salil Mahajan and David J. Erickson and Robert J. Oglesby},
  title = {{M}ultivariate {S}patio-{T}emporal {C}lustering ({MSTC}) as a Data Mining Tool for Environmental Applications},
  booktitle = {Proceedings of the {iEMSs} {F}ourth {B}iennial {M}eeting:  {I}nternational {C}ongress on {E}nvironmental {M}odelling and {S}oftware {S}ociety ({iEMSs} 2008)},
  editor = {Miquel S\`anchez-Marr\`e and Javier B\'ejar and Joaquim Comas and Andrea E. Rizzoli and Giorgio Guariso},
  dates = {7--10 July 2008},
  location = {Barcelona, Catalonia, Spain},
  pages = {1774--1781},
  isbn = {978-84-7653-074-0},
  day = 7,
  month = jul,
  year = 2008,
  abstract = {The authors have applied multivariate cluster analysis to a variety of environmental science domains, including ecological regionalization; environmental monitoring network design; analysis of satellite-, airborne-, and ground-based remote sensing, and climate model-model and model-measurement intercomparison. The clustering methodology employs a $k$-means statistical clustering algorithm that has been implemented in a highly scalable, parallel high performance computing (HPC) application. Because of its efficiency and use of HPC platforms, the clustering code may be applied as a data mining tool to analyze and compare very large data sets of high dimensionality, such as very long or high frequency/resolution time series measurements or model output. The method was originally applied across geographic space and called Multivariate Geographic Clustering (MGC). Now applied across space and through time, the environmental data mining method is called Multivariate Spatio-Temporal Clustering (MSTC). Described here are the clustering algorithm, recent code improvements that significantly reduce the time-to-solution, and a new parallel principal components analysis (PCA) tool that can analyze very large data sets.  Finally, a sampling of the authors' applications of MGC and MSTC to problems in the environmental sciences are presented.}
  author = {Michael Keller and David Schimel and William Hargrove and Forrest Hoffman},
  title = {A Continental Strategy for the {N}ational {E}cological {O}bservatory {N}etwork},
  journal = frontecolenviron,
  note = {Special Issue on Continental-Scale Ecology},
  volume = 6,
  number = 5,
  pages = {282--284},
  doi = {10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2},
  day = 1,
  month = jun,
  year = 2008
  author = {Wesley Kendall and Markus Glatter and Jian Huang and Forrest Hoffman and David E. Bernholdt},
  title = {Web Enabled Collaborative Climate Visualization in the Earth System Grid},
  booktitle = {Proceedings of the International Symposium on Collaborative Technologies and Systems 2008 ({CTS} 2008)},
  dates = {19--23 May 2008},
  location = {Irvine, California, USA},
  pages = {212--220},
  doi = {10.1109/CTS.2008.4543934},
  isbn = {978-1-4244-2248-7},
  day = 19,
  month = may,
  year = 2008,
  abstract = {The recent advances in high performance computing, storage and networking technologies have enabled fundamental changes in current climate research. While sharing datasets and results is already common practice in climate modeling, direct sharing of the analysis and visualization process is also becoming feasible. We report our efforts to develop a capability, coupled with the Earth system grid (ESG), for sharing an entire executable workspace of visualization among collaborators. Evolutionary history of visualizations of research findings can also be captured and shared. The data intensive nature of the visualization system requires using several advanced techniques of visualization and parallel computing. With visualization clients implemented through standard Web browsers, however, the ensuing complexity is made transparent to end-users. We demonstrate the efficacy of our system using cutting edge climate datasets.}
  author = {Robert Sisneros and Markus Glatter and Brandon Langley and Jian Huang and Forrest Hoffman and David J. {Erickson III}},
  title = {Time-Varying Multivariate Visualization for Understanding Terrestrial Biogeochemistry},
  journal = jpconf,
  volume = 125,
  number = 1,
  pages = {012093},
  doi = {10.1088/1742-6596/125/1/012093},
  day = 1,
  month = dec,
  year = 2008,
  abstract = {Petascale computing has brought forth a transformational way of doing science. To the global effort on studying climate change, this shift has enabled not only tools more functional and more powerful than before but also a scientific exploration more comprehensive than before. In this work, we report our efforts to employ recent ultrascale visualization technologies (SciDAC Ultravis) to study model comparison in terrestrial biogeochemistry datasets produced by computation (SciDAC C-LAMP). While many of the current efforts are specific to climate modeling research, our method of location-specific summarizing visualization of extreme and normal relative distribution patterns is generally applicable to other fields of computational sciences.}