% $Header$ % Purpose: Proposal to Information Technology Research (ITR) for National Priorities NSF-04-012 % URL: http://dust.ess.uci.edu/prp/prp_itr/prp_itr.pdf % History: % 1. Army Research Lab (ARL) white paper proposal 2002 (prp_arl.tex) % 2. Modified and extended for 2003 ITR RFP (prp_itr.tex revision 1.1.2.31) % 3. Modified and extended for 2004 ITR RFP (prp_itr.tex tag prp_itr-3_0) % 2004 NSF ITR: % http://www.nsf.gov/pubs/2004/nsf04012/nsf04012.pdf % LOI Deadline: January 14, 2004 % Full proposal deadline: February 24, 2004 % Start Date: 9/1/2004, up to five years, no more than $4M total % Program manager: Stephen Meacham (703) 292-8582, (FAX) 292-9022 % Financial Analyst in charge: Stu Ross Cell (949) 433-3391 % Assistant Financial Analyst: Lisa Rehbaum % ESS Financial Analyst: Liz Ford % Title: ITR-(ASE+NHS)-(dmc+sim) % National Priority Areas: % Advanced Science and Engineering (ASE) % National and Homeland Security (NHS) % Technical Focus Areas: % Innovative approaches to the integration of Data, Models, % Communications, analysis, and/or control systems, including dynamic, % data-driven applications for use in prediction, risk-assessment and % decision-making = (dmc) % Innovation in computational or SIMulation in research or education = (sim) % NSF FastLane Temporary Proposal #6349072 PIN 2987 % Total 5-year budget request: $l,916,375 % 2003 NSF ITR: % http://www.nsf.gov/pubsys/ods/getpub.cfm?ods_key=nsf02168 % Full proposal deadline: December 12, 2002 % Start Date: 9/1/2003, up to five years, no more than $180k yr-1 % Program manager: Stephen Meacham (703) 292-8582, (FAX) 292-9022 % Financial Analyst in charge: Stu Ross Cell (949) 433-3391 % 2002 ARL: % http://www.aro.army.mil/research/arlbaa00/finalarlbaa1.htm % Computational and Information Sciences/Battlefield Environmental Research % Program manager: Dr. Douglas Brown, (505) 678-1222, dbrown@arl.army.mil % NEW Contact: Dr. Jon J. Mercurio, (301) 394-2500/2286, jjmartin@arl.army.mil % Mercurio gave the white paper to Dennis Garvey, dgarvey@arl.army.mil % Garvey will send the white paper to Donald Hoock, Bob Dumais, and Alan Wetmore % Environmental Sciences/Atmospheric Sciences % Program manager: Dr. Walter Bach Jr., (919) 549-4247, bach@arl.aro.army.mil % Bach is interested in science-only proposals, not visualization % Check-out instructions: % cvs -d dust.ess.uci.edu:/home/zender/cvs co -kk -r prp_itr -d prp_itr prp_arl % Tags: % prp_itr-3_0: Version submitted to NSF ITR 20040226 % Usage: See end of file % International collaborators: % Ualikhan Abdibekov % Head research scientist, % Institute of Mathematics % 125, Pushkin Str., Almaty, 480100, Kazakhstan % Telephone: 3272+473695, Fax: 3272+613740 % E-mail: % http://www.tech-db.ru/istc/db/projects.nsf/prjn/K-424 % ISTC appointed Abdibekov to replace original PI Nurlybaev \documentclass[12pt]{article} % Standard packages \usepackage{ifpdf} % Define \ifpdf \ifpdf % We are running PDFLaTeX \usepackage[pdftex]{graphicx} % Defines \includegraphics* \pdfcompresslevel=9 \usepackage{thumbpdf} % Generate thumbnails \usepackage{epstopdf} % Convert .eps, if found, to .pdf when required \else % We are not running PDFLaTeX \usepackage{graphicx} % Defines \includegraphics* \fi % endif PDFLaTeX \usepackage{array} % Table and array extensions, e.g., column formatting \usepackage{datetime} % \xxivtime, \ordinal \usepackage{longtable} % Multi-page tables, e.g., acronyms and symbols \usepackage{makeidx} % Index keyword processor: \printindex and \see \usepackage{mdwlist} % Compact list formats \itemize*, \enumerate* \usepackage{natbib} % \cite commands from aguplus \usepackage{times} % Postscript Times-Roman font KoD99 p. 375 \usepackage{tocbibind} % Add Bibliography and Index to Table of Contents \usepackage{url} % Typeset URLs and e-mail addresses % hyperref is last package since it redefines other packages' commands % hyperref options, assumed true unless =false is specified: % backref List citing sections after bibliography entries % baseurl Make all URLs in document relative to this % bookmarksopen Unknown % breaklinks Wrap links onto newlines % colorlinks Use colored text for links, not boxes % hyperindex Link index to text % plainpages=false Suppress warnings caused by duplicate page numbers % pdftex Conform to pdftex conventions % Colors used when colorlinks=true: % linkcolor Color for normal internal links % anchorcolor Color for anchor text % citecolor Color for bibliographic citations in text % filecolor Color for URLs which open local files % menucolor Color for Acrobat menu items % pagecolor Color for links to other pages % urlcolor Color for linked URLs \ifpdf % We are running PDFLaTeX \usepackage[backref,breaklinks,colorlinks,citecolor=blue,linkcolor=blue,urlcolor=blue,hyperindex,plainpages=false,pdftex]{hyperref} % Hyper-references \pdfcompresslevel=9 \else % We are not running PDFLaTeX \usepackage[backref=false,breaklinks,colorlinks=false,hyperindex,plainpages=false]{hyperref} % Hyper-references \fi % endif PDFLaTeX % Personal packages \usepackage{csz} % Library of personal definitions \usepackage{dmn} % Dimensional units \usepackage{jrn_agu} % AGU-sanctioned journal title abbreviations \usepackage{mystyle} % Renato's style file % Commands which must be executed in preamble \makeglossary % Glossary described on KoD95 p. 221 \makeindex % Index described on KoD95 p. 220 % Commands specific to this file \makeatletter \renewcommand{\fnum@table}{\textbf{\tablename~\thetable}} % Boldface ``Table: #'' \renewcommand{\fnum@figure}{\textbf{\figurename~\thefigure}} % Boldface ``Figure: #'' \makeatother \newcommand{\dmt}{D} % [m] Diameter \newcommand{\calit}{Cal-(IT)$^2$} % Margins \oddsidemargin = 0.0in \evensidemargin = 0.0in \topmargin = -0.4in \textheight = 9in \textwidth = 6.5in \columnsep = 0.25in \headheight = 12pt \headsep = 12pt \footskip = 24pt % Vertical space % 20000717: None of these commands seem to have any effect here \setlength{\textfloatsep}{12pt} % Space between last top float or first bottom float and the text. \setlength{\intextsep}{12pt} % Space left on top and bottom of an in-text float. % \dbltextfloatsep is \textfloatsep for 2 column output. % \dblfloatsep is \floatsep for 2 column output. \setlength{\abovecaptionskip}{12pt} % Space above caption \setlength{\belowcaptionskip}{0pt} % Space below caption % Float placement % NB: Placement of figures is very sensitive to \textfraction \renewcommand\textfraction{0.0} % Minimum fraction of page that is text \setcounter{totalnumber}{73} % Maximum number of floats per page \setcounter{topnumber}{73} % Maximum number of floats at top of page \setcounter{dbltopnumber}{73} % Maximum number of floats at top of two-column page \setcounter{bottomnumber}{73} % Maximum number of floats at bottom of page \renewcommand\topfraction{1.0} % Maximum fraction of top of page occupied by floats \renewcommand\dbltopfraction{1.0} % Maximum fraction of top of two-column page occupied by floats \renewcommand\bottomfraction{1.0} % Maximum fraction of bottom of page occupied by floats \renewcommand\floatpagefraction{1.0} % Fraction of float page filled with floats \renewcommand\dblfloatpagefraction{1.0} % Fraction of double column float page filled with floats \begin{document} \def\prpttl{ITR-(ASE+NHS)-(dmc+sim): Interactive Mesoscale Forecasts, Visualization, and Environmental Planning\\} % Cover page \pagenumbering{roman} %\pagestyle{headings} \pagestyle{plain} \thispagestyle{empty} {\noindent% On the Web at \url{http://dust.ess.uci.edu/prp/prp_itr/prp_itr.pdf}\\ NSF Information Technology Research (ITR) Proposal \hfill Submitted: February~26, 2004\\ Last modified: \today \xxivtime \hfill Next Round Due: December 2004?} \begin{center} \textbf{\Large\prpttl} \bigskip Dr. Charles S. Zender \hfill Dr. Renato B. Pajarola \\ Department of Earth System Science \hfill Department of Computer Science \\ University of California at Irvine \hfill University of California at Irvine \\ \end{center} \vskip 0.5 cm \begin{enumerate*} \item Finalize Hardware/Support Budget (Pajarola, Rehbaum, Ross, Wessel) (done) \begin{enumerate*} \item Visualization Cluster (VC) (Davison, Evans, Pajarola, Wessel) (done) \item Forecast Cluster (FC) (Evans, Wessel) (done) \item Network infrastructure (Davison, Hildebrand, Wessel) (done) (budget is missing \$2535 needed for fiber-pulling labor) \item Visualization Gizmos (Pajarola) (done) \item Text on NACS MPC support and sysadmin (Wessel) (done) \end{enumerate*} \item \calit\ Coordination \begin{enumerate*} \item Ask Yee for visualization/student space (Pajarola) (done) \item Pursue OptIPuter node status (budget implications?) (Zender) (done) \item Include paragraph on applicability to HiPerWall (Zender) (not done) \end{enumerate*} \item Letters of Support/Collaboration/Senior Personnel Inquiries (Zender) \begin{enumerate*} \item Abdibekov (IoM, Aral Sea) (received) \item Chavez (USGS, Mojave) (received) \item Dabdub (UCI MAE, Mojave) (will participate next round) \item Frost (SDSU, Kazakhstan, Mojave) (may participate next round) \item Glantz (NCAR, Kazakhstan) (declined) \item Goulden (ESS, Mojave) (no response) \item Kuester (\calit/HiPerWall) (misplaced) \item Mehrotra (\calit/RESCUE) (received) \item Neff (CU/USGS, Mojave) (received) \item Purvis (Claremont Colleges, Aral Sea) (received) \item Randerson (ESS, Mojave) (no response) \item Reynolds (USGS, Mojave) (received) \end{enumerate*} \item Proposal submitted Feb.~26, 2004 (done) \end{enumerate*} \newpage The next round of ITR proposals is due around December 2004. This proposal is so strong already that I plan to revise and re-submit (again!) if we are declined this round. Here are my initial thoughts on how to make a stronger proposal: \begin{enumerate*} \item Strenghthen Mojave Desert (MD) ROI. Strengthen or remove Aral Sea (AS). \begin{enumerate*} \item Drop Aral Sea ROI if Reviewers' comments too hard to address \item Ramp-up AS without de-emphasizing MD in years 3--5 \item How to entrain Glantz for AS? \item Solicit School of Social Ecology collaborator for AS \item Entrain Israeli group (Rudich?) \item Kazakhstani visualization through OptIPuter (Frost?) \end{enumerate*} \item Describe specific regional projects and environmental planning in text and/or letters of support. \begin{enumerate*} \item CDHS (respiratory studies, valley fever) \item CHP (likely dust/fog road closure locations/seasons) \item DHS (NBC WMD plume scenarios) \item DWP (Airborne contaminants) \item FAA (Santa Ana dust/smoke plume morphology) \item JPL (complement coastal ocean forecasts) \item NPS (Mojave stresses/drought, sources of visibility reduction) \item RESCUE (prototype dust/smoke response plan?) \item SC$^{4}$ (standardize scenarios for future MD simulations) \item SCCOOS (cause of blooms) \item UCI MAE (SCAQMD model eastern boundary eddy dust flux) \item UNCCD (land use/soil treatments to reduce desertification) \item USGS (deposition characterization, valley fever habitat) \end{enumerate*} \item Strengthen forecast/visualization/application links \begin{enumerate*} \item Joerg Meyer (UCI EECS) will Co-PI \item Donald Dabdub (UCI MAE, Mojave) will Co-PI \item Provide reviewer web access to prototype dust storm rendering \end{enumerate*} \item Strengthen project management \begin{enumerate*} \item 0.5 FTE project scientist/web evangelist \end{enumerate*} \end{enumerate*} \clearpage %\onecolumn %\tableofcontents %\listoftables %\clearpage \pagenumbering{arabic} \setcounter{page}{0} \thispagestyle{empty} %\markleft{Mineral Dust Aerosol} %\markright{} %\begin{center} %\textbf{\large\prpttl} %\end{center} \noindent{\large{\textbf{Project Summary.}}}\label{sxn:smr} %\enlargethispage*{0.5in} We propose to improve forecasts and real and accelerated-time visualizations of mesoscale environmental change on multiple timescales to provide infrastructure for scientific, civil, and security-related decisions involving weather and aerosol dispersal. To accomplish this, we will converge existing programs of scientific research on boundary layer aerosol prediction with research on environmental visualization and immersive forecast interaction. Our approach is twofold: 1.~Create turn-key forecasting capability for particle entrainment, dispersal, and deposition using existing aerosol and mesoscale forecast models (\textbf{ASE}, \textbf{NHS}). 2.~Use and improve visualization technology to realistically render the boundary layer forecasts guided by decision-makers' assumptions of natural and anthropogenic land-use disturbance, climate change, and point source emissions (\textbf{dmc,sim}). We call our integration of interactive mesoscale forecasts with visualization capabilities critical for decision-makers the Laboratory for Environmental Planning (LEP). LEP links three sophisticated information technology layers into a flexible environmental planning and decision-making system. LEP's core is the (existing) Weather Research and Forecast (WRF) model with aerosol module enhancements. WRF will simulate Regions of Interest (ROIs), areas of high scientific, civil, and security interest that have significant and variable particulate concentrations on multiple timescales. The second layer is the high-performance visualization front-end for rendering the real and accelerated-time simulations from user specified projections, vantage points, and realistic illumination (\textbf{ASE}). The third layer controls the forecast boundary conditions with interactive devices for decision-makers to feed-back into the simulation and modify the forecast in real time (\textbf{dmc,sim}). LEP initially targets arid regions for their relative visual simplicity, single aerosol type (soil mineral dust), and impacts on downwind populations (\textbf{NHS}). The LEP team has the requisite expertise and data to simulate and evaluate two complementary ROIs that span the spectrum of landscape disturbance from natural to heavily disturbed. The Mojave Desert ROI is a relatively undisturbed, periodically active particulate source upwind of densely populated areas (Los Angeles, Las Vegas). The other ROI is the Aral Sea, a heavily disturbed, persistent source upwind of sparsely populated areas. Both ROIs are susceptible to rapid activation and, potentially, mitigation. Moreover, LEP will visualize any user-prescribed passive particulate emissions, e.g., wildfire smoke (during Santa Anas) and radiological, biological, chemical explosions (i.e., NBC WMD) (\textbf{NHS}) and will be useful in emergency response planning. \textbf{Intellectual Merit:} 1.~Inert tracer and desert aerosol dispersal by mesoscale processes (e.g., gust fronts, dry convection, topography) is little studied and will improve our understanding of fast time-scale visibility and biogeochemical fluxes, and security hazards (\textbf{ASE}, \textbf{NHS}, \textbf{dmc}, \textbf{sim}). 2.~Comparison of satellite and in~situ observations of dust aerosol to rendered forecast data will improve our understanding and model representation of the relative roles of topography, geomorphology, and disturbance in aerosol emission, transport, and deposition (\textbf{ASE}, \textbf{sim}). 3.~Applying advanced rendering techniques to mesoscale forecasts will lead to new and more efficient environmental visualization algorithms (\textbf{ASE}, \textbf{sim}). 4.~The ROIs to be hindcast/forecast and monitored are conducive to science-guided planning and remediation decisions (\textbf{NHS}, \textbf{dmc}, \textbf{sim}). \textbf{Broader Impacts:} LEP 1.~Enhances research and teaching infrastructure with its convergent integration of physically based-forecasting and visualization (\textbf{ASE}). 2.~Benefits society by enabling interactive exploration of the outcomes of land use change and disturbance scenarios on wind erosion and visibility, and the prediction of newly vulnerable landscapes in arid environments undergoing climate change (\textbf{ASE}). 3.~Increases regional security by improving simulation of (and potentially response to) dispersal of specified inert particulate tracers (e.g., NBC WMD) (\textbf{NHS}). \clearpage \begin{center} \textbf{\large \prpttl} \end{center} \csznote{ Mojave ROI: Three instruments with Sensits and anemometers since 1999 Data available every three months 2001--2002: Automatic pictures of sites when wind speeds exceed 6.0 m s-1 Used GOES over-flight imagery on non-cloudy days to pinpoint sources to particular playas Hindcast April 15, 2002 Las Vegas dust storm Sources: Fort Irwin military base, ground-water lowering, playas, recreation Science uses: Environmental Planning/Decision uses: What happens when Fort Irwin size doubles? Epidemiologists examining dust storm tracks looking for respiratory effects Aral Sea ROI: Giles Wiggs Sheffield University Ualikhan Abdibekov Institute of Mathematics (IoM), Almaty, Kazakhstan } % end csznote %--------------------------------------------------------------------------------------------------------- \section{Preamble}\label{sxn:prm} %--------------------------------------------------------------------------------------------------------- The south west United States (SW US) experiences adverse impacts from fast timescale changes in an arid environment. For example, a multi-day dust storm from the Mojave Desert blanketed Las Vegas, Nevada, April~15--17, 2002. It kept inhabitants indoors and inactive, closed construction sites, and \href{http://216.239.57.104/search?q=cache:JFWkow2oPSMJ:www.viewnews.com/2002/VIEW-Apr-26-Fri-2002/pahrump/18558276.html+las+vegas+dust+april+2002&hl=en&ie=UTF-8}{shut down air travel}. In Fall 2003, metropolitan Southern California was downwind of the dry desert regions during an intense and prolonged Santa Ana wind event \cite[][]{Rap03}. Numerous natural and anthropogenic fires sent clouds of \href{http://earthobservatory.nasa.gov/NaturalHazards/natural_hazards_v2.php3?img_id=11813}{smoke}, ash, \href{http://earthobservatory.nasa.gov/NaturalHazards/natural_hazards_v2.php3?img_id=11865}{dust}, and pollutants over much of greater Los Angeles and San Diego. These dramatic events illustrate how high variability, mesoscale weather patterns over desert regions affect millions of American citizens each year \cite[][]{PaE78,Pro99,KJC01,ZeT04}. Recently planners are more concerned that domestic or international terrorists release nuclear, biological, and chemical (NBC) weapons of mass destruction (WMD) in the~US. Planning for atmospheric natural hazards (dust storms, wildfire smoke) at first seems unrelated to preparing for security and WMD hazards. However, both natural and anthropogenic hazards are isomorphic computer modeling and visualization problems insofar as they comprise following the emission, transport, and deposition of passive aerosols (i.e., dust, soot, radionuclides) or gases \cite[][]{Seh80,GBS98}. Concomitant with the increasing awareness of the agricultural, climatic, economic, and respiratory impact of dust storms \cite[e.g.,][]{Gla94,You02} has been a dramatic increase in our understanding and ability to detect and to predict arid region aerosol emission and transport in recent years \cite[e.g.,][]{ShL97,MBA97,WKT98,ZBN03}. This forecast technology has been developed for scientific \cite[e.g.,][]{CRE01}, civil \cite[e.g.,][]{CLM98}, and military \cite[e.g.,][]{HoB93,VHS03} applications. Use of these mesoscale aerosol models in SW US civil planning and homeland security has not kept pace. To help remedy this, we will develop the Laboratory for Environmental Planning (LEP). LEP will produce fast-timescale forecasts of SW US dust and other aerosols, similar to efforts underway to predict intense Gobi and African dust storms \cite[e.g.,][]{SYW03}. Moreover, LEP will integrate scientific forecasting with advanced visualization/rendering and decisionmaking capabilities. The time seems right to integrate recent advances in weather and aerosol forecast technology with advances in interactive visualization and rendering environments so that planners may take full advantage of fast yet quantitative environmental simulations. Let us begin to make civil planners, park rangers, epidemiologists, firefighters, aviation officials, and homeland security personnel familiar with the potential of mesoscale weather and aerosol forecasts to help them understand past events and help them plan for future events. We will help close the forecast-technology-planning gap with technology friendlier to use, easier to understand, and interactively configurable for environmental planning. The decision-makers will control these quantitatively rigorous forecasts and hindcasts in a highly realistic visualization space. Our Information Technology Research (ITR) for National Priorities project would develop a physically based, scientifically evaluated, mesoscale environmental virtual reality and visualization facility. \section{Introduction}\label{sxn:ntr} We propose to improve forecasts and real and accelerated-time visualizations of mesoscale environmental change on multiple timescales to provide infrastructure for scientific, civil, and security-related decisions involving weather and aerosol dispersal. To accomplish this, we will converge existing programs of scientific research on boundary layer aerosol prediction with research on environmental visualization and immersive forecast interaction. Our approach is twofold: 1.~Create turn-key forecasting capability for particle entrainment, dispersal, and deposition using existing aerosol and mesoscale forecast models. 2.~Use and improve visualization technology to realistically render the boundary layer forecasts guided by decision-makers' assumptions of natural and anthropogenic land-use disturbance, climate change, and point source emissions. We call our integration of interactive mesoscale forecasts with visualization capabilities critical for decision-makers the Laboratory for Environmental Planning (LEP). LEP links three sophisticated information technology layers into a flexible environmental planning and decision-making system. LEP's core is the (existing) Weather Research and Forecast (WRF) model with aerosol module enhancements. WRF will simulate Regions of Interest (ROIs), areas of high scientific, civil, and security interest that have significant and variable particulate concentrations on multiple timescales. The second layer is the high-performance visualization front-end for rendering the real and accelerated-time simulations from user specified projections, vantage points, and realistic illumination. The third layer controls the forecast boundary conditions with interactive devices for decision-makers to feed-back into the simulation and modify the forecast in real time. LEP initially targets arid regions for their relative visual simplicity, single aerosol type (soil mineral dust), and impacts on downwind populations. \subsection{Personnel Directly Involved with LEP}\label{sxn:prs} Table~\ref{tbl:prs} lists the primary researchers affiliated with LEP (see supplementary letters of collaboration or biographical sketches). \begin{table} \centering % \centering uses less vertical space than center-environment \begin{minipage}{\hsize} \renewcommand{\footnoterule}{\rule{\hsize}{0.0cm}\vspace{-0.0cm}} % KoD95 p. 111 \caption[Researchers Affiliated with LEP ITR Project]{\textbf{Researchers Affiliated with LEP ITR Project} \label{tbl:prs}} \vspace{\cpthdrhlnskp} \begin{tabular}{r >{\raggedright}p{7.0em}<{} l cccc >{\raggedright}p{11.0em}<{} l } \hline \rule{0.0ex}{\hlntblhdrskp}% & & & \multicolumn{4}{c}{ROI/FOI\footnote{Regions and Fields of Interest and Expertise: MD~=~Mojave Desert; AS~=~Aral Sea; SV~=~Scientific Visualization; EP~=~Environmental Planning}} \\[0.0ex] \cline{4-7} Institution\footnote{Primary institutional and departmental affiliation} & Researcher & LoI\footnote{Level of Involvement: PI~=~Principle Investigator; SP~=~Senior Personnel, integral to accomplishing project goals; Coll.\~=~Collaborator, providing or using information/expertise important to project success; Supp.\~=~Supporter, directs projects that may benefit from LEP; User, will use LEP facility or data at no cost to project.} & MD & AS & SV\footnote{Interests/expertise in scientific visualization, interactive systems, and virtual reality (VR).} & EP\footnote{Interests/expertise in environmental planning, weather-society interaction, emergency response} & Relevant Interests & \\[0.0ex] \hline \rule{0.0ex}{\hlntblntrskp}% UCI ESS & Charlie Zender & PI & $\times$ & $\times$ & & $\times$ & Aeolian erosion, transport, deposition, composition, valley fever & \\[0.5ex] UCI ICS & Renato Pajarola & Co-PI & & & $\times$ & & Scientific visualization, terrain rendering & \\[0.5ex] IOM\footnote{Institute of Mathematics, Kazakhstan} & Ualikhan Abdibekov & SP & & $\times$ & & & Aral Sea dust forecasting, amelioration, salinization & \\[0.5ex] USGS & Pat Chavez & Coll.\ & $\times$ & & & & SW US erosion, satellite, photographic monitoring of source regions & \\[0.5ex] UCI MAE & Donald Dabdub & Co-PI & $\times$ & & & & Regional air quality/atmospheric chemistry/aerosol modeling of LA air-shed & \\[0.5ex] SDSU Geology & Eric Frost & Coll. & $\times$ & $\times$ & & & Aral Sea monitoring, assessment, policy & \\[0.5ex] %NCAR ESIG & Mickey Glantz & User & & $\times$ & & & Climate Impacts on Society & \\[0.5ex] %UCI ESS & Mike Goulden & User & $\times$ & & & & Ecosystem gradients, carbon cycling; Station eddy flux measurements in Southern California & \\[0.5ex] UCI EECS & Falko Kuester & User & & & $\times$ & & Scientific visualization, interactive systems, VR & \\[0.5ex] UCI ICS & Sharad Mehrotra & User & & & & $\times$ & Crisis/Hazard Response and Management & \\[0.5ex] UCI EECS & Joerg Meyer & Co-PI & & & $\times$ & & Large-scale visualization, volume rendering, VR & \\[0.5ex] CU/USGS & Jason Neff & Supp. & $\times$ & & & & SW US land use change, aeolian erosion and biogeochemistry & \\[0.5ex] Claremont & Katie Purvis & Coll. & & $\times$ & & $\times$ & Radionuclide dispersion/exposure via dust; health & \\[0.5ex] %UCI ESS & Jim Randerson & Supp. & $\times$ & & & & Santa Anas, Fire and carbon cycling, fast timescale biogeochemistry & \\[0.5ex] %USGS & Marith Reheis & Coll. & $\times$ & & & & Southwestern U.S. mineral dust particle composition, deposition traps & \\[0.5ex] USGS & Richard Reynolds & SP & $\times$ & & & & Climate change and land use in SW US, station obs.\ of saltation, wind & \\[0.5ex] UCI CE/ESS & Soroosh Sorooshian & User & $\times$ & & & $\times$ & Hydrology, sustainability of semi-arid landscapes & \\[0.5ex] \hline \end{tabular} \end{minipage} \end{table} % end tbl:mdl This non-exhaustive list highlights unique and complementary interests of the research groups. The participants are ``matrixed'' into overlapping fields of interest. The LEP team has the requisite expertise and data to simulate and evaluate two complementary ROIs that span the spectrum of landscape disturbance from natural to heavily disturbed. The Mojave Desert ROI is a relatively undisturbed, periodically active particulate source upwind of densely populated areas (Los Angeles, Las Vegas) \cite[][]{BBL96}. The other ROI is the Aral Sea, a heavily disturbed, persistent source upwind of sparsely populated areas \cite[][]{You02}. Both ROIs are susceptible to rapid activation and, potentially, mitigation \cite[][]{Gla94}. Moreover, LEP will visualize any user-prescribed passive particulate emissions, e.g., wildfire smoke (during Santa Anas) and radiological, biological, chemical explosions (i.e., NBC WMD) and will be useful in emergency response planning. There is no institution in the~US better qualified than UCI to integrate research on arid region erosion, hydrology, and climate change can with research on real-time environmental rendering and simulation interaction. The science arising from our studies of arid region behavior can be leveraged into useful decision-making tools when combined with effective, intuitive visualization and interaction methods. In terms of computing power, the LEP requires two relatively inexpensive, of the shelf clusters of about a dozen PCs each. It is the interdisciplinary collaboration of personnel and entrainment of outside decision-makers that make LEP unique. LEP fills a niche, allowing faculty, post-graduates and students in Earth System Science and Electrical Engineering and Computer Science to integrate their disciplines while learning to apply their scientific specialties with environmental planners working on real-world problems. The first proof-of-concept ROI (the Mojave Desert) is practically in our backyard and we are highly motivated to produce accurate forecasts and stunning visualizations of its behavior now and under forcing from natural (e.g., drought) and anthropogenic (e.g., greenhouse warming) scenarios. Although primarily a simulation and planning tool for arid and semi-arid regions, LEP will produce the mesoscale hindcasts and forecasts necessary to conduct original scientific research on which mesoscale processes control fast-timescale biogeochemical aeolian fluxes. Over the next five years, LEP will allow UCI Earth System Science (ESS) researchers to bridge the scale of their studies from global change to regional and mesoscale change for the first time. Thus LEP will serve as a focus point for ESS's increasing interest in modeling and measuring Southern Californian climate variability and change. %--------------------------------------------------------------------------------------------------------- \section{Background}\label{sxn:bck} %--------------------------------------------------------------------------------------------------------- \subsection{Results from Prior NSF Funding}\label{sxn:prr_NSF} Zender is a Co-PI on ATM-0214430, "Collaborative Proposal: Using Measurements from the Columbia Plateau Eolian System to Improve Global-Scale Models of Mineral-Dust Aerosols", 8/1/2002--7/30/2005. This project has resulted so far in four national meeting presentations with manuscripts in preparation \cite[]{SBG02,SBR03,SBG03,ZFA03}. Our manuscript studies the range of uncertainty in LGM dust mass and radiative budgets to uncertainty in vegetation reconstruction. We show that a significant fraction of the observed LGM increase in Pacific Ocean dust deposition is attributable to vegetation change. Our paper in press \cite[]{GrZ04} explains how the twin processes of saltation and sandblasting (SS) relate to loess formation. These SS~physics were implemented in DEAD and will be used in the LEP proposal for accurate simulation of small (dust) to large (loess and sand) particle entrainment which is especially important to mesoscale visibility. Zender is PI on ATM-0321380 ``Acquisition of an Earth System Modeling Facility (ESMF) for Coupled Climate, Chemistry, and Biogeochemistry Studies''. After negotiating the best price supercomputer through an open bid competition in summer 2003, we awarded IBM the ESMF contract in October 2003. The ESMF opened to early users in early February 2004. It is currently undergoing final acceptance testing by UCI and configuration by IBM prior to being fully devoted to coupled climate studies. As discussed in the Facilities section, this ITR may use the ESMF as a source of real-time \textit{global} (as opposed to mesoscale) coupled climate model simulation data should entrepreneurial members of the visualization team become interested or entrained in the problem of global visualizations. Global visualizations, however, are beyond the scope of this proposal and no funding is presently available or sought to pursue them. Pajarola has not received NSF funding for projects related to this ITR proposal. \subsection{Overview}\label{sxn:ovr} Mineral dust aerosol plays many roles in the environment, including visibility impairment and air quality \cite[]{Pro99,BiZ03}, radiative forcing of climate \cite[]{TLF96,SWB01}, and transport of biogeochemical nutrients to remote ecosystems \cite[]{MaF88,MDG02}. Forecasting dust movement is difficult because of the great spatial and temporal variations in its emissions mechanisms and constraints \cite[]{BSM96,Gil99,PGT02}. Recent research has greatly improved our ability to explain the global cycle of mineral dust distribution on climate timescales \cite[e.g.,][]{TeF94,GCT01,ZBN03}. However, our ability to accurately forecast dust activity at the meso- and synoptic scales ($\sim 10$--$1000$\,\km, timescale of hours to days) remains relatively weak due to inadequate boundary data sets \cite[e.g.,][]{MBA97}, and incomplete understanding of the complex interactions between surface meteorology and soil structure which result in dust emissions \cite[e.g.,][]{MaB95,SRL96,AlG01}. We will investigate the interaction of mesoscale weather systems with dust entrainment and dust storm structure by using and improving our Dust Entrainment And Deposition (\href{http://dust.ess.uci.edu/dead}{DEAD}) model in the mesoscale Weather Research and Forecasting (\href{http://wrf-model.org}{WRF}) model. The resulting mesoscale forecasting system offers novel opportunities to use high speed realistic environmental visualization technologies in scientifically and societally useful ways. The computationally intensive nature of weather forecasting \cite[]{HoB93} and dust entrainment processes \cite[]{SRL96,MBA97} makes high-speed computers necessary to simulate mesoscale events in real time. We will adapt a state-of-the-art environmental visualization system to the back end of the mesoscale weather and dust forecast model. We will use the forecast fields in real time to interactively visualize the boundary layer from user-selected perspectives and anthropogenic emissions sources. In arid and semi-arid environments, mineral dust aerosol is usually the dominant aerosol in terms of mass, visibility impairment, and temporal variability \cite[e.g.,][]{MBN97,CGK02}. Thus our single system will simultaneously forecast and render the most significant features of dynamically changing arid environments. Our goals are to \begin{enumerate*} \item Enhance predictive skill and understanding of mesoscale natural and anthropogenic dust erodibility, emissions, and transport \item Develop efficient physically based visualization technologies and systems for interactive arid environment simulation \item Entrain environmental planners to advise development and use of LEP to maximize societal preparedness for mesoscale environmental change \end{enumerate*} \csznote{The primary aerosol LEP will consider is wind-borne mineral dust. The strongest source, and most studied region, of dust emissions is Saharan Africa \cite[e.g.,][]{MBN97,NTB98,CPH99,RKW03}. By comparison, source regions in Asia are only now receiving adequate attention, and global models still perform relatively poorly there \cite[e.g.,][]{GCT01,ZBN03}, possibly because of difficult-to-assess anthropogenic sources \cite[]{You02}. } % end csznote \csznote{Rendering of the geographic environment and the dynamic boundary layer from the point of view of an observer in the scene requires processing enormous amounts of data including high-resolution terrain surface and volumetric atmosphere representation. Large-scale terrain rendering is a well studied visualization problem \cite[e.g.,][]{GH:95,LKRHFT:96,RG:97,KCH:97,DWSMAM:97,Pajarola:98a,RLIB:99,LP:01,BP:03}. On the other hand, real-time rendering of physically realistic atmospheric effects such as light attenuation and scattering, or clouds is still an extremely difficult volume integration problem \cite[e.g.,][]{NND:96,ES:00,HL:01,ND:01}. The same applies to realistic calculation of shadows, especially in hybrid visualization that combine geometric terrain visualization with volumetric atmospheric visualization \cite[]{LMMH:98}. } % end csznote LEP's three components (scientific, visualization, and planning) are synergistic. To facilitate environmental planning by outside experts, about half of LEP is devoted to an interactive visualization and control system. We will render predicted aerosol (not only dust) plumes above synthetic surfaces constructed from geomorphic terrain processes such as upstream area, local slope, USGS land-use category, and MODIS surface imagery \cite[]{SGS02}. We anticipate learning more about complex linkages between land-use and erodibility by visualizing different combinations of geophysical variables together. The scientific inference of land-use-turbulence-erosion linkages will drive the visualization development in part. Also driving the visualization research is the need to extract and visualize the scientifically meaningful data from the vast data stream generated in real time. LEP's primary non-scientific purpose, to train and assist in environmental planning, depends strongly on the accuracy, realism, and flexibility of the forecasts and visualization system. We expect that planners will exhort the modeling and visualization teams to produce their finest work possible because of the societal importance of their decisions. Planners, in turn, will be able to devise smarter strategies for urban growth/desert disturbance and weather response due to the flexibility and accuracy of LEP. We do \textit{not} intend to develop an operational, $24 \times 7$, global dust forecast system responsive to aviation, civil, and/or military needs (such as the NOGAPS system at the Naval Research Lab) \cite[]{HoB93}. LEP is a system where research forecasts and visualization inform and improve each other. When LEP forecasts a potentially hazardous aerosol event, we will consult with the UC Irvine Responding to Crises and Unexpected Events (RESCUE) Project on whether and how best to disseminate the information (see attached letter of support from Sharad Mehrotra). The central scientific areas that we will address are 1.~Dust storm formation and structure; 2.~Predictability; and 3.~Response to environmental change. The visualization problems that we will address are 1.~Multi-resolution visualization of dynamically changing environments; 2.~Physically realistic rendering of arid terrain and vegetation in changing light conditions; 3.~Optimal communication interfaces between forecast and rendering components. Our scientific and technical research plans to address these questions is given in Section~\ref{sxn:prp}. Appendices contain the Budget Justification, letters of support from collaborators, and a list of Acronyms and Abbreviations. %--------------------------------------------------------------------------------------------------------- \subsection{Scientific Visualization} \label{sxn:bck_vzn} A real-time scientific visualization system powerful enough to allow interactive exploration of mesoscale environmental forecasts depends on efficient solutions and system integration of various components including: large-scale terrain rendering, accurate surface light backscattering, atmospheric attenuation and cloud simulation. In particular, multi-resolution {\em level-of-detail\/} (LOD) approaches as well as a parallel cluster-based visualization system are required to provide the necessary rendering power and scalability to high-resolution mesoscale atmospheric data sets. Below we briefly summarize state-of-the-art rendering algorithms and visualization systems in this context to pinpoint current limitations that will be addressed by research in this project. Efficient real-time terrain visualization algorithms and systems for displaying very large-scale grid-digital elevation models have mainly concentrated on bin-tree or quadtree multi-resolution triangulation methods such as \cite[]{LKRHFT:96,DWSMAM:97,Pajarola:98a,BAV:98,Gerstner:99,EKT:01,LP:01,Pajarola:02} and patch-based rendering \cite[]{Levenberg:02,CGGMPS:03a,CGGMPS:03b}. The common focus is to define an effective hierarchical multi-resolution data structure that allows quick, feature-adaptive and continuous LOD terrain surface extraction. This minimal-complexity terrain surface then allows fast polygonal rendering. The main focus of research in this area has concentrated on rendering large terrains locally on a single computer (node). The few proposed parallel terrain rendering approaches are aimed at shared-memory Symmetric Multi Processing (SMP) or Massively Parallel Processing (MPP) systems \cite[]{VR:91,LDC:96,CRLS:96} and do not take advantage of hardware accelerated polygonal rendering \cite[]{AG:95,LDC:96,CRLS:96}. Hence these previous approaches do directly adapt and scale well to cluster-based systems. Atmospheric attenuation is closely related to the low-albedo case in direct volume rendering (DVR) of color-and-opacity fields defined over a regular 3D grid of volume elements (voxels) \cite[]{KvH:84}. Popular DVR approaches include raycasting \cite[]{Levoy:90}, shear-warping \cite[]{LL:94}, 3D texture mapping \cite[]{CCF:94}, cell projection \cite[]{ST:90} and splatting \cite[]{Westover:90}. Due to the excellent combination of rendering quality and performance (see also comparison in \cite[]{MHBMC:00}), and because of the direct analogy to particles\footnote{which mainly determine atmospheric attenuation} we focus on splatting-based DVR in this project. In this context, much work has been invested into improving performance of footprint rasterization \cite[e.g.,][]{MMSCSY:98,MMC:99,HMSC:00,ZPBG:01b}, occlusion culling \cite[]{MSHC:99} and sparse volume traversal \cite[]{OM:01}. Common splatting methods suffer from a software rasterization of splat-footprints that limits better rendering performance. On the other hand, splatting is among the fastest parallel volume rendering methods \cite[]{LWMT:97}. However, development of cluster-parallel splatting algorithms is largely missing \cite[]{Wittenbrink:98}. Furthermore, real-time rendering of physically realistic atmospheric effects such as light attenuation and scattering, or clouds is an extremely difficult volume rendering problem \cite[e.g.,][]{NND:96,ES:00,HL:01,ND:01} and has not been addressed in a larger scale nor extended to cluster-rendering. %Meyer has studied both large-scale volume rendering algorithms \cite[]{MBTLH:03} as well as hybrid approaches that combine both decimated geometric meshes \cite[]{CM:02,CM:03} and large volumes representing geoscientific models. A major effort of this project is system development of the environmental visualization component. To date no similar visualization systems exist that link an interactive mesoscale weather forecast module with a real-time 3D rendering system. Commercial or semi-commercial systems as Vis5d \cite[]{HS:90} or Open Visualization Data Explorer \cite[]{OVDE} provide high-level API toolkits. However, they are not aimed at realistic rendering, real-time performance on large data sets (e.g. no LOD support) or integration with an interactive mesoscale weather forecast model. Recent developments such as \cite[]{REHL:03,REHL:04} provide isolated technical solutions to individual tasks (i.e. fast realistic cloud rendering) but do not address overall system development and integration. % maybe this should come somewhere else Major system issues previously left out are: (1) The direct coupling of efficient multi-resolution terrain elevation models with ground surface {\em bi-directional radiance distribution function\/} (BRDF) estimates and down-welling flux information from the WRF model that allows for real-time physically realistic ground illumination. (2) Integration of simulated light scattering and attenuation data into multi-resolution volume rendering for physically realistic boundary layer visualization. (3) User input interaction that allows the specification and spatial placement of tracer emissions as well as land-use change as interactive feedback-loop to the WRF model. (4) Design and implementation of a client-server based environmental forecast simulation-visualization system based on cluster-parallel simulation and rendering engines. %--------------------------------------------------------------------------------------------------------- \section{Research Plan}\label{sxn:prp} %--------------------------------------------------------------------------------------------------------- We will accomplish our first goal of providing accurate mineral aerosol forecasts and hindcasts for the visualization system by iterative refinements to WRF-DEAD based on evaluations in the ROIs (Zender), informed by powerful real time visualization technology which allows us to assess and explore model sensitivities and biases (Pajarola). The second goal of our project objectives is to develop a powerful visualization system capable of interactive visualization of the large-scale terrain surface and atmospheric volume data from the WRF model. High-speed network connectivity between the forecast simulation and the visualization engine provides the bandwidth for fast update rates of mesoscale atmospheric data between the two systems. Cluster-based computing on both sides is exploited to achieve accelerated-time weather forecasting and interactive visualization. In the following we outline the research activities in terrain and atmospheric visualization (Section~\ref{sxn:vis}) as well as in system development (Section~\ref{sxn:mth}) that are necessary to achieve our goals for LEP. The third goal, entraining environmental planners to use LEP and guide its development to be applicable to real world problems in the ROIs, is largely a problem of human factors, project coordination, and effective communication. Our methods of addressing these project management issues are discussed in Section~\ref{sxn:prj_crd} below. %--------------------------------------------------------------------------------------------------------- \subsection{Macrophysics and Microphysics of Dust Entrainment and Transport}\label{sxn:mm} The Dust Entrainment And Deposition (DEAD) model \cite[][]{DEAD} is a widely used and extensively tested mineral dust prediction module. DEAD has been used and evaluated in global and regional studies of dust emissions \cite[]{ZBN03,ZNT03,LMZ03,MZL02,MLD03}, radiative forcing \cite[]{CRE01,CRE02}, chemistry \cite[]{BiZ03}, and microphysics \cite[]{GZC02,GrZ04}. Current state-of-the-art dust entrainment models include DEAD \cite[]{ZBN03}, GOCART \cite[]{GCT01}, CARMA \cite[]{CTH03}, and NOGAPS \cite[]{HoB93}. All of these models and others have been used in global forecasts by various institutions. A description of the relative strengths and weaknesses of these models is beyond the scope of this proposal, but the available model evaluations show (in our opinion) that no dust model performs better than DEAD. The physical processes in DEAD are summarized in \cite{ZBN03}, \cite{ZNT03}, and \cite{GrZ04}. In particular, DEAD suits LEP well because it accurately mobilizes large silt and sand-sized particles ($10 < \dmt > 200$\,\um) which settle too quickly to be important in global scale models, but which are extremely important in mesoscale events. A rich variety of physical processes and scientific questions that are neglected in current global scale models become relatively more important at the mesoscale and will be the focus of our scientific investigations. Mesoscale dust forecasts are sensitive to the relative roles of particular dynamic processes in determining total dust mobilization and structure. These mesoscale processes include gust fronts, mountain winds, surface turbulence, and dry and wet convection. Other processes best addressed at the mesoscale rather than the global scale include the role plant phenotype in determining surface roughness lengths and drag partitioning \cite[]{RGL93}, particle asphericity effects on sedimentation \cite[]{Gin03}, fine scale Geo-morphologic and topographic contributions to soil erodibility \cite[]{ZNT03}, and entrainment of large particles in anthropogenically disturbed environments \cite[]{Gil88,BaP99,SCS00}. Recent evidence suggests direct suspension may be more important than saltation-sandblasting in anthropogenic environments (David Chandler, personal communication, 2002; Dale Gillette, personal communication, 2002). As these processes are represented and better understood at the mesoscale, we will transfer (and parameterize) this knowledge for application in our global models. The central scientific areas we will address with this research are \setcounter{enmrfr}{0} % Reset reference counter for this list \begin{enumerate*} \item \enmrfrstp \label{idx_mss} Formation and Structure: \begin{enumerate*} \item What are the relative contributions to dust loading by gust fronts, surface turbulence, mean winds, and dry and wet convection? \item How does the spatial and temporal structure of a dust storm depend on the presence or absence of these elements? \item How are dust storm mass budgets and visibility reductions partitioned between large and small particles under a variety of conditions? \end{enumerate*} \item \enmrfrstp \label{idx_evl} Predictability: \begin{enumerate*} \item To what extent are simulations of mesoscale dust events consistent with synoptic and in-situ observations in the ROIs? \item Which bioclimatic mobilization constraints (e.g., wind speed, soil moisture, vegetation, soil texture, disturbance) most affect dust predictability in the ROIs? Which of these constraints alter most under climate change and thus present environmental planners with the problem of adapting to a dustier climate or exploiting a less turbid climate? \end{enumerate*} \item \enmrfrstp \label{idx_vsb} Response to environmental change: \begin{enumerate*} \item How is dustiness expected to change in ROIs as a result of natural and/or anthropogenic climate and land-use change? \item Which regions are most vulnerable to climatological forecast rainfall changes? \end{enumerate*} \end{enumerate*} This research requires extending the physics in DEAD to account for processes which are relatively more important at the mesoscale than at the global scale. The first such process is the entrainment and transport of large particles. However, large particles are thought to contribute significantly to aerosol mass transport on global scales, although the mechanisms by which large particles remain in suspension are not well understood and consequently are poorly modeled \cite[][]{ZBN03,MSI03,Gin03,CTH03}. The role of large dust particles in visibility reduction increases with proximity to dust source regions, which in the case of military operations may often be local. We are ready to assess the importance of large silt and sand in mesoscale regions using our saltation-sandblasting physics \cite[]{GZC02,GrZ04}. We will extend DEAD to allow direct entrainment of very large sand and gravel sizes using the threshold speed measurements of \cite{BaP99}. This extension will allow realistic deflation to result from extreme events and even helicopter-generated winds. Besides extension to higher wind speeds, DEAD will allow dust production by user-specified anthropogenic sources, such as land use disturbance, vehicle movement, or explosion. Vehicular-generated dust will be parameterized from existing observations based on soil texture, moisture and vehicle type \cite[]{SCS00}. Specification of anthropogenic disturbance and source will be done through offline files initially, and eventually by user-controlled real time input using external controls such as joysticks. LEP will allow the user to visualize natural and anthropogenic dust separately or together, and thus to evaluate the relative strength of anthropogenic disturbance against the natural background. %--------------------------------------------------------------------------------------------------------- \subsection{Boundary Layer Visualization} \label{sxn:vis} The visualization engine has to address realistic ground illumination and rendering of high-resolution mesoscale terrain elevation models as well as visualization of atmospheric attenuation and cloud formation. As indicated in Section~\ref{sxn:bck_vzn} the visualization engine exploits cluster-parallelism for fast rendering. It also features a high-resolution tiled display system capable of 10\,mega-pixel~(Mp) image resolutions (see also Figure~\ref{fgr:display} and Section~\ref{sxn:mth}). In this context, the main problems are efficient data distribution techniques and algorithms for cluster-parallel terrain and atmosphere visualization. The visualization problems that we will confront and address in our project on interactive rendering of forecasts are: \setcounter{enmrfr}{0} % Reset reference counter for this list \begin{enumerate*} \item \enmrfrstp \label{idx_sky} Ground (surface) rendering: \begin{enumerate*} \item To what extent must efficient multi-resolution terrain models be modified to support efficient load-balancing on distributed cluster-based and tiled-display systems? \item What are the main effects for ground illumination from the diffuse sky-/sunlight transfer through the boundary layer atmosphere? How can we efficiently model these for real-time terrain rendering? \end{enumerate*} \item \enmrfrstp \label{idx_dust} Atmosphere (volume) visualization: \begin{enumerate*} \item Efficiently coupling the dynamic large-scale atmospheric aerosol distribution and radiance data with the visualization system, \item Optimizing approximation algorithms for physically correct light scattering and extinction to obtain throughput required for interactive rendering \item Algorithms and data structures for efficient cluster-parallel visualization \end{enumerate*} \end{enumerate*} %------------------------------------------------ \subsubsection{Ground Rendering} \label{sxn:grnd} Terrain surface rendering will be based on our own extensive work on multi-resolution terrain rendering frameworks \cite[]{Pajarola:98,Pajarola:98a,POSSW:98,PW:98,Pajarola:02,PAL:02,BP:03,LPT:03}. This project requires extending such efficient terrain rendering approaches to cluster-parallel tiled-display rendering. In this context, the efficient parallelization and distribution of workload of the rendering task is the major problem (see overviews \cite[]{MCEF:94,Crockett:97}). Existing work on parallel rendering exhibits limiting factors such as requiring all data to reside in main memory \cite[]{SFLS:00,SFL:01,HEBSEH:01} or poor load-balancing \cite[]{CKS:02} for unevenly distributed geometry among display tiles. This work will address the development of a distributed multi-resolution terrain surface representation that supports dynamic load balancing between the rendering nodes. While maximally interleaved and distributed data partitioning supports efficient rendering on each individual rendering node resulting in partial full-resolution images, it imposes a large compositing cost for combining large images from all nodes for each rendered frame. Therefore, sort-first (data partitioning) and sort-last (image compositing) strategies must carefully be combined in this context (see also \cite[]{SFLS:00}). Additionally, this terrain multi-resolution representation will be designed to maintain very large-scale terrain data out-of-core on external (disk) memory and to provide seamless access from all rendering nodes. Ground illumination is mainly dependent on the sky- and sunlight absorption and transport through the boundary layer atmosphere, and reflection properties of the surface. The WRF module computes direct and diffuse down-welling radiance and thus provides the critical terrain surface irradiance information needed for accurate ground illumination. Second, the necessary surface reflection properties will be modeled in form of realistic BRDFs derived from the NASA MODIS and MISR instruments \cite[]{SGS02,TSG02}. Similar to color texturing from satellite images or aerial photographs, sampled BRDF data is interpolated and mapped onto the ground surface. % modeling and pre-computation of multiple-angle down-welling flux information ? %------------------------------------------------ \subsubsection{Atmospheric Visualization} \label{sxn:atm} Boundary layer visibility attenuation and scattering are modeled by a volume rendering approach of sky- and sunlight absorption and transport through the atmosphere. WRF uses atmospheric and particle scattering and absorption properties to calculate radiative fluxes between cells in the atmosphere and thus provides the visualization system with partial results of the scattering equation for direct volume rendering \cite[]{KvH:84}. Hence as scattering is provided by the WRF model, the remaining main rendering task is numerical integration and attenuation of radiative fluxes along the view-direction of the observer similar to the low-albedo case of volume rendering \cite[]{Blinn:82}. The required physical light attenuation and scattering properties can be derived from DEAD and WRF by extending the calculation of upwelling and downwelling irradiance to incorporate scattering in multiple directions for increased visual fidelity. Zender knows the appropriate modifications to make to the WRF radiation code in order to expose more detail to the visualization system where desired. The WRF solar radiation code is based on the accurate, efficient, atmospheric solar radiative transfer code \cite[]{Bri921} used in other climate/weather models such as the NCAR CCM3, CAM, and RegCM2 These models all employ versions of the \href{http://www.cgd.ucar.edu/cms/crm}{CCM3 CRM} radiation code which PI~Zender maintains on behalf of NCAR. For some visualizations we might want to employ more than the standard two-stream adding-doubling approach used for visible radiation in order to obtain realistic radiances at more polar angles \cite[]{STW88,ZBP97,Zen99}. An alternative to increasing the radiation code in WRF is to explore anisotropic scattering phase functions for given particle mixtures that can be computed off-line and accessed via look-up tables. Mesoscale atmospheric models easily constitute of millions or even billions of samples (voxels). Without efficient multi-resolution techniques that can approximate rendering at different {\em levels-of-detail\/} (LODs) and without hardware accelerated rendering, such large volumes cannot be visualized at interactive frame rates. Thus interactive visualization will be achieved by a multi-resolution octree volume representation \cite[e.g.,][]{Samet:89a,LH:91,GG:00} and splatting-based direct volume rendering techniques \cite[e.g.,][]{Westover:90,LH:91,CM:93,MMSCSY:98,MMC:99,HMSC:00,ZPBG:01b}. Similar to \cite[]{CM:93}, our approach will incorporate perspectively projected splatting footprints as texture maps and exploit hardware acceleration by rendering splats as textured sprites. This task will draw on our experience in large-scale volume rendering \cite[]{CM:02,CM:03,MBTLH:03} and result in a combined view-dependent rendering of both the terrain and atmosphere using hybrid rendering approaches \cite[]{LMMH:98,MBTLH:03}. %Meyer has studied both large-scale volume rendering algorithms \cite[]{MBTLH:03} as well as hybrid approaches that combine both decimated geometric meshes \cite[]{CM:02,CM:03} and large volumes representing geoscientific models. %Combined, view-dependent rendering of both the terrain and atmosphere will be accomplished using a hybrid rendering approach \cite[]{LMMH:98,MBTLH:03}. \csznote{ To achieve interactive display performance, parallel rendering is used in this project as outlined in Section~\ref{sxn:bck_vzn}. Therefore, the proposed work will address the issue of cluster-parallel volume splatting that has largely been left out so far \cite[]{Wittenbrink:98}, as well as the development of a distributed out-of-core multiresolution volume data representation for transparent parallel access. For optimized overall load-balancing, this project will also strike for a balance between optimized rendering-load distribution via interleaved rendering (e.g. along the lines of \cite[]{GS:02}) and image compositing costs. } % end csznote %--------------------------------------------------------------------------------------------------------- \subsection{LEP System}\label{sxn:mth} LEP system design and implementation must address the following issues: \begin{enumerate*} \item \enmrfrstp \label{idx_system} How does the time-stepping forecast model (WRF) send 2D and 3D output (tracer concentrations, RGB radiance fields) to the visualization engine's multiresolution terrain and atmosphere rendering model? \item What hardware, software and networking infrastructure components and configurations efficiently support real-time forecast rendering? \item What user-interaction, input, and visualization features best support environmental planning? \end{enumerate*} The basic interaction and visualization mode of LEP will be interactive walk- and fly-through exploration of the simulated ROI, similar to Figure~\ref{fgr:display}~a. This image is from our preliminary terrain rendering system ViRGIS \cite[]{POSSW:98,PW:98}, and does not include the physically realistic simulated ground illumination or atmospheric effects to be incorporated in LEP. Fundamental rendering features will include enabling and disabling various illumination, light scattering and attenuation effects via a mouse- or wand-controlled user-interface (e.g., menus, dialog windows, parameter panels etc.). Further interaction functionality will include specification of boundary condition changes (erodibility, land-use and vegetation type) and anthropogenic disturbances (type, location, duration and magnitude of tracer emissions fluxes), see also Figure~\ref{fgr:lep}. Input modes are tabular data, and, ultimately, interactive manipulation using external controls such as mouse/joystick or wireless hand-held point-and-click devices. One user-interaction scenario the user (i.e., planner or decision-maker) sketches out a region on the screen using the mouse that is converted by the system to a geo-spatial terrain region. Then the user may change the land-use properties of the selected region. Altered properties are fed-back as time-varying boudary conditions to the WRF simulation module and will result in a modified forecast. Moreover, as shown in Figure~\ref{fgr:display}~b, LEP will provide high-resolution images through the use of a tiled multi-screen display system that incorporates in its initial configuration a $3 \times 3$ arrangement of nine LCD panels capable of rendering 10\,Mp. \begin{figure}[ht] \begin{center} \includegraphics*[width=0.9\hsize]{Display}\vfill \end{center} \vskip -8mm \caption{(a)~Example interactive environmental exploration. (b)~Synchronized distributed rendering on tiled-display wall.} \label{fgr:display} \end{figure} Large screen real-estate not only supports large field-of-view and hi-res imagery but also allows for simultaneous display of various atmospheric multi-field data attributes such as convective fluxes, different species (e.g., dust, smoke) and current and time-integrated aerosol deposition. Ultimately, the large display may be partitioned to display different forecast simulations. For increased user immersion, the visualization system will support configurations for stereo-rendering\footnote{via use of stereo shutter-glasses and rendering of separate images for the left and right eye}. To cope with the computational cost of accelerated-time forecasts and interactive visualization of very large environmental data, LEP requires cluster-parallel computing for both tasks as illustrated in Figure~\ref{fgr:lep}. Hence the WRF/DEAD simulation engine is driven by a 10~node (20~CPU) Beowulf cluster, and also includes a large high-performance RAID storage system to archive simulated test-scenarios and to serve as asynchronous data-transfer cache for the visualization system. The forecast simulation software is based on extensions of the Beowulf/MPI based WRF and DEAD modules. The visualization engine is powered by a parallel-rendering cluster consisting of 10~nodes with high-performance graphics cards, each driving one of the LCD displays. It also includes a medium RAID storage system. The rendering software for the tasks outlined in Sections~\ref{sxn:grnd} and~\ref{sxn:atm} will be based on the MPI and Chromium \cite[]{HHNFAKK:02} concurrent computing and rendering libraries. As the forecast system will produce an anticipated data rate of 1--2\,\Gbxs\ every few seconds, even with only few field-attributes provided to the visualization system, LEP incorporates its own multi \Gbxs\ network infrastructure.\footnote{required bandwidth exceeds current production-network backbone available on site} \begin{figure}[ht] \begin{center} \includegraphics*[width=0.9\hsize]{APAVE}\vfill \end{center} \vskip -8mm \caption{System organization of the LEP.} \label{fgr:lep} \end{figure} In hindcast mode, LEP is driven by meteorological inputs from observations such as National Centers for Environmental Prediction (NCEP) reanalyses \cite[e.g.,][]{Kal96} or from archived model data. In forecast mode, the atmospheric model at the core of LEP prognoses the state variables (e.g., wind) required to predict dust emissions and cloud formation. The visualization system of LEP obtains updated aerosol and radiance flux data from the predictive forecast model every few seconds (downlink data transfer). Upon transfer, this data is organized and processed to be easily accessible to the visualization engine's multiresolution terrain and atmosphere rendering. A high-speed RAID system caches the incoming atmospheric field data and provides a transparent single-image disk to all rendering nodes. This allows all nodes to concurrently access the data for rendering from external memory (i.e. via memory-mapped-files). Uplink communication consists of low-bandwidth parameter changes to the WRF/DEAD model and will cause the forecast to temporarily interrupt its calculation to adjust for the modified boundary layer and atmosphere conditions. %--------------------------------------------------------------------------------------------------------- \section{Simulation and Forecast Evaluation}\label{sxn:evl} %--------------------------------------------------------------------------------------------------------- DEAD has a long and ongoing history of evaluation against measurements, including the station and satellite measurements and field campaigns \cite[][]{RCE01,CRE01,CRE02,ZBN03,LMZ03,MZL02,MLD03}. DEAD has also been evaluated against wind-tunnel measurements in the field and laboratory \cite[]{IvW82,GMB98,FMB99,AlG01}. DEAD has been used operationally by P.~Rasch and W.~Collins of NCAR in 72-hour forecasts of global dust aerosol distributions in the \href{http://www.cgd.ucar.edu/cms/match/new_website}{MATCH} Aerosol assimilation framework since 2001, as well as in other GCMs. We are currently evaluating DEAD against near real time station measurements of PM10 and saltation in the Columbia Plateau (data from Washington State University and the Columbia Plateau Air Quality Project). With LEP we will initiate new evaluations against saltation and deposition at multiple stations in the Mojave and against cameras which automatically photograph key playas when wind speeds exceed $6$\,\mxs\ (data from Rich Reynolds, Pat Chavez, and Marith Reheis, USGS) \cite[][]{Reh97}. However, we recognize that the ongoing global and station evaluations are not particularly appropriate for assessing predictions of mesoscale dust storms. To be certain that we improve representation of dust storm processes in LEP, we must also identify and use new sources of satellite data. We will use GOES/TOMS/MODIS imagery/aerosol index/optical depth \cite[][]{WKT98,KKT99,TBH02}. By comparison of model to observations, we will obtain fundamental metrics to assess the forecasting skill of LEP, including: \begin{itemize*} \item Probability of Detection: How often are dust storms observed but not predicted? What commonalities do missed forecasts share? \item False Alarm Rate: How often dust storms predicted but not observed? \item Forecast bias: What is the difference between observed and predicted dust levels? \end{itemize*} These three metrics are appropriate to mesoscale forecasts and may be diagnosed from instrumentation available in the ROIs and from satellite. Because LEP allows interactive specification of anthropogenic sources, simulations may also be evaluated against vehicle-influenced visibility \cite[]{VHS03}. Drought is a primary natural cause of increased dust in semi-arid regions \cite[]{PrN86,NTB98}. Agriculture is a primary anthropogenic source of dust in semi-arid regions \cite[]{Gla94,SCS00,CSK02,You02}. Livestock grazing in both ROIs may be crucial to explaining observed patterns of deflation as much as 30~years after the last anthropogenic activity \cite[]{NRB04}. Collaborator Pat Chavez conducts ongoing photogrammetric measurements and GOES infrared dust detection over the Mojave to assess source region activity. Adequate expenses are requested for travel to the Mojave and to the IoM and Aral Sea in Kazakhstan to train Abdibekov in these methods. In addition, we have assembled a climatology of all detectable dust source regions and their emission efficiency from TOMS satellite observations \cite[]{HBT97,PGT02}, and MODIS aerosol classifications are now a standard product. we are able to identify regions of interest worldwide where dust, both natural and anthropogenic, poses a potential visibility problem. %--------------------------------------------------------------------------------------------------------- \subsection{Broader Impacts}\label{sxn:brd} LEP will combine and enhance existing weather models and display technologies into a coordinated forecast and interactive visualization system of weather and atmospheric information at mesoscale dimensions. This inter-disciplinary research project will advance the penetration of information technology into areas with beneficial application to society, including environmental forecasting, land-use planning and erosion mitigation. Utilizing LEP technology aviation authorities in arid regions could forecast visibility impairment and engine risk due to dust storms. Urban planners downwind of dusty regions could prioritize landscape vulnerability to land use change and forecast traffic warnings. Conservationists could develop ``what if'' visualizations of arid landscapes based on a range of climate, conservation, and land use change scenarios. Health and security sectors could simulate transport and deposition patterns of passive particulates in mesoscale regions, from specified point sources (e.g., dirty bombs). The \href{http://dust.ess.uci.edu/dead}{DEAD model} and visualization system are based on and will continue to be made available as open-source software. Finally, LEP provides an exceptional educational opportunity to rapidly demonstrate and experience the dynamic visual impacts of environmental change on daily to inter-annual timescales (e.g., from dust storms to desertification). Graduate students participating in LEP will benefit from a unique learning experience working at the interface of weather and aerosol forecasting and information technology and display. These students will be advantaged by exposure to scientific questions and applications beyond their own dissertation work. Zender will also display and discuss the mesoscale visualization results to his 350-student lower division course on the Atmosphere. %--------------------------------------------------------------------------------------------------------- \subsection{Synergy with Ongoing Work}\label{sxn:syn} %--------------------------------------------------------------------------------------------------------- This ITR project will enhance adoption and support for DEAD in solving wind-erosion problems in countries with limited scientific infrastructure. This proposal is synergistic with ISTC-funded proposal K-424 by Abdibekov to simulate Aral Sea dust fluxes. Zender is their US project advisor, and they already use DEAD. This proposal is synergistic with NSF ATM-0214430 (discussed in Section~\ref{sxn:prr_NSF}), where Zender requested and was denied funding for mesoscale erosion simulations of the Columbia Plateau in eastern Washington State. This proposal synergizes with a pending proposal to DOE ARM where Dr.~Steven Ghan, PI, and others including Zender seek to prototype a next generation aerosol scheme in WRF and then move it to CCSM. PI~Zender is an affiliate scientist with NCAR and collaborates extensively with scientists there responsible for aerosol implementations in internationally used models including MATCH, CCSM, and WRF. He will ensure that extensions and improvements to the aerosol capabilities of DEAD and WRF are made available to the WRF Chemistry and Aerosol working group, led by Dr.~Peter Hess, and to the NCAR CCSM Atmospheric, Land, and Biogeochemistry Working Groups, led by Drs.~Bill Collins, Gordon Bonan, and Natalie Mahowald. The software technology to expose the key prognostic variables of the running forecast model to interactive modification will be an important contribution useful to many similar community forecast models. \subsection{Extensibility and Technology Transfer}\label{sxn:vsn} Many other ROIs which might benefit from such an integrated mesoscale forecasting and decision-making system. For example, climate change and anthropogenic tracers are both problems confronting the Arctic National Wildlife Refuge (ANWR). In that sense, our initial ROIs are a specific examples of a generic class of problems which could benefit from LEP's capabilities. The UCI ESS Department has national strengths in arid region disturbances, hydrology, and climate change. Once we debug the forecast-visualization-decision process loop, we are committed to expand LEP or transfer our technology to other institutions interested in spearheading similar projects on other regions. % Project Coordination section may be 3 pages in addition to 15 standard pages \section{Project Coordination}\label{sxn:prj_crd} PI~Zender takes overall responsibility for project coordination. Interactions among scientific, visualization, decision-making and field representatives of the LEP team will occur at a variety of pre-planned and informal bi-lateral and multi-lateral, project-wide meetings. Zender directs two other multi-investigator projects, the \href{http://www.ess.uci.edu/esmf}{Earth System Modeling Facility}, similar in scale to the LEP, and the \href{http://nco.sf.net}{netCDF Operators}, a smaller scale, unfunded, OpenSource software project. He makes efficient use of project coordination software such as \href{https://maillists.uci.edu/mailman/listinfo/esmfadm}{Mailman} (for project mailing lists), \href{http://dcs.nac.uci.edu/cgi-bin/wreq/req}{wreq} (a work-request tracking system for prioritizing tasks), and extensive documentation on project Home Pages. These techniques maximize project transparency and minimize confusion that arises through misunderstood responsibilities, requests, and goals. All LEP software design, construction, and modification will employ Concurrent Versioning System (\href{http://www.cvshome.org}{CVS}) to facilitate distributed development. To facilitate collaboration, all model software and data will be made immediately available via the LEP homepage to any interested outside investigator. We believe strongly in unfettered exchange of software and data. Sub-groups of the immediately participating personnel (Table~\ref{tbl:prs}) will naturally form along disciplinary and ROI boundaries. These sub-groups will communicate outside of any pre-planned mechanism, though within a medium (e.g., mail-lists) viewable by all. Phone conferences will be held when immediate ``all hands'' meetings are required. The specific scientific/technical responsibilities of PIs, senior personnel, and collaborators receiving funds are: \begin{enumerate*} \item PI~Zender will direct environmental forecasts and evaluation. Zender will direct the summer workshops and outreach efforts to entrain local educational, civil, and security organizations including CDHS, CHP, DHS, FAA, NPS, and RESCUE. Zender's group will embed DEAD into WRF, study dust storm structure in ROIs, and make and evaluate improvements in dust storm forecasting. The post-doc will conduct original research applying mesoscale forecasts to environmental decisions in the ROIs. The ESS graduate student will work with Zender on micro-to-mesoscale processes, dynamics, prediction, and consequences of Aeolian erosion. \item Co-PI~Pajarola is in charge of environmental visualization and rendering and will direct two UCI graduate students. Pajarola's group will design and implement real time visualization algorithms for WRF/DEAD, along with protocols for efficient interactive and offline specification of anthropogenic disturbances. Pajarola's group will contribute extensively to international synergies by making subsets of LEP forecasts efficiently available through the Web. \item Senior Personnel Joerg Meyer has primary charge of volume rendering and optimizing a multi-resolution data representation to work in a hybrid visualization with Pajarola's view-dependent terrain visualization. The hybrid algorithm will be implemented by a graduate student under Meyer's and Pajarola's joint supervision. \item Senior Personnel Rich Reynolds will provide in situ measurements of Mojave erosion, advise on pilot environmental planning questions, and coordinate LEP with ongoing USGS projects to predict SW US response to natural and anthropogenic forcing. Reynolds is in charge of assessing the visibility impact of the planned doubling of Fort Irwin in the next few years. Fort Irwin is (anecdotally) the largest current dust source in the Mojave \cite[]{VHS03}. \item Collaborator Ualikhan Abdibekov, Institute of Mathematics, Almaty, Kazakhstan (see attach letter of support) is our primary pre-committed \textbf{International Collaboration} and will lead the Aral Sea ROI group. The Aral Sea provides a wonderful end-member for arid region disturbance studies because it is an anthropogenic environmental disaster from which we hope to learn and to which we hope to apply erosion causation and mitigation strategies. Zender is an advisor to Abdibekov on ISTC-funded proposal K-424 to simulate Aral-Sea dust fluxes. Abdibekov will gather and supply in~situ Aral Sea data and liaison between US and international researchers interested in applying LEP in Kazakhstan. Abdibekov will train on LEP in Year~2 and host our field visit to the Aral Sea in Year~3. This will permit Abdibekov's group to contribute to LEP's design for the complex Aral Sea project, and to obtain skills transfer in environmental planning for the Aral Sea based on our Mojave results. \item Collaborator Katie Purvis will use LEP heavily to forecast, understand, and plan for health effects of particulate plumes downwind of the Aral Sea. She will advise Zender's group on how to get science done in Kazakhstan. \item Collaborator Pat Chavez will provide satellite and photogrammetric evaluations of erosion in the Mojave, and use LEP to better understand the roles of bioclimatic controls on erosion. \item Zender, Pajarola, and Meyer will all perform studies of passive tracer releases (e.g., explosions) in the Mojave ROI. Passive tracer studies with prescribed magnitudes and source locations require the least geophysical knowledge and are excellent forecast diagnostics (flow markers). At the same time, these studies are extremely important for igniting interest in LEP from the Civil, Security, and DHS communities. \end{enumerate*} The long-range planning directions that a facility like LEP will take are difficult to predict and subject to pre-emption by emerging threats. For this reason, the above tasks mainly represent the scientific and technical responsibilities which must be carried through to make environmental planning for real-world problems possible. Four distinct types of pre-planned multi-investigator meetings will ensure project cohesion and steady progress to important milestones. Field Site visits (\textsf{FV}), National Conferences (\textsf{NC}), Summer Workshops (\textsf{SW}), and User Visits to LEP (\textsf{UV}). The summer workshops in years three and five bring together students, researchers, and decision-makers to learn and improve the capabilities of LEP in helping to solve real world problems. Invited attendees will represent local educational, civil, and security organizations including California Department of Health Services (CDHS), California Highway Patrol (CHP), Department of Homeland Security (DHS), Federal Aviation Administration (FAA), National Park Service (NPS), and Responding to Crises and Unexpected Events (RESCUE). The project time-line shows how the requested travel funds support intra- and extra-project communication and progress toward milestones. This time-line only details travel involving multiple project investigators. Funds used solely to present investigator-specific results at national and international conferences are requested in the proposal, and are not detailed here: \noindent\textbf{Year~1}. \textit{Milestones}: 1a.~Integrate DEAD with WRF; 1b.~Render first passive tracers; 1c.~Hindcast and render April~15, 2002 dust storm. \textit{Travel}: \begin{enumerate*} \item FV: Three-day visit to Mojave to familiarize modelers with measurement locations, source regions, and terrain (Pajarola, Zender, Reynolds, Chavez, ESS graduate student, post-doc). \end{enumerate*} \textbf{Year~2}. \textit{Milestones}: 2a.~Mesoscale dust storm mass budgets by process; 2b.~Real-time forecast and rendering; 2c.~First forecast interactivity. \textit{Travel}: \begin{enumerate*} \item UV: Rich Reynolds pilots study on Fort Irwin impacts expansion. \item UV: Pat Chavez evaluates modeled Mojave source regions against his satellite and in~situ measurements \item UV: Ualikhan Abdibekov trains on LEP for one week, exchanges information required to commence Aral Sea ROI studies. \item UV: Katie Purvis trains on LEP in Mojave and helps guide development of Aral Sea ROI. \item NC: PI~Zender and ESS Post-doc attend AMS Natural Hazards meeting to present science and to provide hands-on demonstration of LEP forecast/decision technology \end{enumerate*} \textbf{Year~3}. \textit{Milestones}: 3a.~Finish Fort Irwin study; 3b~Hyper-real surface tiling with MISR BRDFs; 3c~Fully interactive forecast boundary conditions. \textit{Travel}: \begin{enumerate*} \item FV: Zender visits Abdibekov in Almaty, Kazakhstan and tours the Aral Sea to familiarize modelers with ROI terrain and available erosion data. \item UV: Katie Purvis pilots study on particulate exposure downwind of Aral Sea ROI. \item NC: Zender and ESS grad student attend AGU Fall AGU meeting to present science and to provide hands-on demonstration of LEP forecast/decision technology to other attendees \item UV: 1--2 unspecified Southern California civil/security planners train on and use LEP \item SW: \textbf{Summer Workshop~I} brings together and train all US LEP participants with civil and security planners from CDHS, CHP, DHS, FAA, NPS, and RESCUE. \end{enumerate*} \textbf{Year~4}. \textit{Milestones}: 4a.~Mojave climate change simulations; 4b.~Initial Aral Sea forecasts; 4c.~Fully hybrid volume rendering/surface rendering. \textit{Travel}: \begin{enumerate*} \item NC: Zender and ESS Post-doc attend AMS Natural Hazards meeting to present science and to provide hands-on demonstration of LEP forecast/decision technology to other attendees \item UV: Katie Purvis pilots study on particulate exposure downwind of Mojave \item UV: 1--2 unspecified Southern California civil/security "decision-makers" train on and use LEP \item UV: Zender and Rich Reynolds conduct pilot study on Valley Fever habitat and dispersal changes with climate \cite[]{KJC01,ZeT04} \end{enumerate*} \textbf{Year~5}. \textit{Milestones}: 5a.~Aral Sea dust storm mitigation studies; 5b.~Visualize LEP forecasts from \calit/UCSD via OptIPuter; 5c.~Visualization-derived improvements to ROI erodibility \textit{Travel}: \begin{enumerate*} \item NC: Zender and ESS grad student attend AGU Fall AGU meeting to present science and to provide hands-on demonstration of LEP forecast/decision technology to other attendees \item UV: Katie Purvis finishes particulate exposure downwind of Aral Sea and Mojave ROIs \item UV: 1--2 unspecified Southern California civil/security "decision-makers" visit to train on and use LEP \item SW: \textbf{Summer Workshop~II}. All US LEP participants interact with, re-acquaint, and train civil and security planners with new LEP features. \end{enumerate*} Due to the complexity of realistically rendering and flying through forecasts, we assume that the visualization facility will be in a rough but usable state by the end of Year~2. Early User Visits, i.e., before Year~3, are reserved for bona fide collaborators and colleagues who will provide feedback on how to improve the system while it is under development. The two all-hands workshops are in years three and five. By that time we expect the interactive visualization system to be much more user friendly, robust, efficient, and versatile. Note that the vast majority of our pre-overhead budget is spent on salaries, benefits and tuition ($\sim 84$\%), and travel and meetings for scientists, students, planners, and decision-makers ($\sim 6$\%), rather than on equipment ($\sim 7$\%). The greatest challenges faced by this project are not computational or networking speed because we can always scale our simulation resolution as needed to obtained the desired visualization throughput. Constructing a system that performs stunning visualizations of mesoscale forecasts will certainly be difficult and will require cutting edge visualization development. Nevertheless, our greatest challenge is to build the facility in such a user-friendly way that students, researcher, and decision-makers see using it as an easy opportunity to approach real-world environmental problems quantitatively yet intuitively. This will require careful leveraging of our modest travel and outreach funding to integrate our talented and interdisciplinary team members. Hence we put more effort up front to outline the human rather than the technical dimensions of our project coordination. \newpage \section*{References}\label{sxn:rfr} \setcounter{page}{1} \thispagestyle{empty} % Bibliography \renewcommand\refname{} \vspace{-24.0pt} \newlength{\oldbaselineskip} \setlength{\oldbaselineskip}{\baselineskip} %\setlength{\baselineskip}{13.574pt} % 1.234 X 11pt \setlength{\baselineskip}{12.0pt} % 1.234 X 11pt \setlength{\bibsep}{4pt} % Space between natbib bibliography items \bibliographystyle{jas} \bibliography{bib,renato} \setlength{\baselineskip}{\oldbaselineskip} % 1.851 X 11pt \newpage \printindex % Requires makeidx KoD95 p. 221 \newpage \subsection{Budget Justification}\label{sxn:bdg_jst} \setcounter{page}{1} \thispagestyle{empty} \begin{verbatim} % NB: Do not use LaTeX formatting in Budget Justification since must % upload into Liz's Word document PERSONNEL Dr. Zender is the lead PI and will oversee the planning and coordination of the project. He will have primary responsibility for the forecasting work and will lead interactions with decision-makers and environmental planners. One month of summer salary each year is requested. Dr. Pajarola is the Co-PI and will have lead responsibilities for the work on visualization. One month of summer salary each year is requested. Dr. Meyer is a Senior Personnel who will have particular responsibility for work on volume rendering. One month of summer salary each year is requested. Rich Reynolds is with the USGS in Denver and is an expert on the Mojave Desert ROI. Pat Chavez is with the USGS in Flagstaff and is an expert on using satellite imagery to study wind erosion, with particular reference to the Mojave Desert. Ualikhan Abdibekov is with the Institute of Mathematics, Almaty, Kazakhstan, and leads their nationally recognized modeling studies on the dessication, deflation, and salinization of the Aral Sea, our other ROI. Katie Purvis is in the Joint Science Department of the Claremont Colleges; her group will use the LEP to understand the distribution of wind-eroded and radionuclide-rich sediment originating near the Aral Sea and affecting Kazakhstani health. These senior personnel will provide expertise and assistance in the field but will draw no salary from the project. To Be Named---Postdoctoral Scholar. 100\% effort will be contributed to the project. Salary is based on a published scale of \$50,472 annually. The post-doc will conduct original research applying mesoscale forecasts to environmental decisions in the ROIs. They will help train the graduate students in this endeavor. To be Named---Graduate Student Researchers~III. Funds are requested to support three non-resident graduate students each year of the project. Salary is estimated using the published scale of \$34,956 annually for GSR III, the level for students who are past the Masters degree but not yet advanced to candidacy. The students will be employed 49\% during the AY and 100\% during the summer, the maximum permissible per UC policy. One student will work with Zender in understanding/improving arid region forecasts and two will assist Pajarola in the visualization work. Student support for Meyer will be sought from other sources. \end{verbatim} \newpage \section{Facilities, Equipment, and Other Resources} \subsection{Computer and Networking} \setcounter{page}{1} \thispagestyle{empty} LEP is well-situated to take advantage of UCI's fastest network connections. The UCI Network Infrastructure provides researchers with 1.0\,\Gbxs\ access to the high-performance network of \calit\ and to the Gb-backbone of UCINet. UCI will upgrade this link to 10\,\Gbxs\ in the near future. This will remove one potential bottleneck to the Forecast Cluster (FC) which was designed to deliver about 1.5\,\Gbxs\ in a typical configuration. The \calit\ building is designed with redundant Gb Ethernet links to the UC Irvine backbone and will support one and ten \Gbxs\ links to other research facilities at UCI. A conceptual network called CalREN-XD (Experimental Development) that will leverage the visualization capabilities of the LEP is under development. An example of this is the ``OptIPuter'', which establishes a private, direct link between UCSD and UCI over the CalREN-HPR or a CalREN-XD circuit. The main impact of the OptIPuter is to operate a computer that has geographically distributed components. The visualization and compute clusters of this ITR will also be interconnected to the OptIPuter. Theoretically, this will allow the Visualization cluster to render on geographically remote displays such as at UCSD. This capability would prove useful in times of natural (e.g., dust storm) or anthropogenic emergency (e.g., radiologic plumes). PI~Zender is director of the Earth System Modeling Facility (ESMF), an NSF-supported MRI facility dedicated to coupled global climate, chemistry, and biogeochemistry simulations. The ESMF is an 88-CPU Power4+ IBM supercomputer with 192\,\GB\ RAM and 32\,\TB\ of RAID storage. Although funding was not requested in this proposal to perform real and accelerated time visualizations of coupled global model simulations, the ESMF is available for groups who are funded for such visualizations. The ESMF will be made available for LEP forecasting if ESMF nodes would otherwise be unused, and if short forecasting benchmarks are needed on machines faster than the forecasting cluster. Should LEP entrain visualization and decision experts interested in global-scale applications, the ESMF will gladly make available accounts and data. Funding for bi-directional 2\,\Gbxs\ connections between the ESMF and UCI's Campus portal is requested as part of PI~Zender's pending SEI proposal. Co-PI~Pajarola is the founder of the Computer Graphics Lab (CGL) in the School of Information and Computer Sciences (ICS) and is part of the Visualization Trust at \calit\. Senior personnel Meyer leads the Creative Interactive Visualization Laboratory (CIVL) research group. The two research labs CGL and CIVL offer various mid-range to high-end graphics workstations, display systems, video editing equipment and printing to support research and software development in scientific visualization. CGL hosts a basic 10-node $3 \times 3$ tiled-display rendering cluster infrastructure that is at the core of the proposed visualization system. As part of this proposal this cluster will be upgraded to satisfy the computational infrastructure needs of LEP. \subsection{Maintenance and Technical Support} \setcounter{page}{1} \thispagestyle{empty} Network and Academic Computing Services \href{http://www.nacs.uci.edu}{NACS} is the largest IT organization at UCI. Dr.~Frank Wessel manages the NACS Research Computing Support Group (RCS). RCS provides customized support and facilitates user access to high-performance computing (HPC) resources, software, training, and development of the UCI research infrastructure. NACS RCS staff led by Dr.~Wessel will facilitate the design, set-up, acquisition, and networking of the Forecast and Visualization clusters. NACS provides free system administration, co-location in the NACS Machine Room, and software sharing to researchers who partner with NACS on Medium Performance Computing (MPC) Beowulf Clusters. In exchange, researchers contribute 25\% of their compute cycles back to the campus. The principal benefit to researchers in joining the MPC partnership is to obtain a robust computing environment at low-recurring cost. The Forecast Cluster (FC) will be situated in the MPC cluster, co-located in the NACS machine room in a NACS-provided rack. The Visualization Cluster (VC) will be remotely located from the Forecast Cluster in Prof.\ Pajarola's laboratory, immediately adjacent to the visualization system. The VC will be administered by IT staff in the School of Information and Computer Science. To implement the required network connection speed, NACS will upgrade network facilities with additional switches and interconnects provided for in the budget. \newpage \section{Acronyms and Abbreviations}\label{sxn:abb} \setcounter{page}{1} \thispagestyle{empty} %\begin{longtable}{ >{\raggedright}p{7.0em}<{} >{\raggedright}p{8.0em}<{} } \begin{longtable}{ r >{\raggedright}p{25.0em}<{} l } & \kill % NB: longtable requires caption as table entry \caption[Acronyms and Abbreviations]{\textbf{Acronyms and Abbreviations}% \label{tbl:abb}} \\ \hline\hline \rule{0.0ex}{\hlntblhdrskp}% Abbreviation & Description & \\[0.0ex] \hline \rule{0.0ex}{\hlntblntrskp}% \endfirsthead % Lines between and \endfirsthead appear at top of table \caption[]{(continued)} \\ % Set label for following pages Abbreviation & Description & \\[0.0ex] \hline \rule{0.0ex}{\hlntblntrskp}% \endhead % Previous block appears at top of every page \endlastfoot % Previous block appears at end of table ACE & Army Corps of Engineers & \\ AMWG & (CCSM) Atmospheric Model Working Group & \\ ANWR & Arctic National Wildlife Refuge & \\ ASE & Advanced Science and Engineering & \\ BRDF & Bi-directional Reflectance Distribution Function & \\ \calit & California Institute for Telecommunications and Information Technology & \\ CAM & Community Atmosphere Model & \\ CARRE & Central Asia Research and Remediation Exchange & \\ SDSU & San Diego State University & \\ CCSM & Community Climate System Model & \\ CDHS & California Department of Health Services & \\ CE & Civil Engineering & \\ CGL & Computer Graphics Lab & \\ CHRS & Center for Hydrometeorology and Remote Sensing & \\ CIVL & Creative Interactive Visualization Laboratory & \\ CLM & Common Land Model & \\ CVS & Concurrent Versions System & \\ DEAD & Dust Entrainment And Deposition Model & \\ DWP & Department of Water and Power & \\ EECS & Electrical Engineering and Computer Science & \\ ESIG & Environmental and Societal Impacts Group & \\ ESMF & Earth System Modeling Facility & \\ ESS & Earth System Science (Department) & \\ FAA & Federal Aviation Administration & \\ FC & Forecast Cluster & \\ FOI & Field Of Interest & \\ FSU & Former Soviet Union & \\ FV & Field site Visits & \\ GB & Gigabyte & \\ GCM & General Circulation Model & \\ GOES & Geostationary Earth Orbiting Satellite & \\ Gb & Gigabit & \\ IC & International Conferences & \\ ICS & Information and Computer Sciences & \\ IOM & Institute of Mathematics (Almaty, Kazakhstan) & \\ ISTC & International Science and Technology Center & \\ JPL & Jet Propulsion Laboratory & \\ LEP & Laboratory for Environmental Planning & \\ LOD & Level-of-detail & \\ MAE & Mechanical and Aerospace Engineering & \\ MISR & Multi-angle Imaging Spectro-Radiometer (satellite instrument) & \\ MNP & Mojave National Preserve & \\ MODIS & Moderate Resolution Imaging Spectroradiometer (satellite instrument) & \\ MPC & Medium Performance Computing & \\ NACS & Network and Computing Services & \\ NASA & National Aeronautic and Space Administration & \\ NBC & Nuclear, Biological, and Chemical weapons & \\ NC & National Conferences & \\ NCAR & National Center for Atmospheric Research & \\ NCEP & National Center for Environmental Prediction & \\ NHS & National and Homeland Security & \\ NPS & National Park Service & \\ OptIPuter & Optical networking Internet Protocol computer \\ PI & Principle Investigator & \\ RAID & Redundant Array of Independent Disks & \\ RCS & Research Computing Services & \\ RESCUE & Responding to Crises and Unexpected Events & \\ RGB & Red Green Blue & \\ ROI & Region Of Interest & \\ RT & Radiative Transfer & \\ SC4 = SC$^{4}$ & Southern California Climate Change Consortium & \\ SCAQMD & South Coast Air Quality Management District & \\ SCCOOS & Southern California Coastal Ocean Observing System & \\ SDSC & San Diego Supercomputer Center & \\ SEI & Science and Engineering Informatics & \\ SP & Senior Personnel & \\ SS & Saltation-Sandblasting & \\ SW & Southwest & \\ SW & Summer Workshops & \\ TB & Terabyte & \\ UNCCD & United Nations Convention to Combat Desertification & \\ UV & User Visits to LEP & \\ VC & Visualization Cluster & \\ WMD & Weapons of Mass Destruction & \\ WRF & Weather Research and Forecasting (model) & \\ \end{longtable} % end tbl:abb \newpage \section{List of All Personnel Associated with Proposal, Collaborators and Co-Editors of Project Senior Personnel, their Post-docs, and their Thesis Advisors}\label{sxn:prs_lst} \setcounter{page}{1} \thispagestyle{empty} % Currently includes: Zender, Pajarola, Meyer, Purvis \begin{enumerate*} \item[] Adam, David, USGS, retired \item[] Agrawal, Divyakant, Computer Science, University of California Santa Barbara \item[] Ammann, C.~A. (NCAR) \item[] Artemyev, O National Nuclear Center, Kurchatov, Kazakhstan \item[] Ayuso, Robert USGS, Reston \item[] Belnap, Jayne USGS, Moab \item[] Bernasek, S.L. Princeton University \item[] Bian, H. (NASA/UMBC) \item[] Bielak, Jacobo (Carnegie Mellon University, CEE) \item[] Bocarsly, Andrew Princeton University \item[] Bonan, G.~B. (NCAR) \item[] Bradbury, J.Platt, USGS, Denver \item[] Busacca, A. (WSU) \item[] Callender, Edward USGS, RI \item[] Canagaratna, M Aerodyne Research, Inc., Billerica, MA \item[] Carlsen, Tina M. Lawrence Livermore National Laboratory \item[] Chambers, Douglas B. USGS, Charleston, WV \item[] Chavez, Pat, Jr., USGS, Flagstaff \item[] Clow, Gary USGS, Denver \item[] Colarco, P. (GSFC) \item[] Collins, W.~D. (NCAR) \item[] Colman, Steven USGS, Woods Hole \item[] Cooper, W.~A. (NCAR) \item[] Cullen, Alison University of Washington \item[] Dean, Walter USGS, Denver \item[] Dillner, Ann Arizona State University \item[] ElZarki, Magda, Computer Science, University of California Irvine \item[] El~Abbadi, Amr, Computer Science, University of California Santa Barbara \item[] Famiglietti, J. (UCI) \item[] Fenves, Gregory~L. (UC Berkeley, CEE) \item[] Forester, Richard USGS, Denver \item[] Fulton, Robert California Desert Consortium, Cal State Fullerton \item[] Gawalt, Ellen University of Chicago \item[] Gaylord, D. (WSU) \item[] Gerstner, Thomas, Applied Mathematics, University of Bonn \item[] Ghertner, Asher University of California, Berkeley \item[] Gill, Tom Texas Tech Univ. \item[] Goldhaber, Martin USGS, Denver \item[] Goldstein, Harland USGS, Denver \item[] Grini, A. (U.~Oslo) \item[] Gross, Markus, Computer Graphics Lab, ETH Zurich \item[] Guidotti, Patrick, Mathematics, University of California Irvine \item[] Hagen, Hans (University of Kaiserslautern, Germany, CS) \item[] Hamann, Bernd (UC Davis, Center for Image Processing and Integrated Computing) \item[] Harriss, Robert C. National Center for Atmospheric Research, Boulder, CO \item[] Herndon, Scott Aerodyne Research, Inc., Billerica, MA \item[] Hinkley, Todd USGS, Denver \item[] Ibraev, Nurlan State Agency for Health Care in East-Kazakhstan Oblast \item[] Jayne, John Aerodyne Research, Inc., Billerica, MA \item[] Jimenez, Jose University of Colorado, Boulder \item[] Jones, Edward~G. (UC Davis, Center for Neuroscience) \item[] Joy, Kenneth~I. (UC Davis, CS) \item[] Jumba, Isaac O. University of Nairobi, Kenya \item[] Kammen, Daniel M. University of California, Berkeley \item[] Kerwin, Micahel Univ. of Denver \item[] Kiehl, J.~T. (NCAR) \item[] Kiehl, J.~T. (NCAR) \item[] Kolb, Chuck Aerodyne Research, Inc., Billerica, MA \item[] Kuester, F. (UCI) \item[] Lamothe, Paul USGS, Denver \item[] Lancaster, Nick Desert Research Inst (Reno) and USGS, Reston \item[] Larson, Edwin Univ. of Colorado \item[] Lu, Gang Millennium Chemicals, Baltimore, MD \item[] Luiszer, Fred Univ. of Colorado \item[] MacKinnon, David USGS, Flagstaff \item[] Mahowald, N.~M. (NCAR) \item[] Meeker, Greg USGS, Denver \item[] Meenakshisundaram, Gopi, Computer Science, University of California Irvine \item[] Miller, Douglas Naval Postgraduate School, Monterey, CA \item[] Miller, Mark E. USGS, Moab \item[] Moore, J.~K. (UCI) \item[] Moscato, Silvina (most recent: USGS, Denver) \item[] Neff, Jason Univ. of Colorado \item[] Okin, G. (U.~Virginia) \item[] Okin, Greg Univ. of Virginia \item[] Olson, Arthur~J. (The Scripps Research Institute, La Jolla) \item[] Orsini, Douglas Metrohm Peak \item[] Pajarola, R. (UCI) \item[] Peterson, Leif E. Baylor College of Medicine \item[] Phillips, Susan USGS, Moab \item[] Rapp, Joshua (most recent: Univ. Vermont) \item[] Rasch, P.~J. (NCAR) \item[] Reheis, Marith USGS, Denver \item[] Robert C. Harriss National Center for Atmospheric Research \item[] Roberts, Helen Univ. of Wales \item[] Rosenbaum, Joseph USGS, Denver \item[] Rossignac, Jarek, Graphics Visualization \& Usability Center, Georgia Tech \item[] Sainz, Miguel, Computer Science, University of California Irvine \item[] Sanford, Robert Univ. of Denver \item[] Schwartz, J.S. Princeton University \item[] Sharber, Anna C. National Research Council \item[] Sides, Stuart USGS, Flagstaff \item[] Silva, Phil Utah State University \item[] Soltesz, Deborah USGS, Flagstaff \item[] Steven L. Bernasek Princeton University \item[] Stojadinovic, Bozidar (UC Berkeley, CEE) \item[] Stucki, Peter, Multimedia Laboratory, University of Zurich \item[] Susin, Antonio, Mathematics, Universita Politecnica de Catalunya Barcelona \item[] Thomas, G.~T. (CU) \item[] Tigges, Richard USGS, retired \item[] Tirado, Francisco, Computer Science, Complutense University Madrid \item[] Torres, O. (NASA GSFC) \item[] Ulsh, Brant A. McMaster University, Ontario Canada \item[] Urban, Frank USGS, Denver \item[] Valero, F.~P.~J. (Scripps) \item[] Velasco, Miguel USGS, Flagstaff \item[] Wandiga, Shem University of Nairobi, Kenya \item[] Weber, Rodney Georgia Institute of Technology \item[] Werner, Cynthia A. Texas A\&M University, College Station \item[] Widmayer, Peter, Theoretical Computer Science, ETH Zurich \item[] Wilhelmi, Olga V. National Center for Atmospheric Research, Boulder, CO \item[] Wischgoll, Thomas (UC Irvine, EECS) \item[] Worsnop, Douglas Aerodyne Research, Inc., Billerica, MA \item[] Yount, James USGS, Denver \item[] Yu, S. (Duke) \item[] Zender, Charlie, Earth System Sciences, University of California Irvine \item[] Zhang, Junfeng Rutgers University, Piscataway, NJ \end{enumerate*} \newpage List is Alphabetical by Surname. \begin{enumerate*} \item[] Collaborators of Zender: \begin{enumerate*} \item[] Ammann, C.~A. (NCAR) \item[] Bian, H. (NASA/UMBC) \item[] Bonan, G.~B. (NCAR) \item[] Busacca, A. (WSU) \item[] Colarco, P. (GSFC) \item[] Collins, W.~D. (NCAR) \item[] Famiglietti, J. (UCI) \item[] Gaylord, D. (WSU) \item[] Grini, A. (U.~Oslo) \item[] Kiehl, J.~T. (NCAR) \item[] Kuester, F. (UCI) \item[] Mahowald, N.~M. (NCAR) \item[] Moore, J.~K. (UCI) \item[] Okin, G. (U.~Virginia) \item[] Pajarola, R. (UCI) \item[] Rasch, P.~J. (NCAR) \item[] Valero, F.~P.~J. (Scripps) \item[] Yu, S. (Duke) \item[] Torres, O. (NASA GSFC) \item[] Thomas, G.~T. (CU) \item[] Kiehl, J.~T. (NCAR) \item[] Cooper, W.~A. (NCAR) \end{enumerate*} \item[] Collaborators of Pajarola: \begin{enumerate*} \item[] El~Abbadi, Amr, Computer Science, University of California Santa Barbara \item[] Agrawal, Divyakant, Computer Science, University of California Santa Barbara \item[] ElZarki, Magda, Computer Science, University of California Irvine \item[] Gerstner, Thomas, Applied Mathematics, University of Bonn \item[] Guidotti, Patrick, Mathematics, University of California Irvine \item[] Meenakshisundaram, Gopi, Computer Science, University of California Irvine \item[] Susin, Antonio, Mathematics, Universita Politecnica de Catalunya Barcelona \item[] Tirado, Francisco, Computer Science, Complutense University Madrid \item[] Zender, Charlie, Earth System Sciences, University of California Irvine \item[] Rossignac, Jarek, Graphics Visualization \& Usability Center, Georgia Tech \item[] Widmayer, Peter, Theoretical Computer Science, ETH Zurich \item[] Gross, Markus, Computer Graphics Lab, ETH Zurich \item[] Stucki, Peter, Multimedia Laboratory, University of Zurich \item[] Sainz, Miguel, Computer Science, University of California Irvine (postdoc) \end{enumerate*} \item[] Collaborators of Meyer: \begin{enumerate*} \item[] Bielak, Jacobo (Carnegie Mellon University, CEE) \item[] Fenves, Gregory~L. (UC Berkeley, CEE) \item[] Jones, Edward~G. (UC Davis, Center for Neuroscience) \item[] Joy, Kenneth~I. (UC Davis, CS) \item[] Olson, Arthur~J. (The Scripps Research Institute, La Jolla) \item[] Stojadinovic, Bozidar (UC Berkeley, CEE) \item[] Hamann, Bernd (UC Davis, Center for Image Processing and Integrated Computing), post-doc. adv. \item[] Hagen, Hans (University of Kaiserslautern, Germany, CS), graduate advisor \item[] Wischgoll, Thomas (UC Irvine, EECS), postgraduate scholar (total number: 1) \end{enumerate*} \item[] Collaborators of Purvis: \begin{enumerate*} \item[] Artemyev, O National Nuclear Center, Kurchatov, Kazakhstan \item[] Bernasek, S.L. Princeton University \item[] Bocarsly, Andrew Princeton University \item[] Canagaratna, M Aerodyne Research, Inc., Billerica, MA \item[] Carlsen, Tina M. Lawrence Livermore National Laboratory \item[] Cullen, Alison University of Washington \item[] Dillner, Ann Arizona State University \item[] Gawalt, Ellen University of Chicago \item[] Ghertner, Asher University of California, Berkeley \item[] Harriss, Robert C. National Center for Atmospheric Research, Boulder, CO \item[] Herndon, Scott Aerodyne Research, Inc., Billerica, MA \item[] Ibraev, Nurlan State Agency for Health Care in East-Kazakhstan Oblast \item[] Jayne, John Aerodyne Research, Inc., Billerica, MA \item[] Jumba, Isaac O. University of Nairobi, Kenya \item[] Jimenez, Jose University of Colorado, Boulder \item[] Kammen, Daniel M. University of California, Berkeley \item[] Kolb, Chuck Aerodyne Research, Inc., Billerica, MA \item[] Lu, Gang Millennium Chemicals, Baltimore, MD \item[] Orsini, Douglas Metrohm Peak \item[] Peterson, Leif E. Baylor College of Medicine \item[] Schwartz, J.S. Princeton University \item[] Sharber, Anna C. National Research Council \item[] Silva, Phil Utah State University \item[] Ulsh, Brant A. McMaster University, Ontario Canada \item[] Wandiga, Shem University of Nairobi, Kenya \item[] Weber, Rodney Georgia Institute of Technology \item[] Werner, Cynthia A. Texas A\&M University, College Station \item[] Wilhelmi, Olga V. National Center for Atmospheric Research, Boulder, CO \item[] Worsnop, Douglas Aerodyne Research, Inc., Billerica, MA \item[] Zhang, Junfeng Rutgers University, Piscataway, NJ \item[] Steven L. Bernasek Princeton University (Graduate advisor) \item[] Robert C. Harriss National Center for Atmospheric Research (Post-Doctoral advisor) \end{enumerate*} \item[] Collaborators of Reynolds: \begin{enumerate*} \item[] Adam, David, USGS, retired \item[] Ayuso, Robert USGS, Reston \item[] Belnap, Jayne USGS, Moab \item[] Bradbury, J.Platt, USGS, Denver \item[] Callender, Edward USGS, RI \item[] Chambers, Douglas B. USGS, Charleston, WV \item[] Chavez, Pat, Jr., USGS, Flagstaff \item[] Clow, Gary USGS, Denver \item[] Colman, Steven USGS, Woods Hole \item[] Dean, Walter USGS, Denver \item[] Forester, Richard USGS, Denver \item[] Fulton, Robert California Desert Consortium, Cal State Fullerton \item[] Gill, Tom Texas Tech Univ. \item[] Goldhaber, Martin USGS, Denver \item[] Goldstein, Harland USGS, Denver \item[] Hinkley, Todd USGS, Denver \item[] Kerwin, Micahel Univ. of Denver \item[] Lamothe, Paul USGS, Denver \item[] Lancaster, Nick Desert Research Inst (Reno) and USGS, Reston \item[] Luiszer, Fred Univ. of Colorado \item[] MacKinnon, David USGS, Flagstaff \item[] Meeker, Greg USGS, Denver \item[] Miller, Douglas Naval Postgraduate School, Monterey, CA \item[] Miller, Mark E. USGS, Moab \item[] Moscato, Silvina (most recent: USGS, Denver) \item[] Neff, Jason Univ. of Colorado \item[] Okin, Greg Univ. of Virginia \item[] Phillips, Susan USGS, Moab \item[] Rapp, Joshua (most recent: Univ. Vermont) \item[] Reheis, Marith USGS, Denver \item[] Rosenbaum, Joseph USGS, Denver \item[] Roberts, Helen Univ. of Wales \item[] Sanford, Robert Univ. of Denver \item[] Tigges, Richard USGS, retired \item[] Sides, Stuart USGS, Flagstaff \item[] Soltesz, Deborah USGS, Flagstaff \item[] Urban, Frank USGS, Denver \item[] Velasco, Miguel USGS, Flagstaff \item[] Yount, James USGS, Denver \item[] Larson, Edwin Univ. of Colorado \end{enumerate*} \end{enumerate*} \newpage \subsection{Supplementary Documents}\label{sxn:spl_doc} \setcounter{page}{1} \thispagestyle{empty} \begin{verbatim} 1. ${DATA}/prp/prp_itr/prp_itr_ltr_nsf.tex 2. ${DATA}/prp/prp_itr/prp_itr_ltr_abdibekov.pdf 3. ${DATA}/prp/prp_itr/prp_itr_ltr_purvis.pdf 4. ${DATA}/prp/prp_itr/prp_itr_ltr_chavez.doc 5. ${DATA}/prp/prp_itr/prp_itr_ltr_sorooshian.pdf 6. ${DATA}/prp/prp_itr/prp_itr_ltr_neff.pdf 7. ${DATA}/prp/prp_itr/prp_itr_clb.pdf 8. ${DATA}/prp/prp_itr/prp_itr_ltr_mehrotra.pdf 9. ${DATA}/prp/prp_itr/prp_itr_abb.pdf \end{verbatim} \csznote{ % Project acronyms Dust Rendering Dust Understanding Simulations Arid Landscape Forecasting and Visualization ALFV Dust Storm Forecasting and Visualization ALFV Environmental Forecasting and Visualization EFAV Arid Environment Forecasting and Visualization EFAV Forecasting and Rendering Environmental Change FAREC Forecasting and Rendering Arid Environments FARAE Forecasting and Rendering Arid Climates FARAC Forecasting and Rendering Arid Dusty Environments FARADE Forecasting and Rendering Arid Landscapes and Dust Storms FARLADS Coordinated Forecasting and Rendering Arid Landscapes and Dust Storms FARLADS Environmental Forecasting and Visualization Laboratory EFAVL Laboratory for Arid Environment Forecasts and Visualization LAEFAV Arid Environment Forecasting and Visualization AEFAV Forecasting and Visualizing Environmental Change in Arid Regions on Multiple Timescales FAVECARMT Forecasting and Visualizing Wind Erosion in Arid Regions on Multiple Timescales FAVWEARMT Wind Erosion Forecasting and Visualization in Arid Regions WEFAVAR Arid Region Wind Erosion Forecasting and Visualization ARWEFAV System for Arid Region Wind Erosion Forecasting and Visualization SARWEFAV System for Wind Erosion Forecasting and Visualization SWEFAV System for Mesoscale Environmental Forecasting and Visualization SMEFV Mesoscale Arid Region Forecast and Visualization Environment MARFVE Mesoscale Arid Region Forecast Interaction and Visualization Environment MARFVIE System for Mesoscale Arid Region Forecasting and Visualization SMARFV Mesoscale Arid Region Forecasting and Visualization Laboratory LEP Mesoscale Arid Region Forecasting and Visualization Interactions MARFVI Mesoscale Arid Region Forecasting and Visualization Interactive Laboratory MARFVIL Mesoscale Environmental Forecasting and Visualization Laboratory MEFVL Mesoscale Environmental Forecasting, Interaction, and Visualization Laboratory MEFVL Laboratory for Interactive Mesoscale Forecasts, Visualization, and Planning Laboratory for Interactive Mesoscale Environmental Forecasts, Visualization, and Planning Laboratory for Interactive Mesoscale Forecasts, Visualization, and Environmental Planning LIMFVEP Laboratory for Environmental Planning LEP Forecasting Visualization Environmental Planning FVEP } % end csznote \csznote{ Dear Everyone, I am writing to describe an NSF ITR proposal that I and a UCI colleague (Renato Pajarola) who specializes in geophysical visualization are preparing entitled ITR-(ASE+NHS)-(dmc+sim): Interactive Mesoscale Forecasts, Visualization, and Environmental Planning This project is to integrate mesoscale forecasts of the Mojave Desert and Aral Sea (aka Regions of Interest or ROIs) into a visualization and decision system called the Laboratory for Environmental Planning (LEP). The one page summary of LEP is attached in PDF format. This five year ITR proposal is due Tuesday 2/24. I am wordy so here's the up-shot: 1. Will you endorse the project with a letter of support? We would need your letter of support by noon Monday 2/23. 2. Would you like to participate? If so, what support (e.g., travel, equipment) should we request so you can effectively participate? Everyone not at UCI should request travel funds to UCI to use LEP. We would need your budget request by Friday noon 2/19. Travel is easy! I've already requested travel funds for some of you that I felt comfortable speaking for. This request is attached below. Travel requests require a one line justification. Equipment requests require item names and one-two line justifications. 3. What are your suggestions/comments/criticisms of the proposal? As you know, the unifying element of my research is desert dust. Until now I have only worked on global and in situ scales. We've recently made great progress in dust simulations, and now I'm highly motivate apply this scientific knowledge in ways that benefit society more tangibly than journal articles. The key scale in understanding, forecasting, and mitigating aeolian erosion and dust emissions, it seems to me, is the mesoscale. LEP is designed to provide state of the art weather and dust forecasts for, initially, two regions of interest, the Mojave and the Aral Sea. The Mojave is upwind of Irvine/LA during the Santa Anas which bring dust our way. It will be our primary proving ground in years 1-2. The Aral Sea is a much more complicated/disturbed region. Our focus will shift to it once we are satisfied with Mojave results. Renato and I have the modeling and visualization skills to build LEP. But only with your help, participation, and guidance will LEP work. Here's why: LEP IS FOR ENVIRONMENTAL PLANNING NOT (just) FORECASTING! We want LEP to be the place where academic, federal, and civil policy and decision-makers come to understand the impacts of natural variability and anthropogenic forcing on the Mojave and the Aral Sea. We will not succeed unless experts in these regions (yes, that means you) use the LEP facility and help guide its development. If you've read this far, then you're probably wondering who else may be involved, and whether LEP will be more than an academic exercise. Allow me to introduce you, and some(!) of your relevant interests: Charlie Zender (UCI ESS; Mojave & Aral Sea): PI aeolian erosion, transport, deposition, composition, valley fever Renato Pajarola : Co-PI, Scientific visualization, terrain rendering Ualikhan Abdibekov (Kazakhstan; Aral Sea): ISTC K424 "Numerical Simulation of Aerohydrodynamic Problems of the Environment Aral Sea Region". Pat Chavez (USGS; Mojave): Southwestern U.S. erosion, satellite monitoring of source regions Donald Dabdub (UCI MAE; Mojave): Regional air quality/atmospheric chemistry/aerosol modeling of LA airshed Mickey Glantz (NCAR; Aral Sea): Climate Impacts on Society Mike Goulden (UCI ESS; Mojave): Ecosystem gradients, carbon cycling, Station eddy flux measurements in So Cal. Falko Kuester (UCI): Scientific visualization, interactive systems, VR Jason Neff (CU, USGS; Mojave): SW US land use change and biogeochemistry Katie Purvis (Claremont; Aral Sea): Resuspension of radionuclides in Kazakhstan; particulates and health Jim Randerson (UCI ESS; Mojave): Santa Anas, Fire and carbon cycling Marith Reheis (USGS Denver; Mojave): Southwestern U.S. mineral dust particle composition, deposition traps Richard Reynolds (USGS Denver; Mojave): Impacts of climatic change and land use on southwestern U.S. Station measurements of saltation, wind, dust events Soroosh Sorooshian (UCI ESS/CE; Mojave): Hydrology and sustainability of semi-arid landscapes I apologize the lateness of this request. A similar, but smaller scale proposal that we submitted last year to ITR received very good reviews. If we are declined again we will probably re-submit to the next ITR round in November, as I'm convinced this is a good idea. The last detail is that if you wish to be a Co-I or Senior Personnel on this proposal, you may not be listed on any other ITR proposals being submitted this round. It would be up to you to verify this. http://dust.ess.uci.edu/prp/prp_itr.pdf is the proposal's location. Currently only the one-page summary (which is attached to this e-mail) is up-to-date. The rest of the body of the proposal is from last year's similar but much smaller ITR proposal, which completely neglected decision-making. The summary makes clear the points that will be re-written. I am happy to discuss any questions you may have by phone or email. I am traveling with good email access between Thursday and Sunday. Although it's late, this could be a great opportunity to get all of us working together with a practical decision-making tool. Thanks! Charlie Dear All, Here are some clarifications of what to do to participate in this ITR, in increasing order of work on your part. Following that is a list of the field travel schedule so you have an idea of the timeline. Budget: 0. We're shooting for a budget under $2M for five years. $1.75M is spoken for by three grad students, one post doc, the forecast/visualization equipment, and two months summer salary. So we're targeting up to $170k+50% overhead for other purposes that would make LEP more useful to you, the scientific/civil experts and decision-makers. $170k over five years split many ways is not a large number, nor is it a hard number. If you need more to fully participate, then make a request and we'll discuss it. To simplify logistics, all requested equipment must reside at UCI. We will only consider student/salary requests from UCI participants because it's too late to worry about sub-contracts etc. We'll have time to arrange sub-contracts if we are declined this round and end up re-submitting in the next ITR round (November?). Levels of involvement: 1. Collaborators: If you like the proposal and would like to use LEP or think that it would benefit your research in some way (e.g., provide boundary conditions to your model, test hypotheses you are interested in, simulate an area you are making measurements in), then please write a short letter of support. Request travel funds if using LEP involves travel/lodging for you! I've been using the nominal figure of $1000 to bring one domestic collaborator to the LEP for one week. Scale to suit. Send me the travel fund request by 12 noon Friday 2/20. Send me the Letter of Support PDF/.doc file by 12 noon Monday 2/23. This makes you a ``Collaborator''---no NSF biosketch or C&P is needed. This is the only option you have if you are on another ITR proposal. 2. Senior Personnel: Senior Personnel are people, from any institution, without whom the proposal technical or science objectives cannot be carried out. There are many people without whom this proposal will not work--- without experts on the Mojave and the Aral Sea this project will be ``just'' a forecasting/visualization proposal. We want more. The LEP vision is a system that people use to inform themselves about the short and long term consequences of land use decisions, freak events (sandstorms, bombs). How will the Mojave respond to drought? to increased precipitation? which parts are optimal to protect from disturbance to reduce aeolian erosion? Same for the Aral Sea. Rich is interested in the potential impacts of Fort Irwin expansion, Katie is interested in dust-borne dispersal of radionuclides in Kazakhstan, USGS and I are interested in valley fever habitats and dispersal, Jim is interested in fast time-scale biogeochemical fluxes from fires, Donald is interested in dust fluxes at the eastern boundary of his LA urban airshed model, and, since the firestorms this Fall, we're all curious about Santa Anas! You decided if it makes sense for you to be more than a Collaborator. If so we'll add you as Senior Personnel. Same spiel on funding. Ask what you need to make your participation effective. Got in situ measurements (sensits, dust traps) to evaluate against? Then join us on the field excursion to examine the Mojave/Aral and show us where your instruments are situated/learn where to situate new instruments. Senior Personnel do not write Letters of Support. Instead, please send me a one-two paragraph text snippet of how you would use LEP for science, decisions, and/or planning. All Senior Personnel must send their NSF biographical sketch (BS), Current and Pending (C&P) funding summary and Social Security number. Budget requests and text are due Friday at noon, paperwork by Monday noon. Guidelines for NSF BS :) and C&P are at http://dust.ess.uci.edu/prp/[cp.doc, cp_nsf.doc, bio_nsf_gdl.pdf] Senior Personnel may not be listed on any other ITR proposals this round. 3. Co-PIs: We don't anticipate anyone else being a Co-PI this round, but we do not rule anything out. Anyone with a very strong inclination to participate and work significantly with LEP through its lifetime should be a Co-PI. Due to time constraing, Co-PIs must be at UCI this round. Modest salary requests (e.g., summer salary) are appropriate. We'd need everything and up to a page of text by Friday noon. Travel Schedule: Field: One round-trip Denver-Irvine with one-week hotel in years two and four for Dr. Rich Reynolds, USGS (senior personnel) (budget at $1000 per trip = $2000 total) Field: One round-trip Flagstaff-Irvine with one-week hotel in year two for Dr. Pat Chavez, USGS (senior personnel) (budget at $1000) Field: One three-day field visit to Mojave in year one. (Pajarola, Zender, Reynolds, Chavez, ESS graduate student and ESS post-doc) Includes four-wheel drive rental and two nights motel. (budget at $1500). Field: One round-trip Almaty, Kazakhstan-Irvine visit for one week in year two by Dr. Ualikhan Abdibe three days on-site at Aral Sea with Dr. Ualikhan Abdibekov (budget at $3000). Meeting: Attend AMS Natural Hazards meeting in years two and four for Zender and ESS post-doc to present science and to demonstrate forecast/decision technology. Includes shipping/rental for computer/projection equipment. (budget at $1000 each + $500 shipping/rental = $2500 per meeting = $5000 total). Meeting: Attend AGU Fall AGU meeting in years three and five for Zender and ESS grad student to present science and to demonstrate forecast/decision technology. Includes shipping/rental for computer/projection equipment. (budget at $1000 each + $500 shipping/rental = $2500 per meeting = $5000 total). LEP: $1000 per year for travel to bring unspecified Southern California civil/security "decision-makers" to UCI to use the LEP in each of years 3, 4, and 5. Total additional cost = $3000. LEP: Weeklong visits to bring Professor Katie Purvis (Claremont College) and students to UCI to use the LEP in each of years 2-5. (budget at $750 per year. Total additional cost = $3000.) Thanks! Charlie Participants: Ualikhan Abdibekov , Pat Chavez , Sharad Mehrotra , Joerg Meyer , Jason Neff , Katie Purvis , Richard Reynolds , Soroosh Sorooshian , Renato Pajarola , Charlie Zender Non-participants: Donald Dabdub , Mickey Glantz , Mike Goulden , Falko Kuester , Jim Randerson , Marith Reheis , } % end csznote \csznote{ % Usage: Place usage here at end of file so comment character % not needed cd ~/prp_itr;make -W prp_itr.tex prp_itr.dvi prp_itr.ps prp_itr.pdf prp_itr.txt;cd - scp ${HOME}/prp_itr/prp_itr.dvi ${DATA}/ps/prp_itr.pdf ${DATA}/ps/prp_itr.ps ${HOME}/prp_itr/prp_itr.tex ${HOME}/prp_itr/prp_itr.txt dust.ess.uci.edu:/var/www/html/prp/prp_itr # NB: latex2html works well on prp_itr.tex latex2html -dir /var/www/html/prp/prp_itr prp_itr.tex # NB: tth chokes on prp_itr.tex cd ${HOME}/prp_itr;tth -a -Lprp_itr -p./:${TEXINPUTS}:${BIBINPUTS} < ${HOME}/prp_itr/prp_itr.tex > prp_itr.html scp prp_itr.html dust.ess.uci.edu:/var/www/html/prp/prp_itr # NB: tex4ht works well on prp_itr.tex cd ${HOME}/prp_itr;htlatex prp_itr.tex scp prp_itr*.css prp_itr*.html dust.ess.uci.edu:/var/www/html/prp/prp_itr # NB: tex4moz works well on prp_itr.tex cd ${HOME}/prp_itr;/usr/share/tex4ht/mzlatex prp_itr.tex scp prp_itr*.css prp_itr*.html prp_itr*.xml dust.ess.uci.edu:/var/www/html/prp/prp_itr Proposal preparation: pdftk A=${DATA}/ps/prp_itr.pdf cat A3 output ${DATA}/prp/prp_itr/prp_itr_smr.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A4-21 output ${DATA}/prp/prp_itr/prp_itr_dsc.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A22-28 output ${DATA}/prp/prp_itr/prp_itr_rfr.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A29 output ${DATA}/prp/prp_itr/prp_itr_bdg_jst.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A30-31 output ${DATA}/prp/prp_itr/prp_itr_fcl.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A32-33 output ${DATA}/prp/prp_itr/prp_itr_abb.pdf pdftk A=${DATA}/ps/prp_itr.pdf cat A34-35 output ${DATA}/prp/prp_itr/prp_itr_clb.pdf Usage: cd ~/prp_itr;make -W prp_itr.tex prp_itr.dvi prp_itr.ps prp_itr.pdf prp_itr.txt;cd - scp ${HOME}/prp_itr/prp_itr.dvi ${DATA}/ps/prp_itr.pdf ${DATA}/ps/prp_itr.ps ${HOME}/prp_itr/prp_itr.tex ${HOME}/prp_itr/prp_itr.txt dust.ess.uci.edu:/var/www/html/prp/prp_itr } % end usage \end{document}