STATMOS Annual Report Report Period: 1 October 2011- 30 September 2012

PARTICIPANTS

Faculty and Senior Personnel Alan Gelfand, Professor, Department of Statistics, Duke Montserrat Fuentes, Professor, Department of Statistics, NCSU Brian Reich, Assistant Professor of Statistics, Department of Statistics, NCSU Lian Xie, Professor of Oceanography, MEAS, NCSU Catherine Calder, Associate Professor, Department of Statistics, OSU Mark Berliner, Professor and Chair, Department of Statistics, OSU Peter Craigmile, Associate Professor, Department of Statistics, OSU Radu Herbei, Assistant Professor, Department of Statistics, OSU Desheng Liu, Assistant Professor, Departments of Geography and Statistics, OSU Tao Shi, Associate Professor, Department of Statistics, OSU Peter Guttorp, Professor, Statistics, UW Dennis Lettenmaier, Professor Civil and Environmental Engineering, UW Donald B. Percival, Professor, Statistics; Senior Mathematician, Applied Physics, UW June Morita, Principal Lecturer, Statistics, UW Paul D. Sampson, Research Professor, Statistics, UW J. Michael Wallace, Atmospheric Sciences, UW Frederick Bingham, Professor, Physics and Physical Oceanography, UNCW Rick Katz, IMAGe Senior Scientist, NCAR Doug Nychka, IMAGe Senior Scientist, NCAR Steve Sain, IMAGe Senior Scientist, NCAR Claudia Tebaldi, Professor, NCAR Jennifer Hoeting, Professor of Statistics, CSU Dan Cooley, Associate Professor of Statistics, CSU Geof Givens, Associate Professor of Statistics, CSU Piotr Kokoszka, Professor of Statistics, CSU Darren Homrighausen, Assistant Professor of Statistics, CSU Michael Stein, Professor of Statistics, UChicago Liz Moyer, Assistant Professor of Geophysical Sciences, UChicago Ian Foster, Professor of Computer Science, UChicago Debashis Mondal, Assistant Professor of Statistics, UChicago Barbara Bailey, Professor, Department of Statistics, SDSU Richard Levine, Professor, Department of Statistics, SDSU Charmaine Dean, Professor & Faculty of Science Dean, UWO Richard Smith, Professor of Statistics, UNC-Chapel Hill Hao Zhang, Professor of Statistics, Purdue Hyonho Chun, Associate Professor, Purdue Eric Gilleland, PhD Project Scientist, NCAR Russ Vose, PhD., Chief, Product Development Branch, Global Climate Application Div.

Postdoctoral Researchers Souparno Ghosh, PostDoc, Duke Elizabeth Mannshardt, Postdoc, Statistics, NCSU Tammy Greasby, IMAGe PostDoc, NCAR Matt Heaton, IMAGe PostDoc, NCAR Will Kleiber, IMAGe PostDoc, NCAR Suz Tolwinski-Ward, IMAGe PostDoc, NCAR William Leeds, Postdoc, UChicago Ying Sun, Postdoc, UChicago Dongsoo Kim, PhD., Remote Sensing Application Div. Graduate Students Inkyung Choi, Statistics, Purdue Xian He, Statistics, Purdue Luke Smith, Statistics, NCSU Kat Sucic, Statistics, NCSU Laura Bohem, Statistics, NCSU Danny Modlin, Statistics, NCSU Chris Krut, Statistics, NCSU Joe Guinness, Statistics, UChicago (now PostDoc, NCSU) Stefano Castruccio, Statistics, UChicago Xiaohui Chang, Statistics, UChicago Myoungji Lee, Statistics, UChicago Jon Bradley, Statistics, OSU Mark Risser, Statistics, OSU Aritra Sengupta, Statistics, OSU Shan Shi, Statistics, OSU Agniva Som, Statistics, OSU Shivi Vaidyanathan, Statistics, OSU Jiangyong Yin, Statistics, OSU Dunke Zhou, Statistics, OSU Shanshan Cai, Geography, OSU Xialin Zhu, Geography, OSU Fangpo Wang, Statistical Science, Duke Lucia Paci, Statistics, Duke Thomas Leininger, Statistical Science, Duke Maria Terres, Statistical Science, Duke Laura Li, Human Centered Design and Engineering, UW Laina Mercer, Statistics, UW Brian Smoliak, Atmospheric Sciences, UW Aaron Zimmerman, Statistics, UW Erin Schliep, Statistics, CSU Grant Weller, Statistics, CSU Brook Russell, Statistics, CSU

Undergraduate Students Sarah Dershowitz, Statistics, UW Solomon Choe, Statistics, UW Amy Kim, Statistics, UW Yuting Ma, U, Applied and Computational Mathematical Sciences, UW Grant Wilder, Geophysical Sciences, UChicago Peter Hansen, Mathematics, UChicago Organizational Partners The Nordic Top Research Initiative SARMA network US Forest Service Other Collaborators and Contacts NOAA Western Alaska Landscape Conservation Cooperative Seattle School District University of Oregon Assistant to Director Leigh Ann Herhold, NCSU

ACTIVITIES AND FINDINGS

Research Team members, largely based at NCSU, have actively worked on the development and implementation of models for spatial extreme weather events. This work has led to manuscripts published in top statistical journals as well as subject matter journals. We have introduced a new model for hurricane forecasting, currently adopted by the NCSU fluid dynamics lab to present their yearly seasonal forecast. A new model for downscaling of deterministic models, has been adopted by EPA to produce their air quality fused maps. The participants have researched time and space scales of variability of sea surface salinity as measured mostly by satellite but also by drifting buoys. There are some indications that the spatial distribution of the time scales of variability as measured by the satellite are similar to those measured by the buoys. This gives a nice measure of confidence in the satellite measurements as they are very innovative and experimental, i.e. not well-validated. Research at the University of Chicago has focused on computational issues relating to the statistical analysis of large spatial and space-time datasets, with applications to high frequency meteorological data and satellite-based observations of total column ozone. A few highlights of this work: we have adapted ideas from the numerical linear algebra community and developed new ones to handle the computational issues in working with large structured and unstructured covariance matrices; using existing methods for evolutionary spectra of time series as a starting point, we have developed an approach to space-time modeling of ARM (Advanced Radiation

Measurement) SGP (Southern Great Plains) surface temperature data (taken every minute) that takes account of changes in variability that occur on the time scale of hours in order to produce conditional simulations of surface temperatures throughout the SGP region based on observation sat around 15 monitoring stations. Research at the University of Washington has focused on uncertainty analysis related to climate data. We have looked at different ways of estimating daily mean temperature from synoptic data, estimation of global temperature, assessment of uncertainty in ranking annual global temperature and methods for up- and downscaling of regional climate models. In addition we have studied the impact of climate change and variability on agricultural production. Education NCSU will offer 9 special topics courses in 2012-2013, with a particular emphasis on the training of our students to develop the computational skills statisticians need to become indispensable in this new area of Big Data. Peter Guttorp will be teaching STAT 593 Statistical Climatology, a case-based course in the spring of 2013 at UW. Michael Stein will be giving a half-day tutorial at the ENVR 2012 meeting entitled "Statistical Models and Methods for Spatial and Spatio-Temporal Data with Strong Local Dependence" and will be re-giving this tutorial as a series of informal talks at UChicago. The Department of Statistics at UChicago offers a year-long sequence in computational methods aimed at statisticians and scientists. The summer school "Ten Lectures on Statistical Climatology" was organized at the University of Washington August 5-9 2012. The school had 65 participants, of which 27 came from STATMOS nodes (U Washington, Colorado State, Chicago, Ohio State, NCAR, North Carolina State, Purdue, SAMSI and U British Columbia).15 participants were from abroad. There were 16 participants from research universities, 5 from regional or teaching universities, 2 from industry and 1 from a government agency. 22 participants were funded by the CBMS-NSF grant, while 17 were funded by the STATMOS NSF research network and 1 by the Pacific Institute for Mathematical Sciences, for a total of 40 funded participants. Doug Nychka from NCAR gave the ten lectures on topics in statistical climatology. Will Kleiber (NCAR), Tamara Greasby (NCAR), Dan Cooley (CSU), Mikyoung Jun (Texas A&M) and Veronica Berrocal (Michigan) gave additional lectures. There was a poster session with 11 posters, six roundtable discussion groups, and two computing sessions. The overall evaluation from the participants was very positive, particularly with respect to the Nychka lectures. Most participants expressed strong interest in participating in similar activities in the future.

Training, Development, and Mentoring There are several opportunities for training, development, and mentoring provided. Four undergraduate students are conducting research projects at UW, ranging from uncertainty in rankings to compiling a list of local outreach projects in climate education. We announced the opportunity to attend a half-day training course for new NARCCAP users. The course was held on Thursday April 12 from 9:30am – 12pm at the NCAR Mesa Lab in Boulder, CO. It was mainly geared toward users working on climate impacts, but all users were welcome.

The collaborations with NCAR helped the team to understand the significance of the results and get the team engaged in very important scientific problems. The graduate students attending the workshops and short courses offered by the team have benefited enormously of this opportunity for interdisciplinary training. Students who made extended visits to other campuses commented on the positive value of attending classes at the institutions they were visiting. This was an unanticipated benefit of the extended visits and we will encourage further students who make extended visits to take advantage of these opportunities. Outreach Activities We have organized invited sessions at the main statistical conferences, and offered short courses for the research outlined in this proposal. We have written a NSF proposal on climate education, which was not funded. This required the development of a project team, investigating local climate education outreach projects (Dershowitz), and compiling a needs assessment. We are expecting to continue some of the team activities under the auspices of STATMOS in spite of the denial of this specific proposal. Peter Craigmile and Peter Guttorp participated in Climate Science Day On The Hill, visiting members of Congress in Washington DC to discuss the role that scientists can offer in climate science matters. 2 Feb 2012. We have also contributed to outreach issues through web page development. We are continually updating and adding material. We also participated in the Joint Statistical Meetings invited session on Statistics and Regional Climate Models (organized and chaired by Guttorp(UW), discussant Fuentes (NCSU), network speakers Smoliak(UW) and Nychka (NCAR)) and the Joint Statistical Meetings invited session on Water and Climate (organized by Katz (NCAR) and Guttorp, chaired by Katz (NCAR), discussant Craigmile (OSU), network speaker Percival) In addition, we gave an introductory overview lecture on Statistics and Climate at the JSM (organized by Guttorp, chaired by Calder (OSU), speaker Guttorp).

We are planning a new journal, Statistics for the Atmosphere and Ocean, which will enable us to serve at the interface of statistics and atmospheric and oceanic sciences. The journal will allow statisticians the opportunity to reach more scientists while publishing cutting-edge statistical methodology. Professor Jennifer Hoeting of Colorado State University will serve as the founding editor of the new journal. Michael Stein has been working with two undergraduate students on projects related to STATMOS research. Grant Wilder is looking at how the choice of GCM affects the results of statistical methods for climate model emulation. Peter Hansen is investigating the use of periodically correlated time series models to capture the variations in monthly temperature and precipitation records at a local level. Peter Guttorp has been working with three undergraduates on research projects involving estimation of daily mean temperature (with Yuting Ma paper in Int. J. Clim.), uncertainty in ranking the warmest years (with Amy Kim) and the co-occurrence of record-breaking weather events (with Solomon Choe). Michael Stein is a co-editor with Michel Dekking, Delft University of Technology, of a special issue of Statistical Science on the general theme of “Mathematics of Planet Earth,” coinciding with the designation of 2013 as a special year for the Mathematics of Planet Earth. The mission of the MPE project is to encourage research in identifying and solving fundamental questions about planet earth, encourage educators at all levels to communicate the issues related to planet earth, and inform the public about the essential role of the mathematical sciences in facing the challenges of our planet. Peter Guttorp (UW) and Steve Sain (NCAR), together with Chris Wikle, co-edited a special issue of Environmetrics on Advances in Statistical Methods for Climate Analysis. STATMOS collaborates with National Climatic Data Center (NCDC) to provide archived environmental data upon request. When requested, NCDC provided 60 year COOP temperature and precipitation data, and 10 years wind data.

CONTRIBUTIONS Contributions of STATMOS Postdocs Elizabeth Mannshardt earned her PhD in Statistics in 2008 from UNC Chapel Hill, she worked as a Postdoctoral Fellow at SAMSI and a Visiting Assistant Professor at Duke University. She then had the opportunity to work as the Assistant Director of The Program in Spatial Statistics and Environmental Statistics in the Department of Statistics at Ohio State University. Since her arrival at NCSU in March 2012, Elizabeth was invited to give a talk at the Purdue Statistics Symposium in June 2012. She also gave this talk at the 2012 JSM meetings, as well as will give this as an invited talk at the ASA ENVR meeting in October 2012. She has submitted to give a talk at the AGU meetings in December: "Time and Space Scales of Variability of Sea Surface Salinity from Aquarius Data" with Fred Bingham at UNC Wilmington, Katarina Sucic, and Montserrat Fuentes. The AGU talk is a STATMOS project just starting up in which all

researchers are members of STATMOS and the project began as a STATMOS collaboration. It is intended as a pilot study for a grant proposal to be submitted to NASA’s Ocean Salinity Science Team in April 2013. Elizabeth is also working on a paper with STATMOS NCSU members Katarina Sucic, Montserrat Fuentes, and Brian Reich "Comparing Exposure Metrics for the Effects of Fine Particulate Matter on Emergency Hospital Admissions" . Most of her previous publications were completed prior to her employment with STATMOS, however she has spent some time since arriving at NCSU finishing up the following: “Statistical Modeling of Extreme Value Behavior in North American Tree-Ring Density Series” Mannshardt, E., Craigmile, P.F., and Tingley, M.P. Accepted, Climatic Change, Aug 2012. In August 2012, she had the opportunity to participate in the STATMOS workshop “Ten Lectures on Statistical Climatology” presented by Doug Nychka. She is actively maintaining research interests and contacts at The Statistical and Applied Mathematical Sciences Institute (SAMSI), a STATMOS node, by participating in their 2012-2013 program on Massive Datasets. She is also a member of the Environment and Climate working group under this program. In addition, she organized an invited session for ENAR 2013, "Statistics of Environmental Health: Considering Spatial Effects and Various Sources of Pollutant Exposure on Human Health Outcomes", which was accepted to the program. Two speakers in this session are STATMOS members – Richard Smith at UNC/SAMSI and Kate Calder at Ohio State University. The Chicago hub has hired two postdocs. Ying Sun came from SAMSI, where she had worked with Montse Fuentes, among others, after completing her Ph.D. with Marc Genton at Texas A&M. She was hired for one year, taking into account that she already had spent a year as a postdoc at SAMSI. In addition to completing work begun at SAMSI, Sun is working on developing estimating equations for Gaussian processes that are both statistically and computationally efficient. These methods are of great interest in the statistical analysis of large spatial and spatio-temporal datasets. She is also interested in statistical issues relating to the NARCCAP experiment. The second Chicago postdoc is William Leeds, who just completed his Ph.D. in Statistics under the supervision of Chris Wikle at the University of Missouri. Chris was hired for what is likely to be a two-year term with funding shared between STATMOS and RDCEP (Center for Robust Decision Making on Climate and Energy Policy), an NSF-supported multi-institution center with its home at the University of Chicago. In particular, Leeds will be working on problems in climate model emulation as it relates to the needs of RDCEP. STATMOS will allow him the flexibility to work on other projects as well if he so chooses, but for now he is focusing on climate model emulation as part of a group that includes faculty from Geophysical Sciences (Elisabeth Moyer), a postdoc in geophysical sciences, and graduates and undergraduates from a range of disciplines. Dorit Hammerling is a postdoctoral fellow in the Program on Statistical and Computational Methodology for Massive Datasets at the Statistical and Applied Mathematical Sciences Institute. She will spend the second year of the postdoctoral fellowship at National Center for Atmospheric Research and the University of Washington with funding from the STATMOS

program. Her thesis research was in the area of spatial statistics developing methods to infer global atmospheric CO2 concentrations from satellite observations at high spatial and temporal resolution. During her postdoctoral fellowship she will work on incorporating spatial information in the prediction of tropical cyclones and hurricanes and the application and further development of spatial statistical methodology to optimally utilize satellite observations of the atmosphere and the oceans. Contributions within Discipline In work focused at NCSU, we have introduced a statistical framework to model distributed lags in space and then we extended it to distributed lags in time. In terms of climate and environmental exposure, addressing such problems is critical since current levels at a particular location may be explained not only by the previous levels at that location but by levels at neighboring locations. Implications for environmental risk assessment are evident. Distributed lag models are also being used in research on climate model emulation at UChicago to produce accurate approximations to GCM output under forcing scenarios for which the model has not been run. Researchers throughout the network have introduced new algorithms to fit large space and space-time datasets. Increasingly, environmental data is being examined over larger regions and over longer time scales. Explanatory models that attempt to capture process features are vital for forecasting purposes, e.g., response to changing climate. Fitting such models pushes computational capabilities, requiring approximation that is efficient while retaining model features. An approach developed at NCSU is already being implemented by the EPA to study ozone and particulate matter levels. The principal purpose of the project regarding time and space scales of variability of sea surface salinity is to determine the spatial and temporal statistical characteristics of the field of sea surface salinity. The data is found through the Aquarius mission, a collaboration between NASA and the Space Agency of Argentina. This is to be related to similar measures of other variables such as rainfall, evaporation, waves, surface wind and currents. The ultimate goal is to understand the ocean's role in the global hydrologic cycle or circulation of freshwater. The fate of the hydrologic cycle is a major question in the face of a warming planet. It is apparent that the cycle is shifting; wet areas are becoming more wet and dry areas dryer. We are still unaware as to how the ocean contributes to this cycle. The purpose of this work is to statistically relate the time and space scales of the relevant processes to those of surface salinity. Computational work at UChicago focuses on fitting Gaussian process models to massive datasets. Iterative methods for solving systems of linear equations are standard in numerical analysis but have been underutilized by statisticians, and we have developed matrix-free iterative algorithms for calculating exactly and approximately score functions for Gaussian likelihoods that are adapted to the settings that arise in the analysis of space-time data.

Contributions to Other Disciplines The grant has helped continue interesting statisticians in climate problems, and climatologists to work with statisticians. We have developed a new framework to assess regional climate models using both up- and downscaling. The methods developed under this award have been applied to hurricane forecasting, modeling of temperature trends and spatial extreme weather events, downscaling of weather and air pollution and in general have contributed to the understanding of the regional impact of climate change. Our work opens up new opportunities in numerical linear algebra as well as providing pathways for statistical ideas to contribute to numerical linear algebra. For example, using standard ideas from factorial designs, we came up with an improvement to a stochastic algorithm for estimating traces of large, implicitly defined matrices. Contributions to Human Resource Development The opportunity for postdoctoral researchers and graduate students to travel to other nodes of the network has been very productive for the participants, as evidenced by the travel reflections at the end of this document. However, there is a serious problem in hiring postdocs with the understanding that they will have to move in a year to another node. We would like to discuss this difficulty with the NSF program officers. Contributions to Resources for Research and Education We have offered short courses (Spain- Carlos III, ENAR-Miami, IBS-Australia, UFRJ, Brazil, Heidelberg, Germany) to continue providing training to our junior investigators and graduate students. Contributions Beyond Science and Engineering Our new methodology for downscaling is adopted by EPA to downscale air quality models. Our methodology for hurricane wind modeling is being considered by NOAA for operational wind forecast. Future Initiatives Elizabeth Mannshardt has organized an invited session for ENAR 2013: Statistics of Environmental Health: Considering Spatial Effects and Various Sources of Pollutant Exposure on Human Health Outcomes. Frederick Bingham will be working with Drs. Montse Fuentes and Elizabeth Mannshardt to analyze the Aquarius data from a spatial statistical perspective. We expect to submit a proposal to NASA in March for further funding. We have proposed a Banff workshop on the impact of oceans on climate variability (Oct 2013) and a Pan-American Advanced Studies Institute on spatiotemporal statistics in Brazil (July 2014). Both have been funded, and will be STATMOS activities.

SEMINARS, TALKS AND PRESENTATIONS

Conference and Workshop Talks June 2010: M. Fuentes, Calibration of deterministic models. Statistical methods for very large datasets conference. Baltimore, MD. June 2010: M. Fuentes, Nonparametric spatial models for extreme temperatures. Interface international conference, Cary, North Carolina March 2011: M. Fuentes, Invited speaker for ENAR 2011. Impact of Pollution on preterm birth. Also gave a short course at ENAR 2011, and leading a luncheon discussion. July, 2011: M. Fuentes, Invited speaker for IMS (Japan). Nonparametric models for spatial extremes. August 2011: M. Fuentes, Invited speaker for JSM 2011. Spatial nonparametric models for the impact of pollution on adverse pregnancy outcomes. Sept 2011: E. Schliep, Evaluating Wetland Health: Multivariate multi-level latent Gaussian process model, Fall Meeting of the Colorado/Wyoming, Chapter of the American Statistical Association, Aurora, Colorado. Oct 2011: M. Fuentes. Spatial modeling of the impact of climate change of ozone levels. Keynote Speaker. Workshop on Mathematics in the Geosciences. Evanston, IL. Dec 2011: M. Fuentes, Plenary speaker for IBS (Australia). Spatial models for the impact of climate change on ozone levels. Dec 2011: Mondal, D. Disease mapping with area and population density information, Statistical Concepts and Methods for the Modern World, Colombo, Sri Lanka. Dec 2011: Katz, R.W., 2011: Economic impact of extreme events: An approach based on extreme value theory. American Geophysical Union Fall Meeting, San Francisco, CA. Jan, 2012: Peter Guttorp, University of Washington: Estimating global temperatures. SAMSI workshop on Uncertainty Quantification for Climate Data at NCDC, Asheville. Jan 2012: B. Reich, Extreme value analysis for evaluating ozone control strategies. SAMSI Workshop on Uncertainty Quantification, Asheville, NC. Jan 2012: Katz, R.W Evidence for clustering of temperatures at high levels based on extreme value theory. American Meteorological Society, 21st Conference on Probability and Statistics in the Atmospheric Sciences, New Orleans, LA.

Jan 2012: Mondal, D. Smoothing wind fields from scatterometer data, 22nd Annual Conference of The International Environmetrics Society, Hyderabad, India. Jan 2012: A.Gelfand, Presidential Invited Address, The International Environmetrics Society, Hyderabad, India. Jan 2012: A.Gelfand, The P.C. Mahalanobis Lectures - Kolkata, Delhi, Bungaluru, India. Feb 2012: E. Schliep, Evaluating Wetland Health: Multivariate Multi-level latent Gaussian Process Model, Front Range Student Ecology Symposium, Fort Collins, CO. March 2012: A.Gelfand, Plenary Lecture, CLAPEM XII, Vina Del Mar, Chile. March 2012: Peter Guttorp, Regional Climate Prediction comparisons via statistical upscaling and downscaling, German Statistics and Probability Days, Mainz, Germany. April 2012: M. Fuentes, Impact of climate change on human health. Biometric Society (ENAR) Regional Meeting, Washington DC. April 2012: B. Reich, Spatiotemporal quantile regression for detecting distributional changes in environmental processes. Biometric Society (ENAR) Regional Meeting, Washington, DC. April 2012: Grant Weller, An investigation of the Pineapple Express phenomenon via bivariate extreme value theory, Colorado/Wyoming chapter of ASA spring meeting, Boulder, CO. April 2012: Grant Weller, An investigation of the Pineapple Express phenomenon via bivariate extreme value theory, SIAM 2012 Conference on Uncertainty Quantification, Raleigh, NC. April, 2012: SSES Conference on Spatial and Environmental Statistics, Department of Statistics, Ohio State University.

STATMOS: A Research Network for Statistical Methods for Atmospheric and Oceanic Sciences Organizer: Catherine Calder Poster Presenters: Tao Shi: Statistical Inference Based on Summaries of Large Spatial Datasets Desheng Liu: Spatio-Temporal Statistical Modeling of Land-Cover Processes Department of Statistics graduate students, Jonathan Bradley, Aritra Sengupta, Shan Shi, Agniva Som, Rui Wang, and Matthew Yin; and Department of Geography graduate student, Xiaolin Zhu all participated in the conference.

May 2012: Katz, R.W., 2012: Improving the simulation of extremes by stochastic weather generators. Workshop on Stochastic Weather Generators, Roscoff, France. May, 2012: B. Reich, A hierarchical Bayesian analysis of max-stable spatial processes. SAMSI Transition Workshop on Uncertainty Quantification, RTP, NC.

May 2012: J. Hoeting, Evaluating wetland health: Avoiding Indexes via a Multivariate Latent Variable Model, National Monitoring Conference of the National Water Quality Monitoring Council, Portland, OR. June 2012: A.Gelfand, Invited Speaker, Symposium on Hierarchical Modeling in the Environmental Sciences, Tokyo, Japan. June 2012: A.Gelfand, Invited Speaker, BayesComp 2012, Tokyo, Japan. June 2012: A.Gelfand, Keynote Speaker, ISBA 2012, Kyoto, Japan. June 2012: Mondal, D. Spatial analysis of environmental bioassays, ISBA World meeting, Kyoto, Japan. June 2012: WNAR/IMS and Graybill Conference, Fort Collins, CO.

J. Hoeting, Multivariate multilevel latent Gaussian process model to evaluate wetland condition, B. Reich, False Discovery Control in Spatial Multiple Testing. D. Mondal, Spatial analysis of areal unit data

June 2012: M. Fuentes, Do we know too much to know anymore? Banquet invited speaker for the SRCOS conference. June 3-6, Jekyll Island, GA. June 2012: P. Guttorp, Statistical issues in climate research. Keynote lecture, NordStat 2012, Umeå, Sweden. July – Aug, 2012: Joint Statistical Meetings, San Diego, CA

Elizabeth Mannshardt, Spatial Modeling to Identify Relationships and Trends Brian Reich, Spatiotemporal quantile regression for detecting distributional changes in environmental processes. B. Smoliak, Assessing the Agronomic Impact of Climate Variability and Change Using Regional Climate Models: A Matter of Scale. Elizabeth Mannshardt, Analyzing Extremes in Environmental Studies Grant Weller, An investigation of the Pineapple Express phenomenon via bivariate extreme value theory D. Cooley, A Model for Extremes on a Regular Spatial Lattice P. Guttorp, Statistics and Climate Science, Introductory Overview Lecture. D. B. Percival, Decline of Arctic Sea-Ice Thickness as Evidenced by Submarine Measurements. A.Gelfand, Inference for Size demography from point pattern data using Integral Projection Models Bo Li, Nonparametric estimation of spatial covariance function Bo Li, Discussion to "Clustering random curves under spatial interdependence with application to service accessibility" by H. Jiang and N. Serban M. Fuentes, Discussion to session Statistics for regional climate models.

P. Craigmile, Spatio-temporal statistical methods applied to the environment; discussant in Water and Climate session. STATMOS organized a topic contributed paper session at JSM 2012 entitled Research in NSF Network ‘Statistics in the Atmospheric and Oceanic Sciences,’ co-sponsored by the ASA Section on Statistics and the Environment, the International Indian Statistical Association, Statistics Surveys Online Journal, and the ASA Advisory Committee on Climate Change Policy. Organizer: Michael L. Stein, University of Chicago Speakers: Conditional Simulation of Nonstationary Spatial-Temporal Processes – Joseph Guinness, University of Chicago. Space-Time Global Models for Climate Ensembles – Stefano Castruccio, Michael L Stein, David J. McInerney and Elisabeth J. Moyer, University of Chicago. Uncertainty Quantification for Regional Climate Model Output Using Space-Time Self-Exciting Point Processes – Souparno Ghosh; Alan Gelfand, Duke University; Stephan Sain, NCAR. Daily Spatio-Temporal Stochastic Weather Simulation – William Kleiber, Rick Katz, NCAR; Balaji Rajagopalan, University of Colorado at Boulder. Spatial Modeling of Global Atmospheric Carbon Dioxide Concentrations from Satellite Observations – Dorit Hammerling, University of Michigan.

July 2012: A. Gelfand, Keynote Speaker, Australian Statistical Congress, Adelaide, Australia. July 2012: Michael Stein, Approximate likelihoods for large spatial and spatio-temporal datasets 8th World Congress in Probability and Statistics. Talks at Other Institutions Oct 2011: D. B. Percival, Decline of Arctic Sea-Ice Thickness as Evidenced by Submarine Measurements. University of Heidelberg, Heidelberg, Germany. (Invited presentation) Feb 2012: Michael Stein, When does the screening effect hold? DePaul University. March 2012: Stefano Castruccio gave poster presentation: Statistical emulation of General Circulation Models. Madison, WI. University of Wisconsin-Madison. March 2012: Elisabeth Moyer, The Transient Response of Precipitation and Ocean Heat Uptake in Changing Climates, Caltech. April 2012: A. Gelfand, Mu Sigma Rho Invited Lecture, Virginia Tech, April 2012. April 2012: Elisabeth Moyer, The Transient Response of Precipitation and Ocean Heat Uptake in Changing Climates, Carnegie-Mellon.

April 2012: J. Hoeting, Departmental Seminar, Arizona State University School of Mathematics and Statistical Science. April 2012: Bo Li, An Approach to Modeling Asymmetric Multivariate Spatial Covariance Structure, IUPUI, Indianapolis, IN. April 2012: Grant Weller, An investigation of the Pineapple Express phenomenon via bivariate extreme value theory, NARCCAP Fourth Users' Meeting, Boulder, CO. May 2012: Schliep, E. M. & J. A. Hoeting. Latent Gaussian Process Model for Mixed Multivariate Continuous and Ordinal Data, (Poster session) The Second Workshop on Bayesian Inference for Latent Gaussian Models with Applications, Trondheim, Norway. May 2012: M. Fuentes, Plenary invited speaker for the 100th year celebration of the Girl Scouts. Raleigh, NC. May–June 2012: Bo Li, Invited panel discussant for the workshop of "Frontiers in the Detection and Attribution of Climate Change", Banff, Canada. June 2012: Michael Stein, Some Background for "Models and Methods for Random Fields in Spatial Statistics" Lund University. (Talk given in the role of opponent for the thesis defense of David Bolin.) 26 June, 2012: Stefano Castruccio: Space time global models for climate ensembles, University of Bergamo, Italy. Sept 2012: Bo Li, invited panel discussant, Massive Data workshop, SAMSI, Research Triangle Park, NC. Interactions Between Nodes Oct 2011: Montserrat Fuentes, North Carolina State University: Nonparametric models for extreme temperatures. University of Chicago, October 3, 2011. Oct 2011: Michael Stein visited OSU.

Stein seminar: Statistical Analysis of Massive Spatio-Temporal Datasets Statistical Climatology Poster Session: Jonathan Bradley(Statistics): Use of AICC to choose spatial basis functions Shanshan Cai (Geography): Enhancing land cover trajectory mapping with MODIS data using a spatio-temporal modeling approach Aritra Sengupta (Statistics): Empirical hierarchical model for counts using the spatial random effects model Shan Shi (Statistics): Whittle methods for regularly and irregularly sampled space-time data Agniva Som (Statistics): Prior choices for functional parameters in an application to atmospheric chemistry

Matthew Yin (Statistics): Modeling stochastic volatility in climatic data Dunke Zhou (Statistics): Statistical analysis of AIRS level 3 quantization data Xiaolin Zhu (Geography): A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images Byrd Polar Research Center tour and discussion with Ellen Mosly-Thompson

Nov 2011: Catherine Calder, visited Purdue University: Space-Time Dynamical Modeling of Aerosol Transport, Nov, 2011: Seminar by Joe Guiness, University of Chicago: Fourier analysis for nonstationary spatial-temporal data. North Carolina State University. Nov, 2011: Seminar by Fabrio Sigrist, ETH Zürich: Postprocessing of Precipitation Forecasts with an SPDE Based Spatio-temporal Model for Large Data Sets. University of Washington Nov 2011: Alan Gelfand, Hierarchial Modeling for Spatial Data Problems, North Carolina State University. Jan 2012: M. Fuentes, Nonparametric models for extreme temperatures. Biostatistics Department, University of Washington. Feb 2012: Bo Li, A Statistical Perspective on Climate Problems, Department of Earth and Atmospheric Science, Department of Earth and Atmospheric Science, Purdue University. Feb 2012: Peter Guttorp visited Colorado State University. Seminar: Regional Climate Prediction comparisons via statistical upscaling and downscaling . Met with graduate students Grant Weller, Huan Wang, Bruce Bugbee, Tingjin Chu, Erin Schliep, John Tipton and Ephraim Frank, and with faculty Jean Opsomer, Piotr Kokoszka, Myung Hee Lee, Jennifer Hoeting, Mervyn Hooten, Dan Cooley and Geof Givens. 27 Feb 2012: Grant Weller, An investigation of the Pineapple Express phenomenon via bivariate extreme value theory, Cooperative Institute for Research in the Atmosphere (CIRA) seminar, CSU. April, 2012: Montserrat Fuentes visited Department at Duke to give a seminar presentation. The following 5 presentations were also prepared for this visit.

Graduate Students (Duke): Fangpo Wang: Spatial and space-time modeling for directional data using projected Gaussian processes Lucia Paci: Spatio-temporal modeling for real-time ozone forecasting Thomas Leininger: Quadratic Scaling Models for Spatial Compositional Data with Application to Forest Fragmentation and Land Use/Land Cover Classification Maria Terres: Analyzing First Flowering Event Data using Survival Models with Time-Varying Covariates

Postdoc (Duke): Souparno Ghosh : Inference for size demography from point pattern data using Integral Projection Models

April, 2012: Michael Stein visited Duke University and gave a seminar presentation

Other presenters: Thomas Leininger, Maria Terres, Lucia Paci, Fangpo Wang, Souparno Ghosh (see Fuentes visit immediately above)

April, 2012: Michael Stein visited NCSU and gave a seminar presentation on Gaussian Likelihood Calculations for Massive Datasets. Several undergraduate and graduate students and postdocs also presented. May –June 2012. BIRS workshop on Detection and Attribution of Climate Change, STATMOS participants: Peter Craigmile, Ohio State ,Tamara Greasby, NCAR, Peter Guttorp, UW, Bo Li, Purdue, Richard Smith, SAMSI, Ying Sun, SAMSI, Claudia Tebaldi, NCAR/Climate Central and Francis Zwiers, U Victoria. June, 2012: 8th International Purdue Symposium on Statistics, Purdue University.

Elizabeth Mannshardt: Statistical modeling of extreme value behavior in North American tree-ring density series Peter Craigmile: Regional climate model assessment using statistical upscaling and downscaling techniques, Montserrat Fuentes, Calibration of deterministic models. Stefano Castruccio: Space time global models for climate ensembles

June 2012: Elisabeth Moyer, The Transient Response of Precipitation and Ocean Heat Uptake in Changing Climates. University of Washington. Aug, 2012: Ten Lectures on Statistical Climatology, by Dough Nychka, NCAR, at University of Washington.

Network participants: Aaron Zimmerman, Myoungji Lee, Jennifer Hoeting, William Kleiber, Tamara Anne Greasby, Bo Li, Elizabeth Mannshardt, Darren Homrighausen, Grant Weller, Stefano Castruccio, Ying Sun, Erin Schliep, Mark D. Risser, Tianji Shi, InKyung Choi, Shivi Vaidyanathan, Joe Guinness, Lan Zou, Whitney K. Huang, Brook Russell, Laina D. Mercer, Desheng Liu, Peter Guttorp, Doug Nychka, Dan Cooley, and Colin Sowder.

Additional network speakers:

William Kleiber: Daily Spatio-Temporal Stochastic Weather Simulation Tamara Greasby: Variability in Annual Temperature Profiles: A Multivariate Spatial Analysis of Regional Climate Model Output Daniel Cooley: Modeling Tail Dependence and Performing Prediction via the Angular Measure

Network poster presenters: Stefano Castruccio: Space time global models for climate ensembles.

Erin Schliep and Jennifer Hoeting: Latent Gaussian Process Model for Mixed Multivariate Continuous and Ordinal Data Grant B.Weller, Daniel S. Cooley, and Stephan R. Sain: An Investigation of the Pineapple Express Phenomenon via Bivariate Extreme Value Theory

Short Courses Oct 2011: M. Fuentes, Spatial nonparametric models for extremes. University of Chicago.

Jan 2012: P. Guttorp: Climate and Statistics, UFRJ, Rio de Janeiro. March 2012: P. Guttorp: Space-time models using Gaussian processes, U. of Heidelberg. April 2012: M. Fuentes, Statistical methodology in atmospheric and oceanic sciences. Duke University. July 2012: A. Gelfand, Hierarchical Modeling for Environmental Processes, CSIRO-QUT, Brisbane, Australia. Connections with other networks Erin Schliep, Colorado State University graduate student, went to INLA workshop at Norwegian Science and Technology University, Trondheim, Norway (SARMA node), funded by STATMOS. Michael Stein (Chicago) served as external examiner at the PhD defense of David Bolin at University of Lund (SARMA node), June 2012. Paul Sampson and Peter Guttorp (UW) sent solar radiation data to Geir-Arne Fuglstad, Norwegian Science and Technology University, Trondheim, Norway (SARMA node) Ida Scheel, University of Oslo, Norway (SARMA node) gave an invited lecture in the session on Statistics and Regional Climate Models at the Joint Statistical Meetings in San Diego, July 30, 2012, funded by SARMA. Ola Haug and Egil Ferkingstad from SARMA node Norwegian Computing Center participated in Ten Lectures on Statistical Climatology, partially funded by SARMA. Michel Msquita, Bjerknes Center/University of Bergen (SARMA node) participated in Ten Lectures on Statistical Climatology. Geir-Arne Fuglstad, Helgi Sigurðarson and Xiangping Hu, Norwegian Science and Technology University, Trondheim (SARMA node) participated in Ten Lectures on Statistical Climatology, partially funded by SARMA.

Peter Guttorp serves on the scientific advisory boards for MERGE (University of Lund, SARMA node) and for CliMath (UK network on climate and mathematics). Peter Guttorp and J. Michael Wallace served on the doctoral committee of Jiansong Zhou, PhD student of MCRN node director Ka-Kit Tung. Papers & Publications on Topics Relevant to STATMOS by Network Personnel (* indicates work directly supported by STATMOS funding) M. Aldrin, M. Holden, P. Guttorp, R. B. Skeie, G. Myhre and T. K. Bentsen (2012) Bayesian estimation of the climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content. Environmetrics 23, 253–271. Alonso, A., Casado, D., López S. and Romo, J. (2012).\ Robust functional classification for time series. Alonso, A., Casado, D. and Romo, J. (2012) Supervised classification of functional data: a weighted derivatives approach. Computational Statistics and Data Analysis, 56, 7, 2334-2346. Arribas, A. and Romo, J. (2012) Robust depth-based estimation in the time warping model. Biostatistics, 13, 3, 398-414. Calder, C. A., Berrett, C., Shi, T., Xiao, N., and Munroe, D. K. (2011) Modeling Space-Time Dynamics of Aerosols Using Satellite Data and Atmospheric Transport Model Output. Journal of Agricultural, Biological, and Environmental Statistics, 16 (4) 495 – 512 V. Berrocal, P. Craigmile and P. Guttorp (2012) Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics 23, 482-492. V.J. Berrocal, A.E. Gelfand and D.M. Holland (2012) Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics (forthcoming). Bradley, J.R., Cressie, N. and Shi, T. (2011) Selection of Rank and Basis Functions in the Spatial Random Effects Model. Proceedings of the Joint Statistical Meetings 2011. Cascos, I., López, A. and Romo, J. (2011) Data depth in multivariate statistics. BEIO 27, 3, 151-174. *Chang, Xiaohui, (2012) Wavelet Analysis in Spatial Interpolation of High-Frequency Monitoring Data. Ph.D. thesis, University of Chicago, Department of Statistics. Chang, Howard, Fuentes, M., and Frey, C. (2012). Time Series Analysis of Personal Exposure to Ambient Air Pollution and Mortality Using an Exposure Simulator, Journal of Exposure Science and Environmental Epidemiology.

Cheng, G., Zhao, Y. and Li, B. (2012) Empirical likelihood inferences for the semiparametric additive isotonic regression Journal of Multivariate Analysis, Vol. 112, 172-182. Cooley D., Davis R.A., Naveau P. (2012) Approximating the Conditional Density Given Large Observed Values via a Multivariate Extremes Framework, with Application to Environmental Data. Annals of Applied Statistics. Cooley, D., Cisewski, J., Erhardt, R., Jeon, S., Mannshardt, E., Omolo, B., Sun, Y. (2012) A Survey of Spatial Extremes: Measuring Spatial Dependence and Modeling Spatial Effects. REVSTAT, Vol 10, No. 1, pp. 135-165. Durand, M.T. and D. Liu. (In Press). The need for prior information in characterizing snow water equivalent from microwave brightness temperatures. Remote Sensing of Environment. Franco, A., Lillo, R. and Romo, J. (2012) Comparing residual quantile residual life functions by confidence bands. Lifetime Data Analysis, 18, 195-214. Franco, A., Lillo, R. and Romo, J. (2011) The percentile of residual life up to time t0: ordering and aging properties. Journal of Statistical Planninig and Inference, 141, 3554-3563. Franco, A., Lillo, R., Romo, J. and Shaked, M. (2011) Percentile residual life orders. Applied Stochastic Models for Business and Industry, 27, 235-252. A. Finley, S. Banerjee and A.E. Gelfand, A.E. (2012) Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes. Journal of Geographic Systems (forthcoming). *Foley KM, Reich BJ, Napelenok SL. Bayesian analysis of a reduced-form air quality model. Accepted, Environmental Science & Technology. Fuentes, M. (2012). Spectral methods, chapter in the Encyclopedia of Environmetrics. Wiley. Fuentes, M., and Banarjee, S. (2012). Bayesian Modeling for Large Spatial Datasets. WIREs Computational Statistics, accepted. Fuentes, M. and Foley, K. (2012). Ensemble methods, Spectral methods, chapter in the Encyclopedia of Environmetrics. Wiley. Fuentes M, Henry JB, Reich BJ. Nonparametric Spatial Models for Extremes: Application to Extreme Temperature Data. Accepted, Extremes. Fuentes M, Reich BJ. Multivariate spatial nonparametric modeling via kernel processes mixing. Accepted, Statistica Sinica. Gelfand, A.E. (2012) Hierarchical Modeling for Spatial Data Problems, Spatial Statistics, 1, 30-39.

Gelfand, A.E., S. Banerjee and A. Finley (2012) Spatial design for knot selection in knot-based dimension reduction models, In: Spatio-temporal design: Advances in efficient data acquisition, Eds: J. M. Mateu and W. Mueller, J.Wiley and Sons (forthcoming). Gelfand, A.E., S. Ghosh and J.S. Clark (2012) Inference for size demography from point pattern data using Integral Projection Models (Invited paper with discussion) Journal of Agricultural, Biological and Environmental Statistics (forthcoming). Gelfand, A.E., S.K. Sahu and D.M. Holland (2012) On the Effect of Preferential Sampling in Spatial Prediction Environmetrics (forthcoming). S. Ghosh, A.E. Gelfand and T. Molhave (2012) Attaching uncertainty to deterministic spatial interpolations, Statistical Methodology, 9, 1-2, 251-264 S. Ghosh, A.E. Gelfand, K. Zhu, and J.S. Clark (2012) The k-ZIG: flexible modeling for zero-inflated counts, Biometrics (forthcoming). Givens, Geof H. and Jennifer A. Hoeting (2012) Computational Statistics, John Wiley & Sons, New York, 504 pages, 2nd edition. T. Gneiting, H. Sevcikova and D. B. Percival (2012) Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data, Statistical Science, 27, no. 2, pp. 247-277. [looks at Arctic sea-ice thickness] Gu, Y., Wan, S., Shi, T., Liu, Y., Clothiaux, E. E., and Yu, B. (2012) Multiple-kernel Learning based on Unmixing Algorithm for Estimation of Cloud Fractions with MODIS and CloudSat Data. Proceedings of IEEE International Geoscience and Remote Sensing Symposium 2012. R. Guha Niyogi, A.O. Finley, and S. Banerjee and A.E. Gelfand (2012) Adaptive Gaussian Predictive Process Models for Large Spatial Datasets, Environmetrics (forthcoming). Guinness, J.(2012) Nonstationary Models for Spatial-Temporal Processes. University of Chicago, Ph.D. thesis. Guinness, J. and Stein, M. L. Transformation to approximate independence for locally stationary Gaussian processes. Submitted to Journal of Time Series Analysis. Gurney, K. R., Castillo, K., Li, B. and Zhang, X., (2012) A positive carbon feedback to ENSO and volcanic aerosols in the tropical terrestrial biosphere Global Biogeochemical Cycles, Vol. 26, GB1029, doi:10.1029/2011GB004129. P. Guttorp (2012) Climate statistics and public policy. Statistics, Politics and Policy. DOI: 10.1515/2151-7509.1055

P. Guttorp, S. Sain and C. Wikle (2012) Editorial: Advances in Statistical Methods for Climate Analysis. Environmetrics 23, 363. M.J. Heaton and A.E. Gelfand (2012) Kernel Averaged Predictors for Spatio-Temporal Regression Models, Spatial Statistics (forthcoming). Heaton, M.J., Katzfuss, M., Ramachandar, S., Pedings, K., Gilleland, E., Mannshardt-Shamseldin, E., Smith, R.L (2011) Spatio-Temporal Models for Large-scale Indicators of Extreme Weather. Environmetrics, Vol 22, Issue 3, pp. 294-303. Herbei, R. and Berliner, L. M., (2012) Estimating ocean circulation : a likelihood-free MCMC approach via a Bernoulli factory. Submitted. Hitczenko, M. and Stein, M. L. (2012) Some theory for anisotropic processes on the sphere. Statistical Methodology, 9, 211—227. Johnson, D. S. and J. A. Hoeting (2011) Bayesian Multimodel Inference for Spatial Regression Models. PLoS ONE 6(11): e25677. doi:10.1371/journal.pone.0025677. Johnson, D. S. and J. A. Hoeting (2011) Properties of Graphical Regression Models for Multidimensional Categorical Data, Statistics and Probability Letters. 81, 1471-1475. Katz, R.W. (2012) Economic impact of extreme events: An approach based on extreme value theory. In Extreme Events: Observations, Modeling and Economics, M. Ghil, J. Urrutia-Fucugauchi, and M. Chavez (eds.), Geophysical Monograph Series, American Geophysical Union (accepted). Katz, R.W. (2012) Extremal events. In Encyclopedia of Environmetrics, Second Edition, A.H. El-Shaarawi and W.W. Piegorsch (eds.), Wiley, Chichester, U.K. (in press). Katz, R.W. (2012) Hydrological extremes. In Encyclopedia of Environmetrics, Second Edition, A.H. El-Shaarawi and W.W. Piegorsch (eds.), Wiley, Chichester, U.K. (in press). Katz, R.W. (2012) Statistical methods for nonstationary extremes. In Hydrologic Extremes in a Changing Climate: Detection, Analysis and Uncertainty, A. AghaKouchak, D. Easterling, K. Hsu, S. Schubert, and S. Sorooshian (eds.), Springer (in press). Kim, Y., R.W. Katz, B. Rajagopalan, G.P. Podestá, and E.M. Furrer (2012) Reducing overdispersion in stochastic weather generators using a generalized linear modeling approach. Climate Research, 53, 13–24. Kleiber, W., R.W. Katz, and B. Rajagopalan (2012) Daily spatio-temporal precipitation simulation using latent and transformed Gaussian processes. Water Resources Research, 48, W01523, doi:10.1029/2011WR011105.

Kumar, S., R. Lal, and D. Liu. (In Press). A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma. Laniado, H., Lillo, R., Pellerey, F. and Romo, J. (2011) Portfolio selection through an extremality stochastic order. Insurance: Mathematics and economics, 51, 1, 1-9. G. Jona Lasinio, A.E. Gelfand and M. Jona Lasinio (2012) Analyzing spatial directional data using wrapped Gaussian processes, Annals of Applied Statistics (forthcoming). *Lee, M. (2012) Local Properties of Irregularly Observed Gaussian Fields. Ph.D. thesis, University of Chicago, Department of Statistics. Li, B. and Smerdon, J. E. (2012) Defining spatial assessment metrics for evaluation of paleoclimatic field reconstructions of the Common Era Environmetrics, Vol. 23, 394-406. López, S. and Romo, J. (2011) A half-region depth for functional data. Computational Statistics and Data Analysis, 55, 1679-1695. López, S., Romo, J. and Torrente, A. (2012) depthTools: an R package for a robust analysis of gene expression data. K. Lum and A.E. Gelfand (2012) The asymmetric Laplace process for spatial quantile regression, (with discussion) Bayesian Analysis 7, 235-276. *Y. Ma and P. Guttorp (2012) Estimating daily mean temperature from synoptic climate observations. International Journal of Climate. doi: 10.1002/joc.3510 Mannshardt, P. F. Craigmile, and M. P. Tingle (2012) Statistical modeling of extreme value behavior in North American tree-ring density series. Climatic Change, doi: 10.1007/s10584-012-0575-5 Mannshardt-Shamseldin, E., Smith, R.L., Sain, S. R., Mearns, L., Cooley, D. (2010) Downscaling Extremes: A Comparison of Extreme Value Distributions in Point Source and Gridded Precipitation Data. Annals of Applied Statistics, Vol 4, No. 1, pp. 484-502. Martín Barragán, B., Lillo, R. and Romo, J. (2012) Interpretable support vector machines for functional data. European Journal of Operational Research. Accepted. Matthews, J., Mannshardt, E, and Gremaud, P. (2012) Uncertainty quantification for climate observations. Accepted, The Bulletin of the American Meteorological Society. Mayordomo, S., Peña, J.I. and Romo, J. (2011) Does liquidity affect the price discovery process in credit derivative markets? European Journal of Finance, 17, 9-10, 851– 881. Merrill, S. C., S. M. Walter, F. B. Peairs, E. M. Schliep (2012) The distribution of European corn borer moths in sprinkler irrigated corn. Submitted to Journal of Economic Entomology.

Modlin D, Fuentes M, Reich BJ (2012) Circular conditional autoregressive modeling of vector fields. Environmetrics, 23, 46–53. Moreno, M. and Romo, J. (2012) Bootstrap unit roots tests under infinite variance. Journal of Time Series Analysis, 33, 1, 32-47. Moreno, M. and Romo, J. (2012) Robust unit roots tests with autoregressive errors. D. Mondal and D. B. Percival (2012) M-Estimation of Wavelet Variance, Annals of the Institute of Statistical Mathematics, 64, no. 1, pp. 27-53. D. Mondal and D. B. Percival (2012) Wavelet Variance Analysis for Random Fields on a Regular Lattice, IEEE Transactions on Image Processing, 21, no. 2, pp. 537-549. Myoungji Li, (2012) Local Properties of Irregularly Observed Gaussian Fields. Ph.D. thesis, University of Chicago, Department of Statistics. D. B. Percival and D. Mondal (2012) A Wavelet Variance Primer, in Time Series Analysis: Methods and Applications (Handbook of Statistics 30), edited by T. Subba Rao, S. Subba Rao and C. R. Rao, Amsterdam: Elsevier, pp. 623-657. *Reich BJ. (2012) Spatiotemporal quantile regression for detecting distributional changes in environmental processes. Journal of the Royal Statistical Society: Series C, bf 64, 535-553. Reich, B., and Fuentes, M (2012) Accounting for design in spatial modeling, chapter in Spatio-Temporal design: advances in efficient data acquisition. Wiley. Editors, Mateu and Werner. Reich. B., and Fuentes, M. (2012) Nonparametric Bayesian models for a spatial covariance. Accepted, Statistical Methodology. Reich BJ, and Fuentes M (2012) Nonparametric Bayesian models for a spatial covariance. Statistical Methodology, 9, 265-274. Reich BJ, Kalendra E, Storlie CB, Bondell HD, Fuentes M (2012) Variable selection for high-dimensional Bayesian density estimation: Application to human exposure simulation. Journal of the Royal Statistical Society: Series C, 61, 47--66. *Reich BJ, Shaby BA. A hierarchical max-stable spatial model for extreme precipitation. Accepted, Annals of Applied Statistics. Schliep, E. M. & J. A. Hoeting (2012) Multivariate Multilevel Latent Gaussian Process Model to Evaluate Wetland Health. Under revision for Journal of Agricultural, Biological, and Environmental Statistics.

Shaby B, Reich BJ (2012) Comment on ``Statistical Modeling of Spatial Extremes'' by Davison, Padoan, and Ribatet. Statistical Science, 17, 197-198. Sillmann, J., M. Croci-Maspoli, M. Kallache, and R.W. Katz (2011) Extreme cold winter temperatures in Europe under the influence of North Atlantic atmospheric blocking. Journal of Climate, 24, 5899–5913. Stein, M. L. (in press). On a class of space-time intrinsic random functions. Bernoulli. Stein, M. L. (in press). Statistical Properties of Covariance Tapers. Journal of Computational and Graphical Statistics. Stein, M. L. (2012) Simulation of Gaussian random fields with one derivative. Journal of Computational and Graphical Statistics, 21, 155--173. Stein, M. L. (2012) When does the screening effect hold? Annals of Statistics, 39, 2795--2819. Stein, M. L., Chen, J. and Anitescu, M. (2012) Difference filter preconditioning for large covariance matrices. SIAM Journal on Matrix Analysis and Applications, 33, 52--72. Stein, M. L., Chen, J. and Anitescu, M. Stochastic approximation of score functions for Gaussian processes. Under revision for Annals of Applied Statistics. Tingley, M. and Li, B. (2012) Comments on "Reconstructing the NH mean temperature: Can underestimation of trends and variability be avoided?" Journal of Climate, Vol. 25, 3441-3446. M. Tingley, P. F. Craigmile, M. Haran, B. Li, E. Mannshardt, and B. Rajaratnam 2012) Piecing together the past: Statistical insights into paleoclimatic reconstructions. Quaternary Science Reviews, Volume 35, 5 March 2012, Pages 1–22. J. M. Wallace, Q. Fu, B. V. Smoliak, P. Lin, and C. M. Johanson (2012) Simulated Versus Observed Patterns of Warming over the Extratropical Northern Hemisphere Continents during the Cold Season. Proc. Natl. Acad. Sci. USA., doi:10.1073/pnas.1204875109 Warren, J, Fuentes, M, Herring, A, and Langlois, P. (2012) Spatial-Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure. Biometrics, accepted. Warren, J. Fuentes, M. Herring, A, and Langlois, P. (2012) Spatial-temporal Model for Cardiac Congenital Anomalies and Ambient Air Pollution Risk Assessment. Environmetrics, invited paper. Weller G., Cooley D, and Sain S. (2012) An investigation of the Pineapple Express phenomenon via bivariate extreme value theory. Environmetrics, 23(5): 420-439. DOI: 10.1002/env.2143.

Zhou, Jingwen, Chang, Howard H., and Fuentes, Montserrat (2012) Estimating the Health Impact of Climate Change with Calibrated Climate Model Output. Journal of Agricultural, Biological, and Environmental Statistics, accepted. Zhou, Jingwen, Fuentes, Montserrat, and Davis, Jerry (2011) Calibration of numerical model output using nonparametric spatial density functions. Journal of Agricultural, Biological, and Environmental Statistics, 16, no4, pp. 531-553 Zhou, D. and Shi, T. (2011). Statistical Inference based on Distances between Empirical Distributions with Applications to AIRS Level 3 Data. Proceedings of the NASA Conference on Intelligent Data Understanding (CIDU) 2011. Zhu, X., D. Liu, and J. Chen. (In Press). A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment. Reflections of Network Interactions: Myoungji Lee, U. Chicago Travel to Purdue 11-12-07 to 11-12-13. Mentor: Hao Zhang Goals: I met with Professor Zhang and discussed the problem I have been working on with my advisor Professor Stein. Estimation of local variogram parameters, which determine behavior of variogram near origin, has been studied for regularly observed data using increments. We have proposed estimation methods that work for both regularly and irregularly spaced observations. Goals well met. New directions: Professor Zhang gave me valuable advice on constructing the composite likelihood and made me have new perspectives of the problem. He pointed out my work is connected with microergodic parameters in Matern or exponential covariance models in Gaussian processes. Correspondingly he suggested what would be valuable work to undertake by using known results on microergodic parameters. Interactions: Professor Li suggested a probable further work by relating local variogram parameters with long range dependence parameter. Professor Hyonho Chun suggested valuable insights on the simulation comparison I was doing. I also met several PhD students in Purdue University. They are Professor Zhang's students, Cheng Liu, Juan Hu, Xian He, Whitney Huang and Yen-Ning Huang, and Professor Li's students, Luis Barboza and Inkyung Choi. I also talked with Meena Choi and Hyokun Yun, Ph.D students in Purdue majoring biostatistics and machine learning. They all showed great interest to my work and this visiting program as well. Positive aspects: It was awesome to get to know people working on spatial statistics. Professor Zhang and Li gave me new point of views in my dissertation and I could get a sense of frontier works by hearing from students what they are working on and what are their primary concerns. This could be more productive: Making a long term visit will be helpful. Interested in more visits: Yes Xian He, Purdue Visit to Chicago, 12-05-20 to 12-05-26. Mentor: Michael Stein

Plan: I am working on quantifying model uncertainty and would like to eventually make use of observations to tune up the parameters in a physical model. One thing I would like to discuss with you about is the choice of multivariate covariance function in the statistical emulation approach as in Conti and O'Hagan 2009 who used the proportional model. Reflection: I visited Prof. Michael Stein of Department of Statistics in University of Chicago During May 21-May25. It was really a nice, pleasant and productive travel. Prof. Stein gave me many advises and suggestions on my research which I would work on after I went back. I found so many new problems and potential improvement that I did not realize before I came here. Also, I learned a lot from Prof. Stein's students, especially there is one student whose research is similar as mine. Every day, I have dinner with different people and communicated with them. They gave me many useful advice. In addition, the academic environment of UChicago inspired me a lot. I really appreciate this opportunity and thanks to all the people in the department. Erin Schliep, Colorado State Visit to Norwegian University of Science and Technology, Trondheim, 12-05-21 to 12-06-06. Mentor Håvard Rue Coals: Attend a workshop on latent Gaussian models and learn about the integrated nested Laplace approximation (INLA) method. Collaborate with Håvard Rue and Finn Lindgren to implement a multivariate likelihood for ordinal data into the R package for INLA. The goals were well met. New directions: Many applications of latent Gaussian models included spatial, temporal, and spatio-temporal random effects. I am working on a new research project for disease mapping in cattle and was able to get some new ideas as well as make contact with fellow researchers in this area of whom I can consult. Interactions: Håvard Rue - The implementation of a multivariate likelihood for ordinal data in INLA Finn Lindgren/Daniel Simpson - Stochastic partial differential equations for mapping a Gaussian field to a Gaussian Markov random field. Thiago Martins - The theory of INLA Oli Geirsson - Using the SPDE approach for the mapping of the GF to the GMRF within Markov Chain Monte Carlo simulations Positive aspects: Making international contacts with students/faculty that work in similar fields. It was great to get fresh ideas for problems that I am currently working on as well as spur future research ideas. I enjoyed the opportunity to share my work with fellow students, post-docs and faculty and to work with Håvard Rue on implementing my model in INLA. This could be more productive: Next time I have an extended visit at another institution, I hope to be working on more projects in which I can consult with fellow researchers. It would be great to have multiple projects in various stages in which I can get feedback or fresh ideas. Interested in more visits: Yes. It was a great visit. I really appreciated the opportunity to collaborate with the people at NTNU and other European institutions. Elizabeth Mannshardt, NCSU Travel to Purdue Statistics Symposium, 12-06-21 to 12-06-24. Mentor Hao Zhang Plan: This proposal of work is written with regard to a potential visit to Dr. Hao Zhang and Dr. Bo Li in the Department of Statistics at Purdue University, in conjunction with the 8th International Purdue Symposium on Statistics. I have been asked to speak at the symposium, as well as to visit the department to meet with Dr. Zhang and Dr. Li. Dr. Zhang and Dr. Li are both

involved with the STATMOS program through the Purdue node. They are working with spatial statistics and climate change, which correlates well with my current research interests. Reflection: Purdue Statistics Symposium, Jun 22-24, 2012: I attended and presented at the 8th International Purdue Symposium on Statistics. The speaker sessions were very engaging, and provided a great forum for seeing relevant work and fostering good discussion. I was able to meet with an on-going SAMSI research group in Paleoclimate that I am still actively participating in, as well as with Dr. Bo Li, a Purdue STATMOS member. I also had the opportunity to talk with Dr. Hao Zhang, the STATMOS director at Purdue, about our mutual research interests and goals, and upcoming opportunities. While on campus at Purdue, I met with a group of graduate students to talk about the STATMOS program and its opportunities. There were several students in attendance, and all were very interested in the various branches of STATMOS. We discussed visiting travel between universities, courses and workshops offered at the universities in conjunction with the STATMOS program, the launch of the new journal associated with STATMOS, and the upcoming annual STATMOS meeting. The students had not been aware of many of these opportunities, and were excited to learn what STATMOS has to offer. Thank you for the opportunity to travel to the Purdue Statistics Symposium. Luke Smith, North Carolina State Visit to University of Washington, Seattle, 12-07-06 to 12-08-03. Mentor: Paul Sampson Goals: 1) Get feedback on air pollution research for my dissertation 2) Get feedback on quantile regression and extremes research for my dissertation 3) Learn more about current spatial modeling at University of Washington. These goals were partially met. New directions: The faculty I talked with helped me in several ways. Seeing the UW research in air pollution helped give structure to my air pollution project. The smoothing spline perspective and its connections to likelihood inference enhanced my understanding of spatial modeling. Interactions: Paul Sampson - air pollution and spatial modeling; Lianne Sheppard - air pollution; Adam Szpiro - spatial modeling; Jon Wellner - density estimation; Environmental biostatistics group - spatial modeling, exposure estimation, quantile regression. Positive aspects: The departments of statistics and biostatistics were welcoming and generous with their time, especially the four individuals listed above. Further, they were all pleasant people whom you would want to spend time with outside of work. I also had the opportunity to meet several students, all of whom were bright and lots of fun to talk to. This could be more productive: I wrongly assumed that someone at UW would be working on quantile regression, but I should have checked into that more thoroughly before leaving. Some faculty, including those involved with STATMOS, were unresponsive to e-mail. Interested in more visits: I really appreciate the time the faculty members and students spent with me. Many thanks to the UW statistics and biostatistics department. While I was unable to take him up on the opportunity, I also appreciate the invitation from Paul Sampson to show me Seattle. My time in Seattle was very productive and I would certainly encourage students to participate in STATMOS. Inkyung Choi, Purdue University. Visited NCAR, 06-25-2012 to 08-10-2012. Mentor: Doug Nychka Goals: My research interest involves the statistical analysis of climate data. The goals of the visit were to identify statistical questions regarding scientific problems in climatology and atmospheric/oceanic sciences and to develop collaboration with scientists and statisticians

New Directions: Statistical research on climate model data has mostly been focused on the synthesis of various model outputs and construction of mean structure. This visit led me to think about how model data reconstruct a covariance structure which again led me numerous subsequent questions such as how to compare covariance structures that are derived from different sources of data with different scales. Interactions: Dr. Doug Nychka (the work I'd been doing and statistically interesting problems on climate data), Dr. Linda Mearns (NARCCAP data, climatologist's view on statistical analysis on climate data, how important it is for climatologists and statisticians to collaborate) and Dr. Will Kleiber (the work I'd been doing and how to extend stationary covariance model to nonstationary one). Positive Aspects: I think the most positive aspect of my visit was that I got to meet and talk with some of leading experts in the field. Also, during the visit, I attended several events that NCAR hosted such as Annual WRF user's workshop and talks given by Dr. Inez Fung and Amy McGovern. They helped me understand how climate models work, how climatologists work with data and what kinds of data are of climatologist's interest. This could be more productive: It was good that I could explore various problems which suits my goal of the visit, but it might have been more productive if I had a specific research problem. Will Kleiber, NCAR Visit to University of Washington, Seattle, 12-08-05 to 12-08-11 Mentor Peter Guttorp Plan: I plan on giving a lecture introducing and discussing the issues of stochastic weather simulation during Doug Nychka's symposium on statistical climatology. Reflection: The 10 lectures on statistical climatology were very well put together. The invited sessions fit within the framework of Doug's lectures, and the discussion sections covered appropriate and relevant topics for the variety of specialties involved with the workshop. All talks and lectures were well attended, with impressive Q&A sections afterwards. Tamara Greasby, NCAR Visit to University of Washington, Seattle, 12-08-05 to 12-08-11 Mentor: Peter Guttorp Plan: Give one of the young researcher lectures at Doug Nychka's lectures on statistical climatology. Reflection: The ten lectures on statistical climatology were great! Doug did a great job of developing the concepts from simple to incredibly complicated. He was able to portray his research, in a difficult field, to a variety of expertise levels through a solid introduction and by discussing the methodology at two levels. He introduced each method at the conceptual level in addition to including the detailed math. In addition to the quality of the lectures, the roundtables provided a good opportunity to discuss issues in my field and to learn the perspectives of participants in other fields. It was also good to get to know other participants. The lectures given by the people other than Doug were also nice. It was good to see the different areas of research. It also provided a nice break in the day. Jennifer Hoeting, Colorado State Visit to University of Washington, Seattle, 12-08-05 to 12-08-10 Mentor: Peter Guttorp Plan: I will attend the 10 Lectures in Statistical Climatology conference. This will be important for me to educate myself for the start of the new journal.

Reflection: I attended the terrific 10 lectures seminars by Doug Nychka and other NCAR affiliates. The conference was well attended by an international group of researchers and graduate students. The conference was a great learning experience for all and a great chance for people from all over the world to interact about statistical models for climate. Bo Li, Purdue University Visit to NCAR, Colorado, 7-01-2012 to 7-14-2012 Plan and Reflection: I visited NCAR from July 1 to July 14, 2012. I had conversations with Doug Nychka, Steve Sain, Linda Mearns, Will Kleiber, and Rick Katz. We identified problems to collaborate with. With Linda Mearns, we plan to assess the impact of climate change on human health; with Steve Sain and Will Kleiber, we plan to build probabilistic model for regional climate model output; with Doug Nychka, we plan to evaluate the climate model output using observations. Xiaohui Chang, Graduate Student, Chicago State University. Spent Winter Quarter at Ohio State University, 10-21-2011. Mentor: Peter Graigmile Plan and Reflection: I’m interested in visiting Professor Peter Craigmile at Ohio State University for collaboration work in space time modeling of meteorological variables collected from monitoring stations using a wavelet based approach. Stein (2009) studied this problem using a Fourier method in time domain, but wavelet method is a more natural alternative due to wavelets’ localization property. Wavelets also decorrelate stochastic processes, thus simplifying analysis and modeling of wavelet transforms. Nonstationarity can be further imposed on the covariance structure of wavelet transforms. Our previous work using high frequency temporal data collected at sparse monitoring stations under Atmospheric Radiation Measurement (ARM) program has shown that a simple wavelet based model is able to produce accurate spatial interpolations with low uncertainties. There are clear signs of nonstationary in time as seen from the occasional bursts of increased variability. Our current solution is to remove nonstationarity by constructing a single volatility function in time that is the same for all spatial locations, and dividing data by this volatility function. Although this approach works for the purpose for modeling ARM data, it does not treat stationarity as a special case of nonstationarity. With results and shortcomings from our exploratory work in mind, we propose to use wavelet methods for both generating models and analyzing data. We’ll start off from generating stationarity time series models using wavelet transforms, expand to space time models, and then generalize to nonstationary models by allowing the covariance of wavelet transforms to depend on time. Using similar techniques from our earlier work, we’ll analyze our space time data via wavelet-based covariance. As stationary is being considered as a special case of nonstationarity under this framework, a test for stationarity can also be proposed along the way. In addition, since most meteorological processes are not fully symmetric in their space time covariance structures, our method involving wavelet decomposition is also able to capture different phase relationships in the processes across different scales. There are other aspects of the problem that are also important for this study. The

choice of wavelet filters matters because different filters have different decorrelation power and filters also need to match up with the smoothness of data being analyzed. Compared to other asymptotic theory work that has been done for wavelet transforms, studying the wavelet transforms asymptotics from a fixed domain is more relevant here. We’d like to have a single continuous time model in time that is not affected by the frequency of observation. Among all the meteorological variables provided by ARM program, we have only analyzed air pressure, but this nonstationarity feature is present in several other variables as well, particularly strong in variables that may be affected by passage of fronts, which could be further associated with extreme weather and climate problems in general. Data collected from atmospheric and oceanic sciences are mostly nonstationary space time data. This wavelet based approach allows for simplification of covariance structure in modeling and analyzing nonstationary processes and may prove its usefulness in many other applications. Professor Craigmile is one of the most active researchers applying wavelet methodologies in statistics and has many recent work on space time climate modeling and nonstationarity. I believe I’ll benefit very much from working under his guidance for an extended visit. I value this excellent research opportunity offered by NSF network program to learn directly from experts working on climate science problems that I’m interested in and also work with students who share similar research interests as me. All in all, I’m truly grateful and looking forward to be part of this network program. Myoungji Lee, University of Chicago. Visited Department of Statistics, Purdue University Mar. 22–May 12, 2012. Mentor: Professor Hao Zhang Briefly summarize the goals of your visit as described in your research plan for the visit.

During the visit, professor Zhang advised me on my dissertation work, the determination of the variogram at the origin.

To what extent were these goals met? Not met? My work had been improved greatly through discussions with Professor Zhang. I attained a new perspective and knew the importance of the problem I had worked on.

Did your visit lead to your undertaking any new research directions not anticipated in your research plan? If so, describe briefly.

Yes, thanks to Professor Zhang, I get to know that there are broad research about local times on multifractal Brownian motions, on which I possibly do further work in the future.

List all people (students/postdocs/faculty) with whom you had significant, substantive interactions and the topics you discussed with each.

I had weekly meeting with Professor Zhang. And I discussed my research with Professor Li, graduate students Inkyung Choi, Cheng Liu, Juan Hu, Xian He, Whitney Huang and Yen-Ning Huang. They told me the subjects they are working on too and we discussed the relationship between our research.

What were the most positive aspects of your visit? I was introduced to many people who are interested in the similar subjects as I am. It was interesting to know what they are working on. I also enjoyed Professor Zhang’s course, Spatial Statistics.

In what ways might your visit have been more productive? I am very satisfied with everything given to me, including office, computing support, etc., during my visit to Purdue.