Browsing by Author "Jerry M. Davis, Committee Member"

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Analysis of Model QPF Errors During the 2-4 December 2000 Snowstorm in North Carolina
(2006-01-31) Caldwell, Raymond Jason; Gary M. Lackmann, Committee Chair; Jerry M. Davis, Committee Member; Allen J. Riordan, Committee Member
Model forecasts of an early season snowstorm for 2 – 4 December 2000 followed the historical blizzard of 24 – 25 January 2000 that dumped 20.3 inches of snowfall at the Raleigh-Durham International Airport. Much like the January 2000 storm, operational models exhibited a significant lack of skill, particularly in the realm of quantitative precipitation forecasting. As early as 1800 UTC 1 December, operational models from the National Centers for Environmental Prediction (NCEP), including the 32-kilometer Eta, generated liquid equivalent precipitation totals approaching two inches for the Raleigh–Durham metropolitan area. Later forecasts indicated as much as 2.77 inches of precipitation would fall. In reality, only a trace of precipitation was observed at the Raleigh–Durham airport. A local, real–time version of the fifth-generation, mesoscale modeling system (MM5) was operational at the time of the event and provided a much-improved forecast scenario compared to the NCEP Eta model. Remarkably, the initial conditions and lateral boundary conditions in the MM5 were identical to those used to initialize the Eta model at 1200 UTC 2 December. In this study, an examination of the potential sources of error in the quantitative precipitation forecast is performed to challenge prior studies that suggest that data quality issues with sea surface temperature analyses led to spurious precipitation generation. The study includes a case study of the 2 – 4 December snowstorm, model sensitivity experiments, and quasi-geostrophic analysis to identify and diagnose the quantitative precipitation errors in the Eta model and the superior forecast guidance available from the local MM5 model. The case study showed that several potential sources of model error existed including missing upper air soundings, sea surface temperatures, model design, and misdiagnosed topographic flow. This study will test the hypothesis that errors at the 500–hPa level led to limited precipitation early in the period and, hence, produced errors in the cold air damming, coastal front, and cyclogenesis in later periods responsible for the heaviest precipitation in model forecasts. Results from sensitivity experiments with the MM5 model failed to exhibit significant differences in the representation of topographically induced phenomena or the westward extent of the precipitation shield into central North Carolina. The Eta model produced an anomalously strong 850 hPa jet at the North Carolina coast which transported warm air and moisture inland over the region. Better representation of the initial 500–hPa shortwave trough and associated vorticity maximum in the MM5 model is shown in the results to strengthen the low-level damming episode and shift the coastal front farther offshore. The results of this study provide basis for further investigation into both models and concludes that the effect of the upper–level forcing on the evolution of the low-level topographically induced flow and the surface–based forcing of upper–level dynamics can be of equal magnitude and importance in winter season precipitation forecasting. Results of this study will be coupled with local efforts to improve forecasting through conceptual model development by providing operational forecasting with the knowledge that individual models can have independent and opposing response to initial condition errors based on the physical and dynamical make–up of the mesoscale modeling system.
Modeling and Prediction of Nonstationary Spatial Environmental Processes
(2002-08-19) Barber, Jarrett Jay; Montserrat Fuentes, Committee Chair; Peter Bloomfield, Committee Member; Jerry M. Davis, Committee Member; Marc G. Genton, Committee Member; Marcia L. Gumpertz, Committee Member
Spatial data are often collected for the purpose of producing spatial predictions (i.e., maps), the accuracy of which relies on a good estimate of the spatial covariance. Traditional geostatistical methods for spatial interpolation assume covariance stationarity. However, spatial data often exhibit nonstationary covariance, and traditional methods can produce maps that are misleading. Some existing approaches to nonstationarity feature process models which lead naturally to a globally defined covariance but do not retain a familiar interpretation in terms of local stationarity, while other approaches focus on local stationarity but rely on ad hoc methods for calculating covariance. We present a different approach with a relatively simple but useful model for space-time data. The model is simultaneously defined everywhere (globally) and leads immediately to a globally defined covariance, and, locally, the model behaves like a stationary process. A nonparametric approach to estimating the nonstationary spatial covariance is presented along with some asymptotic properties. The approach is particularly suited to time-rich, spatially-sparse networks. We illustrate this nonparametric approach for spatial prediction of atmospheric pollution data collected periodically from an EPA environmental monitoring network. We also propose an alternative, parametric approach to estimation and prediction using a Bayesian formulation of a nonstationary spatial model.
Multivariate Spatial-Temporal Modeling of Environmental-Health Processes
(2008-11-25) Choi, Jungsoon; Montserrat Fuentes, Committee Chair; Jerry M. Davis, Committee Member; Sujit K. Ghosh, Committee Member; John F. Monahan, Committee Member; Brian J. Reich, Committee Member; Hao H. Zhang, Committee Member
In many applications in environmental sciences and epidemiology, data are often collected over space and time. In some cases, the spatial-temporal data of interest are multivariate, and these multivariate spatial-temporal processes often have a complicated dependency structure. Hence, multivariate spatial-temporal modeling is a very challenging task. In this study, we develop statistical models to effectively account for multivariate spatial-temporal dependency structures of air pollution concentrations and human health outcomes. Fine particulate matter (PM2.5) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM2.5 has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain better understanding about the spatial-temporal distribution of each component of the total PM2.5 mass, and also to estimate how the composition of PM2.5 changes with space and time. We introduce a multivariate spatial-temporal model for speciated PM2.5. Our hierarchical framework combines different sources of data and accounts for potential bias. In addition, a spatiotemporal extension of the linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We apply our framework to speciated PM2.5 data in the United States for the year 2004. In addition, the chemical composition of PM2.5 varies across space and time so the association between PM2.5 and mortality could change with space and season. Thus, we develop and implement a multi-stage Bayesian framework that provides a very broad and flexible approach to studying the spatial-temporal associations between mortality and population exposure to daily PM2.5 mass, while accounting for different sources of uncertainty. In the first stage, we map ambient PM2.5 air concentrations using all available monitoring data and an air quality model (CMAQ) at different spatial and temporal scales. In the second stage, we examine the spatial-temporal relationships between the health end-points and the exposures to PM2.5 by introducing a spatial-temporal generalized Poisson regression model. We adjust for time-varying confounders, such as seasonal trends. A common seasonal trends model uses a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. Thus, instead the number of the basis functions is treated as an unknown parameter in our Bayesian model, and we use a space-time stochastic search variable selection method. The framework is illustrated using a data set in North Carolina for the year 2001.
Numerical Studies of Synoptic and Mesoscale Environments Conducive to Heavy Rainfall in Tropical and Extratropical Systems
(2003-11-13) Thurman, James Arnold; Michael L. Kaplan, Committee Member; Jerry M. Davis, Committee Member; Simon W. Chang, Committee Member; Yuh-Lang Lin, Committee Chair
The purpose of this research was to examine the environments conducive to heavy rainfall production, specifically a landfalling hurricane, Hurricane Floyd (September 1999) and an Alpine event, MAP IOP-2B (September 1999). In addition to studying the two events independently, a third study examined the link between Floyd's extratropical transition and IOP-2B given that the two events occurred a few days apart. Analysis of observations of both events led to the formation of the hypothesis that the coupling of transverse ageostrophic circulations over a pre-existing low-level confluence zone was a key precursor to heavy rainfall production. In both cases, a low-level confluence zone was found from the observations and simulations. For Floyd, the confluence zone developed as warm easterly winds ahead of the hurricane became juxtaposed with cooler northeast winds just inland over North Carolina and Virginia. In IOP-2B, the confluence zone developed as southerly winds from the Mediterranean became juxtaposed with easterly and southeasterly winds from eastern Italy. These easterlies and southeasterlies developed as southeast winds from the Adriatic Sea impinged upon the eastern Alps, and turned west in the form of a barrier jet. Also, in both cases, upper level diffluence, due to a split flow, became juxtaposed over the low-level confluence, enhancing the upward motion. MM5 simulations for both events revealed coupled thermally direct and thermally indirect circulations over the low-level confluence zone with their rising branches coupled over the zone, proving the hypothesis. Simulations of Floyd's extratropical transition showed a link existed between Floyd and IOP-2B. Parcels from Floyd's upper level circulation reached Italy around the time the heavy rainfall developed in IOP-2B. Simulations with and without latent heat release demonstrated the importance of latent heat release in maintaining the upper-level jets and split flow which in turn, aided in the maintenance of convection. Latent heat release was also found to be important in maintaining the strength of the transverse ageostrophic circulations
Reparametrized Dynamic Space-Time Models and Spatial Model Selection
(2006-08-07) Lee, Hyeyoung; Jerry M. Davis, Committee Member; Montserrat Fuentes, Committee Member; David A. Dickey, Committee Member; Sujit K. Ghosh, Committee Chair
Researchers in diverse areas such as environmental and health sciences are increasingly facing working with space-time data. Often the dimension of space-time data sets can be very large and moreover, space-time processes are often complicated in that the dependence structure across space and time is non-trivial, often non-separable and non-stationary in space and/or time. Hence, space-time modeling is a challenging task and in particular parameter estimation can be problematic due to the high dimensionality. We propose a reparametrization approach to fit dynamic space-time models with an unstructured covariance function. Our modeling contribution is to present unconstrained reparametrization for a covariance matrix in dynamic space-time models. Using this unconstrained reparametrization method, we are able to implement the modeling of a high dimensional covariance matrix that automatically maintains the positive definiteness constraint. We illustrate the use of this reparametrization method by applying our model to a set of atmospheric nitrate concentration data. We also consider the problem of model selection for spatial data. The issue of model selection in spatial models has rarely been addressed in the literature, though it is very important. To address this problem, we consider selection criteria such as the Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC). The performance of these selection criteria are examined using Monte Carlo simulations. In particular, the ability of these criteria to select the correct model is evaluated.
Symmetry and Separability in Spatial-Temporal Processes
(2005-12-15) Park, Man Sik; Montserrat Fuentes, Committee Chair; Peter Bloomfield, Committee Member; David A. Dickey, Committee Member; Sastry G. Pantula, Committee Member; Jerry M. Davis, Committee Member
Symmetry is one of most standard assumptions that are needed for a covariance function in spatial statistics. However, many studies in spatial research fields show that environmental data have complex spatial-temporal dependency structures that are difficult to model and estimate, due to the lack of symmetry and other standard assumptions of a covariance function. So, not much literature exists in statistics about asymmetric covariance functions and formal tests for lack of symmetry in spatial-temporal processes. In this study, we introduce certain types of symmetry in spatial-temporal processes and propose new classes of asymmetric spatial-temporal covariance models by using spectral representations. We also clarify the relationship between symmetry and separability and introduce nonseparable covariance models. Based on the proposed concept of symmetry in spatial-temporal processes, new formal tests for lack of symmetry are proposed in this study by employing spectral representations of the spatial-temporal covariance function. The advantage of the tests is that simple analysis of variance (ANOVA) approaches are employed for detecting lack of symmetry inherent in spatial-temporal processes. Our new classes of covariance models are applied to the methods for the fine particulate matters with a mass median diameter less than 2.5 $mu m$ ($mbox[PM]_[2.5]$) observed from U.S. Environmental Protection Agency (EPA). We evaluate the performance of the tests by a simulation study and, finally, apply to the $mbox[PM]_[2.5]$ daily concentration calculated by the Models-3/Community Multiscale Air Quality (CMAQ) modeling system with the spatial resolution of $36km imes 36 km$.
Three-Dimensional Modeling of Ozone and Particulate Matter: Model Improvement and Evaluation
(2008-05-02) Liu, Ping; Jerry M. Davis, Committee Member; S. T. Rao, Committee Member; Fred H. M. Semazzi, Committee Member; Yang Zhang, Committee Chair; Viney P. Aneja, Committee Member