Browsing by Author "Katzfuss, Matthias"

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CAREER: Data Assimilation for Massive Spatio-Temporal Systems Using Multi-Resolution Filters
Statistics; TAMU; https://hdl.handle.net/20.500.14641/215; National Science Foundation
The research supported by this award will produce powerful and scalable open-source software for data assimilation in large spatio-temporal systems with varying degrees of nonlinearity. It will lead to improved inference, forecasts, diagnostics, downscaling, and calibration using data assimilation in many fields of science with direct impact on society, including weather forecasting, climate studies, renewable energy, and pollution monitoring. Despite the great importance and highly statistical nature of data assimilation, there is a lack of statisticians involved in this research area. Thus, the educational component of this project revolves around bridging the gap between the statistics and data-assimilation communities, and getting more statisticians involved in the latter. The principal investigator will develop approaches for filtering inference on high-dimensional states that can outperform existing methods in linear and nonlinear settings. The novel approaches are based on the multi-resolution approximation, a state-of-the-art method for spatial covariance approximations that employs many adaptive, compactly supported basis functions at multiple resolutions. Algorithmic implementations of the methods are highly scalable and can take full advantage of massively parallel high-performance computing systems. Validation, testing, and comparison of the methods will be carried out using realistic observations simulated from models of varying complexity.
CAREER: Data Assimilation for Massive Spatio-Temporal Systems Using Multi-Resolution Filters
Statistics; TAMU; https://hdl.handle.net/20.500.14641/215; National Science Foundation
The research supported by this award will produce powerful and scalable open-source software for data assimilation in large spatio-temporal systems with varying degrees of nonlinearity. It will lead to improved inference, forecasts, diagnostics, downscaling, and calibration using data assimilation in many fields of science with direct impact on society, including weather forecasting, climate studies, renewable energy, and pollution monitoring. Despite the great importance and highly statistical nature of data assimilation, there is a lack of statisticians involved in this research area. Thus, the educational component of this project revolves around bridging the gap between the statistics and data-assimilation communities, and getting more statisticians involved in the latter. The principal investigator will develop approaches for filtering inference on high-dimensional states that can outperform existing methods in linear and nonlinear settings. The novel approaches are based on the multi-resolution approximation, a state-of-the-art method for spatial covariance approximations that employs many adaptive, compactly supported basis functions at multiple resolutions. Algorithmic implementations of the methods are highly scalable and can take full advantage of massively parallel high-performance computing systems. Validation, testing, and comparison of the methods will be carried out using realistic observations simulated from models of varying complexity.
Investigation and Forecast Improvements of Tornadoes in Landfalling Tropical Cyclones
Atmospheric Sciences; TAMU; https://hdl.handle.net/20.500.14641/222; DOC-National Oceanic and Atmospheric Administration
Tornadoes in recent landfalling tropical cyclones (TCs) in the United States underscore the threat these phenomena pose to society and the unique forecast challenge they present to operational forecasters. Despite a fairly robust body of research in this area, significant gaps in our knowledge remain regarding the tropical cyclone tornado (TCTOR) climatology, radar-based storm attributes, and near-cell environments of tornadic and nontornadic convective cells in TCs. Moreover, recent improvements in observational networks (e.g., nationwide dual-polarization radar) and high-resolution operational models afford opportunities to study these phenomena in greater detail. Leveraging and expanding existing collaborations between the NWS and Texas A&M, this study seeks to advance our understanding of TCTOR cell attributes and environments, focusing on differences between verified tornado warnings and false alarms. The second major goal of this project is to improve the operational forecasting and warning decision process through integration of observed cell attributes and modeled near-cell environments. Specific objectives under these larger goals include: O1: Build a database of all tornadoes and tornado warnings in TCs in the United States since the NEXRAD dual-polarization upgrade that includes radar-based storm attributes and near-storm environment information from model analyses. O2: Assess the skill of high-resolution model analyses and forecasts in depicting 1) the low-level, near-cell environment for convective cells in TCs and 2) forecast proxies for low-level rotation (e.g., updraft helicity). O3: Compare near-cell environment and storm attribute information between verified warnings and false alarms in the climatology to determine differences that may be leveraged to reduce false alarms. O4: In partnership with NWS collaborators, assess the performance of current radar, high-resolution NWP, and storm-environment based TCTOR forecasting practices and heuristics. O5: In partnership with NWS collaborators, improve and streamline TCTOR warning practices using information gained from this climatology, including development and evaluation of probabilistic hazard information (PHI) produced by a statistical model trained on data produced in our climatological database. This proposed three-year project is relevant to the CSTAR program under the primary objective of engaging university researchers with operational NWS forecasters to improve basic understanding, forecasting, and warning accuracy for high impact weather events. Specifically, the proposed research addresses Program Priority 1b: “Improving application of Numerical Weather Prediction (NWP) information in the forecast and warning process at various time scales.” The proposed research also addresses Program Priority 1cii: through “developing Probabilistic Hazard Information (PHI).
Records of Fused and Assimilated Satellite Carbon Dioxide Observations and Fluxes From Multiple Instruments SRS# 1707951
Statistics; TAMU; NASA - Jet Propulsion Lab
Statement of Work The Texas A&M team consists of Dr. Matthias Katzfuss, an assistant professor in the Department of Statistics. As part of this project, he would work with Dr. Braverman and Dr. Nguyen on fusing XCO2 and other ancillary fields from different instruments. He would lead the effort on: (1) a parallel version of the SSDF algorithm, and (2) compression of the error covariance matrix so that it can be provided as part of the fused and gap-filled products. Additionally, he would collaborate with Dr. Hobbs on validation and modification of the EFDR algorithm for identifying spatio-temporal anomalies in the flux maps obtained from DA systems.
World Meeting of the International Society for Bayesian Analysis 2022
Statistics; TAMU; National Science Foundation
This award provides travel support for US-based participants in the 2022 World Meeting of the International Society for Bayesian Analysis (ISBA), to be held from June 25 to July 1, 2022, in Montreal, Canada. The conference themes are theory, modeling, and applications of Bayesian statistics. The focus of this award is on funding for junior researchers (graduate students and postdoctoral researchers) from U.S.-based institutions to travel to the conference. Emphasis will be placed on supporting women and members of underrepresented groups. Participating in the conference will inform junior statisticians about key problems and methods that shape research in modern Bayesian statistics and provide them with opportunities to learn from more established researchers and to build mentoring and collaborative relationships. More information on the conference is available on the meeting web page: https://isbawebmaster.github.io/ISBA2022/ Statisticians play an indispensable role in making decisions based on noisy, complex data structures. Statistical methods find application in myriad areas, including biomedical research, environmental science, finance, marketing, psychology, public health, and genomics. Within statistics, the Bayesian approach offers many appealing features: Bayesian methods provide a coherent framework for integrating information from different sources and communicating findings and conclusions using probabilities; Bayesian hierarchical models can capture different sources of variability in data and processes; relevant knowledge can be incorporated easily; and all relevant uncertainties are coherently propagated and incorporated into the final inference. Consequently, Bayesian analyses are commonplace across a wide variety of application areas. ISBA 2022 will bring together a diverse international community of researchers and practitioners who develop and use Bayesian statistical methods to share recent findings, exchange ideas, and discuss new, challenging questions. As a truly international meeting, it will provide participants with access to ideas and colleagues from other countries with whom they may not ordinarily interact. Meeting organizers hope to provide a venue that facilitates the exchange of ideas and cross-fertilization, is welcoming to young researchers, and promotes collaborations and interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
World Meeting of the International Society for Bayesian Analysis 2022
Statistics; TAMU; https://hdl.handle.net/20.500.14641/215; National Science Foundation
This award provides travel support for US-based participants in the 2022 World Meeting of the International Society for Bayesian Analysis (ISBA), to be held from June 25 to July 1, 2022, in Montreal, Canada. The conference themes are theory, modeling, and applications of Bayesian statistics. The focus of this award is on funding for junior researchers (graduate students and postdoctoral researchers) from U.S.-based institutions to travel to the conference. Emphasis will be placed on supporting women and members of underrepresented groups. Participating in the conference will inform junior statisticians about key problems and methods that shape research in modern Bayesian statistics and provide them with opportunities to learn from more established researchers and to build mentoring and collaborative relationships. More information on the conference is available on the meeting web page: https://isbawebmaster.github.io/ISBA2022/ Statisticians play an indispensable role in making decisions based on noisy, complex data structures. Statistical methods find application in myriad areas, including biomedical research, environmental science, finance, marketing, psychology, public health, and genomics. Within statistics, the Bayesian approach offers many appealing features: Bayesian methods provide a coherent framework for integrating information from different sources and communicating findings and conclusions using probabilities; Bayesian hierarchical models can capture different sources of variability in data and processes; relevant knowledge can be incorporated easily; and all relevant uncertainties are coherently propagated and incorporated into the final inference. Consequently, Bayesian analyses are commonplace across a wide variety of application areas. ISBA 2022 will bring together a diverse international community of researchers and practitioners who develop and use Bayesian statistical methods to share recent findings, exchange ideas, and discuss new, challenging questions. As a truly international meeting, it will provide participants with access to ideas and colleagues from other countries with whom they may not ordinarily interact. Meeting organizers hope to provide a venue that facilitates the exchange of ideas and cross-fertilization, is welcoming to young researchers, and promotes collaborations and interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria