- Expanding Access to Open Environmental Data Advancements and Next Steps , BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY (2022)
- Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6 , GEOSCIENTIFIC MODEL DEVELOPMENT (2020)
- Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections , REMOTE SENSING (2020)
- Five years of Florida Current structure and transport from the Royal Caribbean Cruise ShipExplorer of the Seas , Journal of Geophysical Research (2008)
- Tidal variations of flow convergence, shear, and stratification at the Rio de la Plata estuary turbidity front , Journal of Geophysical Research (2008)
- On the limiting aerodynamic roughness of the ocean in very strong winds , Geophysical Research Letters (2004)
- Seasonal and interannual studies of vortices in sea surface temperature data , International Journal of Remote Sensing (2004)
The proposed research in collaboration with Arizona State University addresses fundamental questions at the intersection of climate dynamics and civil engineering aimed at improving design of infrastructure systems under climate change. The main goal of the proposed project is to advance knowledge on changes in the generating mechanisms of sub-daily and daily extreme precipitation (EP) and use this new knowledge to develop a novel physics-driven statistical framework to inform the development of improved nonstationary intensity-duration-frequency (IDF) curves. Two research hypotheses will be investigated: (1) The occurrence and/or thermodynamic and dynamic components of the generating mechanisms of EP are changing in time, leading to changes in IDF design values; and (2) improved nonstationary IDF curves can be developed through statistical models of mixed populations that incorporate information on changes in the generating mechanisms of EP simulated by climate models. The research hypotheses will be tested using hourly and daily rainfall records, atmospheric reanalyses, and climate simulations in multiple regions of U.S. spanning a wide range of dominant mechanisms of EP.
Marine air temperature drives climate processes which have far-reaching downstream impacts including an increase in extreme cyclones, sea level rise, and coastal flooding. Monitoring of this important climate indicator is imperative for decision makers and stakeholders to plan climate adaptation and mitigation efforts on local and regional scales: a long-term research goal for NOAA. As a key contributor to the estimation of global mean surface temperature, MAT observations are essential for climate monitoring. High quality MAT data also play a crucial role in understanding air-sea fluxes and its impact on the physical and biological processes in the ocean and coastal system. This project will result in a new observation-based (in situ and satellite) ocean synthesis dataset for climate monitoring and modeling applications. The new high-resolution global MAT dataset will be developed using a state-of-art machine learning framework. The improved spatial and temporal resolution and coverages provided by HIRSMAT will contribute to the associated NOAA objective to improve scientific understanding of the changing climate system and its impacts. Furthermore, the project will increase the use and utility of field campaign data observations (e.g., DYNAMO and ATOMIC) by using them for independent evaluation. The resultant dataset will be provided in an obs4MIPS-compliant format for efficient, ready access by the modeling community to perform comparisons with relevant CMIP6 output variables.
The NOAA Cooperative Institute for Satellite Earth System Studies (CISESS) in North Carolina (hosted by NC State University) will utilize Oak Ridge Associated Universities (ORAU) to collaboratively contribute to the advancement of NOAA's Air Resources Laboratoryâ€™s Atmospheric Turbulence and Diffusion Division mission-related research efforts in the development of capabilities to better predict air quality impacts from wildfires. ORAU and its consortium partner(s) will work to improve the performance of operational air quality modeling systems, based on current knowledge and computing constraints through activities including the update of emissions estimates of trace gases and smoke particles that are responsible for the formation of high ozone and PM2.5 concentrations downwind based on recent measurements, development of a prototype wildfire smoke ensemble product from existing NOAA smoke-enabled models, development of a fire behavior module for higher-resolution air quality forecasting models, and development of standardized evaluation tools that will help future efforts at model improvements.
The NOAA Cooperative Institute for Satellite Earth System Studies (CISESS) will be formed through a national consortium of academic and nonprofit institutions, with leadership from the University of Maryland College Park (UMCP) and North Carolina State University (NCSU). NCSU's North Carolina Institute for Climate Studies (NCICS) will operate the North Carolina arm of CISESS. CISESSÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ primary objective is to document the natural atmosphere-ocean-land-biosphere components of the Earth system and how they interact with human activities as a coupled system through collaborative and transformative research activities and to transition that research into operational applications that produce societal benefits. The CISESS ConsortiumÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s main goals are to 1) support the NESDIS mission of providing ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œsecure and timely access to global environmental data and information from satellites and other sources to both promote and protect the NationÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s environment, security, economy, and quality of lifeÃƒÂ¢Ã¢â€šÂ¬Ã‚Â; 2) promote and augment the research needed to carry out NOAAÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s mission ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œto understand and predict changes in climate, weather, oceans, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resourcesÃƒÂ¢Ã¢â€šÂ¬Ã‚Â; and 3) deliver innovative research products, education, training, and outreach aligned with these missions.
It is well understood that stress on the human body from high temperatures is exacerbated by high humidity. The National Weather Service uses a familiar metric (the heat index) that incorporates temperature and humidity to quantify this effect. However, long-term U.S. monitoring of heat stress indicators relevant to human health has been limited to the period from around the middle of the 20th century to present because of a lack of digitally available humidity data prior to 1948. Extending the availability of heat stress metrics that combine both temperature and humidity over the past century is critically important for providing historical context to current heat wave trends. The Climate Database Modernization Program (CDMP)funded the entry of early hourly weather observations going back to the late 19th century. This set of data has never been publicly available but is now being incorporated into the new Global Historical Climatology Network-hourly (GHCNh) of the National Centers for Environmental Information (NCEI). This study will provide the scientific basis for new heat wave monitoring products by characterizing, for the first time, the features of heat waves in the contiguous United States over a century-plus time frame that includes the 1930s Dust Bowl era and uses human healthÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“relevant heat metrics that incorporate humidity. This proposal will target the high-priority climate risk area of extreme heat using a dataset that was previously unavailable for climate analysis. It will provide a new monitoring indicator suite (heat index and related metrics and circulation features) extending back to 1895. The longer period of analysis, encompassing the epic 1930s heat waves, will advance understanding about recent trends in heat waves and the nature of multidecadal variability.
The climate crisis, in combination with other social and environmental stressors, negatively influences human health; its impact varies across age groups and life stages. Pregnancy is an understudied critical window of susceptibility. Few studies have focused on the impact of prenatal exposure to climate stressors on pregnant populations and infants in the Southeastern US, despite pronounced trends in climate warming, an escalating maternal death rate, and persistent maternal and infant health disparities in the region. An important science gap remains in identifying national climate change and health surveillance indicators, particularly for understanding the relationship between climate change and pregnancy risks. Our long-term goal is to develop clinic-based and public health interventions to reduce the adverse impact of climate change during pregnancy. The overarching goal of this population-based study is to examine extreme temperature-sensitivities in maternal and infant health risk during critical windows of pregnancy and advance understanding of the social-environmental drivers of health disparities in a changing climate. We will achieve the following two specific aims: Aim 1. Characterize the relationship between exposure to cold and hot ambient temperature extremes and adverse maternal and infant health outcomes during critical periods of pregnancy; Aim 2. Examine the joint effects of prenatal exposure to temperature extremes and socio-environmental stressors on excess maternal and infant health risks. The new NIH Climate Change and Health Strategic Initiative has prioritized protecting the health of pregnant populations in the face of extreme temperatures. This low-cost retrospective birth cohort study will advance understanding on how social and environmental conditions that occur during pregnancy interact with climate change stressors to negatively affect the health of pregnant populations and their infants. Our expected outcomes will include: 1) the quantification of prenatal exposure to climate change as a risk multiplier during pregnancy; and 2) the identification of maternal and infant surveillance indicators for tracking the health effects of climate change. The proposed research is innovative because results will 1) show how climate and social stressors interact to exacerbate climate-health risks during pregnancy, and 2) aid in hypothesis generation on the pathways that contribute to climate resilience and the reduction of maternal and infant health risks. This work has significant potential to be transformative to the field through the identification of maternal or infant health outcomes as potential surveillance health indicators of climate change impacts, which can be leveraged to measure local and state-level health interventions and policy changes in the United States. Results will address a well-cited research need from the NIH Strategic Climate & Health Initiative on the timing of exposure to climate stressors and associated maternal and infant complications and the critical windows of susceptibility during the prenatal period.
Accurate measurement and accounting of global precipitation is an essential prerequisite to carry out global drought monitoring, and it is equally critical for gauging initial conditions for drought forecasting (as well as validation). The Global Drought Information System (GDIS) follows the World Meteorological Organization (2012) recommendation of converting the precipitation into Standardized Precipitation Index (SPI). This project would provide both a comprehensive, state of the art global precipitation archive and a global archive for drought monitoring. Drought indices, such as SPI are a measure or proxy comparing the current availability of water relative to long-term water availability for that month or time period across a long term time record. Most of the deployed drought monitoring tools, including drought indices, used around the world evaluate broad scale conditionsÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âat, for example, large tracts of one degree latitude and longitude (as found in the case of GermanyÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s DWD Global Precipitation Climatology Centre (GPCC) precipitation archive). The European Commission Joint Research Centre Global Drought Observatory (GDO) is an example of this, carrying out drought monitoring over one geographical degree squares. However, details of fine resolution, local drought conditions are needed, both for residents within these areas and for documentation of drought patterns across the terrestrial globe, to analyze patterns of global warming and drought. This project upgrades the spatial resolution over which precipitation is measured globally, while at the same time, modernizing the pipeline of providing precipitation to overcome legacy quality control issues and latency issues (bringing drought monitoring to near real time (NRT) conditions, dispensing with, for example, the time delay of a month, commonly encountered when using GPCC precipitation for regional or global drought monitoring.
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) records reflect that there were 20 U.S. weather/climate disaster events in 2021 that each resulted in losses exceeding $1 billion and that the annual number of such impactful events has significantly increased over the last four decades under changing climate conditions. NOAA is currently focused on improving and expanding equitable access to its weather and climate prediction capabilities and services to help prepare communities for the increasingly devastating impacts of these more frequent intense weather/climate events including extreme precipitation, hurricanes, flooding, drought, extreme heat, and wildfires. NC State University scientists working in the NOAA Cooperative Institute for Satellite Earth System Studies in North Carolina (CISESS NC) will utilize remotely sensed and in situ observations to further the publicâ€™s awareness and understanding of Earth System variability, change, prediction, and projection and enhance NOAA weather and climate prediction capabilities and services. CISESS NC will complete collaborative research and development activities to enhance NOAA weather and climate prediction capabilities and services to help society and especially vulnerable and underserved communities better understand, prepare and plan for, adapt, and respond to climate change and its impacts.
Health departments and healthcare professionals need reliable information to effectively prepare and warn constituents of pending natural and biological threats caused by drought. However, drought warning systems are currently limited as public health officials are just starting to investigate drought human health impacts. To address this issue, a collaborative, multi-institution team of investigators will assess the relationship between drought indicators and health outcomes to assist health officials to develop effective warning systems. It is expected that drought related health outcomes will be unique for different regions of the United States due to population (e.g. race/ethnicity, age groups, occupation, rural or urban status, and access to existing health care) drought severity disparities. The proposed study will evaluate multiple drought indices to identify potential regional health outcomes across the U.S. These findings will benefit public health professionals or emergency planners by showing utility for certain drought indicators in predicting health outcomes and enable the production of regionally based specialized messaging for at-risk populations. Findings will be synthesized to specific regions [e.g. Drought Early-Warning Systems (DEWS) and Climate Division regions] of the United States to assist with dissemination. NCSU scientists will provide climate science and climate data analysis expertise in support of the project objectives.
Machine learning (ML) and other big data synthesis and prediction techniques make it possible to use artificial intelligence (AI) for decision making. With these pivotal technology advances and the emerging field of data-centric AI, robust research needs to occur alongside these innovations in several other dimensions. The complexity makes it imperative to ensure that AI, and especially ML, promotes open science values â€“ particularly reproducibility and the use of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. Researchers studying these areas need sufficient collective organizing capacity to keep pace alongside ML adoption. The proposed Research Coordination Network (RCN) will concentrate on three themes: FAIR in ML, AI readiness, and reproducibility. Existing networks will be used to build the RCN, creating a network of networks. Experts and affinity groups related to ML will be engaged to understand emerging best practices, resources to leverage, and how to stimulate experimentation that quantifies the relationship between the FAIRness of data and how easily and efficiently ML algorithms can be applied, as well as need for awareness and new research in ML reproducibility. This RCN will result in an interconnected community of practice that will increase capacity and understanding of the current environment and interactions between AI usage in Computing Science and application of the FAIR Principles to illuminate sources of irreproducibility.