- 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) 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.
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.
Severe maternal morbidity (SMM) is on the rise in the U.S. Literature has established links between environmental exposures and adverse pregnancy outcomes, including poor air quality, tobacco smoke, and chemical toxicants. Few studies have examined the impact of climate and weather-related changes on maternal health, and no studies have examined the impact of climate change on changing patterns in SMM. Climate change serves as a threat multiplier by which existing health disparities and other vulnerabilities related to social and environmental determinants are amplified, accelerated, or worsened. Research has consistently demonstrated the complex interactions between socio-environmental characteristics that co-occur within an individual's geographic residence, drive inequalities in environmental exposures, and reduce accessibility and availability of health services as major drivers in maternal health status. This project will examine the impact of climate change-a known environmental determinant- on maternal health risks for pregnant women, specifically by investigating the association between ambient temperature and hospital delivery hospitalizations for severe maternal morbidity (SMM)to inform the development of a population-based indicator to be used in national, state- and community-level climate-health surveillance efforts.
This collaborative project with the University of California Santa Cruz (UCSC) will tackle the problem of obtaining state of the art climate data products from several, partially overlapping, geostationary satellites. The proposed research will advance the ÃƒÂ¯Ã‚Â¬Ã‚Âeld of geostatistics by developing probabilistic methods for multivariate, non-stationary ÃƒÂ¯Ã‚Â¬Ã‚Âelds in space and space-time. NCSU will provide the expertise related to applied mathematical methods and knowledge of the remote sensing systems under study as well as the proposed data retrieval methods and processing protocols. The utility of the developed models can be extended to any climate variable derived from a constellation of geostationary satellites. Given that multiple international agencies operate similar instruments focused on viewing diÃƒÂ¯Ã‚Â¬Ã¢â€šÂ¬erent parts of the world, the project will strengthen international cooperation for better Earth system understanding, and leverage the investments in instrumentation to foster global scientiÃƒÂ¯Ã‚Â¬Ã‚Âc know-ledge. This project will showcase the role of computational and data-enabled science as a focus of interdisciplinary activities, displaying the power of statistical inference to produce eÃƒÂ¯Ã‚Â¬Ã¢â€šÂ¬ective tools for environmental studies and climate policy making.
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.
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.
Kelvin waves and easterly waves are among the most prominent modes of synoptic-scale convective variability in the tropics. Recent studies suggest that interactions between these waves can lead to tropical cyclogenesis. However, many questions remain regarding how these waves affect one another and how cyclogenesis ensues. The most significant ways that Kelvin waves might affect easterly waves relate to their modulation of low-level winds, which may alter the background shear and gradient of vorticity and enhance wave-mean flow interaction. The Kelvin wave westerlies could also enhance surface enthalpy fluxes within the easterly wave, which would lead to intensification through diabatic heating. While the kinematic view of the interaction appears simple, the inherent dynamics are expected to be complex and nonlinear. The imminent launch of the CYGNSS constellation of satellites will provide an unprecedented opportunity to observe and model these interactions. The high spatial and temporal resolution of CYGNSS is ideally suited for studying Kelvin waves and easterly waves. They have a phase speed of ~20 m sÃƒÂ¢Ã‹â€ Ã¢â‚¬â„¢1 relative to one another and each have wavelengths of just 2000ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“4000 km. The proposed research aligns clearly with NASA objectives by leveraging CYGNSS data to investigate this important problem. Case studies from the first year of CYGNSS data will be compared with climatological composites from MERRA-2 and TRMM/GPM. This investigation will identify the evolution of surface winds and enthalpy fluxes in Kelvin and easterly waves separately as well as during interactions between them. The role of mesoscale convective systems (MCS) in mediating the interaction will also be investigated. The final phase of this project will use idealized simulations of the interactions between Kelvin waves and easterly waves. These simulations will shed light into the causal relationships between changes in the surface winds and fluxes derived from CYGNSS observations and changes in the amplitude of easterly waves. Particular focus will be placed effect of moisture and vorticity advections as well as surface energy fluxes.