Weather and Water
Using Weather Data to Create Models and Tools to Predict Coastal Impacts
Understanding and predicting climate-related issues along coastlines is important for management and coastal resiliency.
We provide scientific information and tools on connections between climate, weather and coastal impacts from hazards including storms, rising seas, floods, and changing air/water temperatures.
Satellite remote sensing technology and large-scale weather patterns are used to test and develop coastal change indicators, metrics and models to inform coastal managers on adverse ecological situations affecting vital resources, habitats and people.
Why We Care
Significant impact and damage to our coastal environments can occur due to changing climate, and increasingly more severe and frequent weather events.
These weather – water events and subsequent damage are of great concern for people living in affected coastal communities and the vital ecosystems and resources we protect.
We use climatological data obtained nearly simultaneously over a relatively large area of the atmosphere and coastal water bodies from NOAA and other sources to create a framework to assess indicators of predictability of anomalous events damaging our coastal environments.
We determine how climate change driven extremes interact with, or compound existing risks and impacts on coastal ecosystems.
We determine how processes can be tracked, how impacts can be predicted and translated into environmental indicators which allow managers to take proactive measures.
What We Are Doing
We use satellite-derived data, in situ conditions and atmospheric patterns over large geographic areas as inputs to predict changes in coastal ecosystems.
Results are analyzed and reassessed to eventually determine what weather-driven indicators can be used to measure or model a particular resilience characteristic.
Examples
Sea Surface Temperature Used to Model Weather Driven Coastal Change That Can Cause Sea Turtle Hypothermia and Mortality.
This project assessed cold snap mortality and hypothermia in loggerhead and green sea turtles in south Florida coastal ecosystems to develop a biological cold stress index using satellite sea surface temperature and weather pattern forcing.
This work allows sea turtle rescue workers to be better prepared for when the next cold snap mortality or hypothermia event may occur.
Development of a Water Clarity Index for the Great Lakes as a Climate Indicator
Great Lakes
Over the last several decades, multiple environmental issues have led to dramatic changes in the water clarity of the Great Lakes.
While many of the key factors are well-known and have direct anthropogenic origins, climatic variability and change can also impact water clarity at various temporal scales, but their influence is less often studied.
Building upon examination of the univariate relationships between synoptic-scale weather patterns and water clarity, this research utilized nonlinear autoregressive models with exogenous input (NARX models) to explore the multivariate climate-to-water clarity relationship.
Models trained on the observation period (1997–2016) are extrapolated back to 1979 to reconstruct a daily-scale historical water clarity dataset, and used in a reforecast mode to estimate real-time forecast skill.
The Models indicate climate variability is a contributing factor to the fluctuation of water clarity.
A goal of this research is to enhance awareness of the long-term changes and variation taking place in water clarity across the entire Great Lakes system.
Real-time satellite monitoring and forecasting of water clarity conditions will lead to better management response to water clarity events.
Additionally, improved recreational activity planning for users such as divers, kayakers, and snorkelers, will increase regional tourism and facilitation of public access through NOAA, the National Park Service and other protected area management agencies in the region.
Anomalous Coastal Sea Level Variability Patterns Along the Atlantic Coast and Their Linkages to Synoptic-Scale Weather Forcing
Tide Gauge Station Locations for Anomalous Coastal Sea Level Variability Pattern Study Along the Atlantic Coast
This study examined sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data in the context of synoptic weather pattern forcing.
Altimetry- derived sea level anomaly (SLA) data between 1993 and 2018 were compared with Self Organizing Map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their inter-relationships with high water-level conditions from tide gauges.
Pearson Product correlation maps between daily tide gauge residuals and daily-scale altimetry-derived sea level anomaly values.
Self-Organizing Map (SOM) of mean sea level pressure (SLP) data for the study region.
Nuisance or blue-sky floods are increasing along many US coastlines.
While the drivers of such events are numerous, we identified key contributing weather risk patterns and environmental factors linked to regional and locally high water conditions along the Atlantic Coast.
The predictability of elevated sea levels and nuisance floods are highly dependent upon atmospheric drivers including wind and circulation patterns, and
when applied in a tested modeling framework, may prove useful for predicting future floods at various time scales.
