Harmful algal blooms(habs) are recurring phenomena in lake erie, and pose a very real health threat to inhabitants and animal life in the exposed regions, primarily the lands adjacent to the western…

This is a $107,692.20 grant awarded by the National Aeronautics and Space Administration (NASA) Shared Services Center to Florida Atlantic University, a public university in Boca Raton, Florida. The grant is for research to improve the accuracy of remote sensing data used in the National Oceanic and Atmospheric Administration's (NOAA) forecasting model for harmful algal blooms (HABs) in Lake Erie.

The research will utilize newly available autonomous radiometric data collected from a NASA Aerosol Robotic Network-Ocean Color (AERONET-OC) node in western Lake Erie to improve the atmospheric correction and bio-optical algorithms used to process MODIS-Aqua ocean color satellite imagery. This improved satellite data will then be integrated into NOAA's three-dimensional hydrodynamic model to provide more accurate HAB forecasts to support mitigation planning by water treatment facilities, beach managers, and the public. The grant does not have a set-aside designation.

Harmful algal blooms(habs) are recurring phenomena in lake erie, and pose a very real health threat to inhabitants and animal life in the exposed regions, primarily the lands adjacent to the western and central. NOAA s great lakes environmental research laboratory (glerl) has developed a HAB the current and predicted locations of habs based on the coupling of NASA ocean color products with a hydrodynamic model. The forecast model uses NASA ocean color remote sensing data to initialize the particle field in a three-dimensional hydrodynamic model, which is simulated 3 TO5 days into the future. These predicted HAB maps are used by water plant managers to help plan mitigation, by public beach managers to plan for beach closures, and by the public to plan for recreational activities on the lake. Remote sensing plays a critical role in establishing the disseminated HAB forecast. However, a number of issues limit the effectiveness of ocean color observations over lake eire. These include atmospheric correction errors that propagate into downstream products, BIO-optical algorithm limitations owing to the complexity of the in-water optics, and the ability of cyanobacteria colonies to rapidly disperse or concentrate in the water column owing to gas vacuoles within cell structures. We are proposing to utilize newly available autonomous radiometric data collected from a NASA aeronet-OC node in western lake erie to improve NASA remote sensing image products for use in NOAA s lake erie forecasting and obsercational system. The radiometric data will be collected by a robotic seaprism sun photometer, which will be fixed to a fixed tower in western lake erie that is part of NOAA s realtime coastal observational network (recon), and the site of one of its HAB monitoring stations. Spectral measurements of atmospheric and water properties will be collected by the seaprism at hourly invervals. The seaprism data will be combined with an in-water buoy system measuring water and refinement. The seaprism data will be used to guide the optimal configuration for atmospheric correction schemes for modis-aqua ocean color images. The high temporal frequency of seaprism measurement will also be used to validate the three-dimensional forecast model, which incorporates cyanobacteria colony dispersion and accumulation. Ultimately, improving remote sensing products will improve predictive forecasts, and that is the aim of this proposal. On a broader level, the work proposed here will help shape strategies for ocean color studies of habs in other regions.