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Anderson, CR, Kudela RM, Kahru M, Chao Y, Rosenfeld LK, Bahr FL, Anderson DM, Norris TA.  2016.  Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system. Harmful Algae. 59:1-18.   10.1016/j.hal.2016.08.006   AbstractWebsite

Toxic algal events are an annual burden on aquaculture and coastal ecosystems of California. The threat of domoic acid (DA) toxicity to human and wildlife health is the dominant harmful algal bloom (HAB) concern for the region, leading to a strong focus on prediction and mitigation of these blooms and their toxic effects. This paper describes the initial development of the California Harmful Algae Risk Mapping (C-HARM) system that predicts the spatial likelihood of blooms and dangerous levels of DA using a unique blend of numerical models, ecological forecast models of the target group, Pseudo-nitzschia, and satellite ocean color imagery. Data interpolating empirical orthogonal functions (DINEOF) are applied to ocean color imagery to fill in missing data and then used in a multivariate mode with other modeled variables to forecast biogeochemical parameters. Daily predictions (nowcast and forecast maps) are run routinely at the Central and Northern California Ocean Observing System (CeNCOOS) and posted on its public website. Skill assessment of model output for the nowcast data is restricted to nearshore pixels that overlap with routine pier monitoring of HABs in California from 2014 to 2015. Model lead times are best correlated with DA measured with solid phase adsorption toxin tracking (SPATI') and marine mammal strandings from DA toxicosis, suggesting long-term benefits of the HAB predictions to decision making. Over the next three years, the C-HARM application system will be incorporated into the NOAA operational HAB forecasting system and HAB Bulletin. (C) 2016 Elsevier B.V. All rights reserved.

Kahru, M, Kudela RM, Anderson CR, Mitchell BG.  2015.  Optimized merger of ocean chlorophyll algorithms of MODIS-Aqua and VIIRS. Ieee Geoscience and Remote Sensing Letters. 12:2282-2285.   10.1109/lgrs.2015.2470250   AbstractWebsite

Standard ocean chlorophyll-a (Chla) products from currently operational satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Visible Infrared Imager Radiometer Suite (VIIRS) underestimate medium and high in situ Chla concentrations and have approximately 9% bias between each other in the California Current. By using the regional optimization approach of Kahru et al., we minimized the differences between satellite estimates and in situ match-ups as well as between estimates of the two satellite sensors and created improved empirical algorithms for both sensors. The regionally optimized Chla estimates from MODIS-Aqua and VIIRS have no bias between each other, have improved retrievals at medium to high in situ Chla, and can be merged to improve temporal frequency and spatial coverage and to extend the merged time series.

Kahru, M, Kudela RM, Anderson CR, Manzano-Sarabia M, Mitchell BG.  2014.  Evaluation of satellite retrievals of ocean chlorophyll-a in the California Current. Remote Sensing. 6:8524-8540.   10.3390/rs6098524   AbstractWebsite

Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, all sensors produced a coefficient of determination (R-2) between 0.79 and 0.88 and a median absolute percent error (MdAPE) between 21% and 27%. However, at in situ Chla > 1 mg m(-3), only products from MERIS (both the ESA produced algal_1 and NASA produced chlor_a) maintained reasonable accuracy (R-2 from 0.74 to 0.52 and MdAPE from 23% to 31%, respectively), while the other sensors had R-2 below 0.5 and MdAPE higher than 36%. We show that the low accuracy at medium and high Chla is caused by the poor retrieval of remote sensing reflectance.