Branch of the SoG-bloomcast project that uses an ensemble forecast.
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Updated
Aug 29, 2024 - Jupyter Notebook
Branch of the SoG-bloomcast project that uses an ensemble forecast.
assess the impact of 2024 Central American fires on surface ocean properties in the Gulf of Mexico
Superseded by https://github.com/SalishSeaCast/SOG-Bloomcast-Ensemble. SoG-bloomcast is a an operational oceanography project to provide daily updated forecasts of the first spring phytoplankton bloom in the Strait of Georgia on the British Columbia south coast. In addition to being part of an active research program on the physics and biogeoche…
Planktos is an image recognition model tailored for classifying various species of phytoplankton. Built using cutting-edge machine learning algorithms, this tool enables researchers and environmentalists to automate and accelerate the identification process, enhancing studies in marine biology and ecology.
Popcycle is an R package that offers reproducible analytical tools to analyze flow cytometry data collected by SeaFlow
This is the base code of the University of Victoria (UVic) Earth System Climate Model version 2.9 with the Model of Ocean Biogeochemistry and Isotopes (MOBI).
This work is part of the PhytoArk project.
Southern Ocean phytoplakton observations stored in a relational SQL database
A PyTorch implementation of the Multi-Mode CNN to reconstruct Chlorophyll-a time series in the global ocean from oceanic and atmospheric physical drivers
Calculating future changes in Arctic phytoplankton using the emergent constraint method
Provides Mathematica code to generate results from "Modeling the Metabolic Evolution of Mixotrophic Phytoplankton in Response to Rising Ocean Surface Temperatures"
Function that calculates the maximum phytoplankton growth rate at specified temperature(s) according to the Eppley growth curve
Functions and a walkthrough to visualize common photosynthesis-irradiance relationships
from first principles to end to end ecosystem models
Calculate various MHWs(Marine Heat Waves) properties from daily SST datas
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