Accurate short-range (e.g., 7 days) microalgae growth forecasts will be beneficial for both the production and harvesting of microalgae. This study developed an operational microalgae growth forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2) hydrodynamic model, and ensemble data assimilation (DA). The novelty of this study is the use of ensemble DA to sequentially update the BGM model's initial condition (IC) with the assimilation of measured biomass optical density to improve short-range biomass forecasting skills. The forecasting system was run in pseudo-real-time and validated against observed Monoraphidium minutum 26B-AM growth in two outdoor pond cultures located in Mesa, Arizona, United States. We found the DA forecasting system could improve the 7-day microalgae forecasting skill by about 85% on average compared to model forecasts without DA. These results suggest the potential accuracy of biomass growth forecasts may be sufficient to inform real-time operational decisions, such as pond operation and harvest planning, for commercial-scale microalgae production.
Keywords: biomass forecasting; biomass growth model; ensemble data assimilation; harvest planning; particle filtering; pond operation.
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