An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting.

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

Real-time ensemble microalgae growth forecasting with data assimilation.

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.