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.

中文翻译：

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基于数据同化的实时集成微藻生长预测

准确的短期（例如，7 天）微藻生长预测将有利于微藻的生产和收获。本研究开发了一个可操作的微藻生长预测系统，该系统由 Huesemann 藻类生物质生长模型 (BGM)、二维模块化水生模拟系统 (MASS2) 水动力模型和集合数据同化 (DA) 组成。本研究的新颖之处在于使用集成 DA 来顺序更新 BGM 模型的初始条件 (IC)，同时吸收测量的生物量光密度，以提高短期生物量预测技能。该预测系统以伪实时方式运行，并针对观察到的 Monorapidium minutum进行了验证26B-AM 在位于美国亚利桑那州梅萨的两个室外池塘养殖中生长。我们发现，与没有 DA 的模型预测相比，DA 预测系统可以将 7 天微藻预测技能平均提高约 85%。这些结果表明，生物量增长预测的潜在准确性可能足以为商业规模微藻生产的实时运营决策提供信息，例如池塘运营和收获计划。