An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast capabilities, and the need to match forecast and validation resolutions.

中文翻译：

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可操作的藻华预报系统的技能评估

对佛罗里达州西南部有害藻华 (HAB) 的业务预报系统进行了分析，以提高预报技巧。由有毒的甲藻、短卡氏藻引起的有害细菌会导致贝类中毒并刺激呼吸道。除了预测新的水华及其范围之外，在水华事件期间，HAB 预测每周两次，结合使用卫星图像产品、风力预测以及从之前的观测和研究得出的基于规则的模型。这些预测包括：识别、强化、运输、范围和影响；后者对公众来说最为重要。识别包括将新的水华识别为有害细菌，并根据涉及水采样的运行监测计划进行验证。强度预测的频率要低得多，只能利用单一特定水华的卫星数据进行评估。还根据水样评估了赤潮区的范围和迁移预测。由于预测和可用验证数据的分辨率，无法在小于 30 公里的尺度上解析技能。最初，呼吸道刺激预测是使用轶事信息（唯一可用的数据）进行分析的，这些数据对主要呼吸系统事件存在偏差，导致预测准确度超过 90%。当救生员每天两次观察的系统计划得到实施时，可以对预测进行有意义的评估。结果显示，70% 的时间预测可以识别出所有救生员海滩发生的呼吸事件。然而，任何特定海滩的误报率都很高（80%）。由于预测是在县级的一半到整个县级进行的，因此验证数据的分辨率降低到县级，将误报率降低到22%（准确度为78%）。该研究表明了系统抽样的重要性，即使在使用定性描述符时也是如此，使用验证分辨率来评估预测能力，以及匹配预测和验证分辨率的必要性。