Stumpf Richard P, Tomlinson Michelle C, Calkins Julie A, Kirkpatrick Barbara, Fisher Kathleen, Nierenberg Kate, Currier Robert, Wynne Timothy T
NOAA, National Ocean Service, 1305 East-West Highway, 9th floor, Silver Spring, MD 20910, USA.
J Mar Syst. 2009 Feb 20;76(1-2):151-161. doi: 10.1016/j.jmarsys.2008.05.016.
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.
对佛罗里达州西南部有害藻华（HABs）的业务预报系统进行了预报技能分析。由有毒甲藻短裸甲藻引起的有害藻华会导致贝类中毒和呼吸道刺激。除了预测新的藻华及其范围外，在藻华事件期间每周进行两次有害藻华预报，采用卫星衍生图像产品、风预报以及基于先前观测和研究得出的规则模型相结合的方法。这些预报包括：识别、强化、扩散、范围和影响；其中影响对公众最为重要。识别包括将新的藻华识别为有害藻华，并根据涉及水样采集的业务监测计划进行验证。强化预报的频率要低得多，只能用单种藻华的卫星数据进行评估。有害藻华的范围和扩散预报也根据水样进行评估。由于预报分辨率和可用验证数据的原因，在小于30公里的尺度上无法分辨技能。最初，使用轶事信息（唯一可用的数据）对呼吸道刺激预报进行分析，这些信息偏向于重大呼吸道事件，导致预报准确率超过90%。当实施了救生员每日两次系统观测计划后，预报可以得到有意义的评估。结果表明，预报在70%的时间里能识别出所有救生员海滩发生的呼吸道事件。然而，在任何给定海滩上出现假阳性预报的比例都很高（80%）。由于预报是在半个到整个县的层面进行的，验证数据的分辨率降低到了县一级，将假阳性率降低到了22%（准确率为78%）。该研究表明了系统采样的重要性，即使使用定性描述符时也是如此，使用验证分辨率来评估预报能力，以及匹配预报和验证分辨率的必要性。
