Coastal flooding operational forecasting in the US is limited to short-range temporal scales (3–7 days), which limits the response time for emergency preparation and planning. The sub-seasonal prediction project (SubX), which produces weather forecasts with a lead time of up to four weeks, provides an opportunity to assess the potential for creating probabilistic flood forecasts with longer lead times. Using the ADCIRC hydrodynamic model for coastal storm surge, two major hurricanes, Isabel (2003) and Katrina (2005), were used as case studies to test coastal flood predictions induced by wind and pressure fields generated from five global weather models within SubX. The storm surges simulations are forced by Sea Level Pressure (SLP) and 10 m winds fields from SubX models for a lead-time of up to 30 days before storm landfall. The subseasonal surge forecasts are evaluated temporally and spatially at 1–4 weeks lead-time against the NOAA tide gages observations and a verification dataset derived by forcing the storm surge model with wind and pressure fields from the NCEP-Reanalysis. The results are evaluated in terms of lead-time and forecast skill metrics. The storm surge forecast skill is measured using the mean square error skill score (MSESS) relative to the verification dataset and an approximate of the climatology. A skill score greater than 0.55 is considered here useful for flood forecasting. The multi-model ensemble (MME) mean surge forecasts driven by several members of SubX models demonstrate skill greater than 0.55 up to a 4-day and 10-day lead for Katrina and Isabel, respectively. A sharper decrease in MSESS was noted from week 1 to week 3 lead-times for Katrina, in comparison to Isabel. Some ensemble members forecasted hurricanes and storm surges as early as 3–4 weeks lead-time. However, due to the offsets developed in the timing and magnitude of the peak at these lead-times, and based on a sample size of only two events, it is hard to establish the significance of these longer lead-time results. While a follow up study involving flood reforecasts over the entire SubX reforecast period (1999–2015) is needed to support more robust statistics of the forecast skill, our case studies demonstrate the feasibility of probabilistic flood forecasting at subseasonal timescales using the SubX models.

美国的沿海洪水业务预报仅限于短期时间尺度（3-7 天），这限制了应急准备和规划的响应时间。次季节预测项目 (SubX) 可生成提前期长达 4 周的天气预报，为评估创建提前期更长的概率性洪水预报的潜力提供了机会。使用沿海风暴潮的 ADCIRC 水动力模型，两个主要飓风 Isabel (2003) 和 Katrina (2005) 被用作案例研究，以测试由 SubX 内五个全球天气模型产生的风和压力场引起的沿海洪水预测。风暴潮模拟由来自 SubX 模型的海平面压力 (SLP) 和 10 m 风场强制进行，提前期最长可达风暴登陆前 30 天。在 1-4 周的提前期，根据 NOAA 潮汐计观测值和验证数据集对次季节潮汐预报进行时间和空间评估，该数据集是通过使用 NCEP-再分析中的风和压力场强制风暴潮模型得出的。结果根据提前期和预测技能指标进行评估。风暴潮预报技能是使用相对于验证数据集的均方误差技能分数 (MSESS) 和气候学的近似值来衡量的。此处认为大于 0.55 的技能分数可用于洪水预测。由 SubX 模型的几个成员驱动的多模型集合 (MME) 平均激增预测表明，Katrina 和 Isabel 分别领先 4 天和 10 天的技能超过 0.55。与 Isabel 相比，Katrina 的第 1 周到第 3 周的交货时间明显减少了 MSESS。一些整体成员早在 3-4 周的准备时间就预测了飓风和风暴潮。然而，由于在这些提前期峰值的时间和幅度产生了偏移，并且基于只有两个事件的样本量，因此很难确定这些较长提前期结果的显着性。虽然需要在整个 SubX 重新预测期间（1999-2015 年）进行涉及洪水重新预测的后续研究，以支持更可靠的预测技能统计数据，但我们的案例研究证明了使用 SubX 模型在次季节时间尺度上进行概率洪水预测的可行性。由于在这些提前期峰值的时间和幅度产生了偏移，并且基于只有两个事件的样本量，因此很难确定这些较长提前期结果的显着性。虽然需要在整个 SubX 重新预测期间（1999-2015 年）进行涉及洪水重新预测的后续研究，以支持更可靠的预测技能统计数据，但我们的案例研究证明了使用 SubX 模型在次季节时间尺度上进行概率洪水预测的可行性。由于在这些提前期峰值的时间和幅度产生了偏移，并且基于只有两个事件的样本量，因此很难确定这些较长提前期结果的显着性。虽然需要在整个 SubX 重新预测期间（1999-2015 年）进行涉及洪水重新预测的后续研究，以支持更可靠的预测技能统计数据，但我们的案例研究证明了使用 SubX 模型在次季节时间尺度上进行概率洪水预测的可行性。