Evaluating storm surge predictability on subseasonal timescales for flood forecasting applications: A case study for Hurricane Isabel and Katrina

Abstract

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.