Forecasting Extreme Heat Events: Updates to the Subseasonal Excessive Heat Outlook System

Exposure to extreme heat is one of the most common causes of weather related fatalities in the United States. For instance, it had the highest 2006-2015 average number of cases, despite being commonly under-diagnosed. Emergency preparedness is an important tool for mitigating the impacts of extreme heat events on public health. Development of extended weather forecast systems focusing on these events is therefore potentially invaluable. The Subseasonal Excessive Heat Outlook System (SEHOS) was previously developed at the University of Maryland and the NOAA Climate Prediction Center (CPC) for use with the NOAA’s Global Ensemble Forecast System (GEFS). This system utilizes dynamical model forecasts of temperature and relative humidity in the day-8 through day-14 window to quantify the likelihood, duration and start-date of heat events. It was also used in real-time through the 2016 heat season, internally, at the CPC.

This presentation describes recent advancements in the SEHOS forecast system. Specifically, this will overview the extension of the system to include the European Centre for Medium-Range Weather Forecast (ECMWF) model. The bulk of the ECMWF evaluation was based on the 2016 version of the model (CY14R2) and its hindcast. Current real-time monitoring of the 2017 heat season, using the current model version (CY41R1), is also discussed. The ECMWF forecasts drive the SEHOS forecast system with sub-daily air temperatures and daily mean dew point temperatures from multiple ensemble members. The SEHOS forecast system then, in turn, forecasts the heat event statistics in the day-8 through day-14 window. Forecast calibrations were examined for sensitivity to region and calendar date, as well as to the reference dataset - which included both observational and reanalysis products. The ability and forecast skill of the ECMWF SEHOS forecasts were robustly examined including spatially, temporally, and by event type (e.g. temperature vs heat index). The disparities between the SEHOS system with the ECMWF and GEFS forecasts, and possible model combination methodologies will also be considered. Lastly, the advantages in using both models for the SEHOS forecast system will be discussed.