For the Heat Index, It’s about Both the Heat and the Humidity ... and about Choices in Calculation Methodologies

The diagnostic metric known as the “heat index” is a function of temperature and humidity that is often used as a quantitative measure associated with human heat exposure. More complete than merely considering temperature, but less complex than other heat exposure diagnostic quantities that require additional meteorological variables (e.g., wind speed, solar radiation), the heat index (and other metrics computed from temperature and humidity alone) aims to represent the combined effects of two weather factors that are measured at a wide range of locations and are routinely output by many numerical weather and climate models. Accordingly, heat index values are considered in forecasts issued by the National Weather Service (NWS), incorporated into heat warning systems used by governmental entities, and appear as diagnostic measures in many scientific publications, especially interdisciplinary works linking public health with weather and climate. Yet, there is more than one way to calculate or estimate heat index values, and it has been shown that there are cases for which the algorithm used or other procedural choices can lead to varying levels of agreement across methods (e.g., Anderson et al., 2013.)
Here we present results for the northeastern United States that compare estimates of daily maximum summertime heat index values computed following different procedures. We employ the widely-used NWS polynomial estimation technique and the relatively new extended heat index method (Lu and Romps, 2022.) Data sources we use that have hourly near surface air temperatures and humidity include select weather stations (mostly airport locations) and ERA-5 gridded reanalysis data. For those data sources, the daily maximum heat index (HImax) can be determined simply as the maximum of the 24 hourly heat index values. HImax can also be estimated from daily maximum temperature (tasmax) and minimum relative humidity (hursmin), the implicit assumption being that the hour when the maximum heat index occurs is when the diurnal cycle of temperature reaches its peak and relative humidity is at its minimum for the day. Estimating the heat index using daily tasmax and hursmin data increases the number of data sources that can be considered, including outputs of many CMIP6 global climate models (GCMs) for both the historical period and future projections. Because GCMs exhibit biases relative to observations, we use two different bias correction approaches to refine GCM heat index values and compare their results. In addition to illustrating some of the different heat index calculation approaches that may be used, we also summarize results regarding which of the different choices examined make a bigger difference than others.
References:
Anderson, G. B., M. L. Bell, and R. D. Peng, 2013: Methods to Calculate the Heat Index as an Exposure Metric in Environmental Health Research. Environmental Health Perspectives, 121, 1111–1119, https://doi.org/10.1289/ehp.1206273.
Lu, Y.-C., and D. M. Romps, 2022: Extending the Heat Index. Journal of Applied Meteorology and Climatology, 61, 1367–1383, https://doi.org/10.1175/JAMC-D-22-0021.1.