Affiliation:
1. East‐West Center Honolulu HI USA
2. Water Resource Research Center University of Hawai'i at Mānoa Honolulu HI USA
3. Institute for Astronomy University of Hawaiʻi at Mānoa Honolulu HI USA
4. Department of Civil and Environmental Engineering University of Hawai'i at Mānoa Honolulu HI USA
5. Department of Natural Resources and Environmental Management University of Hawai'i at Mānoa Honolulu HI USA
6. Department of Geography and Environment University of Hawai'i at Mānoa Honolulu HI USA
7. Information and Technology Services Honolulu HI USA
Abstract
AbstractGridded air temperature data are required in various fields such as ecological modeling, weather forecasting, and surface energy balance assessment. In this work, a piecewise multiple linear regression model is used to produce high‐resolution (250 m) daily maximum (Tmax), minimum (Tmin), and mean (Tmean) near‐surface air temperature maps for the State of Hawaiʻi for a 32‐year period (1990–2021). Multiple meteorological and geographical variables such as the elevation, daily rainfall, coastal distance index, leaf area index, albedo, topographic position index, and wind speed are independently tested to determine the most well‐suited predictor variables for optimal model performance. During the mapping process, input data scarcity is addressed first by gap‐filling critical stations at high elevation using a predetermined linear relationship with other strongly‐correlated stations, and second, by supplementing the training dataset with station data from neighboring islands. Despite the numerous covariates physically linked to temperature, the most parsimonious model selection uses elevation as its sole predictor, and the inclusion of the additional variables results in increased cross‐validation errors. The mean absolute error of resultant estimated Tmax and Tmin maps over the Hawaiian Islands from 1990 to 2021 is 1.7°C and 1.3°C, respectively. Corresponding bias values are 0.01°C and −0.13°C, respectively for the same variables. Overall, the results show the proposed methodology can robustly generate daily air temperature maps from point‐scale measurements over complex topography.
Funder
Office of Experimental Program to Stimulate Competitive Research
Publisher
American Geophysical Union (AGU)
Subject
General Earth and Planetary Sciences,Environmental Science (miscellaneous)