Abstract
Ground-truthing results are presented for a new 1-km air temperature product downscaled for New York City (NYC) from ∼12 km North American Land Data Assimilation System (NLDAS) air temperature data using 1 km moderate resolution imaging spectroradiometer surface temperature data. The downscaled product was compared against a unique highly spatially resolved ground-level ambient air temperature dataset collected through the New York City Community Air Survey (NYCCAS), a neighborhood level air pollution and temperature monitoring network, for the years 2009 and 2010. This work focuses on the spatial variation in daily minimum temperatures within the five counties that comprise NYC (∼784 km2). Overall, the downscaled daily minimum temperature was well correlated with ground station data, with NYCCAS minimum temperatures being slightly higher. Minimum temperature R2 values were 0.9 and 0.92, and mean absolute errors were 0.69°C and 0.86°C for years 2009 and 2010, respectively. The smallest differences between NYCCAS and the downscaled data were seen at lower temperatures, in less densely urbanized areas, and in areas with higher vegetative cover, suggesting systematic bias in the downscaled data related to land-use. The 1-km dataset discerned neighborhood level temperature differences in high-density urban situations with heterogeneous land cover.
Highlights
As the frequency of extreme heat days has risen with the increase of anthropogenic global warming in recent years[1,2] so has the availability of weather data with higher spatial and temporal resolutions
The final nationwide dataset will be disseminated via the Centers for Disease Control and Prevention (CDC) Environmental Public Health Tracking Network to be used as an alternative to ground-based or 12-km North American Land Data Assimilation System (NLDAS) surface temperature data in vulnerability mapping or potentially for the calibration or validation of other models
Areas within the NLDAS grid cell for which the moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) composite is warmer than the NLDAS grid cell mean will result in a maximum or minimum air temperature that is warmer than the NLDAS air temperature recorded for the respective day
Summary
As the frequency of extreme heat days has risen with the increase of anthropogenic global warming in recent years[1,2] so has the availability of weather data with higher spatial and temporal resolutions. Prior ground-truthing has not been done for a downscaled NLDAS dataset or for air temperature in a highly variable urban landscape such as NYC. The accuracy of the 1-km downscaled data in highly variable landscapes, such as cities, will be evaluated and improved through the ground-truthing process in NYC. The final nationwide dataset will be disseminated via the Centers for Disease Control and Prevention (CDC) Environmental Public Health Tracking Network to be used as an alternative to ground-based or 12-km NLDAS surface temperature data in vulnerability mapping or potentially for the calibration or validation of other models
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