Validation of North American Land Data Assimilation System Phase 2 (NLDAS-2) Air Temperature Forcing and Downscaled Data with New York State Station Observations

Climate and various meteorological factors including temperature and associated heat exposure metrics are related to environmental health risk factors and the incidence of health effects. While meteorological station data provide a good source of point measurements, temporal and spatially consistent temperature data are needed for health studies. Reanalysis data such as the North American Land Data Assimilation System’s (NLDAS) 12-km gridded product are an effort to resolve spatio-temporal environmental data issues. With funding from the NASA Applied Sciences Program, the NLDAS 12-km air temperature product has been downscaled to 1-km using MODIS Land Surface Temperature patterns. Previously, limited validation of the native 12-km NLDAS reanalysis data has been undertaken. Our objective is to evaluate the accuracy of both the 12-km and 1-km downscaled products using the US Historical Climatology Network station data geographically dispersed across New York State for 2006, 2009, and 2010. These years were selected by a climatological analysis over the past 10 years that found 2006 to be a reference year, 2009 cool year and 2010 warm year. Statistical methods including correlation, scatterplots, time series and summary statistics to determine the accuracy of the remotely-sensed maximum and minimum temperature products. The specific effects of elevation and percent slope on remotely-sensed temperature product accuracy were determined with 10-m digital elevation data. National Land Cover Land Use data was used to determine the effect of water mixed pixels on the accuracy of the NLDAS products. Results show the downscaled temperature product improves accuracy over the native 12-km temperature product with average correlation improvements from 0.82 to 0.87 for minimum and 0.76 to 0.85 for maximum temperatures. Higher percent slope was generally related to lower corrections between stations and remotely sensed products. Stations in pixels with higher percentages of open water tended to have higher correlations. However, the benefits vary temporally and geographically. Our results will inform health studies using remotely-sensed temperature products to determine health risk from excessive heat by providing a more robust assessment of the accuracy of the 12-km NLDAS product and additional accuracy gained from the 1-km downscaled product. Also, the results have been used by the National Weather Service to determine temperature thresholds heat warning systems and final data products evaluated for inclusion in the Centers of Disease Control and Prevention (CDC) Environmental Public Health Tracking Network as a resource for the health community.