A paper entitled “Validation of North American land data assimilation system Phase 2 (NLDAS-2) air temperature forcing and downscaled data with New York State station observations” by Maury Estes (UAH/ST11), Tabassum Insaf and Adeyeye (New York State Health Dept.), Al-Hamdan (USRA/Univ. of Miss.) and Crosson (USRA) was published 12/7/21.
While some validation of 12 km North American Land Data Assimilation System (NLDAS-2) meteorological re-analysis data and various downscaled remotely sensed products has been done more validation is needed to encourage applied uses of these data. The purpose of this study was to evaluate the 12 km NLDAS-2 data and downscaled NLDAS-2 1 km remote sensing data, which can be used as substitutes for in situ station data, particularly in areas where station data are unavailable. We evaluated both the 12 km remote sensing meteorological re-analysis data and a 1 km downscaled product with data from 35 station observations throughout New York State. 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 terrain slope was related to lower correlations between stations and remotely sensed products.
Our results 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. The work was supported by the NASA Applied Sciences Health and Air Quality program.
Read the paper at: https://www.sciencedirect.com/science/article/pii/S2352938521002068.
Time series of climate stations with best (top of each panel) and worst (bottom of each panel) correlations per year for NLDAS 12 km data (left column of each panel) and downscaled remotely sensed 1 km data (right column of each panel) for minimum temperatures (°F) in 2006.