Evaluation of Near-Surface Air Temperature From Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this paper, we evaluate the near-surface air temperature datasets from the ERA-Interim (ERAI), Japanese 55-Year Reanalysis, Modern-Era Retrospective Analysis for Research and Applications Version 2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r
²
> 0.95) and that more modern reanalysis, such as ERAI, had lower errors [root-mean-square difference (RMSD) ~0.9 °C than the older, lower resolution datasets, such as NCEP1 (RMSD ~2.4 °C). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated growing degree day (GDD) during the growing season to estimate the peak normalized difference vegetation index signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.