Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration

Abstract

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

“…On a larger scale, Zhong et al (2018) analysed observations of winter maximum snow depth in the former Soviet Union, Mongolia and China, finding an average positive trend of 0.6 cm per decade from 1966 through 2012. Increases in snow depth dominated especially north of 50 • N, extending to milder regions than one would expect based on GCM projections for the future (Räisänen, 2008). Whether such differences reflect a problem in the models or have resulted from multidecadal internal variability in the atmospheric circulation (Deser et al, 2012;Mankin and Diffenbaugh, 2015) is still an open question.…”

“…Following Räisänen (2008), the monthly snowfall is written as F P , where P is the monthly precipitation and F is the fraction of precipitation that falls as snow. SWE then becomes…”

A Comparison between One-Step and Two-Step Nesting Strategy in the Dynamical Downscaling of Regional Climate Model COSMO-CLM at 2.2 km Driven by ERA5 Reanalysis

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Metrology for Agriculture and Forestry 2019

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