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Highlights • ERA-Iterim reanalysis weather variables were used for computing daily PM-ET o. • The accuracy of weather variables estimation was assessed and temperature was corrected for elevation. • Various bias correction approaches were tested for ET o REAN using cross-validation. • Bias correction used data aggregated quarterly to consider seasonality of climate. • Additive bias correction relative to the nearest grid point was selected because accurate and simple. Abstract This study aims at assessing the accuracy of estimating daily grass reference evapotranspiration (PM-ET o ) computed with ERA-Interim reanalysis products, as well as to assess the quality of reanalysis products as predictors of daily maximum and minimum temperature, net radiation, dew point temperature and wind speed, which are used to compute PM-ET o. With this propose, ET o computed from local observations of weather variables in 24 weather stations distributed across Continental Portugal were compared with reanalysis-based values of ET o (ET o REAN ). Three different versions of these reanalysis-based ET o were computed: (i) an (uncorrected) ET o based on the individual weather variables for the nearest grid point to the weather station; (ii) the previously calculated ET o corrected for bias with a simple bias-correction rule based only on the nearest grid point; and (iii) the ET o corrected for bias with a more complex rule involving all grid points in a 100 km radius of the weather station. Both bias correction approaches were tested aggregating data on a monthly, quarterly and a single overall basis. Cross-validation was used to allow evaluating the uncertainties that are modelled independently of any forcing; with this purpose, data sets were divided into two groups. Results show that ET o REAN without bias correction is strongly correlated with PM-ET o (R2>0.80) but tends to over-estimate PM-ET o , with the slope of the regression forced to the origin b 0 ≥ 1.05, a mean RMSE of 0.79 mm day−1, and with EF generally above 0.70. Cross-validation results showed that using both bias correction methods improved the accuracy of estimations, in particular when a monthly aggregation was used. In addition, results showed that using the multiple regression correction method outperforms the additive bias correction leading to lower RMSE, with mean RMSE of 0.57 and 0.64 mm day−1 respectively. The selection of the bias correction approach to be adopted should balance the ease of use, the quality of results and the ability to capture the intra-annual seasonality of ET o. Thus, for irrigation scheduling operational purposes, we propose the use of the additive bias correction with a quarterly aggregation. [ABSTRACT FROM AUTHOR]

ABSTRACT Computing crop reference evapotranspiration ( ETo) with the FAO Penman-Monteith method ( PM-ETo) requires maximum and minimum air temperature, shortwave radiation, relative air humidity and wind speed. These data are often not available, thus requiring alternative computation procedures. Although some proposed approximations may provide ETo values with small estimation errors, the physics of the ET processes may then not be well described. The use of reanalysis data, which is common in climate studies, represents an alternative to observation data for the weather variables referred above, when these are not available. This study focuses on the use of the National Center for Environmental Prediction/National Center for Atmospheric Research ( NCEP/ NCAR) blended reanalysis products with gridded data sets for the computation of PM-ETo in the Iberian Peninsula. A monthly time step was adopted. The PM-ETo time series computed with the blended reanalysis data sets were compared with those obtained using observations for 130 weather stations in the Iberian Peninsula. Results show that the PM-ETo computed with blended reanalysis compares well with the series computed from observation data (average root mean square error, RMSE = 0.49 mm day−1). The weather variables derived from reanalysis were also compared with observation data. Results supported the quality of ETo computations because, overall, there was a good match between solar radiation (average RMSE = 1.76 MJm−2 day−1) and maximum temperature (average RMSE = 1.48 °C) derived from reanalysis and in situ observations. By contrast, the wind speed from reanalysis highly overestimated observations and this is likely a reason for the slight overestimation of ETo computed from reanalysis (percentage bias, PBIAS>20% in 89% of cases). In addition, the reanalysis products are apparently influenced by modelled warming, which contributes to overestimation of the minimum temperature and, to a lesser extent, of the relative humidity. The spatial pattern of accuracy indicators reveals that poorer results correspond to the southern and south-eastern coastal areas of Iberia, where climate is semi-arid. The compatibility of the PM-ETo computed with monthly inputs and of the daily ETo cumulated to the month using the PM-ETo equation was confirmed, thus allowing to extend conclusions of this study to daily computations. Alternative reanalysis products were also assessed. Tests for ERA-Interim reanalysis products revealed overestimation of ETo and those for NCEP/ NCAR Reanalysis II have shown large underestimation. Results suggest that the blended reanalysis products are suitable for the estimation of ETo in Iberia since they integrate an appropriate correction of radiation and temperature, which proved essential for the good estimation results obtained. [ABSTRACT FROM AUTHOR]

This study evaluates the accuracy of daily reference evapotranspiration (ET o), computed according to the FAO Penman-Monteith equation by using a set of input weather variables obtained by blending ERA5-Land (ERA5L) reanalysis data with surface incoming solar radiation (R s) provided by the instruments on board the Meteosat geostationary satellites, operationally delivered by the Satellite Applications Facility on Climate Monitoring (CM-SAF). Performance assessment was carried out in Sicily (southern Italy) by using data from 38 automatic ground weather stations (AWSs) for years 2003–2020. ERA5L and CM-SAF data were first downscaled and bias-corrected with a calibration dataset; ERA5L air temperature data were also downscaled by lapse-rate correction. ET o estimates obtained with the blended ERA5L and CM-SAF validation dataset (ERA5L+CM-SAF) were compared with two other ET o estimates respectively obtained by using ERA5L and interpolated ground weather data (IGD). Performance indicators of the IGD dataset were evaluated by recursively applying universal kriging or ordinary kriging to the observed weather data, according to a cross-validation procedure. R s provided by CM-SAF outperformed R s obtained by ground interpolation, thus confirming the convenience of using bias-corrected CM-SAF data even when ground observations are available in the study area. ET o estimates with ERA5L+CM-SAF showed a normalized RMSE of 12%, outperforming ERA5L ET o estimates while performing comparably to ET o estimates obtained with the IGD dataset. The results suggested that the proposed blended dataset is a good proxy for interpolated ground weather observations in the assessment of ET o at regional scale when weather measurements cannot be easily gathered or in data-sparse regions. • Shortwave radiation by CM-SAF outperforms ERA5-Land shortwave radiation. • ET o is estimated by blending CM-SAF satellite-based radiation and ERA5-Land data. • ET o computed by the blended dataset is as accurate as ET o by interpolated ground data. • Blended datasets are good proxy for interpolated observations in data-sparse regions. • Shortwave radiation by CM-SAF outperforms spatially interpolated pyranometer data. [ABSTRACT FROM AUTHOR]

This study assesses the accuracy of estimating daily grass reference evapotranspiration (PM-ET o) using daily shortwave radiation (R s) and reference evapotranspiration (ET REF) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (R s ERA5) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study's use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ET o estimation. The first assessment focused on comparing LSA-SAF estimates of R s (R s LSA-SAF) against ground stations (R s ground). The results showed a good matching between the two R s data sets for continental Portugal but a tendency for R s LSA-SAF to under-estimate R s ground in the cloudy islands of Azores. ET o values computed using R s LSA-SAF data and observed temperature, humidity and wind speed (ET o LSA-SAF) were then compared with PM-ET o estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ET o were quality checked for surface aridity effects. It was observed that ET o LSA-SAF is strongly correlated with PM-ET o (R2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d−1, and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ET o LSA-SAF over-estimated PM-ET o. This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ET REF product (ET REF LSA-SAF) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ET o derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the R s LSA-SAF and R s ERA5 data, which showed a superiority of the LSA-SAF product for ET o estimations (ET o PMT LSA-SAF). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ET o PMT and ET REF LSA-SAF. Differently, results showed that using the R s LSA-SAF in the PMT approach (ET o PMT LSA-SAF) produces significantly better RMSE results than ET o PMT and ET REF LSA-SAF. Overall, the performed assessment allows concluding that the use of R s LSA-SAF, and to a lesser extent the use of the R s ERA5 , highly improves the accuracy of computation of ET o when R s observations are not available, including when only temperature data are accessible. The use of the ET REF LSA-SAF product is a good alternative when observed weather data are not available. • Satellite LSA-SAF solar radiation (R s) closely fits ground R s in continental sites leading to highly accurate PM-ET o estimates. • Humid, windy and cloudy climate of oceanic islands lead to pixel heterogeneities and over-estimation trends for R s and ET o • ERA5 reanalysis R s show a behavior similar to LSA-SAF product but with slightly lower accuracy. • Reference ET product of LSA-SAF over continental sites are accurate but highly uncertain for the oceanic islands. • LSA-SAF and ERA5 R s data led to improved accuracy when used to compute PM-ET o relative to computing with temperature only. [ABSTRACT FROM AUTHOR]