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

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

_{0}) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET

_{0}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 ET

_{0}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 ET

_{0}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 ET

_{0}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 ET

_{0}.

## 1. Introduction

_{0}), which is evapotranspiration from a well-watered hypothetical reference crop according to the FAO-56 Penman–Monteith equation [15].

_{0}. Even in highly developed countries, ground weather stations recording variables with consistent accuracy over time are generally available only in a few sites, as part of relatively sparse monitoring networks or very often far from rural areas [16]. In some countries, such as Italy, data availability is also constrained by the fact that the monitoring networks are managed by several regional and national services, which adopt different policies in data storage and distribution.

_{0}, the best solution is to first interpolate the input variable (e.g., the weather variables) and then, calculate the model output (e.g., ET

_{0}) [20]. This rule was verified by recent studies applied to the regional assessments of ET

_{0}[21,22,23,24].

_{0}with a reduced set of variables, such as temperature [25] or temperature and radiation [26]. However, these methods introduce empirical parameters that need to be locally calibrated, e.g., [27,28] and then possibly regionalized [29,30,31]. Moreover, these empirical equations are more exposed to errors in case of non-stationarities as those induced by climate change [32].

_{0}, e.g., [34].

_{0}estimates in different regions of the world. Boulard et al. [39] computed ET

_{0}by using ERA-Interim reanalysis, verifying their accuracy for water balance studies in northeastern France. Srivastava et al. [40] verified that ET

_{0}estimated with ERA-Interim, outperformed those obtained with NCEP/NCAR in England. These results were confirmed in similar studies conducted in the Iberian Peninsula [41]. As with all numerical weather products, reanalysis data must be bias-corrected prior to applying them to a specific region [42,43,44]. Paredes et al. [45] provided a comprehensive analysis of the impact of different bias correction methods applied to ERA-Interim reanalysis data for ET

_{0}estimates across Continental Portugal.

_{0}estimate performances based on MESCAN-SURFEX and ERA5-Land weather data and compared them with ET

_{0}estimate performances obtained by spatially interpolating weather variables with geostatistical procedures. A geostatistical cross-validation procedure was designed and applied for evaluating ET

_{0}estimate performances based on the spatial interpolation of point observations. The study was conducted for Campania Region (Southern Italy), in the irrigation seasons of years 2008–2018.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Figure 1 shows the location of the region, bounded between the coastline of the Tyrrhenian Sea and the mountains of the Southern Apennines. The relief map, also displayed in Figure 1, reveals the topographic complexity of the study area, characterized by two main plains and by an irregular orography in proximity of the sea and in the inner part of the region, that highly influences the spatial variability of the weather [51].

^{−1}to 4.64 m s

^{−1}with cv from 0.23 to 0.47; solar radiation, RS, ranged from 224 W m

^{−2}to 269 W m

^{−2}with cv from 0.26 to 0.35.

#### 2.2. Reanalysis Data

#### 2.3. Geostatistical Interpolation of Ground Sensor Data and Cross-Validation for Evaluating Performances

#### 2.4. Reference Evapotranspiration Model

_{0}, was computed by using daily weather data retrieved from ground sensors at AWS sites and, alternately, by using bias-corrected reanalysis data and spatially interpolated data from ground sensors by cross-validation.

_{0}(mm day

^{−1}) is computed with the FAO Penman–Monteith equation, as follows:

- $\lambda $ is the latent heat of vaporization;
- Δ is the slope of the vapor pressure curve (kPa °C
^{−1}); - γ is the psychometric constant (kPa °C
^{−1}); - T is the daily mean air temperature at 2 m height (°C) computed as the average between the daily maximum (T
_{max}) and minimum (T_{min}) air temperature at 2 m height at the highest available temporal resolution of data; - WS is the wind speed at 2 m height above ground (m s
^{−1}) computed from the wind speed at 10 m above the ground by multiplying for a factor of about 0.75 given by the logarithmic equation of the wind speed profile [15]; - VPD is the daily vapor pressure deficit (kPa), computed as function of T
_{max}and T_{min}and relative humidity, RH. If available, dew point temperature (T_{dew}) is used for computing VPD, instead of RH); - R
_{n}is the net radiation at the crop surface (MJ m^{−2}day^{−1}) and G is the soil heat flux density (MJ m^{−2}day^{−1}). The net radiation (R_{n}) was calculated as the difference between the incoming net shortwave radiation and the outgoing net long-wave radiation. The incoming net shortwave radiation is a fraction of the incoming shortwave solar radiation (RS), computed for the reference crop by setting the albedo equal to 0.23.

_{0}, computed with ground-based weather data at the 18 AWSs.

_{0}values over the region are 2.78 mm day

^{−1}in April, 3.51 mm day

^{−1}in May, 4.55 mm day

^{−1}in June, 4.94 mm day

^{−1}in July, 4.00 mm day

^{−1}in August and 2.80 mm day

^{−1}in September. The minimum mean value over a month of daily ET

_{0}is registered in September, 2.21 mm day

^{−1}and the maximum in July, 5.54 mm day

^{−1}. The mean coefficient of variation is minimum in July, 0.17 and maximum in April, 0.32.

#### 2.5. Statistical Indicators for Performance Analysis

_{0}was, respectively compared with ET

_{0}computed by using the bias-corrected reanalysis weather data and the spatially interpolated weather data from ground sensors as input.

_{0}on the examined j-th month computed over the analyzed years (values specified in Table 3), in order to consistently compare monthly performances.

## 3. Results

#### 3.1. Performance Analysis on Weather Data

_{0}computations: a common scenario to many regions of the world. Appendix B shows the sensitivity of kriging interpolator to the density of the network for what concerns air temperature data. For all other weather variables, the kriging estimates at each AWSs where conditioned to just the remaining 17 AWSs, for which the corresponding observations were available.

^{−1}to 2.19 m s

^{−1}, with mean RMSE equal to 1.16 m s

^{−1}and maximum RMSE equal to 2.78 m s

^{−1}. Solar radiation BIAS ranges from −30 W m

^{−2}to 20 W m

^{−2}, with mean RMSE equal to 30 W m

^{−2}and a maximum RMSE at 53 W m

^{−2}. VPD BIAS varies over the region from −0.19 kPa to 0.30 kPa, with a mean RMSE of 1.50 kPa and a maximum RMSE of 2.24 kPa. The worst performances in terms of RMSE are observed at AWSs n. 1, 9 and 13 for T, at AWSs n. 5, 10, 12 for WS, at AWSs n. 1, 10, 12 for RS and at AWSs n. 1, 6, 13 for VPD.

#### 3.2. Performance Analysis on Reference Evapotranspiration

_{0}averaged over the entire irrigation season of years 2008–2018. The following subsection will present monthly based results.

_{0}in the period 2008–2018 for the whole region. The spread of the box plots refers to the variability of the statistical indicators among the 18 AWS sites. ET

_{0}was computed by, respectively using weather data from kriging interpolation, UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). Figure 5 reveals that ET

_{0}computed with weather data estimated by kriging is generally more accurate and precise than reanalysis data. However, the differences in ET

_{0}estimate performances among datasets are negligible: the differences in RMSE between kriging estimator and E5L are on average less than 3% of the mean daily ET

_{0}and the differences in RMSE between kriging estimator and UMS are on average less than 8% of the mean daily ET

_{0}. The differences among databases are even smaller in terms of BIAS.

_{0}computed with kriging interpolated observations varies from −0.49 mm day

^{−1}to 0.59 mm day

^{−1}with a mean value of 0.04 mm day

^{−1}; the RMSE from 0.32 mm day

^{−1}to 0.86 mm day

^{−1}with a mean value of 0.56 mm day

^{−1}. The BIAS of the daily ET

_{0}computed with UMS weather data varies from −0.31 mm day

^{−1}to 0.51 mm day

^{−1}with a mean value of 0.08 mm day

^{−1}; the RMSE from 0.70 mm day

^{−1}to 1.23 mm day

^{−1}with a mean value of 0.87 mm day

^{−1}. The BIAS of the daily ET

_{0}computed with E5L weather data varies from −0.53 mm day

^{−1}to 0.55 mm day

^{−1}with a mean of 0.21 mm day

^{−1}; the RMSE ranges from 0.44 mm day

^{−1}to 1.04 mm day

^{−1}with a mean of 0.67 mm day

^{−1}.

#### Performance Analysis on Monthly Basis

_{0}estimate performances on a monthly basis. The statistical indicators of Equations (4–5) are averaged for each month and normalized by the corresponding monthly mean of the daily ET

_{0}observed at each AWSs (Table 3).

_{0}computed by using weather data from kriging interpolation of ground sensor data.

_{75}+ 1.5(p

_{75}−p

_{25}), where p

_{25}and p

_{75}are, respectively the 25th and 75th percentiles of the monthly values in the years 2008–2018.

_{0}monthly mean; nRMSE ranges between 8% and 21% of the corresponding observed ET

_{0}monthly mean, with an average nRMSE value of 14%.

_{0}estimate performances of each reanalysis dataset and the corresponding performances of the kriging estimator (given by Figure 6).

_{0}monthly mean and nRMSE ranges between 11% and 32% of the corresponding observed ET

_{0}monthly mean

_{,}with an average value of 22%. For E5L, nBIAS ranges between −12% and 8% of the corresponding observed ET

_{0}monthly mean and nRMSE ranges between 7% and 27% of the corresponding observed ET

_{0}monthly mean, with an average value of 17%.

_{0}estimates based on UMS and E5L occurs in July, which is also the month with highest ET

_{0}in the study region. UMS performances drop in April, May and slightly in September. When the aerodynamic term of Penman Monteith equation is prevalent over the radiation term, the uncertainty about VPD and wind speed exerts a major role in the estimation of ET

_{0}. Hence, the performance of reanalysis models in April and May is influenced by the larger BIAS and RMSE (Figure 4).

## 4. Discussion and Conclusions

_{0}) requires dataset of multiple weather variables, recorded by networks of automatic weather stations (AWSs). The spatial distribution of these AWSs is generally too coarse and irregular for assessing the spatial variability of weather variables, such as solar radiation and wind [76].

^{2}. The spatial density of this network is relatively high if compared with those examined by previous studies on this topic: Paredes et al. [45] analyzed data of 24 stations in Portugal (about 92,000 km

^{2}) while Martins et al. [41] collected data observed at 130 station across the entire Iberian Peninsula (597,000 km

^{2}). Indeed, the access to observed weather data can be even more difficult for areas extending across regional or national boundaries. In Italy, for instance, weather monitoring networks are managed by regional and national services, without a common data sharing policy and data distribution platform [77]. Pan European gridded agrometeorological maps released to support the EU Common Agricultural Policy, are derived by interpolating observed weather data collected in the different Member States, with uneven distributions, not consistent with what would be required based on the topographic complexity (e.g., Agri4Cast Resources Portal, Gridded Agro-Meteorological Data in Europe [78]).

_{0}. This result testifies that the choice of the gridded product should be not merely driven by its nominal spatial resolution. ERA5-Land benefits of the latest ERA5 global reanalysis, downscaled from 31 km to 9 m by applying the lapse rate correction to account for the influence of the altitude on the spatial structure of the weather variables. UMS is instead a downscaled product of UERRA-HARMONIE regional reanalysis at spatial resolution of 11 km, which boundary conditions were defined by the ERA-Interim global reanalysis, which production ceased in 2019, being substituted by the more advanced ERA5.

_{0}estimate performances based on the examined reanalysis data were satisfactory if compared with those obtained by spatially interpolating the observed data. Reanalysis-based ET

_{0}presented average nBIAS lower than 5%. Average nRMSE ranged from 17% with E5L to 22% with UMS. The values of these performance indicators were not far from those obtained by kriging interpolation: average nBIAS of 1% and average nRMSE of 14%.

_{0}estimate performances based on E5L and UMS were better than those presented by Martins et al. [41] for the Iberian Peninsula based on NCEP/NCAR blended reanalysis data, and those presented by Parades et al. [45] for Continental Portugal based on the ERA-Interim.

_{0}estimates were small: ET

_{0}predicted with E5L and the kriging estimator presented average differences in nRMSE smaller than 3%; while in the comparison between UMS and kriging estimator, the differences in nRMSE were smaller than 8%. The corresponding differences were even smaller and negligible in terms of nBIAS.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A. Kriging Interpolation

**x**, Z(

**x**), where

**x**is the vector containing a pair of coordinates, such as longitude and latitude. In this framework, an observed value of the variable of interest at a sampling location, z(

**x**), represents a realization of the random variable Z(

_{i}**x**). The set of random variables {Z(

_{i}**x**), Z(

_{1}**x**), …, Z(

_{2}**x**)} constitutes a random function for which only one realization, given by the set of observed values at the m sampling locations {z(

_{m}**x**), z(

_{1}**x**), …, z(

_{2}**x**)}, is known.

_{m}**x**using information available at the m sampling locations {

_{k}**x**,

_{1}**x**,...,

_{2}**x**}. This can be carried out by expressing Z(

_{m}**x**) as a linear combination of {Z(

_{k}**x**), Z(

_{1}**x**), …, Z(

_{2}**x**)} as follows:

_{m}_{i}, for i= 1, …m, are obtained by solving the kriging system that provide the optimal (minimum variance) unbiased linear estimator [73]. In particular, ordinary kriging assumes the hypothesis of constant unknown mean, μ, over the search neighborhood of

**x**. The ordinary kriging system can be written as follows:

_{k}**x**), the semi-variogram of the field unambiguously describes the spatial autocorrelation of the sampling locations and it can be estimated from data as follows:

## Appendix B. The Network of Temperature Sensors and Sensitivity of the Kriging Estimator to Network Density

**Figure A1.**Eighteen ground weather station sites (red triangles) and 59 ground temperature station sites (orange triangles): 77 stations in total.

**Figure A2.**Boxplots of (

**a**) BIAS and (

**b**) RMSE of temperature estimates at the 18 AWSs obtained by spatial interpolation via kriging of two different networks made up of 77 stations and 18 stations, respectively.

## References

- Magnan, A.K.; Ribera, T. Global adaptation after Paris Climate mitigation and adaptation cannot be uncoupled. Science
**2016**, 352, 1280–1282. [Google Scholar] [CrossRef] - Salerno, F. Adaptation Strategies for Water Resources: Criteria for Research. Water
**2017**, 9, 805. [Google Scholar] [CrossRef] [Green Version] - Thenkabail, P.S.; Biradar, C.M.; Turral, H.; Noojipady, P.; Li, Y.J.; Vithanage, J.; Dheeravath, V.; Velpuri, M.; Schull, M.; Cai, X.L.; et al. An Irrigated Area Map of the World (1999) Derived from Remote Sensing. Research Report # 105; International Water Management Institute: Battaramulla, Sri Lanka, 2006; p. 74. Available online: http://www.iwmigiam.org (accessed on 10 July 2014).
- Wriedt, G.; van der Velde, M.; Aloe, A.; Bouraoui, F. A European irrigation map for spatially distributed agricultural modelling. Agric. Water Manag.
**2009**, 96, 771–789. [Google Scholar] [CrossRef] - Siebert, S. Global Map of Irrigation Areas Version 4.0.1.; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2007; Available online: http://www.fao.org/nr/water/aquastat/quickWMS/irrimap.htm (accessed on 21 July 2012).
- Döll, P.; Siebert, S. Global modelling of irrigation water requirements. Water Resour. Res.
**2002**, 38, 8:1–8:10. [Google Scholar] [CrossRef] - Thoidou, E. Climate adaptation strategies: Cohesion policy 2014–2020 and prospects for Greek regions. Manag. Environ. Q.
**2017**, 28, 350–367. [Google Scholar] [CrossRef] - Consoli, S.; Vanella, D. Comparisons of satellite-based models for estimating evapotranspiration fluxes. J. Hydrol.
**2014**, 513, 475–489. [Google Scholar] [CrossRef] - Vuolo, F.; D´Urso, G.; De Michele, C.; Bianchi, B.; Cutting, M. Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia. Agric. Water Manag.
**2015**, 147, 82–89. [Google Scholar] [CrossRef] - Campos, I.; Balbontin, C.; González-Piqueras, J.; González-Dugo, M.P.; Neale, C.; Calera, A. Combining water balance model with evapotranspiration measurements to estimate total available water soil water in irrigated and rain-fed vineyards. Agric. Water Manag.
**2016**, 165, 141–152. [Google Scholar] [CrossRef] - Calera, A.; Campos, I.; Osann, A.; D’Urso, G.; Menenti, M. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors
**2017**, 17, 1104. [Google Scholar] [CrossRef] [Green Version] - Chirico, G.B.; Pelosi, A.; De Michele, C.; Falanga Bolognesi, S.; D’Urso, G. Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: An application in southern Italy. J. Agric. Sci.
**2018**, 156, 702–710. [Google Scholar] [CrossRef] - Longo-Minnolo, G.; Vanella, D.; Consoli, S.; Intrigliolo, D.S.; Ramírez-Cuestac, J.M. Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard. Agric. Water Manag.
**2020**, 231, 105967. [Google Scholar] [CrossRef] - Pelosi, A.; Villani, P.; Falanga Bolognesi, S.; Chirico, G.B.; D’Urso, G. Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts. Sensors
**2020**, 20, 1740. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements, Irrigation and Drain—FAO Irrigation and Drainage Paper No. 56; FAO: Rome, Italy, 1998. [Google Scholar]
- Battisti, R.; Bender, F.D.; Sentelhas, P.C. Assessment of different gridded weather data for soybean yield simulations in Brazil. Theor. Appl. Climatol.
**2018**, 135, 237–247. [Google Scholar] [CrossRef] - Senay, G.B.; Verdin, J.P.; Lietzow, R.; Melesse, A.M. Global daily reference evapotranspiration modeling and evaluation. J. Am. Water Resour. Assoc.
**2008**, 44, 969–979. [Google Scholar] [CrossRef] - Lewis, C.S.; Geli, H.M.E.; Neale, C.M.U. Comparison of the NLDAS weather forcing model to agrometeorological measurements in the western United States. J. Hydrol.
**2014**, 510, 385–392. [Google Scholar] [CrossRef] - Strong, C.; Khatri, K.B.; Kochanski, A.K.; Lewis, C.S.; Allen, L.N. Reference evapotranspiration from coarse-scale and dynamically downscaled data in complex terrain: Sensitivity to interpolation and resolution. J. Hydrol.
**2017**, 548, 406–418. [Google Scholar] [CrossRef] [Green Version] - Heuvelink, G.B.M.; Pebesma, E.J. Spatial aggregation and soil process modelling. Geoderma
**1999**, 89, 47–65. [Google Scholar] [CrossRef] - McVicar, T.R.; Van Niel, T.G.; Li, L.-T.; Hutchinson, M.F.; Mu, X.-M.; Liu, Z.-H. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. J. Hydrol.
**2007**, 338, 196–220. [Google Scholar] [CrossRef] - Raziei, T.; Pereira, L.S. Spatial variability analysis of reference evapotranspiration in Iran utilizing fine resolution gridded datasets. Agric. Water Manag.
**2013**, 126, 104–118. [Google Scholar] [CrossRef] [Green Version] - Raziei, T.; Pereira, L.S. Estimation of ET
_{0}with Hargreaves-Samani and FAO-PM temperature methods for a wide range of climates in Iran. Agric. Water Manag.**2013**, 121, 1–18. [Google Scholar] [CrossRef] - Tomas-Burguera, M.; Vicente-Serrano, S.M.; Grimalt, M.; Begueri, S. Accuracy of reference evapotranspiration (ET
_{0}) estimates under data scarcity scenarios in the Iberian Peninsula. Agric. Water Manag.**2017**, 182, 103–116. [Google Scholar] [CrossRef] [Green Version] - Hargreaves, G.H.; Samani, Z.A. Reference crop evapotranspiration from temperature. Trans. ASAE
**1985**, 1, 96–99. [Google Scholar] [CrossRef] - Priestley, C.H.B.; Taylor, R.J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev.
**1972**, 100, 81–92. [Google Scholar] [CrossRef] - Pereira, A.R. The Priestley–Taylor parameter and the decoupling factor for estimating reference evapotranspiration. Agric. For. Meteorol.
**2004**, 194, 50–63. [Google Scholar] [CrossRef] - Pelosi, A.; Chirico, G.B.; Falanga Bolognesi, S.; De Michele, C.; D’Urso, G. Forecasting crop evapotranspiration under standard conditions in precision farming. In Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2019—Proceedings, Portici, Italy, 24–26 October 2019; pp. 174–179. [Google Scholar]
- Mendicino, G.; Senatore, A. Regionalization of the Hargreaves Coefficient for the Assessment of Distributed Reference Evapotranspiration in Southern Italy. J. Irrig. Drain. Eng.
**2013**, 139, 349–362. [Google Scholar] [CrossRef] - Jung, C.G.; Lee, D.R.; Moon, J.W. Comparison of the Penman-Monteith method and regional calibration of the Hargreaves equation for actual evapotranspiration using SWAT-simulated results in the Seolma-cheon basin, South Korea. Hydrol. Sci. J.
**2016**, 61, 793–800. [Google Scholar] [CrossRef] - Senatore, A.; Parrello, C.; Almorox, J.; Mendicino, G. Exploring the Potential of Temperature-Based Methods for Regionalization of Daily Reference Evapotranspiration in Two Spanish Regions. J. Irrig. Drain. Eng.
**2020**, 146, 05020001. [Google Scholar] [CrossRef] - Ren, X.; Martins, D.S.; Qu, Z.; Paredes, P.; Pereira, L.S. Daily reference evapotranspiration for hyper-arid to moist sub-humid climates in Inner Mongolia, China: II. Trends of ET
_{0}and weather variables and related spatial patterns. Water Resour. Manag.**2016**, 30, 3793–3814. [Google Scholar] [CrossRef] - Bojanowski, J.S.; Vrieling, A.; Skidmore, A.K. A comparison of data sources for creating a long-term time series of daily gridded solar radiation for Europe. Sol. Energy
**2014**, 99, 152–171. [Google Scholar] [CrossRef] - Cammalleri, C.; Ciraolo, G. A simple method to directly retrieve reference evapotranspiration from geostationary satellite images. Int. J. Appl. Earth Obs. Geoinf.
**2013**, 21, 149–158. [Google Scholar] [CrossRef] - Sheffield, J.; Goteti, G.; Wood, E.F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim.
**2006**, 19, 3088–3111. [Google Scholar] [CrossRef] [Green Version] - Kanamitsu, M.; Ebisuzaki, W.; Woollen, J.; Yang, S.H.; Hnilo, J.J.; Fiorino, M.; Potter, G.L. NCEP—DOE AMIP-II reanalysis (R-2). Bull. Am. Meteor. Soc.
**2002**, 83, 1631–1643. [Google Scholar] [CrossRef] - Rienecker, M.M.; Suarez, M.J.; Gelaro, R.; Todling, R.; Bacmeister, J.; Liu, E.; Bosilovich, M.G.; Schubert, S.D.; Takacs, L.; Kim, G.K.; et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim.
**2011**, 24, 3624–3648. [Google Scholar] [CrossRef] - Dee, D.P.; Uppala, S.M.; Simmons, A.J.; Berrisford, P.; Poli, P.; Kobayashi, S.; Andrae, U.; Balmaseda, M.A.; Balsamo, G.; Bauer, D.P.; et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc.
**2011**, 137, 553–597. [Google Scholar] [CrossRef] - Boulard, D.; Castel, T.; Camberlin, P.; Sergent, A.S.; Bréda, N.; Badeau, V.; Rossi, A.; Pohl, B. Capability of a regional climate model to simulate climate variables requested for water balance computation: A case study over northeastern France. Clim. Dyn.
**2016**, 46, 2689–2716. [Google Scholar] [CrossRef] - Srivastava, P.K.; Han, D.; Rico-Ramirez, M.A.; Islam, T. Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmos. Sci. Lett.
**2013**, 28, 4419–4432. [Google Scholar] [CrossRef] - Martins, D.S.; Paredes, P.; Raziei, T.; Pires, C.; Cadima, J.; Pereira, L.S. Assessing reference evapotranspiration estimation from reanalysis weather products. An application to the Iberian Peninsula. Int. J. Climatol.
**2017**, 37, 2378–2397. [Google Scholar] [CrossRef] - Tian, D.; Martinez, C.J. Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts. J. Hydrol.
**2012**, 475, 350–364. [Google Scholar] [CrossRef] - Pelosi, A.; Medina, H.; Van den Bergh, J.; Vannitsem, S.; Chirico, G.B. Adaptive Kalman filtering for post-processing of ensemble numerical weather predictions. Mon. Weather Rev.
**2017**, 145, 4837–4854. [Google Scholar] [CrossRef] - Medina, H.; Tian, D.; Srivastava, P.; Pelosi, A.; Chirico, G.B. Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions. J. Hydrol.
**2018**, 562, 502–517. [Google Scholar] [CrossRef] - Paredes, P.; Martins, D.S.; Pereira, L.S.; Cadima, J.; Pires, C. Accuracy of daily estimation of grass reference evapotranspiration using era-interim reanalysis products with assessment of alternative bias correction schemes. Agric. Water Manag.
**2018**, 210, 340–353. [Google Scholar] [CrossRef] - Bauer, P.; Thorpe, A.; Brunet, G. The quiet revolution of numerical weather prediction. Nature
**2015**, 525, 47–55. [Google Scholar] [CrossRef] [PubMed] - Ridal, M.; Olsson, E.; Unden, P.; Zimmermann, K.; Ohlsson, A. Uncertainties in Ensembles of Regional Re-Analyses. Deliverable D2.7 HARMONIE Reanalysis Report of Results and Dataset 2017. Available online: http://www.uerra.eu/component/dpattachments/?task=attachment.download&id=296 (accessed on 18 July 2019).
- Bazile, E.; Abida, R.; Verelle, A.; Le Moigne, P.; Szczypta, C. MESCAN-SURFEX Surface Analysis. Deliverable D2.8 of the UERRA Project 2017. Available online: http://www.uerra.eu/publications/deliverable-reports.html (accessed on 18 July 2019).
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 Global Reanalysis. Q. J. R. Meteorol. Soc.
**2020**. submitted. [Google Scholar] - Copernicus Climate Change Service. ERA5-Land Hourly Data from 2001 to Present ECMWF. Available online: https://cds.climate.copernicus.eu/doi/10.24381/cds.e2161bac (accessed on 18 July 2019).
- Pelosi, A.; Furcolo, P. An amplification model for the regional estimation of extreme rainfall within orographic areas in Campania region (Italy). Water
**2015**, 7, 6877–6891. [Google Scholar] [CrossRef] [Green Version] - Pelosi, A.; Medina, H.; Villani, P.; D’Urso, G.; Chirico, G.B. Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system. Agric. Water Manag.
**2016**, 178, 106–118. [Google Scholar] [CrossRef] - Soci, C.; Bazile, E.; Besson, F.; Landelius, T. High-resolution precipitation reanalysis system for climatological purposes. Tellus A
**2016**, 68, 1–19. [Google Scholar] [CrossRef] [Green Version] - UERRA Regional Reanalysis for Europe on Single Levels from 1961 to 2019. Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-single-levels?tab=form (accessed on 18 July 2019).
- ERA5-Land Hourly Data from 1981 to Present. Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form (accessed on 12 January 2020).
- UERRA Product User Guide. Available online: http://datastore.copernicus-climate.eu/documents/uerra/D322_Lot1.4.1.2_User_guides_v3.3.pdf (accessed on 18 July 2019).
- Copernicus Regional Reanalysis for Europe (CERRA). Available online: https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra (accessed on 10 February 2020).
- ERA5-Land: Data Documentation. Available online: https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation (accessed on 12 January 2020).
- Hofstra, N.; Haylock, M.; New, M.; Jones, P.; Frei, C. Comparison of six methods for the interpolation of daily, European climate data. J. Geophys. Res. Atmos.
**2008**, 113, D21110. [Google Scholar] [CrossRef] [Green Version] - Luo, W.; Taylor, M.C.; Parker, S.R. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int. J. Clim.
**2008**, 28, 947–959. [Google Scholar] [CrossRef] - Berndt, C.; Haberlandt, U. Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density. J. Hydrol. Reg. Stud.
**2018**, 15, 184–202. [Google Scholar] [CrossRef] - Rehman, S.; Ghori, S.G. Spatial estimation of global solar radiation using geostatistics. Renew. Energy
**2000**, 21, 583–605. [Google Scholar] [CrossRef] - Alsamamra, H.; Ruiz-Arias, J.A.; Pozo-Vazquez, D.; Tovar-Pescador, J. A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agric. For. Meteorol.
**2009**, 149, 1343–1357. [Google Scholar] [CrossRef] - Journée, M.; Bertrand, C. Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements. Remote Sens. Environ.
**2010**, 114, 2692–2704. [Google Scholar] [CrossRef] - Prudhomme, C.; Reed, D. Mapping extreme rainfall in a mountainous region using geostatistical techniques: A case study in Scotland. Int. J. Climatol.
**1999**, 19, 1337–1356. [Google Scholar] [CrossRef] - Jarvis, C.H.; Stuart, N. A comparison among strategies of interpolating maximum and minimum daily air temperatures. Part I: The selection of “guiding” topographic and land cover variables. J. Appl. Meteorol.
**2001**, 40, 1060–1074. [Google Scholar] [CrossRef] - Hudson, G.; Wackernagel, H. Mapping temperature using kriging with external drift: Theory and example from Scotland. Int. J. Climatol.
**1994**, 14, 77–91. [Google Scholar] [CrossRef] - Stahl, K.; Moore, R.D.; Floyer, J.A.; Asplin, M.G.; McKendry, I.G. Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agric. For. Meteorol.
**2006**, 139, 224–236. [Google Scholar] [CrossRef] - Di Piazza, A.; Lo Conti, F.; Viola, F.; Eccel, E.; Noto, L.V. Comparative Analysis of Spatial Interpolation Methods in the Mediterranean Area: Application to Temperature in Sicily. Water
**2015**, 7, 1866–1888. [Google Scholar] [CrossRef] [Green Version] - Odeh, I.O.A.; McBratney, A.B.; Chittleborough, D.J. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma
**1995**, 67, 215–226. [Google Scholar] [CrossRef] - Holdaway, M.R. Spatial modelling and interpolation of monthly temperature using kriging. Clim. Res.
**1996**, 6, 215–225. [Google Scholar] [CrossRef] [Green Version] - Ahmed, S.; de Marsily, G. Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resour. Res.
**1987**, 23, 1717–1737. [Google Scholar] [CrossRef] - Journel, A.G.; Huijbregts, C.J. Mining Geostatistics; Academic Press: London, UK, 1978. [Google Scholar]
- Cressie, N. Statistics for Spatial Data; John Wiley and Sons: New York, NY, USA, 1993. [Google Scholar]
- Furcolo, P.; Pelosi, A.; Rossi, F. Statistical identification of orographic effects in the regional analysis of extreme rainfall. Hydrol. Process.
**2016**, 30, 1342–1353. [Google Scholar] [CrossRef] - Viggiano, M.; Busetto, L.; Cimini, D.; Di Paola, F.; Geraldi, E.; Ranghetti, L.; Ricciardelli, E.; Romano, F. A new spatial modeling and interpolation approach for high-resolution temperature maps combining reanalysis data and ground measurements. Agric. For. Meteorol.
**2019**, 276, 107590. [Google Scholar] [CrossRef] - Pelosi, A.; Furcolo, P.; Rossi, F.; Villani, P. The characterization of extraordinary extreme events (EEEs) for the assessment of design rainfall depths with high return periods. Hydrol. Process.
**2020**, 1–17. [Google Scholar] [CrossRef] - Agri4Cast Resources Portal, Gridded Agro-Meteorological Data in Europe. Available online: https://agri4cast.jrc.ec.europa.eu/DataPortal/ (accessed on 18 July 2019).
- Journel, A.G.; Alabert, F.G. New Method for Reservoir Mapping. J. Pet. Technol.
**1990**, 40, 7. [Google Scholar] [CrossRef] - Gomis-Cebolla, J.; Jimenez, J.C.; Sobrino, J.A.; Corbari, C.; Mancini, M. Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests. Int. J. Appl. Earth Obs. Geoinf.
**2019**, 80, 280–294. [Google Scholar] [CrossRef] - Perera, K.C.; Western, A.W.; Nawarathna, B.; George, B. Forecasting daily reference evapotranspiration for Australia using numerical weather prediction outputs. Agric. For. Meteorol.
**2014**, 125, 305–313. [Google Scholar] [CrossRef] - Ricard, S.; Anctil, F. Forcing the Penman-Montheith Formulation with Humidity, Radiation, and Wind Speed Taken from Reanalyses, for Hydrologic Modeling. Water
**2019**, 11, 1214. [Google Scholar] [CrossRef] [Green Version]

**Figure 2.**Ground weather station sites (red triangles) and reanalysis grid points (

**a**) UERRA MESCAN-SURFEX (UMS), blue circles; (

**b**) ERA5-Land (E5L), green and gray circles. The gray circles are grid points located on the sea, for which E5L does not release data, being the reanalysis focused for the grid points located on the land.

**Figure 4.**Differences between statistical indicators of reanalysis data (UMS and E5L) and kriging (Figure 3): (

**a**) absolute BIAS; (

**b**) RMSE.

**Figure 5.**(

**a**) BIAS and (

**b**) RMSE of ET

_{0}computed with weather data from kriging interpolation, UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). Spread of the box plots refers to the variability of the statistical indices among the 18 AWS sites. Circles indicate the mean values. Central lines in the box indicate the median, the box edges are drawn at the 25th (p

_{25}) and 75th (p

_{75}) percentiles. Whiskers extend to the most extreme data values, excluding the outliers. Values are considered as outliers when larger than p

_{75}+ 1.5(p

_{75}− p

_{25}) or smaller than p

_{25}− 1.5(p

_{75}− p

_{25}).

**Figure 6.**Statistical performance of the kriging estimator by cross-validation: (

**a**) nBIAS; (

**b**) nRMSE. The indicators are normalized by the observed monthly mean value of ET

_{0}at the automatic weather station (AWS) site of interest (Table 2) to allow comparison. Marker size is proportional to the value p

_{75}+ 1.5(p

_{75}−p

_{25}) where the 25th (p

_{25}) and 75th (p

_{75}) percentiles are related to the interannual variability of the statistics.

**Figure 7.**Differences between statistical indices of reanalysis data (UMS and E5L) and kriging (Figure 6): (

**a**) absolute nBIAS; (

**b**) nRMSE. Indices are normalized by the observed monthly mean value of ET

_{0}at the AWSs site of interest to allow comparison.

ID | Name | Elevation (m) | Latitude (°) | Longitude (°) |
---|---|---|---|---|

1 | Agerola METEO | 848 | 40°38′49″ N | 14°32′28″ E |

2 | Ariano Irpino METEO | 631 | 41°11′49″ N | 15°8′10″ E |

3 | Benevento METEO | 236 | 41°6′54″ N | 14°49′30″ E |

4 | Cellole METEO | 9 | 41°11′46″ N | 13°50′17″ E |

5 | Conza della Campania METEO | 770 | 40°51′43″ N | 15°16′55″ E |

6 | Lago Patria METEO | 1 | 4056′31″ N | 14°1′19″ E |

7 | Montella METEO | 515 | 40°50′17″ N | 15°2′20″ E |

8 | Montesano Marcellana METEO | 552 | 40°15′22″ N | 15°39′50″ E |

9 | Nisida METEO | 88 | 40°47′38″ N | 14°9′50″ E |

10 | Postiglione METEO | 660 | 40°33′43″ N | 15°14′13″ E |

11 | Rocca d’Evandro METEO | 62 | 41°25′30″ N | 13°52′48″ E |

12 | S.Bartolomeo METEO | 750 | 41°25′19″ N | 15°2′28″ E |

13 | San Marco Evangelista METEO | 31 | 41°1′12″ N | 14°20′38″ E |

14 | S.Salvatore Telesino METEO | 167 | 41°14′49″ N | 14°28′23″ E |

15 | Salerno METEO | 13 | 40°38′38″ N | 14°50′13″ E |

16 | Torre Orsaia METEO | 413 | 40°7′55″ N | 15°27′32″ E |

17 | Alife | 117 | 41°20′20″ N | 14°20′2″ E |

18 | Battipaglia | 64 | 40°36′40″ N | 14°58′34″ E |

Station | T (°C) | WS (m s^{−1}) | VPD (kPa) | RS (W m^{−2}) | ||||
---|---|---|---|---|---|---|---|---|

ID | Mean | cv | Mean | cv | Mean | cv | Mean | cv |

1 | 18.0 | 0.27 | 2.08 | 0.37 | 0.73 | 0.75 | 224 | 0.35 |

2 | 18.7 | 0.27 | 3.17 | 0.38 | 0.8 | 0.67 | 256 | 0.29 |

3 | 20.5 | 0.23 | 1.95 | 0.40 | 0.9 | 0.51 | 254 | 0.28 |

4 | 21.3 | 0.19 | 2.04 | 0.26 | 0.66 | 0.49 | 259 | 0.26 |

5 | 18.2 | 0.29 | 4.64 | 0.47 | 0.74 | 0.76 | 258 | 0.3 |

6 | 21.5 | 0.19 | 2.60 | 0.33 | 0.65 | 0.46 | 263 | 0.25 |

7 | 18.0 | 0.25 | 1.34 | 0.42 | 0.72 | 0.54 | 240 | 0.32 |

8 | 18.4 | 0.24 | 1.55 | 0.23 | 0.63 | 0.57 | 246 | 0.29 |

9 | 23.1 | 0.19 | 3.31 | 0.39 | 0.87 | 0.56 | 269 | 0.25 |

10 | 18.9 | 0.26 | 4.28 | 0.43 | 0.84 | 0.66 | 248 | 0.33 |

11 | 21.1 | 0.21 | 1.27 | 0.39 | 0.79 | 0.54 | 233 | 0.31 |

12 | 17.9 | 0.30 | 4.40 | 0.41 | 0.71 | 0.83 | 258 | 0.32 |

13 | 23.4 | 0.20 | 2.03 | 0.33 | 1.13 | 0.49 | 243 | 0.28 |

14 | 21.4 | 0.21 | 1.99 | 0.45 | 0.9 | 0.56 | 246 | 0.28 |

15 | 23.5 | 0.19 | 2.10 | 0.24 | 1.12 | 0.43 | 249 | 0.26 |

16 | 20.8 | 0.22 | 2.62 | 0.26 | 0.84 | 0.61 | 251 | 0.3 |

17 | 20.8 | 0.21 | 1.2 | 0.42 | 0.91 | 0.53 | 242 | 0.28 |

18 | 22.4 | 0.20 | 1.33 | 0.33 | 1.13 | 0.46 | 254 | 0.26 |

**Table 3.**Monthly statistics for Food and Agriculture Organization of the United Nations (FAO) Penman–Monteith reference evapotranspiration, ET

_{0}(mm day

^{−1}), averaged over years 2008–2018.

Station | April | May | June | July | August | September | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

ID | Mean | cv | Mean | cv | Mean | cv | Mean | cv | Mean | cv | Mean | cv |

1 | 2.56 | 0.42 | 3.08 | 0.34 | 4.04 | 0.23 | 4.29 | 0.20 | 3.31 | 0.30 | 2.22 | 0.28 |

2 | 2.70 | 0.33 | 3.43 | 0.29 | 4.62 | 0.23 | 5.16 | 0.20 | 4.08 | 0.25 | 2.72 | 0.28 |

3 | 2.75 | 0.28 | 3.59 | 0.24 | 4.66 | 0.18 | 5.04 | 0.16 | 4.05 | 0.22 | 2.77 | 0.22 |

4 | 2.70 | 0.25 | 3.57 | 0.20 | 4.43 | 0.17 | 4.71 | 0.12 | 3.90 | 0.19 | 2.86 | 0.19 |

5 | 2.68 | 0.45 | 3.34 | 0.36 | 4.64 | 0.25 | 5.31 | 0.23 | 4.23 | 0.28 | 2.85 | 0.40 |

6 | 2.75 | 0.26 | 3.69 | 0.19 | 4.66 | 0.16 | 4.85 | 0.13 | 3.98 | 0.19 | 2.97 | 0.20 |

7 | 2.47 | 0.32 | 3.09 | 0.29 | 4.12 | 0.20 | 4.38 | 0.17 | 3.36 | 0.23 | 2.30 | 0.29 |

8 | 2.59 | 0.32 | 3.18 | 0.28 | 4.12 | 0.19 | 4.46 | 0.16 | 3.60 | 0.21 | 2.41 | 0.24 |

9 | 3.18 | 0.28 | 4.08 | 0.22 | 5.16 | 0.17 | 5.54 | 0.14 | 4.67 | 0.22 | 3.58 | 0.21 |

10 | 2.91 | 0.40 | 3.47 | 0.36 | 4.74 | 0.25 | 5.33 | 0.21 | 4.38 | 0.27 | 3.13 | 0.30 |

11 | 2.65 | 0.29 | 3.33 | 0.26 | 4.29 | 0.20 | 4.59 | 0.16 | 3.63 | 0.24 | 2.21 | 0.28 |

12 | 2.52 | 0.43 | 3.20 | 0.36 | 4.51 | 0.27 | 5.29 | 0.25 | 4.20 | 0.31 | 2.68 | 0.40 |

13 | 3.12 | 0.28 | 3.90 | 0.24 | 4.93 | 0.20 | 5.15 | 0.14 | 4.18 | 0.23 | 3.04 | 0.22 |

14 | 2.94 | 0.32 | 3.59 | 0.27 | 4.56 | 0.22 | 4.90 | 0.17 | 4.07 | 0.25 | 2.85 | 0.23 |

15 | 3.05 | 0.25 | 4.00 | 0.20 | 4.99 | 0.15 | 5.29 | 0.11 | 4.30 | 0.20 | 3.13 | 0.18 |

16 | 2.99 | 0.36 | 3.55 | 0.30 | 4.52 | 0.22 | 5.05 | 0.16 | 4.33 | 0.23 | 3.16 | 0.27 |

17 | 2.63 | 0.29 | 3.30 | 0.26 | 4.26 | 0.21 | 4.56 | 0.17 | 3.72 | 0.22 | 2.62 | 0.21 |

18 | 2.87 | 0.26 | 3.71 | 0.21 | 4.69 | 0.15 | 4.96 | 0.12 | 4.03 | 0.20 | 2.92 | 0.18 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Pelosi, A.; Terribile, F.; D’Urso, G.; Chirico, G.B.
Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. *Water* **2020**, *12*, 1669.
https://doi.org/10.3390/w12061669

**AMA Style**

Pelosi A, Terribile F, D’Urso G, Chirico GB.
Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. *Water*. 2020; 12(6):1669.
https://doi.org/10.3390/w12061669

**Chicago/Turabian Style**

Pelosi, Anna, Fabio Terribile, Guido D’Urso, and Giovanni Battista Chirico.
2020. "Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration" *Water* 12, no. 6: 1669.
https://doi.org/10.3390/w12061669