Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Abstract

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

“…Actually, NRMSE and NMBE (Figure 1), when computing ETo from the PM method, led to worst results than when estimating reference evapotranspiration when using the temperature-based methods; in fact, PM proved to be inefficient (EF < 0.75, Figure 1) for more than half of the locations. These can be explained due to low accuracy in RH and u 2 estimation by NASA POWER, as discussed by Rodrigues and Braga [46]. Similar results were found by Negm et al [44] for Sicily, with RMSE varying from 0.68 to 1.27 mm d −1 and MBE varying between −0.39 and 0.73 mm d −1 .…”

“…Besides the raw NASA POWER data, two additional datasets were used in this study. Following the bias correction equations proposed by Rodrigues and Braga [46], two corrected datasets were obtained for each location: a regionally bias-corrected set; and a locally bias-corrected data series.…”

“…The application includes several methods to compute ETo depending on the available weather data. However, to estimate ETo, and based on the conclusions drawn by Rodrigues Braga [46], where a bias correction of NASA POWER weather datasets proved to significantly improve the accuracy of reanalysis products, only three different methods (Table 2) were selected to evaluate the impact of adopting reduced weather datasets. Those three different methods were selected based on the requirement of different levels of weather variables: PM where a full weather dataset is required; HS where only Tmax and Tmin need to be used; and MaxTET, where only Tmax is mandatory.…”

“…In order to improve its accuracy, reanalysis data may require corrections using observation-based datasets in order to amend for anomalies that arise from land sur-face modeling [41]. Recent studies aimed to evaluate the performance of NASA POWER data [32,[41][42][43][44][45][46][47]. Those studies showed that there is a significant agreement between NASA POWER reanalysis and observed data for most weather parameters (mostly air temperature and solar radiation).…”

“…Those studies showed that there is a significant agreement between NASA POWER reanalysis and observed data for most weather parameters (mostly air temperature and solar radiation). However, Rodrigues and Braga [46] concluded that a bias correction of NASA POWER reanalysis products tends to significantly improve the goodness of fit when compared with land-observed data.…”