Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy

Negm, Amro; Jabro, Jalal D.; Provenzano, Giuseppe

doi:10.1016/j.agrformet.2017.05.022

Cited by 29 publications

(15 citation statements)

References 21 publications

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“…Additionally, a local bias correction of NASA POWER Tmax, Tmin and Rs improved the mean RMSE values from 1.91 • C, 2.01 • C and 2.10 MJ m −2 day −1 to 1.59 • C, 1.58 • C and 1.89 MJ m −2 d −1 , respectively. Similar results for those variables were found by Bai et al [24] in China, by Monteiro et al [26] in Brazil and by Negm et al [25] in Italy. White el al.…”

Section: Discussionsupporting

confidence: 88%

“…However, for coastal stations the estimation still needs improvement. Similar conclusions were drawn by Negm et al [25], Monteiro et al [26] and Aboelkhair et al [1]. Nonetheless, one can conclude that NASA POWER can simulate RH with good accuracy, when compared with observed data.…”

Section: Discussionsupporting

confidence: 86%

“…Results tend to improve with a bias correction; however, the lowest NRMSE is of 19.6%, thus one can conclude that wind speed lacks the performance showed by the remaining weather variables. Previous studies [25,26,32] also found that NASA POWER fails to estimate wind speed with acceptable accuracy. Despite the improvements if a bias correction is performed, the NASA POWER wind speed reanalysis data show unsatisfactory correlation and does not agree with most of the ground observations and still needs improvement.…”

Section: Discussionmentioning

confidence: 89%

“…They concluded that there is a close relation between NASA POWER and the observed data with a RMSE of 4.0 • C, 3.2 • C and 3.4 MJ m -2 d -1 for maximum and minimum temperatures, and solar radiation, respectively. Negm et al [25] assessed the suitability of NASA POWER to estimate reference evapotranspiration through daily maximum, minimum and average air temperatures, relative humidity, global solar radiation and wind speed, in Sicily, Italy. Results showed a RMSE for those variables, respectively of 3.6 • C, 5.0 • C, 3.2 • C, 12.2%, 2.7 MJ m -2 d -1 and 2.4 m s −1 , allowing them to conclude that NASA POWER had agreement with the corresponding measured values on ground weather stations.…”

Section: Introductionmentioning

confidence: 99%

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

Rodrigues

Braga

2021

Agronomy

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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.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

Rodrigues

Braga

2021

Agronomy

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“…During the last decades, a number of models have been developed and applied to simulate mass exchange processes in the soil-plant-atmosphere (SPA) continuum [11][12][13]. Several models have been proposed to simulate all the components of the water balance, including actual crop evapotranspiration [14], in particular under arid and semi-arid conditions [15,16], deep percolation [17], and water and solute transport [18,19].…”

Section: Of 16mentioning

confidence: 99%

Assessing the Performance of Thermal Inertia and Hydrus Models to Estimate Surface Soil Water Content

et al. 2017

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Abstract:The knowledge of soil water content (SWC) dynamics in the upper soil layer is important for several hydrological processes. Due to the difficulty of assessing the spatial and temporal SWC dynamics in the field, some model-based approaches have been proposed during the last decade. The main objective of this work was to assess the performance of two approaches to estimate SWC in the upper soil layer under field conditions: the physically-based thermal inertia and the Hydrus model. Their validity was firstly assessed under controlled laboratory conditions. Thermal inertia was firstly validated in laboratory conditions using the transient line heat source (TLHS) method. Then, it was applied in situ to analyze the dynamics of soil thermal properties under two extreme conditions of soil-water status (well-watered and air-dry), using proximity remote-sensed data. The model performance was assessed using sensor-based measurements of soil water content acquired through frequency (FDR) and time domain reflectometry (TDR). During the laboratory experiment, the Root Mean Square Error (RMSE) was 0.02 m 3 m −3 for the Hydrus model and 0.05 m 3 m −3 for the TLHS model approach. On the other hand, during the in situ experiment, the temporal variability of SWCs simulated by the Hydrus model and the corresponding values measured by the TDR method evidenced good agreement (RMSE ranging between 0.01 and 0.005 m 3 m −3 ). Similarly, the average of the SWCs derived from the thermal diffusion model was fairly close to those estimated by Hydrus (spatially averaged RMSE ranging between 0.03 and 0.02 m 3 m −3 ).

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Assessing Daily ERA5-Land Reanalysis Data to Estimate Actual Evapotranspiration of Olive Orchards in Sicily

Ippolito

Provenzano

2023

Lecture Notes in Civil Engineering

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Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy

Cited by 29 publications

References 21 publications

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

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

Assessing the Performance of Thermal Inertia and Hydrus Models to Estimate Surface Soil Water Content

Assessing Daily ERA5-Land Reanalysis Data to Estimate Actual Evapotranspiration of Olive Orchards in Sicily

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