Assessment of NASA POWER reanalysis products as data resources alternative for weather monitoring in West Sumbawa, Indonesia

Environmental Department, PT Amman Mineral Nusa Tenggara (AMMAN), West Sumbawa, Indonesia

^* Corresponding author: luky.darman@amman.co.id

Abstract

The climatic reanalysis datasets represent a crucial form of data that can help to address the shortage of data observations. This study evaluates the accuracy of NASA POWER reanalysis data by comparing it with AMMAN Environmental Department in-situ observations from four weather stations in West Sumbawa, collected from 2013 to 2022. The reanalysis data includes surface daily average temperatures, average wind speed, wind direction, relative humidity, and rainfall. The statistical analysis used in this research includes Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to measure the differences between the observed and predicted values, both of which expressed in the same unit as the variable being measured. The comparison of 10 years of historical data revealed that the temperature parameter had the lowest RMSE and MAE values of 0.003 and 0.18, respectively, while the highest values were 0.46 and 5.11. For humidity, the lowest RMSE and MAE values were 0.001 and 3.6, respectively, and the highest values were 1.3 and 14.2. The RMSE and MAE values for rainfall were 0.02 and 0.9 (lowest) and 2.2 and 5.7 (highest). Regarding wind speed, the lowest RMSE and MAE values were 0.001 and 0.27, while the highest values were 0.07 and 1. Finally, the lowest RMSE and MAE values for wind direction were 0.1 and 6.6, and the highest values were 15.7 and 67.6. The comparison between the predicted and observed values showed a relatively high level of similarity for temperature, rainfall and wind speed parameters. However, parameters such as humidity and wind direction resulted varying degrees of deviation between the weather station data and the NASA POWER reanalysis data. These results highlight both the accuracy and discrepancy in the reanalysis data, emphasizing the importance of considering the limitations of such datasets when addressing the shortage of observational data.