Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey

Hasan Karaman, Çağrı
Akyürek, Sevda Zuhal
Near-surface air temperature is a key variable used in a wide range of applications showing the environmental conditions of the Earth. Standard meteorological observations generally provide the best estimation with high accuracy over time for a small area of influence. However, considerable uncertainty arises when point measurements are extrapolated or interpolated over much larger areas. Satellite and reanalysis products have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. Thus, spatial patterns of air temperature can be derived from these products. In this study, we evaluate the performance of several reanalysis products of near-surface air temperature to determine the best product in estimating daily and monthly air temperatures across the complex terrain of Turkey. European Center for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), (AgERA5), (ERA5-Land), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and the Japanese 55-year Reanalysis (JRA-55) products are assessed with 1120 ground-based gauge stations for the period 2015–2019 over complex terrain having different climate classes according to Köppen-Geiger classification scheme and land surface types. Moreover, several traditional and more sophisticated machine learning downscaling algorithms are applied to increase the products’ spatial resolution. The agreement between ground observations and the different products and the downscaled temperature product is investigated by using a set of commonly employed statistical estimators of mean absolute error (MAE), correlation coefficient (CC), root-mean-square error (RMSE), and bias. Performance analysis of reanalysis air temperature products with ground-based observations on daily and monthly time series has shown that among the five datasets, the AgERA5 product is superior in estimating air temperature over Turkey, both seasonally and annually. Compared to the other datasets, ERA5 and ERA5-Land show similar performance and reach the second highest performance score at both daily and monthly time steps. In terms of improving product performance with spatial downscaling, among the distance-based methods, the best overall performance is obtained by bicubic interpolation with a slight increase in the product performance in monthly and daily time series. However, depending on the season, the Random Forest algorithm's performance is far superior to all other methods used in this study.
Advances in Space Research


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Citation Formats
Ç. Hasan Karaman and S. Z. Akyürek, “Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey,” Advances in Space Research, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149905254&origin=inward.