Evaluation of NASA POWER Climatic Data against Ground-Based Observations in The Mediterranean and Continental Regions of Turkey

Öz

The weather reanalysis datasets are very advantageous data types worldwide that fill the gaps of missed measuring data and are alternatives that compensate for the scarcity of observed climate data. The main purpose of this study was to evaluate the effect of horizontal distance, altitude, and climatic regions compared to sea level on NASA POWER reanalysis data for daily temperature variables, relative humidity, and wind speed observed in meteorology stations in the Mediterranean and Continental regions of Turkey. For this purpose, three different meteorology stations (Antalya airport, Elmalı, Teffenni) from the Mediterranean region with different distances and elevations compared to sea level and one station (Ankara) far from the Mediterranean region with continental climate were selected. The statistical approach used to compare observed and estimated values in this study was determination coefficient (R2), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Mean Bias Error (MBE). The results showed a high relation between the POWER reanalysis dataset and observed data for all parameters except wind speed. For daily maximum, minimum and mean temperature, the R2 and NSE achieved higher than 0.91 and 0.88 respectively, while the mean bias error MBE ranged between -3 °C up to +2 °C and the RMSE was less than 4 °C in all stations. Additionally, POWER estimated data correlation accuracy for temperature variables increased toward higher altitudes in the study area. Similarly, this performance was followed by relative humidity, increasing relation accuracy toward higher elevated regions. The R2 was higher than 0.69 in higher altitudes and less than 0.4 in lower elevations. The MBE for relative humidity ranges -2% in Antalya to +9% in Ankara, and the RMSE attained less than 13.81% in all regions. The POWER daily wind speed did not show relation with observed data without adjusting for elevation and seasonal bias correction. Overall, it was concluded that the NASA POWER dataset could predict temperature and relative humidity over study area and give a promising result if used in research, water, and agricultural decision-making where observation data are not available.

Anahtar Kelimeler

• Decision-making
• Estimation
• Global atmospheric models
• Meteorological data
• Reanalysis data

Evaluation of NASA POWER Climatic Data against Ground-Based Observations in The Mediterranean and Continental Regions of Turkey

Öz

İklimsel parametreler için yeniden analiz veri kümeleri, dünya çapında ölçülemeyen ya da eksik verilere alternatifler olan çok avantajlı veri türleridir. Bu çalışmanın temel amacı, ülkemizin Akdeniz ve karasal iklime sahip bölgelerindeki meteoroloji istasyonlarında ölçülen ve NASA POWER yeniden analiz yöntemiyle tahmin edilen günlük sıcaklık, bağıl nem ve rüzgâr hızı parametrelerine ait verilerin deniz seviyesine göre yatay mesafe, yükseklik ve iklim bölgelerinin etkisine bağlı olarak değerlendirmektir. Bu amaçla, Akdeniz bölgesinden deniz seviyesine göre farklı yatay uzaklıkta ve kotlarda üç farklı meteoroloji istasyonu (Antalya havalimanı, Elmalı, Teffenni) ile Akdeniz bölgesine uzak karasal iklime sahip bir istasyon (Ankara) seçilmiştir. Bu çalışmada ölçülen ve tahmin edilen değerleri karşılaştırmak için, determinasyon katsayısı (R2), Nash-Sutcliffe Verimliliği (NSE), Ortalama Kareler Hatasının Karekökü (RMSE), Normalleştirilmiş Ortalama Kareler Hatasının Karekökü (NRMSE) ve Ortalama Yanlı Hatası (MBE) performans kriterleri kullanılmıştır. Sonuçlar, rüzgâr hızı dışındaki tüm parametreler için POWER yeniden analiz veri seti ile gözlemlenen veriler arasında yüksek bir ilişki göstermiştir. Günlük maksimum, minimum ve ortalama sıcaklık için, R2 ve NSE sırasıyla 0.91 ve 0.88'den daha yüksek bir değere ulaşırken, MBE -3 °C ile +2 °C arasında değişkenlik gösterdi. İstasyonların tamamında RMSE’nin 4 °C az olduğu belirlenmiştir. Ayrıca, sıcaklık değişkenleri için POWER tahmininin veri doğruluğu, yükselen irtifaya bağlı olarak artış göstermiştir. Ortalama bağıl nem için de benzer sonuçlar elde edilmiştir. R2, yüksek irtifalarda 0,69'dan fazla ve alçak irtifalarda 0,4'ten düşük olarak elde edilmiştir. Tüm bölgelerde RMSE değerinin %13.81'den daha az olduğu saptanmıştır. POWER günlük rüzgâr hızı, farklı yükseklik ve iklim tiplerinde gözlemlenen verilerle iyi bir ilişki göstermemiştir. Sonuç olarak, NASA POWER veri setinin çalışma alanı üzerindeki sıcaklık ve bağıl nemi tahmin edebileceği ve gözlem verilerinin bulunmadığı araştırma, su ve tarımsal karar verme süreçlerinde kullanılması durumunda umut verici sonuçlar verebileceği sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Karar verme
• Tahmin
• Küresel atmosferik modeller
• Meteorolojik veriler
• Yeniden analiz verileri

Kaynakça

1. Alramlawi, K., Fistikoglu, O. (2022). Estimation of Intensity-Duration-Frequency (IDF) Curves from Large Scale Atmospheric Dataset by Statistical Downscaling. Teknik Dergi, 33(1): 11591-11615.
2. Aboelkhair, H., Mostafa, M., El Afandi, G. (2019). Assessment of agro-climatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2m against ground observations over Egypt. Advances in Space Research, 64: 129–142.
3. Bicer, A. (2020). Temperature and Relative Humidity Models of the Malatya City. MTU Journal of Engineering and Natural Sciences, 1(1): 11-18.
4. Bai, J., Chen, X., Dobermann, A., Yang, H., Cassman, K., Zhang, F. (2010). Evaluation of NASA satellite- and model-derived weather data for simulation of maize yield potential in China. Agronomy Journal, 102: 9–16.
5. Bao, X., Zhang, F. (2013). Evaluation of NCEP–CFSR, NCEP–NCAR, ERA-interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan Plateau. Journal of Climate, 26: 206–214.
6. Chandler, W.S., Stackhouse, P.W. Jr., Hoell, J.M., Westberg, D., Zhang, T. (2013). NASA Prediction of Worldwide Energy Resource High-Resolution Meteorology Data for Sustainable Building Design. Conference of American Solar Energy Society, April 16-20. Baltimore, Maryland, U.S.A
7. Chen, G., Iwasaki, T., Qin, H., Sha, W., (2014). Evaluation of the warm-season diurnal variability over East Asia in recent reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA. Journal of Climate, 27 (14): 5517–5537.
8. Chen, S., Gan, T.Y., Tan, X., Shao, D., Zhu, J. (2019). Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China. Climate Dynamics, 53(10), 737–757.

9. Daly, C. (2006). Guidelines for assessing the suitability of spatial climate datasets. International Journal of Climatology, 26: 707–721.
10. Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.N., Vitart, F. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): 553–597.
11. Demircan, M., Gurkan, H., Eskioğlu, O., Arabaci, H., Coşkun, M. (2017). Climate change projections for Turkey: Three models and two scenarios. Turkish Journal of Water Science & Management. 1(1): 22-43.
12. Henseler, J., Ringle, C.M., Sinkovics, R.R. (2009). The Use of Partial Least Squares Path Modeling In International Marketing, Sinkovics, R.R. and Ghauri, P.N. (Ed.) New Challenges to International Marketing (Advances in International Marketing, Vol. 20), Emerald Group Publishing Limited, Bingley, pp. 277-319.
13. Irvem, A., Ozbuldu, M. (2019). Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey. Hindawi Advances in Meteorology, 2019: 1-11.
14. Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.K., Hnilo, J.J., Fiorino, M., Potter, G.L. (2002). Ncep–doe amip-ii reanalysis (r-2). Bulletin of the American Meteorological Society, 83(11): 1631–1644.
15. Kobayashi, S., Yukinari, O.T.A., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Miyaoka, K. (2015). The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan, 93(1): 5–48.
16. Konukcu, F., Deveci, H., Altürk, B. (2020). Modelling of the effect of climate change on wheat yield in thrace region with AquaCrop and WOFOST models. Journal of Tekirdag Agricultural Faculty, 17(1): 77-96.
17. Koster, R.D., Suárez, M.J., Ducharne, A., Stieglitz, M., Kumar, P. (2000). A catchment-based approach to modeling land surface processes in circulation model 1. Model structure. Journal of Geophysical Research Atmospheres, 105: 24809-24822.
18. Kuzay, M., Tuna M., Tombul, M. (2022). Determining the relationship of evapotranspiration with precipitation and temperature over Turkey. Journal of Agricultural Sciences, 28(3): 525-534.
19. Malanotte-Rizzoli, P., Bergamasco, A. (1989). The circulation of the eastern Mediterranean. Oceanologica Acta, 12(4): 335-351.
20. Marzouk, O.A. (2021). Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements. Heliyon, 7(3): 1-19.
21. MGM, 2022. Instruments and devices used in meteorology (Turkish). Turkish State Meteorological Service https://mgm.gov.tr/genel/meteorolojikaletler.aspx?s=9 Accessed: 13.02.2022.
22. Monteiro, A.L., Sentelhas, P.C., Pedra, G.U. (2018). Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugar cane yield simulation. International Journal of Climatology, 38: 1571–1581.
23. NASA POWER (2022). POWER Data Methodology https://power.larc.nasa.gov/docs/methodology/ Accessed: 13.02.2022
24. Negm, A., Jabro, J., Provenzano, G. (2017). 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. Agricultural and Forest Meteorology, 244–245: 111–121.
25. 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. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14): 3624–3648.
26. Rodrigues, G.C., Braga, R.P. (2021). Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy, 11 (6): 1-17.
27. Schneider, D.P., Deser, C., Fasullo, J., Trenberth, K.E. (2013). Climate data guide spurs discovery and understanding. EOS, Transactions, American Geophysical Union, 94 (13):121–122.
28. Sener, M., Yuksel, A.N., Konukcu, F. (2007). Evaluation of Hayrabolu Irrigation Scheme in Turkey Using Comparetive Performance Indicators. Journal of Tekirdag Agricultural Faculty, 4(1): 43-54.
29. Tan, E. (2019). Evaluation of NCEP/NCAR reanalysis precipitable water data comparing to radiosonde observations for Turkey. Cumhuriyet Science Journal, 40: 527-535.
30. Tuzcu Kokal, A., Musaoğlu, N. (2021). Monitoring chlorophyll-a and sea surface temperature with satellite data derived from multiple sensors. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XLIII(B3): 515-520.
31. White, J.W., Hoogenboom, G., Stackhouse P.W., Hoell, J.M. (2008). Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Journal of Agricultural and Forest Meteorology, 148: 1574–1584.
32. Willmott, C.J., Matsuura, K. (2006). On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. International Journal of Geographical Information Science, 20: 89-102.