ERA5 ve MERRA-2 Yeniden Analiz Veri Setlerinin Ege Bölgesi Genelinde Değerlendirilmesi

Yıl 2024, , 9 - 21, 23.01.2024

Gülşah Tulger Kara , Tolga Elbir

https://doi.org/10.21205/deufmd.2024267602

Cited By: 1

Öz

Yeniden analiz verileri, bir bölgedeki iklim verisini saatlik bazda tanımladıkları için atmosfer bilimlerinde en yaygın kullanılan veri setleri arasında yer almaktadır. Bu çalışmada beşinci nesil Avrupa Orta Menzilli Hava Tahmini Merkezi (ECMWF) küresel iklimin atmosferik yeniden analizi (ERA5) ve Araştırma ve Uygulamalar için Modern Çağ Retrospektif Analizi, sürüm 2 (MERRA2) olmak üzere iki yeniden analiz veri seti, 1963–2020 döneminde Türkiye'nin Ege Bölgesi'nde yerden 2 m yükseklikte hava sıcaklığı, ortalama deniz seviyesi basıncı ve rüzgar hızı parametreleri için değerlendirilmiştir. Saatlik yeniden analiz verileri bölgede bulunan 20 meteoroloji istasyonundan elde edilen gözlemlerle karşılaştırılmıştır. Veri kümelerinin performanslarını değerlendirmek için ortalama hataların karekökü (RMSE), korelasyon katsayısı (R) ve ortalama sapma hatası (MBE) gibi çeşitli istatistiksel parametreler kullanılmıştır. Sonuçlar, hava sıcaklığının ve ortalama deniz seviyesi basıncının, bölgedeki MERRA-2 yeniden analiz verileri ile daha iyi temsil edildiğini, buna karşın ERA5 yeniden analiz verileri ile rüzgar hızının daha başarılı temsil edildiğini göstermiştir. Ortalama deniz seviyesi basıncı için daha yüksek R değerine (>0,98) ve daha düşük RMSE değerine sahip olan MERRA-2, 11 istasyonda daha iyi performans göstermiştir. Hava sıcaklığı için genel olarak yüksek bir R değerine (>0,94) sahip olan MERRA-2 yeniden analiz veri seti, 12 istasyonda daha iyi performans göstermiştir. Bölgedeki rüzgar hızı için ERA5 veri setinin genel R değeri 0,58 olup ERA5 ile rüzgar hızı için 13 istasyonda daha başarılı bir performans elde edilmiştir. Çalışma sonucunda elde edilen bulgular, seçilen parametrelerde hangi veri setinin bölgeyi daha iyi temsil ettiğini göstermesi açısından bir kılavuz niteliği taşımaktadır.

Anahtar Kelimeler

Yeniden analiz, MERRA-2, ERA5, hava sıcaklığı, ortalama deniz seviyesi basıncı, rüzgar hızı

Evaluation of ERA5 and MERRA-2 Reanalysis Datasets over the Aegean Region, Türkiye

Öz

Reanalysis products are among the most-used datasets in the atmospheric sciences since they comprehensively describe the observed climate at sub-daily intervals in a region. Two reanalysis datasets, namely, the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis of global climate (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), were evaluated for the representation of air temperature at 2 m, mean sea level pressure and wind speed over the Aegean Region of Türkiye during the period 1963–2020. Hourly reanalysis data were compared with observations in 19 meteorological stations in the region. Several statistical parameters, such as root mean square error (RMSE), correlation coefficient (R), and mean bias error (MBE), were used to evaluate the performances of the datasets. The results indicated that air temperature and mean sea level pressure are generally better represented by the MERRA-2 reanalysis in the region, whereas the ERA5 reanalysis dataset better represents wind speed. MERRA-2 had lower RMSE and slightly better performance at 11 stations with high R (>0.98) for mean sea level pressure. The MERRA-2 reanalysis dataset had a high overall R (>0.94) for air temperature and performed better at 12 stations. The overall regional R-value for the ERA5 wind speed dataset was 0.58, and ERA5 showed better performance at 13 individual stations for wind speed. Our results guide which reanalysis dataset better represents the regional climate characteristics for selected parameters.

Anahtar Kelimeler

Reanalysis, MERRA-2, ERA5, air temperature, sea level pressure, wind speed

Kaynakça

• Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thepaut, J.N. 2020. The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146. 730, p. 1999-2049.
• Gelaro, R., McCarty, W., Suarez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A.M., Gu, W., Kim, G.K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M.,Zhao, B. 2017. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate, 30. 14, p. 5419-5454.
• Adame, J.A., Lope, L., Sorribas, M., Notario, A., Yela, M. 2020. SO2 measurements in a clean coastal environment of the southwestern Europe: Sources, transport and influence in the formation of secondary aerosols, Science of The Total Environment, 716. p. 137075.
• Adame, J.A., Notario, A., Cuevas, C.A., Lozano, A., Yela, M.,Saiz-Lopez, A. 2019. Recent increase in NO2 levels in the southeast of the Iberian Peninsula, Science of The Total Environment, 693. p. 133587.
• Dong, L.C., Li, S.W., Yang, J., Shi, W.X., Zhang, L. 2020. Investigating the performance of satellite-based models in estimating the surface PM2.5 over China, Chemosphere, 256.
• Nabavi, S.O., Haimberger, L., Abbasi, E. 2019. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms, Atmospheric Pollution Research, 10. 3, p. 889-903.
• Notario, A., Gutierrez-Alvarez, I., Adame, J.A. 2020. Atmospheric benzene measurements in the main metropolitan and industrial areas of Spain from 2014 to 2017, Atmospheric Research, 238, 104896.
• Chen, S., Gan, T.Y., Tan, X.Z., Shao, D.G., Zhu, J.Q. 2019. Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China, Climate Dynamics, 53. 1-2, p. 737-757.
• Li, X.X. 2020. Heat wave trends in Southeast Asia during 1979-2018: The impact of humidity, Science of the Total Environment, 721, 137664.
• Camargo, L.R., Valdes, J., Macia, Y.M., Dorner, W. 2019. Assessment of on-site steady electricity generation from hybrid renewable energy systems in Chile, Applied Energy, 250. p. 1548-1558.
• Olauson, J., Bergkvist, M. 2015. Modelling the Swedish wind power production using MERRA reanalysis data, Renewable Energy, 76. p. 717-725.
• Michaud-Dubuy, A., Carazzo, G., Tait, S., Le Hir, G., Fluteau, F.,Kaminski, E. 2019. Impact of wind direction variability on hazard assessment in Martinique (Lesser Antilles): The example of the 13.5 ka cal BP Bellefontaine Plinian eruption of Mount Pelee volcano, Journal of Volcanology and Geothermal Research, 381. p. 193-208.
• Karami, K. 2019. Inter-reanalysis differences of the temperature trends in the MERRA-2.0 and ERA-Interim: comparison of the middle and lower atmosphere, Theoretical and Applied Climatology, 137. 3-4, p. 2549-2558.
• Miao, H.Z.Y., Dong, D.H., Huang, G., Hu, K.M., Tian, Q.,Gong, Y.F. 2020. Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets, Energy, 200, 117382.
• Sharmar, V., Markina, M. 2020. Validation of global wind wave hindcasts using ERA5, MERRA2, ERA-Interim and CFSRv2 reanalyzes, Climate Change: Causes, Risks, Consequences, Problems of Adaptation and Management, 606, 012056.
• Olauson, J. 2018. ERA5: The new champion of wind power modelling?, Renewable Energy, 126. p. 322-331.
• Wang, C.X., Graham, R.M., Wang, K.G., Gerland, S., Granskog, M.A. 2019. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, Cryosphere, 13. 6, p. 1661-1679.
• Jiang, H., Yang, Y.P., Bai, Y.Q., Wang, H.Z. 2020. Evaluation of the Total, Direct, and Diffuse Solar Radiations From the ERA5 Reanalysis Data in China, Ieee Geoscience and Remote Sensing Letters, 17. 1, p. 47-51.
• Bao, X.H., Zhang, F.Q. 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. 1, p. 206-214.
• de Lima, J.A.G., Alcantara, C.R. 2019. Comparison between ERA Interim/ECMWF, CFSR, NCEP/NCAR reanalysis, and observational datasets over the eastern part of the Brazilian Northeast Region, Theoretical and Applied Climatology, 138. 3-4, p. 2021-2041.
• Kong, B., Liu, N., Lin, L.N., He, Y., Wang, Y.J., Pan, Z.D. 2019. Assessment of meteorological variables and heat fluxes from atmospheric reanalysis and objective analysis products over the Bering Sea, International Journal of Climatology, 39. 11, p. 4429-4450.
• Turkish State Meteorological Service 2020. 2019 Administration Activity Report, Strategy Development Department, Ankara, www.mgm.gov.tr.
• Tan, E. 2019. Evaluation of NCEP/NCAR Reanalysis Precipitable Water Data Comparing to Radiosonde Observations for Turkey, Cumhuriyet Science Journal, 40, 2, 527 - 535.
• Yanbolu, M., Akpınar, A., Çakmak, R.E., Bingölbali, B. 2018, Karadeniz Üzerinde ERA-20C, ERA-20CM ve CERA-20C İklim Modellerine Ait Rüzgar Hızı ve Dalga Tahmin Performanslarının Değerlendirmesi, 9. Kıyı Mühendisliği Sempozyumu, 01-03 Kasım, Adana.
• Yilmaz, M. 2022. Türkiye Üzerinde ERA5 Saatlik Hava Sıcaklığı Verilerinin Doğrulanması, Doğal Afetler ve Çevre Dergisi, 8. 2, p. 207-220.
• Elbir, T. 2004. A GIS based decision support system for estimation, visualization and analysis of air pollution for large Turkish cities, Atmospheric Environment, 38. p. 4509-4517.
• Elbir, T., Müezzinoğlu, A., Bayram, A. 2000. Evaluation of some air pollution indicators in Turkey, Environment international, 26. 1-2, p. 5-10.
• Kara, M., Mangir, N., Bayram, A., Elbir, T. 2014. A Spatially High Resolution and Activity Based Emissions Inventory for the Metropolitan Area of Istanbul, Turkey, Aerosol and Air Quality Research, 14. 1, p. 10-20.
• Tuygun, G.T., Altuğ, H., Elbir, T., Gaga, E.E. 2017. Modeling of air pollutant concentrations in an industrial region of Turkey, Environmental science and pollution research international, 24. 9, p. 8230-8241.
• Turkish State Meteorological Service 2021. Evaluation of Temperature and Precipitation for January 2021, Department of Climate and Agricultural Meteorology, Ankara, www.mgm.gov.tr.
• Yılmaz, E., Darende, V. 2021. Türkiye’de yağış ölçümü yapılan manuel-otomatik meteoroloji gözlem istasyonu verilerinin karşılaştırılması, Türk Coğrafya Dergisi, 77. p. 53 - 66.
• Carslaw, D. 2019. Worldmet: Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD), R package version 0.8.7.
• National Oceanic and Atmospheric Administration (NOAA) 2009. Integrated Surface Database https://www.ncdc.noaa.gov/isd.
• Copernicus Climate Change Service (C3S) 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate Copernicus Climate Change Service Climate Data Store, https://cds.climate.copernicus.eu/cdsapp#!/home.
• Alves, J.M.R., Miranda, P.M.A. 2013. Variability of Iberian upwelling implied by ERA-40 and ERA-Interim reanalyses, Tellus Series a-Dynamic Meteorology and Oceanography, 65, 1, 19245.
• Ramon, J., Lledo, L., Torralba, V., Soret, A., Doblas-Reyes, F.J. 2019. What global reanalysis best represents near-surface winds?, Quarterly Journal of the Royal Meteorological Society, 145. 724, p. 3236-3251.
• Bosilovich, M.G., Lucchesi, R., Suarez, M. 2016. MERRA-2 : File Specification, p. 73, http://gmao.gsfc.nasa.gov/pubs/office_notes.
• Molod, A., Takacs, L., Suárez, M.J., Bacmeister, J.T. 2014. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geoscientific Model Development, 8. p. 1339-1356.
• Molod, A., Takacs, L., Suárez, M.J., Bacmeister, J.T., Song, I.S., Eichmann, A.F. 2012. The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna, 20120011790, 28, NASA, April 30, 2012.
• Wickham, H., François, R., Henry, L., Müller, K. 2020. A Grammar of Data Manipulation [R package dplyr version 1.0.2].
• Fernandez-Lopez, J., Schliep, K. 2019. rWind: download, edit and include wind data in ecological and evolutionary analysis, Ecography, 42. 4, p. 804-810.
• Jones, R.W., Renfrew, I.A., Orr, A., Webber, B.G.M., Holland, D.M., Lazzara, M.A. 2016. Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica, Journal of Geophysical Research-Atmospheres, 121. 11, p. 6240-6257.
• Sheridan, L.M., Krishnamurthy, R., Gorton, A.M., Shaw, W.J., Newsom, R.K. 2020. Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations, 54, 6.
• Chopde, N.R., Nichat, M.K. 2013. Landmark Based Shortest Path Detection byUsing A* and Haversine Formula, International Journal of Innovative Research in Computer and Communication Engineering, 1. p. 298-302.
• Wonohardjo, E.P., Kusuma, G.P. 2019. Air Pollution Mapping using Mobile Sensor Based on Internet of Things, Procedia Computer Science, 157. p. 638-645.
• Yang, J.H., Yang, M.S. 2005. A control chart pattern recognition system using a statistical correlation coefficient method, Computers & Industrial Engineering, 48. 2, p. 205-221.
• Neville, A.M. 1968. Basic statistical methods for engineers and scientists. Intertext student editions, ed. J.B. Kennedy. London: Intertext. 490 pp.
• Carslaw, D.C., Ropkins, K. 2012. openair - An R package for air quality data analysis, Environmental Modelling & Software, 27-28. p. 52-61.
• Graham, R., Cohen, L., Ritzhaupt, N., Segger, B., Graversen, R., Rinke, A., Walden, V., Granskog, M., Hudson, S. 2019. Evaluation of Six Atmospheric Reanalyses over Arctic Sea Ice from Winter to Early Summer, Journal of Climate, 32. p. 4121-4143.
• Fredriksen, L.-E. Year. An evaluation of the reanalyses ERA-Interim and ERA5 in the Arctic using N-ICE2015 data. 2018.
• Gupta, P., Verma, S., Bhatla, R., Chandel, A.S., Singh, J., Payra, S. 2020. Validation of Surface Temperature Derived From MERRA-2 Reanalysis Against IMD Gridded Data Set Over India, Earth and Space Science, 7. 1, p. e2019EA000910.
• Santos, J., Sakagami, Y., Haas, R., Passos, J., Machuca, M., Correa Radunz, W., Dias, E., Lima, M. 2019. Wind Speed Evaluation of MERRA-2, ERA-Interim and ERA-5 Reanalysis Data at a Wind Farm Located in Brazil. 1-10.
• Kim, H.-G., Kim, J.Y., Kang, Y.-H. 2018. Comparative Evaluation of the Third-Generation Reanalysis Data for Wind Resource Assessment of the Southwestern Offshore in South Korea, Atmosphere, 9, 2, 73.
• Staffell, I., Pfenninger, S. 2016. Using bias-corrected reanalysis to simulate current and future wind power output, Energy, 114. p. 1224-1239.
• Gualtieri, G. 2022. Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review, Renewable and Sustainable Energy Reviews, 167. p. 112741.