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NCEP/NCAR Modelinin Yağışa Geçebilir Su Buharı Miktarındaki Başarısının Türkiye’deki Radyosonda (Radyozonde) Gözlemleri ile Karşılaştırılarak Değerlendirilmesi

Year 2019, , 527 - 535, 30.06.2019
https://doi.org/10.17776/csj.393237

Abstract

NCEP/NCAR Reanaliz Projesi
(NNRP) modelinin yağışa geçebilir su buharı miktarı verileri, Türkiye'nin 8
istasyonundan alınan radyosonde verileriyle 2015-2017 yılları için
karşılaştırılarak değerlendirilmiştir. NNRP verilerinden gözlem noktalarına
karşılık gelen zaman serilerini oluşturabilmek için iki yöntem kullanılmıştır.
İlk yöntemde ilgili istasyona en yakın grid noktasından zaman serisi
oluşturulmuştur. İkinci yöntem ise, istasyon konumuna yakın olan grid
noktalarının ağırlıklı etkilerini dikkate alabilmek için bilinear interpolasyon
yönteminin NNRP verilerine uygulanmasıdır. NNRP ve radyosonda verilerinin zaman
aralığı, 0000 Z ve 1200 Z saatleri için, 12 saattir. NNRP modelinin PW çıktısı
gözlemlerle karşılaştırılırken zaman serileri grafiksel olarak değerlendirilmiş,
hata analizleri (Ortalama Mutlak Hata (MAE), Kök Ortalama Kare Hata (RMSE) ve
Kök Ortalama Kare Hata (nRMSE)) yapılmış, uygunluk test sonuçları (Cp ve PBIAS)
belirlenmiş ve olasılık yoğunluk fonksiyonları (PDF) grafiklendirilmiştir. İstasyonların
çoğunun hata analizi, bilinear enterpolasyon yönteminin, bir interpolasyon
tekniği uygulamadan seçilen en yakın grid noktasının değerlerinden daha
uygulanabilir olduğunu göstermektedir. Gözlemlerin hata içermediği kabulü ile,
NNRP verilerinin nRMSE'lerinin, Türkiye'nin 6 istasyonu için (Ankara,
Diyarbakır, Erzurum, Isparta, İstanbul ve İzmir) %10'dan az olduğu belirlenmiştir.
Kıyıya yakın olan diğer 2 istasyon (Adana ve Samsun) için de nRMSE %13.8'dir.
Bu sonuçlar, çözünürlüğünün düşük olmasından dolayı NNRP modelinin lokal nem
etkilerini doğru kestiremediğini göstermektedir. Olasılık yoğunluk
fonksiyonlarının (PDF) karşılaştırmaları ise NNRP modelinin aşırı değerleri
yakalamadaki başarısının düşük olduğunu belirtmektedir.

References

  • Precipitable water, Glossary of Meteorology, American Meteorological Society, http://glossary.ametsoc.org/wiki/Precipitable_water. Accessed: 29 Jan 2018.
  • Kalnay E., Kanamitsu M., Kistler R., Collins W., Deaven D., Gandin L., Iredell M., Saha S., White G., Woollen J., Zhu Y., Chelliah M., Ebisuzaki W., Higgins W., Janowiak J., Mo K.C., Ropelewski C., Wang J., Leetmaa A., Reynolds R., Jenne R. and Joseph D., The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77 (1996) 437-471.
  • Xie P. and Arkin P.A., Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin of the American Meteorological Society, 78 (1997) 2539–2558.
  • Trenberth K.E. and Guillemot C.J., Evaluation of the atmospheric moisture and hydrological cycle in the NCEP/NCAR reanalyses, Clim Dyn, 14 (1998) 213–231.
  • Bromwich D.H., Fogt R. L., Hodges K. I. and Walsh J. E., A tropospheric assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar regions, J. Geophys. Res., 112 (2007) D10111.
  • Ma L., Zhang T., Frauenfeld O.W., Ye B., Yang D. and Qin D., Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 Reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China, Journal of Geophysical Research, 114 (2009) D09105. https://doi.org/10.1029/2008JD011178.
  • Sun Q., Miao C., Duan Q., Ashouri H., Sorooshian S. and Hsu K.-L., A review of global precipitation data sets: Data sources, estimation, and inter- comparisons. Reviews of Geophysics, 56 (2018) https://doi.org/10.1002/2017RG000574.
  • Trenberth K.E., Fasullo J. and Smith L., Trends and variability in column integrated atmospheric water vapor. Climate Dyn., 24, 7–8 (2005) 741–758.
  • Fistikoglu O. and Okkan U., Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River basin in Turkey, ASCE J Hydrol Eng, 16(2) (2011) 157–164.
  • Tatli H., Statistical complexity in daily precipitation of NCEP/NCAR reanalysis over the Mediterranean basin. Int. J. Climatol., 34 (2014) 155–161.
  • Bozkurt D., Turuncoglu U., Sen O.L., Onol B. and Dalfes H.N., Downscaled simulations of the ECHAM5, CCSM3 and HadCM3 global models for the eastern Mediterranean–Black Sea region: evaluation of the reference period, Clim Dyn, 39(1–2) (2012) 207–225.
  • National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 1994, updated monthly. NCEP/NCAR Global Reanalysis Products, 1948-continuing. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. http://rda.ucar.edu/datasets/ds090.0/.Accessed: 28 JAN 2018.
  • University of Wyoming, College of Engineering, Upper Air Radiosonde Data. http://weather.uwyo.edu/upperair/sounding.html. Accessed: 28 JAN 2018.
  • Turkish State Meteorological Service, Statistics Report of Relative Humidty, https://www.mgm.gov.tr/FILES/resmi-istatistikler/Turkiye-Ortalama-Nem.pdf. Accessed: 1 FEB 2018.
  • Mauricio, Z.-B., hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.3-10. http://CRAN.R-project.org/package=hydroGOF, (2017).
  • Topcu, S. Atmosferdeki Yağışa Geçebilir Su Buharı Miktarının Hesaplanması, Coğrafya Araştırmaları Dergisi, Cilt 1, Sayı 1 (1989). In Turkish. Accessed 1 FEB 2018. http://tucaum.ankara.edu.tr/wp-content/uploads/sites/280/2015/08/cadata1_9.pdf

Evaluation of NCEP/NCAR Reanalysis Precipitable Water Data Comparing to Radiosonde Observations for Turkey

Year 2019, , 527 - 535, 30.06.2019
https://doi.org/10.17776/csj.393237

Abstract




















Precipitable Water (PW) data
of NCEP/NCAR Reanalysis Project (NNRP) model is evaluated by comparing to
radiosonde data obtained from 8 locations of Turkey for the years between 2015
and 2017. Two methods are utilized to extract NNRP data for the observation
locations. In the first method, the nearest NNRP grid point to the radiosonde
locations is selected. The second method is the application of bilinear
interpolation method on NNRP data to include the weighted effects of
corresponding grid locations related with the observation sites. Both NNRP and
radiosonde data have 12 h interval for the times 0000 Z and 1200 Z. PW output
of NNRP model is compared to observations by means of graphical evaluation of
time series, error analyses (Mean Absolute Error (MAE), Root Mean Square Error
(RMSE), and Root Mean Squared Error (nRMSE)), goodness of fit tests (Cp and
PBIAS), and probability density functions (PDF). Error analyses of most of the
observation locations indicate that bilinear interpolation method is better
than utilizing the nearest grid value data which is not obtained by applying
any interpolation technique. Error analyses indicate that nRMSEs of NNRP data
for PW analyses are less than 10% for 6 locations of Turkey (Ankara,
Diyarbakir, Erzurum, Isparta, Istanbul, and Izmir) if it is assumed that the
observations have no errors for the years between 2015 and 2017. nRMSEs of the
other 2 coastal locations (Adana and Samsun) are the same as 13.8% and this may
indicate that local moisture sources of these locations are greater than mesoscale
moisture fields, since NNRP data may not capture local effects well due to its
spatial resolution. Comparisons of probability density functions (PDF) of these
data sets show that NNRP model may not be successful in capturing extreme
values.

References

  • Precipitable water, Glossary of Meteorology, American Meteorological Society, http://glossary.ametsoc.org/wiki/Precipitable_water. Accessed: 29 Jan 2018.
  • Kalnay E., Kanamitsu M., Kistler R., Collins W., Deaven D., Gandin L., Iredell M., Saha S., White G., Woollen J., Zhu Y., Chelliah M., Ebisuzaki W., Higgins W., Janowiak J., Mo K.C., Ropelewski C., Wang J., Leetmaa A., Reynolds R., Jenne R. and Joseph D., The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77 (1996) 437-471.
  • Xie P. and Arkin P.A., Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin of the American Meteorological Society, 78 (1997) 2539–2558.
  • Trenberth K.E. and Guillemot C.J., Evaluation of the atmospheric moisture and hydrological cycle in the NCEP/NCAR reanalyses, Clim Dyn, 14 (1998) 213–231.
  • Bromwich D.H., Fogt R. L., Hodges K. I. and Walsh J. E., A tropospheric assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar regions, J. Geophys. Res., 112 (2007) D10111.
  • Ma L., Zhang T., Frauenfeld O.W., Ye B., Yang D. and Qin D., Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 Reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China, Journal of Geophysical Research, 114 (2009) D09105. https://doi.org/10.1029/2008JD011178.
  • Sun Q., Miao C., Duan Q., Ashouri H., Sorooshian S. and Hsu K.-L., A review of global precipitation data sets: Data sources, estimation, and inter- comparisons. Reviews of Geophysics, 56 (2018) https://doi.org/10.1002/2017RG000574.
  • Trenberth K.E., Fasullo J. and Smith L., Trends and variability in column integrated atmospheric water vapor. Climate Dyn., 24, 7–8 (2005) 741–758.
  • Fistikoglu O. and Okkan U., Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River basin in Turkey, ASCE J Hydrol Eng, 16(2) (2011) 157–164.
  • Tatli H., Statistical complexity in daily precipitation of NCEP/NCAR reanalysis over the Mediterranean basin. Int. J. Climatol., 34 (2014) 155–161.
  • Bozkurt D., Turuncoglu U., Sen O.L., Onol B. and Dalfes H.N., Downscaled simulations of the ECHAM5, CCSM3 and HadCM3 global models for the eastern Mediterranean–Black Sea region: evaluation of the reference period, Clim Dyn, 39(1–2) (2012) 207–225.
  • National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 1994, updated monthly. NCEP/NCAR Global Reanalysis Products, 1948-continuing. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. http://rda.ucar.edu/datasets/ds090.0/.Accessed: 28 JAN 2018.
  • University of Wyoming, College of Engineering, Upper Air Radiosonde Data. http://weather.uwyo.edu/upperair/sounding.html. Accessed: 28 JAN 2018.
  • Turkish State Meteorological Service, Statistics Report of Relative Humidty, https://www.mgm.gov.tr/FILES/resmi-istatistikler/Turkiye-Ortalama-Nem.pdf. Accessed: 1 FEB 2018.
  • Mauricio, Z.-B., hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.3-10. http://CRAN.R-project.org/package=hydroGOF, (2017).
  • Topcu, S. Atmosferdeki Yağışa Geçebilir Su Buharı Miktarının Hesaplanması, Coğrafya Araştırmaları Dergisi, Cilt 1, Sayı 1 (1989). In Turkish. Accessed 1 FEB 2018. http://tucaum.ankara.edu.tr/wp-content/uploads/sites/280/2015/08/cadata1_9.pdf
There are 16 citations in total.

Details

Primary Language English
Journal Section Engineering Sciences
Authors

Elcin Tan 0000-0001-7677-6073

Publication Date June 30, 2019
Submission Date February 11, 2018
Acceptance Date May 17, 2019
Published in Issue Year 2019

Cite

APA Tan, E. (2019). Evaluation of NCEP/NCAR Reanalysis Precipitable Water Data Comparing to Radiosonde Observations for Turkey. Cumhuriyet Science Journal, 40(2), 527-535. https://doi.org/10.17776/csj.393237