Evaluation of NCEP/NCAR Reanalysis Precipitable Water Data Comparing to Radiosonde Observations for Turkey
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.
Keywords
NCEP/NCAR Reanalysis Data Project,Precipitable Water,normalized Root Mean Square Error
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.