TY - JOUR
T1 - To what extent does high-resolution dynamical downscaling improve the representation of climatic extremes over an orographically complex terrain?
AU - El-Samra, R.
AU - Bou-Zeid, Elie R.
AU - El-Fadel, M.
N1 - Funding Information:
The authors thank the United States Agency for International Development for providing support for this work through the USAID-NSF PEER initiative (grant number—AID-OAA-A_I1_00012) in conjunction with support from the US National Science Foundation under grant number CBET-1058027. Elie Bou-Zeid was funded by the US National Science Foundation’s Sustainability Research Network Cooperative Agreement 1444758. The supercomputing resources were provided by the NCAR through project P36861020. We would also like to thank Dr. D. Li at the University of Boston for his technical support, and LNMS, LARI, AREC, Dr. R. Al Khodari at the LNMS, Dr. M. Traboulsi at the Lebanese University, and Dr. S. Katafago at the Litani Water Authority for their help in data acquisition.
Funding Information:
Acknowledgements The authors thank the United States Agency for International Development for providing support for this work through the USAID-NSF PEER initiative (grant number—AID-OAA-A_I1_ 00012) in conjunction with support from the US National Science Foundation under grant number CBET-1058027. Elie Bou-Zeid was funded by the US National Science Foundation’s Sustainability Research Network Cooperative Agreement 1444758. The supercomputing resources were provided by the NCAR through project P36861020. We would also like to thank Dr. D. Li at the University of Boston for his technical support, and LNMS, LARI, AREC, Dr. R. Al Khodari at the LNMS, Dr. M. Traboulsi at the Lebanese University, and Dr. S. Katafago at the Litani Water Authority for their help in data acquisition.
Publisher Copyright:
© 2017, Springer-Verlag GmbH Austria.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - The Weather Research and Forecasting (WRF) model was applied as a downscaling tool over an orographically complex terrain along the Eastern Mediterranean. It was forced with the National Centers for Environment Prediction (NCEP) Final Analysis (FNL) (resolution 1°) for the years 2003 (a cold and wet year) and 2010 (a hot and dry year) and nested at sequential horizontal resolutions of 9 and 3 km. This study focuses on the assessment of simulated temperature and precipitation against data from an observational network over the study area. The observations comprise rain gauges and temperature stations with records of both daily average and/or maximum and minimum temperatures. The yearly precipitation validation shows that the WRF simulation has good agreement with the observed data, with a percentage bias of 3.80% in 2010. The errors in various extreme indices (such as minimum and maximum temperatures, number of hot or frost days, and rainfall intensity) were reduced by the downscaling, marking a large improvement over FNL analysis data in the description of temperature variability and extremes. These improvements support the benefits of dynamic downscaling over complex terrain, which can reduce the errors associated with mesoscales that are not resolved by the coarser driving model, and establish the skill of WRF for such downscaling.
AB - The Weather Research and Forecasting (WRF) model was applied as a downscaling tool over an orographically complex terrain along the Eastern Mediterranean. It was forced with the National Centers for Environment Prediction (NCEP) Final Analysis (FNL) (resolution 1°) for the years 2003 (a cold and wet year) and 2010 (a hot and dry year) and nested at sequential horizontal resolutions of 9 and 3 km. This study focuses on the assessment of simulated temperature and precipitation against data from an observational network over the study area. The observations comprise rain gauges and temperature stations with records of both daily average and/or maximum and minimum temperatures. The yearly precipitation validation shows that the WRF simulation has good agreement with the observed data, with a percentage bias of 3.80% in 2010. The errors in various extreme indices (such as minimum and maximum temperatures, number of hot or frost days, and rainfall intensity) were reduced by the downscaling, marking a large improvement over FNL analysis data in the description of temperature variability and extremes. These improvements support the benefits of dynamic downscaling over complex terrain, which can reduce the errors associated with mesoscales that are not resolved by the coarser driving model, and establish the skill of WRF for such downscaling.
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U2 - 10.1007/s00704-017-2273-8
DO - 10.1007/s00704-017-2273-8
M3 - Article
AN - SCOPUS:85029600663
SN - 0177-798X
VL - 134
SP - 265
EP - 282
JO - Theoretical and Applied Climatology
JF - Theoretical and Applied Climatology
IS - 1-2
ER -