To what extent does high-resolution dynamical downscaling improve the representation of climatic extremes over an orographically complex terrain?

• Published in: Theoretical and applied climatology 2018-10, Vol.134 (1-2), p.265-282
• Main Authors: El-Samra, R., Bou-Zeid, E., El-Fadel, M.
• Format: Article
• Language: English
• Subjects: Analysis; Aquatic Pollution; Atmospheric precipitations; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climate science; Climatic extremes; Climatology; Computer simulation; Data; Data processing; Earth and Environmental Science; Earth Sciences; Errors; Gauges; Maximum temperatures; Meteorological research; Minimum temperatures; Numerical weather prediction; Original Paper; Precipitation; Rain; Rain gauges; Rainfall; Rainfall intensity; Resolution; Temperature; Temperature effects; Temperature variability; Terrain; Waste Water Technology; Water Management; Water Pollution Control; Weather; Weather forecasting
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-017-2273-8

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.