Climatic features of the Red Sea from a regional assimilative model
Dasari, Hari Prasad
Challa, Venkata Srinivas
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/622115
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AbstractThe Advanced Research version of Weather Research and Forecasting (WRF-ARW) model was used to generate a downscaled, 10-km resolution regional climate dataset over the Red Sea and adjacent region. The model simulations are performed based on two, two-way nested domains of 30- and 10-km resolutions assimilating all conventional observations using a cyclic three-dimensional variational approach over an initial 12-h period. The improved initial conditions are then used to generate regional climate products for the following 24 h. We combined the resulting daily 24-h datasets to construct a 15-year Red Sea atmospheric downscaled product from 2000 to 2014. This 15-year downscaled dataset is evaluated via comparisons with various in situ and gridded datasets. Our analysis indicates that the assimilated model successfully reproduced the spatial and temporal variability of temperature, wind, rainfall, relative humidity and sea level pressure over the Red Sea region. The model also efficiently simulated the seasonal and monthly variability of wind patterns, the Red Sea Convergence Zone and associated rainfall. Our results suggest that dynamical downscaling and assimilation of available observations improve the representation of regional atmospheric features over the Red Sea compared to global analysis data from the National Centers for Environmental Prediction. We use the dataset to describe the atmospheric climatic conditions over the Red Sea region. © 2016 Royal Meteorological Society.
CitationViswanadhapalli Y, Dasari HP, Langodan S, Challa VS, Hoteit I (2016) Climatic features of the Red Sea from a regional assimilative model. International Journal of Climatology. Available: http://dx.doi.org/10.1002/joc.4865.
SponsorsThis research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia and the Saudi ARAMCO-KAUST Marine Environmental Research Center (SAKMERC). This research made use of the resources of the Supercomputing Laboratory and/or computer clusters at KAUST. The NCEP FNL, prepbufr global observational datasets were obtained from http://rda.ucar.edu. TRMM-3B42 rainfall estimates were downloaded from the TRMM NASA GFSC server. CCMP datasets were downloaded from podaac.jpl.nasa.gov. CMORPH rainfall estimates were downloaded from the Climate Prediction Center.