Comparative assessment of RAMS and WRF short-term forecasts over Eastern Iberian Peninsula using various in-situ observations, remote sensing products and uncoupled land surface model datasets

The Regional Atmospheric Modeling System (RAMS) and the Weather Research and Forecasting (WRF) mesoscale models are being used for weather and air quality studies as well as forecasting tools in Numerical Weather Prediction (NWP) systems. In the current study, we perform a comparative assessment of these models under distinct typical atmospheric conditions, classified according to the dominant wind flow and cloudiness, over Eastern Iberian Peninsula. This study is focused on the model representation of key physical processes in terms of meteorology and surface variables during a 7-days period in summer 2011. The hourly outputs produced by these two models are compared not only with observed standard surface variables, measured at different permanent weather stations located over the region of study, but also with different surface remote sensing products and uncoupled Land Surface Models (LSM) datasets. Confronting RAMS and WRF, the current study highlights relevant differences over areas near the coast when mesoscale circulations or Eastern synoptic advections are developed over the region of study. A higher moisture content is observed under these atmospheric conditions, due to the moisture transport by the sea breeze inland. In this regard, it has been found that the Eastern wind field simulated by WRF reaches inland areas and comprises a larger sea breeze extension than RAMS. This sea breeze development impacts meteorology and surface variables in locations not too close to the coast, but still affected by land-sea winds. Additionally, WRF remains more windy and moister than RAMS at night-time, while alike results are found under Western synoptic advections. The results obtained in the current paper show differences under distinct dominant atmospheric conditions, which outline further research in this field in order to achieve more general conclusions.