Syntax and Semantic

In the part 1 we introduced WRF and discussed the meteorological basics of Advanced Research WRF. In this part we discuss the concept of nesting and explain different system parts of WRF. But first we'll define two basic terms. Grid: Raw atmospheric data layed out as a discrete grid for an area of the map. Figure 1 [1] Grid examples Domian: A model created by processing grid information and making it ready for processing by WRF Model. Nesting: Nesting is a way of getting high resolution data from low resolution data.That's achieved by introducing more grid or grids to the initial state. Those finer grain grids force the need for more high resolution data. That data is obtained by interpolating data from coarse grain grids or could be user input. Figure 2 [1] Nested grid WRF System Components: Figure3 [1] WRF system components As it appears in figure 1, the system has 3 main components: Preprocessor (WPS), the WRF Model and Post Processor. The WPS is responsible for pre...

My goal here is to give a simple overview of the WRF for computer science students. The Weather Resarch and Forecasting (WRF, pronounced "worf") model is an open-source numerical weather prediction (NWP) and atmospheric simulation system designed for both research and operational applications [1]. This system was developed as joint effort between National Center of Atmospheric Research, National Oceanic and Atmospheric Administration (NOAA), Air Force Weather Agency and others. In this post ,and the posts to follow, I'll try to explain the WRF from two different perspectives, the first being Meteorology and the second being Computer Science. I am here concerned with the Advanced Research WRF (ARW) subset of WRF. Weather Prediction Basics: Weather Prediction means to predict the state of the atmosphere in terms of (pressure, temperature, speed of wind, etc). Our atmosphere is controlled by flow and the conditions of the air across the planet and by predicting that fl...