WRF-ARW Data importing: VAPOR GUI can directly import WRF-ARW output data, with no data conversion. From the VAPOR GUI Data menu, click "Import WRF-ARW data into default session". Then select all the WRF-ARW files to visualize. They must be on the same grid and the same level of nesting.
WRF-ARW Data conversion: Visualization of very large WRF-ARW datasets in VAPOR GUI can be sluggish if you import the data as above. To improve interactivity, convert the WRF-ARW data to a VAPOR VDC, and then, from the GUI Data menu, click "Load a Dataset into default session". See Creating a .vdf file and Populating a VDC for instructions on performing this conversion.
Image preparation: Georeferenced images displayed in the VAPOR scene are useful for providing a geospatial context for the visualization. Some images are pre-installed with VAPOR; others can be downloaded from Web Mapping Services. These images can be mapped to the terrain, indicating mountain heights other terrain features. Instructions for preparing terrain images are provided at Obtain terrain images: getWMSImage.sh . Options for displaying images are discussed under Image rendering.
Analysis capabilities: When visualizing WRF-ARW data, several weather-related variables can be calculated in Python and immediately visualized, using the vapor_wrf module. The available derived variables include cloud-top temperature, equivalent potential temperature, radar reflectivity, vorticity and potential vorticity, relative humidity, wind shear, sea-level pressure, dewpoint temperature, and temperature in degrees Kelvin. These capabilities are described in the Vapor Python Guide
Additional resources: Several online tutorials that deal with WRF and VAPOR are available:
The Hurricane Katrina Tutorial is a short overview of some ways to visualize WRF data with VAPOR, based on a simulation of Hurricane Katrina.
Georgia Weather Case Study : A quick-start tutorial showing how to use VAPOR to explore a WRF data set.
VAPOR User’s Guide for WRF Typhoon Research : A guide to using VAPOR to visualize typhoon Jangmi, including instructions for preparing satellite images and NCL plots to visualize in VAPOR.
Using NCL with VAPOR to visualize WRF-ARW data: This is a tutorial showing how to make NCL plots of WRF data that can be embedded and visualized in a VAPOR 3D scene.
