Welcome back to AWIPS Tips! Today we're diving into more capabilities of python-awips! Previously, we've discussed how to access model data and how to access and write out specific GOES imagery. Today, we're switching gears slightly from meteorological data, to focus on how to access and use map data.
While meteorological data are great, they usually need additional information for full context and interpretation. That is where map data becomes so important. Because python-awips is just one of many Python libraries available, there are numerous ways to access useful map data (think of cartopy for example). However, to keep things simpler, most relevant data can simply be accessed from an EDEX server. One additional benefit of using python-awips for this task is that a user can easily query and limit by County Warning Areas (CWAs), which is not a common functionality of other python libraries.
In our example Notebook titled Map Resources and Topography, we walk through how to add additional map data, layer by layer, to create a robust contextual plot. A summarized graphic of the process is as follows:
Accessing map data is done in the same manner as accessing other data in EDEX. The first step is to create a data request object and use the 'maps' data type:
request = DataAccessLayer.newDataRequest('maps')
From this point, we set many different attributes on the request object. Most notably we change the 'table' identifier several times throughout the Notebook:
A full list of all available tables can be found as an appendix on our website.
It is also important to note, in order to be efficient, we only pull back data that are relevant to the geographical location we're interested in. This is accomplished by creating all subsequent data requests with a filter known as an 'envelope':
request = DataAccessLayer.newDataRequest('maps', envelope=envelope)
Geographic filters are useful for creating fast and efficient notebooks and can be done when accessing any data type on EDEX (not just map resources).
By adding one data set after another, we can see how much more context the map information offers to our overall plot. In this particular example, we don't actually have any meteorological data, but our resulting output is still a valuable product on its own. All of the map sources used in this Notebook can be used to help add context to products from models, satellites, radars and more!
We hope you found this week's entry useful, please check back in two weeks for the next blog post, where we dive into viewing soundings in CAVE.
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